Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2022, Volume 1 (Lecture Notes in Networks and Systems, 491) 9811941920, 9789811941924

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Lecture Notes in Networks and Systems 491

Paramartha Dutta · Satyajit Chakrabarti · Abhishek Bhattacharya · Soumi Dutta · Vincenzo Piuri   Editors

Emerging Technologies in Data Mining and Information Security Proceedings of IEMIS 2022, Volume 1

Lecture Notes in Networks and Systems Volume 491

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

Paramartha Dutta · Satyajit Chakrabarti · Abhishek Bhattacharya · Soumi Dutta · Vincenzo Piuri Editors

Emerging Technologies in Data Mining and Information Security Proceedings of IEMIS 2022, Volume 1

Editors Paramartha Dutta Department of Computer and System Sciences Visva-Bharati University Santiniketan, West Bengal, India

Satyajit Chakrabarti Department of Computer Science and Engineering Institute of Engineering and Management Kolkata, West Bengal, India

Abhishek Bhattacharya Department of Computer Application and Science Institute of Engineering and Management Kolkata, West Bengal, India

Soumi Dutta Department of Computer Application and Science Institute of Engineering and Management Kolkata, West Bengal, India

Vincenzo Piuri Department of Computer Science University of Milan Milan, Italy

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-19-4192-4 ISBN 978-981-19-4193-1 (eBook) https://doi.org/10.1007/978-981-19-4193-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This volume presents proceedings of the 3rd International Conference on Emerging Technologies in Data Mining and Information Security IEMIS 2022, which took place in the Institute of Engineering and Management in Kolkata, India, from February 23 to 25, 2022. The volume appears in the series “Lecture Notes in Networks and Systems” (LNNS) published by Springer Nature, one of the largest and most prestigious scientific publishers, in the series which is one of the fastest-growing book series in their program. LNNS is meant to include various high-quality and timely publications, primarily conference proceedings of relevant conference, congresses and symposia but also monographs, on the theory, applications and implementations of broadly perceived modern intelligent systems and intelligent computing, in their modern understanding, i.e., including tools and techniques of artificial intelligence (AI), computational intelligence (CI)—which includes data mining, information security, neural networks, fuzzy systems, evolutionary computing, as well as hybrid approaches that synergistically combine these areas—but also topics such as—network security, cyber-intelligence, multiagent systems, social intelligence, ambient intelligence, web intelligence, computational neuroscience, artificial life, virtual worlds and societies, cognitive science and systems, perception and vision, self-organizing and adaptive systems, e-learning and teaching, human-centered and human-centric computing, autonomous robotics, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis—various issues related to network security, big date, security and trust management, to just mention a few. These areas are at the forefront of science and technology and have been found useful and powerful in a wide variety of disciplines such as engineering, natural sciences, computer, computation and information sciences, ICT, economics, business, e-commerce, environment, health care, life science and social sciences. The LNNS book series is submitted for indexing in ISI Conference Proceedings Citation Index (now run by Clarivate), EI Compendex, DBLP, SCOPUS, Google Scholar and SpringerLink and many other indexing services around the world. IEMIS 2022 is an annual conference series organized at the School of Information Technology, under the aegis of Institute of Engineering and Management. Its idea came from the heritage of the other two cycles of events: IEMCON and UEMCON, which were v

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organized by the Institute of Engineering and Management under the leadership of Prof. (Dr.) Satyajit Chakrabarti. In this volume of “Lecture Notes in Networks and Systems,” we would like to present results of studies on selected problems of data mining and information security. Security implementation is the contemporary answer to new challenges in threat evaluation of complex systems. Security approach in theory and engineering of complex systems (not only computer systems and networks) is based on multidisciplinary attitude to Information theory, technology and maintenance of the systems working in real (and very often unfriendly) environments. Such a transformation has shaped natural evolution in topical range of subsequent IEMIS conferences, which can be seen over the recent years. Human factors likewise infest the best digital dangers. Work force administration and digital mindfulness are fundamental for accomplishing all-encompassing cybersecurity. This book will be of extraordinary incentive to a huge assortment of experts, scientists and understudies concentrating on the human part of the Internet and for the compelling assessment of safety efforts, interfaces, client focused outline and plan for unique populaces, especially the elderly. We trust this book is instructive yet much more than it is provocative. We trust it moves, driving per user to examine different inquiries, applications and potential arrangements in making sheltered and secure plans for all. The Programme Committee of the IEMIS 2022 Conference, its organizers and the editors of these proceedings would like to gratefully acknowledge participation of all reviewers who helped to refine contents of this volume and evaluated conference submissions. Our thanks go to all respected keynote speakers: Prof. Seyedali Mirjalili, Prof. Md. Abdur Razzak, Prof. Rafidah Md. Noor, Prof. Xin-She Yang, Prof. Reyer Zwiggelaar, Dr. Vincenzo Piuri, Dr. Shamim Kaiser and to our all session chairs. Thanking all the authors who have chosen IEMIS 2022 as the publication platform for their research, we would like to express our hope that their papers will help in further developments in design and analysis of engineering aspects of complex systems, being a valuable source material for scientists, researchers, practitioners and students who work in these areas. Santiniketan, India Kolkata, India Kolkata, India Kolkata, India Milan, Italy

Paramartha Dutta Satyajit Chakrabarti Abhishek Bhattacharya Soumi Dutta Vincenzo Piuri

About This Book

This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2022) held at the Institute of Engineering and Management, Kolkata, India, on February 23–25, 2022. Data mining is a current well-known topic in mirroring the exertion of finding learning from information. It gives the strategies that enable supervisors to acquire administrative data from their heritage frameworks. Its goal is to distinguish legitimate, novel, possibly valuable and justifiable connection and examples in information. Information mining is made conceivable by the very nearness of the expansive databases. Information security advancement is an essential part to ensure open and private figuring structures. Notwithstanding how strict the security techniques and parts are, more affiliations are getting the chance to be weak to a broad assortment of security breaks against their electronic resources. Network-intrusion area is a key protect part against security perils, which have been growing in rate generally. This book comprises high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT) and information security, etc.

About the Conference Welcome to the 3rd International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2022) which was held in February 23–25, 2012, in Kolkata, India. As a premier conference in the field, IEMIS 2022 provides a highly competitive forum for reporting the latest developments in the research and application of information security and data mining. We are pleased to present the proceedings of the conference as its published record. The theme this year is Crossroad of Data Mining and Information Security, a topic that is quickly gaining vii

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About This Book

traction in both academic and industrial discussions because of the relevance of privacy preserving data mining (PPDM) model. IEMIS is a young conference for research in the areas of information and network security, data sciences, big data and data mining. Although 2018 was the debut year for IEMIS, it has already witnessed significant growth. As evidence of that, IEMIS received a record 610 submissions. The authors of submitted papers come from 35 countries and regions. Authors of accepted papers are from 11 countries. We hope that this program will further stimulate research in information security and data mining and provide practitioners with better techniques, algorithms and tools for deployment. We feel honored and privileged to serve the best recent developments in the field of data mining and information security to you through this exciting program. Dr. Satyajit Chakrabarti President of IEM Group, India Chief Patron, IEMIS 2022

Contents

Computational Intelligence An Interpretive Saga of SQL Injection Attacks . . . . . . . . . . . . . . . . . . . . . . . Saloni Manhas Numerical Simulation of Boundary Layer Flow of MHD Influenced Nanofluid Over an Exponentially Elongating Sheet . . . . . . . . . . . . . . . . . . . Debasish Dey and Rupjyoti Borah

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Driver Drowsiness Detection and Traffic Sign Recognition System . . . . . Ruchi Pandey, Priyansha Bhasin, Saahil Popli, Mayank Sharma, and Nikhil Sharma

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Application of Data Visualization: Realization of Car Rental System . . . Rajendrani Mukherjee, Drick Datta, Gargi Ganguly, Srinjini Bandopadhyay, Chirantan Chakraborty, Birabrata Pal, and Deborup Chatterjee

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Physical and Mental Health Problem’s Technical Resolutions . . . . . . . . . . Priyanshu Joshi, Samviti Bhardwaj, Abhishek Patel, and Priyanka

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Entropy Generation Analysis of MHD Fluid Flow Over Stretching Surface with Heat and Mass Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Debasish Dey and Madhurya Hazarika A Stacking Ensemble Framework for Android Malware Prediction . . . . Abhishek Bhattacharya, Soumi Dutta, Salahddine Krit, Wen Cheng Lai, Nadjet Azzaoui, and Adriana Burlea-Schiopoiu A Comparative Analysis of Performances of Different Ensemble Approaches for Classification of Android Malwares . . . . . . . . . . . . . . . . . . . Abhishek Bhattacharya, Soumi Dutta, Mohammad Kamrul Hasan, Kusum Yadav, Dac-Nhuong Le, and Pastor Arguelles Jr

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Natural Language Processing in Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Sai Pranav, Mehar Mutreja, Devansh Punj, and Pronika Chawla CT Image Denoising Using Bilateral Filter and Method Noise Thresholding in Shearlet Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rashmita Sehgal and Vandana Dixit Kaushik

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Reactive Mass Diffusion in Viscoelastic Fluid Past a Stretchable Exponential Sheet Due to Variation in Wall Concentration . . . . . . . . . . . . 107 Kamal Debnath and Sankar Singha Technology Adoption for Facilitating Knowledge Management Practices in Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Arpana Kumari and Arun Kumar Singh Slip Flow and Heat Transition for Hydromagnetic Elastico-viscous Fluid Past a Flat Moving Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Kamal Debnath and Bikash Koli Saha Comprehensive Analysis of Various Distance Metrics on Colour-Based CBIR System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Shaheen Fatima Numerical Simulation of MHD Viscous Fluid Flow Over a Porous Stretching Surface with the Effects of Power-Law Heat and Mass Flux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Ashim Jyoti Baruah and Rupjyoti Borah Free Convective Oscillatory Flow of Visco-Elastic Dusty Fluid in a Channel with Inclined Magnetic Field . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Hridi Ranjan Deb Study of Power Law Fluid Flow Through a Stretched Vertical Surface with Viscous Dissipation and Its Rheology . . . . . . . . . . . . . . . . . . . . 179 Debasish Dey and Bhagyashree Mahanta A Simulation of Nanofluid Flow with Variable Viscosity and Thermal Conductivity Over a Vertical Stretching Surface . . . . . . . . . 189 Debasish Dey, Rajesh Kumar Das, and Rupjyoti Borah Soret and Dufour Effects on MHD Micropolar Fluid Flow with Heat and Mass Transfer Past a Horizontal Plate in Porous Medium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Krishnandan Verma Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Jayashree S. Shetty, Nisha P. Shetty, Vedant Rishi Das, Vaibhav, and Diana Olivia

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From the Perspective of Digital Transformation: Amazon’s Tryst with Competition Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Sunishi Tiwari Secured Quantum Key Distribution Encircling Profuse Attacks and Countermeasures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Veerraju Gampala, Balajee Maram, and A. Suja Alphonse Early Parkinson Disease Detection Using Audio Signal Processing . . . . . 243 Mohit Bansal, Satya Jeet Raj Upali, and Sukesha Sharma Unveiling the Success Behind Tesla’s Digital Marketing Strategy . . . . . . . 251 Pankaj Pathak, Vikash Yadav, Samaya Pillai, Subhadeep Das, and Gaurav Kansal A Comparative Survey of Consensus Algorithms Based on Proof of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Poonam Rani and Rajul Bhambay Advance Computing Smart Spy in the Online Video Calls Without Notifying the Concerned and Alerting the Nearest Receiver Contacts . . . . . . . . . . . . 271 N. Ravinder, S. Hrushikesava Raju, B. Venkateswarlu, Durga Bhavani Dasari, B. Revathi, and Harika Lakshmi Sikhakolli An Exploratory Study on Internet of Things for Covid-19 Pandemic . . . 283 Rydhm Beri and Raju Kumar A Study of E-commerce Platform Issues Shared by Developers on Stack Overflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Yusuf Sulistyo Nugroho, Syful Islam, Dedi Gunawan, Yogiek Indra Kurniawan, Md. Javed Hossain, and Mohammed Humayun Kabir Emerging Trends in Multimedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Shreyas Vijay, Prince Mann, Renu Chaudhary, and Aniket Rana IoT in Medical Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Aniket Rana, Anushka Kalra, Siddharth Gautam, and Shreyas Vijay Gesture Recognition System for Real-Time Interaction in Dynamic Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Meghna Nandy, Swagata Sinha, Basudev Halder, Kallol Bera, and Deep Suman Dev Chaos-Based Image Encryption with Salp Swarm Key Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Supriya Khaitana, Shrddha Sagar, and Rashi Agarwal

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Role of IOT in Automated Hydroponic System: A Review . . . . . . . . . . . . . 349 Pooja Thakur and Manisha Malhotra AI-Based Real-Time Surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Himani Mittal, Himanshu Tripathi, and Shivansh Shrish Tripathi Smart Car with Safety Features and Accident Detection Alert System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Subhadip Ghosh, Soumen Maity, Sourav Chowdhury, and Sanjay Chakraborty CARGIoT: Concept Application Review in Green Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Raveena Yadav and Vinod Kumar Heterogeneous BigData Analysis in IoT Cloud Environment . . . . . . . . . . . 387 P. V. Manjusha Nambiar and E. Anupriya IRIS: A Pragmatic Approach to Build an Integrated and Robust IOT System to Counter Malware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Gaytri Bakshi, Romil Verma, and Rohil Chaudhry Quantum Implementation of Reversible Logic Gates Using RCViewer+ Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 Shaveta Thakral, Pratima Manhas, and Jyoti Verma Intelligent Process Automation for Detecting Unauthorized Entry by Actors in IoT Imbedded Enterprise Setting . . . . . . . . . . . . . . . . . . . . . . . 419 Amit K. Nerurkar and G. T. Thampi Path Segmentation for Visually Impaired People Using U-Net Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Amit Chaudhary and Prabhat Verma Segmentation of Sidewalk for Visually Impaired Using Convolutional Network U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 Amit Chaudhary and Prabhat Verma Hybrid Security for Data in Cloud Computing: A Review . . . . . . . . . . . . . 441 R. Mary Sheeba and R. Parameswari Issues of Commodity Market and Trade Finance in India and Its Solutions Using Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Swapnil Sonawane and Dilip Motwani Optimal Selection of Cloud Service Provider Using MCDM Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 M. Krithika and A. Akila Plant Species Recognition from Leaf-Vein Structures Using ResNets . . . 471 Abdul Hasib Uddin and Abu Shamim Mohammad Arif

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IoT-Enabled Automated Analysis and Classification of COVID-19 Disease in Lung CT Images Based on Edge Computing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 Ayman Qahmash Secure 6G Communication in Smart City Using Blockchain . . . . . . . . . . . 487 Saikat Samanta, Achyuth Sarkar, and Yaka Bulo Social Engineering Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Yuvraj Singh Saini, Lucky Sharma, Pronika Chawla, and Sanidhya Parashar On-Device Emotional Intelligent IoT-Based Framework for Mental Health Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Godson D’silva, Jagprit Batra, Amit Bhoir, and Akanksha Sharma Energy Minimization of Cloud Computing Data Center Strategies, Research Questions: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Abhineet Anand, M. Arvindhan, Naresh Kumar Trivedi, Ajay Kumar, and Raj Gaurang Tiwari Network Security and Telecommunication Scope of Machine Learning in Mobile Wireless Sensor Networks . . . . . . . 533 Kavita Gupta, Sandhya Bansal, and Ajay Khurana An Overview of Cybercrimes and Its Impact: Indian Scenario . . . . . . . . . 545 Ashvarya Chaudhary An Insight on Latest Technologies of Cyber Security . . . . . . . . . . . . . . . . . . 555 Aditya Bansal, Raghav Goel, Shagun Sharma, Kanupriya Verma, Megha Bhushan, and Ashok Kumar Progressive Web Apps (PWAs)—Alternate to Mobile and Web . . . . . . . . . 565 Sarwar Ali, Chetna Grover, and Renu Chaudhary An Analysis of Legal and Cybersecurity Issues in Internet of Things . . . 577 Gagandeep Kaur, Rishabh Malhorta, and Vinod Kumar Shukla Incorporation of Secure Channel Communications Over Multi-tenant Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Rati Shukla, Rahul Shrivastava, Shivaji Sinha, Anurag Mishra, and Vikash Yadav Review of Detection of Packets Inspection and Attacks in Network Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597 Sai Kalki Jajula, Khushboo Tripathi, and Shalini Bhaskar Bajaj Route Optimization for Waste Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605 Minal Sahu, Purvi Sharma, Hitesh Kumar Sharma, Tanupriya Choudhury, and Bhupesh Kumar Dewangan

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Impact of Dispersion Schemes and Sensing Models on Performance of Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 Mini, Ashok Pal, and Tanupriya Choudhury A Research Perspective of VANET Applications: A Review . . . . . . . . . . . . 627 Payal Kaushal, Meenu Khurana, and K. R. Ramkumar Remote Authentication of Fingerprints Using Meaningful Visual Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 Surajit Goon, Debdutta Pal, and Souvik Dihidar A Novel Approach to Ensure the Security of Question Papers Using Visual Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 Surajit Goon, Debdutta Pal, and Souvik Dihidar NO PHISHING! Noise Resistant Data Resampling in Majority-Biased Detection of Malicious Websites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 Arghasree Banerjee, Kushankur Ghosh, Rahul Sen, Aritro Chakraborty, Sudipta Roy Chowdhury, and Sankhadeep Chatterjee A Review on Analysis and Development of Quantum Image Steganography Technique for Data Hiding . . . . . . . . . . . . . . . . . . . . . . . . . . . 663 Sonia Thind and Anand Kumar Shukla Forecasting the Growth in Covid-19 Infection Rates . . . . . . . . . . . . . . . . . . 673 Soumi Dutta, Abhishek Bhattacharya, Prithwidip Das, Shayan Pal, Ratna Mandal, Ahmed J. Obaid, Wen Cheng Lai, Ambuj Kumar Agarwal, and Ben Othman Soufiene Exoplanet Hunting Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 687 Nitin Tyagi, Prakriti Arora, Renu Chaudhary, and Jatin Bhardwaj Technical Review and Data Analysis of Expert System Development . . . . 703 Rashmi Pandey, Anand Kumar Pandey, Krishn Kumar Joshi, and Ravi Rai Self-Driving Car Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 709 Rishabh Kumar, Tarun Sharma, Renu Chaudhary, and Vibhor Singh Impacts on Health Frameworks of Big Data Analytics: A Review . . . . . . 721 Naresh Kumar Trivedi, Abhineet Anand, Ajay Kumar, Umesh Kumar Lilhore, and Raj Gaurang Tiwari Upgrading Search Link Priority by Content Analysis . . . . . . . . . . . . . . . . . 731 Ayushi Prakash, Sandeep Kumar Gupta, and Mukesh Rawat Stock Price Analysis Using LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 Shyamala Boosi, Chetana Tukkoji, Archana S. Nadhan, and A. Usha Ruby

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Analysis on Potential Use of Crowdsourcing in Different Domain Using Metasynthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 Nivedita Kasturi, S. G. Totad, and Goldina Ghosh Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757

Editors and Contributors

About the Editors Dr. Paramartha Dutta is currently a Professor in the Department of Computer and System Sciences in, Visva-Bharati University, Shantiniketan, India. He did Bachelors and Masters in Statistics from ISI, Kolkata, India. Subsequently, he did Master of Technology in Computer Science from ISI, Kolkata, India. He did Ph.D. (Engineering) from BESU, Shibpore, India. He is a Co-author of eight authored books apart from thirteen edited books and more than 200 and 40 research publications in peerreviewed journals and conference proceedings. He is a Co-inventor of 17 published patents. He is a Fellow of IETE, Optical Society of India, IEI, Senior Member of ACM, IEEE, Computer Society of India, International Association for Computer Science and Information Technology and Member of Advanced-Computing and Communications Society, Indian Unit of Pattern Recognition and AI—the Indian Affiliate of the International Association for Pattern Recognition, ISCA, Indian Society for Technical Education, System Society of India. Dr. Satyajit Chakrabarti is Pro-vice Chancellor, University of Engineering and Management, Kolkata and Jaipur Campus, India, and Director of Institute of Engineering and Management, IEM. As the Director of one of the most reputed organizations in Engineering and Management in Eastern India, he launched a PGDM Program to run AICTE approved Management courses, Toppers Academy to train students for certificate courses and Software Development in the field of ERP solutions. Dr. Chakrabarti was Project Manager in TELUS, Vancouver, Canada, from February 2006 to September 2009, where he was intensively involved in planning, execution, monitoring, communicating with stakeholders, negotiating with vendors and cross-functional teams and motivating members. He managed a team of 50 employees and projects with a combined budget of $3 million.

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Editors and Contributors

Dr. Abhishek Bhattacharya is Assistant Professor at Institute of Engineering and Management, India. He has completed his Ph.D. (Engineering), BIT, Mesra. He is certified as Publons Academy Peer Reviewer, 2020. His research interests are Data Mining, Cyber security and Mobile Computing. He has published 25 conference and journal papers in Springer, IEEE, IGI Global, Taylor & Francis etc. He has 3 Book Chapters in Taylor & Francis Group EAI. He is Peer Reviewer and TPC Member in different international journals. He was the Editor in IEMIS 2020, IEMIS 2018 and special issues in IJWLTT. He is the Member of several technical functional bodies such as IEEE, IFERP, MACUL, SDIWC, Internet Society, ICSES, ASR, AIDASCO, USERN, IRAN, IAENG. He has published 3 patents. Dr. Soumi Dutta is Associate Professor at Institute of Engineering and Management, India. She has completed her Ph.D. (Engineering), IIEST, Shibpur. She received her B.Tech. (IT) and M.Tech. (CSE) securing 1st position (Gold medalist), from MAKAUT. She is certified as Publons Academy Peer Reviewer, 2020 and Certified Microsoft Innovative Educator, 2020. Her research interests are Data Mining, OSN Data Analysis and Image Processing. She has published 30 conference and journal papers in Springer, IEEE, IGI Global, Taylor & Francis, etc. She has 5 Book Chapters in Taylor & Francis Group and IGI-Global. She is Peer Reviewer and TPC Member in different international journals. She was Editor in CIPR-2020, CIPR2019, IEMIS-2020, CIIR-2021, IEMIS-2018 special issues in IJWLTT. She is Member of several technical functional bodies such as IEEE, ACM, IEEE, IFERP, MACUL, SDIWC, Internet-Society, ICSES, ASR, AIDASCO, USERN, IRAN, IAENG. She has published 4 patents. She has delivered more than Keynote talks in Different International Conferences. Vincenzo Piuri has received his Ph.D. in computer engineering at Polytechnic of Milan, Italy. He is Full Professor in computer engineering at the University of Milan, Italy (since 2000). His main research interests are: AI, computational-intelligence, intelligent-systems, machine-learning, pattern analysis and recognition and many more. Original results have been published in 400+ papers in international journals, conferences proceedings, books and book chapters. He is Fellow of the IEEE, Distinguished Scientist of ACM and Senior Member of INNS. He is President of the IEEE Systems Council (2020–21) and IEEE Region 8 Director-elect (2021–22) and has been IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE CIS, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council and Vice President for Membership of the IEEE CIS. He has been Editor-in-Chief of the IEEE Systems Journal (2013–19). He is Associate Editor of the IEEE Transactions on Cloud Computing and has been Associate Editor of the IEEE Transactions on Computers, the IEEE Transactions on Neural Networks, the IEEE Transactions on Instrumentation and Measurement and IEEE Access. He received the IEEE Instrumentation and Measurement Society Technical Award (2002) and the IEEE TAB Hall of Honor (2019).

Editors and Contributors

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Contributors Agarwal Ambuj Kumar Chitkara University, Chandigarh, Punjab, India Agarwal Rashi Department of MCA, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India Akila A. School of Computing, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India Ali Sarwar HMRITM, Delhi, India Anand Abhineet Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Anupriya E. KL University, Hyderabad, India Arguelles Pastor Jr University of Perpetual Help System DALTA, Las Piñas, Philippines Arif Abu Shamim Mohammad Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh Arora Prakriti HMRITM, Delhi, India Arvindhan M. Galgotias University, Greater Noida, UP, India Azzaoui Nadjet University Kasdi Merbah Ouargla, Ouargla, Algeria Bajaj Shalini Bhaskar Department of Computer Science, Amity University, Gurugram, India Bakshi Gaytri School of Computer Science, University of Petroleum and Energy Studies, Bidholi, Dehradun, Uttrakhand, India Bandopadhyay Srinjini Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India Banerjee Arghasree Department of Computing Science, University of Alberta, Edmonton, Canada Bansal Aditya Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Bansal Mohit UIET, Panjab University, Chandigarh, India Bansal Sandhya M. M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, India Baruah Ashim Jyoti Department of Mathematics, Namrup College, Namrup, Assam, India Batra Jagprit HERE Technologies, Mumbai, India

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Editors and Contributors

Bera Kallol Department of Computer Science and Engineering, Neotia Institute of Technology, Management and Science, Kolkata, West Bengal, India Beri Rydhm University Institute of Computing, Chandigarh University, Mohali, India Bhambay Rajul Netaji Subhas University of Technology, New Delhi, India Bhardwaj Jatin HMRITM, Delhi, India Bhardwaj Samviti HMRITM, Delhi, India Bhasin Priyansha Tata Consultancy Services, Gurugram, Haryana, India Bhattacharya Abhishek Institute of Engineering and Management, Kolkata, India Bhoir Amit HERE Technologies, Mumbai, India Bhushan Megha School of Computing, DIT University, Dehradun, India Boosi Shyamala GITAM Deemed to Be University, Bengaluru, Karnataka, India Borah Rupjyoti Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, India Bulo Yaka Department of Electronics and Communication Engineering, National Institute of Technology, Arunachal Pradesh, Jote, India Burlea-Schiopoiu Adriana University of Craiova, Craiova, Romania Chakraborty Aritro Department of Computer Science & Engineering, University of Engineering & Management, Kolkata, India Chakraborty Chirantan Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India Chakraborty Sanjay Computer Science and Engineering, Techno International Newtown, Kolkata, India Chatterjee Deborup Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India Chatterjee Sankhadeep Department of Computer Science & Technology, University of Engineering & Management, Kolkata, India Chaudhary Amit Harcourt Butler Technical University, Kanpur, India Chaudhary Ashvarya University Institute of Computing, Chandigarh University, Chandigarh, Punjab, India Chaudhary Renu Branch of Information Technology, HMRITM, New Delhi, Delhi, India Chaudhry Rohil Deloitte USI, Hyderabad, India

Editors and Contributors

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Chawla Pronika Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Choudhury Tanupriya Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies (UPES), Bidholi Campus, Dehradun, Uttarakhand, India Chowdhury Sourav Computer Science and Engineering Department, JIS University, Kolkata, India Chowdhury Sudipta Roy Department of Computer Science & Engineering, University of Engineering & Management, Kolkata, India D’silva Godson HERE Technologies, Mumbai, India Das Rajesh Kumar Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, India Das Subhadeep Symbiosis Institute of Digital and Telecom Management Symbiosis International (Deemed University), Pune, India Das Vedant Rishi Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Das Prithwidip Institute of Engineering and Management, Kolkata, India Dasari Durga Bhavani Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, India Datta Drick Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India Deb Hridi Ranjan Silchar Collegiate School, Silchar, Assam, India Debnath Kamal Department of Mathematics, The Assam Royal Global University, Guwahati, Assam, India Dev Deep Suman Department of Computer Science and Engineering, Neotia Institute of Technology, Management and Science, Kolkata, West Bengal, India Dewangan Bhupesh Kumar Department of CSE, O. P. Jindal University, Raigarh, India Dey Debasish Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, India Dihidar Souvik Department of Computer Science, Eminent College of Management and Technology, Barasat, West Bengal, India Dutta Soumi Institute of Engineering and Management, Kolkata, India Fatima Shaheen Department of Applied Electronics, Gulbarga University, Kalaburagi, Karnataka, India

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Editors and Contributors

Gampala Veerraju Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India Ganguly Gargi Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India Gautam Siddharth Branch of Information Technology, NSUT, New Delhi, Delhi, India Ghosh Goldina Institute of Engineering & Management, Kolkata, West Bengal, India Ghosh Kushankur Department of Computing Science, University of Alberta, Edmonton, Canada Ghosh Subhadip Computer Science and Engineering Department, JIS University, Kolkata, India Goel Raghav Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Goon Surajit Department of Computer Science and Engineering, Brainware University, Barasat, West Bengal, India Grover Chetna HMRITM, Delhi, India Gunawan Dedi Universitas Muhammadiyah Surakarta, Surakarta, Indonesia Gupta Kavita University Institute of Computing, Chandigarh University, Gharuan, India Gupta Sandeep Kumar Department of Computer Science Engineering, Dr Kedar Nath Modi University, Tonk, Rajasthan, India Halder Basudev Department of Computer Science and Engineering, Neotia Institute of Technology, Management and Science, Kolkata, West Bengal, India Hasan Mohammad Kamrul Universiti Kebangsaan Malaysia, Bangi, Malaysia Hazarika Madhurya Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, India Hossain Md. Javed Noakhali Science and Technology University, Noakhali, Bangladesh Hrushikesava Raju S. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, India Islam Syful Noakhali Science and Technology University, Noakhali, Bangladesh Jajula Sai Kalki Department of Computer Science, Amity University, Gurugram, India Joshi Krishn Kumar ITM University, Gwalior, India

Editors and Contributors

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Joshi Priyanshu HMRITM, Delhi, India Kabir Mohammed Humayun Noakhali Science and Technology University, Noakhali, Bangladesh Kalra Anushka Branch of Computer Science Engineering, Mahavir Swami Institute of Technology, Sonipat, India Kansal Gaurav ABES Engineering College, Ghaziabad, Uttar Pradesh, India Kasturi Nivedita KLE Technological University, Hubli, Karnataka, India; PES University, EC Campus, Bengaluru, Karnataka, India Kaur Gagandeep University of Petroleum and Energy Studies, Dehradun, India Kaushal Payal Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India Kaushik Vandana Dixit Department of Computer Science and Engineering, HBTU, Kanpur, India Khaitana Supriya School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India Khurana Ajay University School of Business, Chandigarh University, Gharuan, India Khurana Meenu Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India Krit Salahddine Ibn Zohr University, Agadir, Morocco Krithika M. School of Computing, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India Kumar Ajay Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Kumar Ashok School of Computer Application, Lovely Professional University, Phagwara, Punjab, India Kumar Raju University Institute of Computing, Chandigarh University, Mohali, India Kumar Rishabh HMRITM, Delhi, India Kumar Vinod Department of Computer Science and Engineering, Delhi Technological University, Delhi, India Kumari Arpana Symbiosis Centre for Management Studies, Noida, India Kurniawan Yogiek Indra Universitas Jenderal Soedirman, Purwokerto, Indonesia Lai Wen Cheng National Taiwan University of Science and Technology, Taipei, Taiwan

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Editors and Contributors

Le Dac-Nhuong Haiphong University, Haiphong, Vietnam Lilhore Umesh Kumar Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Mahanta Bhagyashree Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, India Maity Soumen Computer Science and Engineering Department, JIS University, Kolkata, India Malhorta Rishabh University of Petroleum and Energy Studies, Dehradun, India Malhotra Manisha Chandigarh University, Chandigarh, India Mandal Ratna Institute of Engineering and Management, Kolkata, India Manhas Pratima Manav Rachna International Institute of Research and Studies, Faridabad, India Manhas Saloni University Institute of Computing, Chandigarh University, Mohali, India Manjusha Nambiar P. V. KL University, Hyderabad, India Mann Prince Branch of Information Technology, HMRITM, New Delhi, Delhi, India Maram Balajee Chitkara University Institute of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh, India Mary Sheeba R. Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, India Mini Research Scholar, Dept. of Mathematics, Chandigarh University, Gharuan (Mohali), Punjab, India; Assistant Professor, S.A.Jain College, Ambala City, Haryana, India Mishra Anurag ABES Engineering College, Ghaziabad, Uttar Pradesh, India; GLA University, Mathura, Uttar Pradesh, India Mittal Himani Department of E&C, R.K.G.I.T. Ghaziabad, Ghaziabad, India Motwani Dilip Vidyalankar Institute of Technology, Mumbai, India Mukherjee Rajendrani Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India Mutreja Mehar Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Nadhan Archana S. GITAM Deemed to Be University, Bengaluru, Karnataka, India

Editors and Contributors

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Nandy Meghna Department of Computer Science and Engineering, Neotia Institute of Technology, Management and Science, Kolkata, West Bengal, India Nerurkar Amit K. Thadomal Shahani Engineering College, Mumbai, India Nugroho Yusuf Sulistyo Universitas Indonesia

Muhammadiyah

Surakarta,

Surakarta,

Obaid Ahmed J. Faculty of Computer Science and Maths, Kufa University, Kufa, Iraq Olivia Diana Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Pal Ashok Professor, Dept. of Mathematics, Chandigarh University, Gharuan (Mohali), Punjab, India Pal Birabrata Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India Pal Debdutta Department of Computer Science and Engineering, Brainware University, Barasat, West Bengal, India Pal Shayan Institute of Engineering and Management, Kolkata, India Pandey Anand Kumar ITM University, Gwalior, India Pandey Rashmi ITM University, Gwalior, India Pandey Ruchi Hanu Software Solutions, Greater Noida, India Parameswari R. Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, India Parashar Sanidhya Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Patel Abhishek HMRITM, Delhi, India Pathak Pankaj Symbiosis Institute of Digital and Telecom Management Symbiosis International (Deemed University), Pune, India Pillai Samaya Symbiosis Institute of Digital and Telecom Management Symbiosis International (Deemed University), Pune, India Popli Saahil GGSIPU, Delhi, India Prakash Ayushi Department of Computer Science Engineering, Dr Kedar Nath Modi University, Tonk, Rajasthan, India Priyanka HMRITM, Delhi, India Punj Devansh Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India

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Qahmash Ayman Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia Rai Ravi ITM Gwalior, Gwalior, India Ramkumar K. R. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India Rana Aniket Branch of Information Technology, HMRITM, New Delhi, Delhi, India Rani Poonam Netaji Subhas University of Technology, New Delhi, India Ravinder N. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, India Rawat Mukesh Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, U.P., India Revathi B. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, India Ruby A. Usha GITAM Deemed to Be University, Bengaluru, Karnataka, India Sagar Shrddha School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India Saha Bikash Koli Department of Mathematics, The Assam Royal Global University, Guwahati, Assam, India Sahu Minal University of Glasgow, Glasgow, UK Sai Pranav D. Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Saini Yuvraj Singh Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Samanta Saikat Department of Computer Science and Engineering, National Institute of Technology, Arunachal Pradesh, Jote, India Sarkar Achyuth Department of Computer Science and Engineering, National Institute of Technology, Arunachal Pradesh, Jote, India Sehgal Rashmita Department of Computer Science and Engineering, HBTU, Kanpur, India Sen Rahul University of Engineering and Management, Kolkata, India Sharma Akanksha HERE Technologies, Mumbai, India Sharma Hitesh Kumar School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, India

Editors and Contributors

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Sharma Lucky Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India Sharma Mayank GGSIPU, Delhi, India Sharma Nikhil Delhi Technological University, Delhi, India Sharma Purvi Cognizant Technology Solutions, Bengaluru, India Sharma Shagun Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Sharma Sukesha UIET, Panjab University, Chandigarh, India Sharma Tarun HMRITM, Delhi, India Shetty Jayashree S. Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Shetty Nisha P. Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Shrivastava Rahul Sanganan IT Solution, Noida, Uttar Pradesh, India Shukla Anand Kumar University Institute of Computing, Chandigarh University, Gharuan, Mohali, Punjab, India Shukla Rati Motilal Nehru National Institute of Technology, Prayagraj, Uttar Pradesh, India Shukla Vinod Kumar Department of Engineering and Architecture, Amity University, Dubai, UAE Sikhakolli Harika Lakshmi Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, India Singh Arun Kumar Institute of Management Studies, Ghaziabad, India Singha Sankar Department of Mathematics, The Assam Royal Global University, Guwahati, Assam, India Singh Vibhor HMRITM, Delhi, India Sinha Shivaji JSS Academy of Technical Education, Noida, Uttar Pradesh, India Sinha Swagata Department of Computer Science and Engineering, Neotia Institute of Technology, Management and Science, Kolkata, West Bengal, India Sonawane Swapnil Vidyalankar Institute of Technology, Mumbai, India Soufiene Ben Othman University of Sousse, Sousse, Tunisia Suja Alphonse A. Karunya Institute of Technology and Sciences, Coimbatore, India

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Editors and Contributors

Thakral Shaveta Zeal College of Engineering and Research, Pune, India Thakur Pooja Chandigarh University, Chandigarh, India Thampi G. T. Thadomal Shahani Engineering College, Mumbai, India Thind Sonia University Institute of Computing, Chandigarh University, Gharuan, Mohali, Punjab, India Tiwari Raj Gaurang Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Tiwari Sunishi School of Law, University of Petroleum & Energy Studies, Dehradun, India Totad S. G. KLE Technological University, Hubli, Karnataka, India Tripathi Himanshu Department of CS, R.K.G.I.T. Ghaziabad, Ghaziabad, India Tripathi Khushboo Department of Computer Science, Amity University, Gurugram, India Tripathi Shivansh Shrish Department of E&C, R.K.G.I.T. Ghaziabad, Ghaziabad, India Trivedi Naresh Kumar Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India Tukkoji Chetana GITAM Deemed to Be University, Bengaluru, Karnataka, India Tyagi Nitin HMRITM, Delhi, India Uddin Abdul Hasib Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh Upali Satya Jeet Raj UIET, Panjab University, Chandigarh, India Vaibhav Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Venkateswarlu B. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, India Verma Jyoti Manav Rachna International Institute of Research and Studies, Faridabad, India Verma Kanupriya Thapar Institute of Engineering and Technology, Deemed to Be University, Patiala, Punjab, India Verma Krishnandan Department of Mathematics, Dibrugarh, Assam, India Verma Prabhat Harcourt Butler Technical University, Kanpur, India Verma Romil Ernst and Young GDS, Noida, Uttarpradesh, India

Editors and Contributors

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Vijay Shreyas Branch of Information Technology, HMRITM, New Delhi, Delhi, India Yadav Kusum College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia Yadav Raveena Department of Computer Science and Engineering, Delhi Technological University, Delhi, India Yadav Vikash Department of Technical Education, ABES Engineering College, Ghaziabad, Uttar Pradesh, India; Department of Technical Education, Lucknow, Uttar Pradesh, India

Computational Intelligence

An Interpretive Saga of SQL Injection Attacks Saloni Manhas

Abstract Web applications security is indeed the most talked about and a vital topic of discussion in cyber security world. In the recent years, websites have become a main target for hackers. Attackers exploit vulnerabilities present in the web applications. Application level attacks are increasing day by day in websites. Some of the main application level attacks are SQL injections, cross-site scripting attacks, cookie poisoning attack, command injection attack, etc. According to OWASP, injection attacks occupy first place in breaching website security. There are several government, private and e-commerce websites that become victim of SQL injection attack each year. This paper includes the detailed study of mitigation strategies proposed by various researchers related to SQL injection attack. Furthermore, defence mechanism to safeguard web applications from SQLi attacks and future research directions is also indicated. Keywords Structured query language injection (SQLi) · OWASP · Vulnerabilities · Websites · Malicious query · Attacks · Detection

1 Introduction It is very evident that most of the advances seen in the info technology sphere served both the great guys and the bad. Consequently, we have observed a high increase in the amount of spyware and web site exploits observed throughout websites. Therefore, in past years, one need to be an efficient programmer, social engineer and service administrator to misuse any website. But, today due to technological advancement, a person only needs basics of programming to compromise hundreds of websites [1]. All injection attacks are not limited to SQL injection, but it is a main and widely performed attack. It has been a prevailing attack for over fifteen years. If the data furnished by consumer is poorly validated then consumer may change a malevolent SQL claims and can do discretionary code on the mark process or modify S. Manhas (B) University Institute of Computing, Chandigarh University, Mohali, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_1

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S. Manhas

Fig. 1 SQLi severity level in web applications [2]

the table of contents of database [2]. Figure 1 highlights the areas where developers have to be more trained in order to understand the severity of web application attacks. SQLi can result in illegal exposure of sensitive data, such as customer credit card information and organizations may suffer from significant remediation costs after a SQLi attack [3]. SQL injection is a class of code that takes profit of deficiency of establishment of user input. It was an injection method that was used to violate data-oriented applications, in which harmful SQL queries were entered into an entry field for execution [1]. SQL injections utilize security loopholes in the database and open a path for hackers to do what they desire. The main purpose is to bypass the server level in the web application to achieve control over backend [4]. SQL injection attack continues to hold a position in OWASP list of web application attacks till year 2017 [5]. This paper focuses on different aspects of SQLi attacks and its tautologies.

2 Overview of SQL Injection Attacks SQL injections are implemented to exploit web applications which use client-side data in SQL statements. Thus, intruders gain illegitimate access to a database, allowing intruders to manipulate or view authorized information [6]. There are different methods through which SQL injections can occur [7]: • Injection through cookies—Websites that contains cookies are vulnerable to SQL injection attacks. Attacker can easily perform an attack in the cookies. • Second-order injection—This attack gets executed when malevolent query stored in the database is used. • Injection through user input—By modifying user queries to insert SQL commands, attackers can perform SQLi attack. Figure 2 shows the process of SQLIi attacks. SQL malicious query is injected by attacker which is further processed by database and query response is generated.

An Interpretive Saga of SQL Injection Attacks

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Fig. 2 General process of SQL injection [8, 9]

2.1 Types of SQL Injection Attacks There are various types of SQL injections attacks that can invade user’s privacy through different attack patterns [10]. The detailed description of each attack is given below [11]: A.

Tautologies

With SQL, your tautology is a statement that is definitely at all times true. SQL tautology can assault the job associated with injecting your pass code on a number conditional phrases order that the country’s outcome often be true. To illustrate, “Select * from worker where WorName = ‘ ’ or 1=1 - - and Password= ‘yyyy’ ” Here, ‘or 1=1’ is the SQL tautology. B.

Illegal/Logically Incorrect Queries

When error texts are displayed by database due to an incorrect query, this attack triggers by taking advantage of the error messages. These error messages from database contain helpful substance thus allowing hacker to find out the insecure factor in a practical application. C.

Union Query

This SQL injections assault is normally known as declaration injections attack. Attackers fit other terms in to the initial SQL statements. This assault is conducted by just getting sometimes a UNION query or maybe your firm stand out with the application “:” in to an insecure parameter. “SELECT * from Library where Books = ’ ‘ union select * from customer- - and Password = ’anypwd”’. The above written query becomes the union of two SELECT queries.

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S. Manhas

Piggy-Backed Queries

Assailant tries to supplement extra queries to the primary query string in piggybacked queries. In this process, the first query is primary, however, the consequent queries are injected. This assault is quite harmful as assaulter can use it to shoot virtually any type of SQL command. For example, “SELECT data FROM employee WHERE signup = ‘xyz’ AND pin=0; drop table employee”

E.

Stored Procedures

To perform this attack, aggressor focuses on stored procedures present in the DBMS. Stored procedure returns true/false for authorized/unauthorized user. To accomplish SQLIA, invader inputs “,;DROP;- -” for username or password. After this, stored procedure creates the following query: SELECT record FROM users WHERE login= ‘joe’ AND pass= ‘ ’; DROP; - AND pin=” This specific attack design functions for instance a piggy-backed attack. F.

Inference

This approach strike releases questions that will spark a databases or a questionnaire towards conduct yourself for a strange mode is dependent upon the result of a query. These types of approaches let your burglar towards get data out of databases and additionally space vulnerable parameter inference attack consists of following techniques: Blind Injection. An attacker frames a set of questions that have a Boolean result. If the reply is true, then application will behave appropriately, if answer is untrue, then it will generate an error. In this manner, intruder gets indirect outcome from database. G.

Timing attacks

H.

Alternate Encodings

To refrain the filter and signature-based checks, the intruder alters their injection strings known as alternate encoding technique, such as hexadecimal, ASCII and Unicode can be used in conjunction. I.

n – band SQLi

This attack type is can be easily exploited as compared to other SQLi attack types. Attacker launches malevolent codes into web application and obtains result from the database with the help of malevolent codes and database results. Results are usually displayed on the attacker’s screen [12].

An Interpretive Saga of SQL Injection Attacks

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3 Literature Survey Over the past two decades, researchers have done quality work in the area of SQLi attacks. By injecting a malicious SQL query into an entry field, attacker can get access of the details of the user. Stripslashes () and real-string function () are used by escaping special characters for SQL prevention. Some techniques to prevent SQL injections are as following [7]: • • • •

Input encoding Checking input types Identification of all input sources Positive pattern matching.

Authors have added that how SQL injections are used to poison cookies and steal information [13]. Defence mechanisms were discussed, where query parameterization, validating input and hashing functions points were discussed to prevent SQL injection attack. In this paper, authors have presented an overview on how to do a systematic research on web application vulnerabilities containing empirical research, containing various web-based vulnerabilities [14]. Various tables containing data characteristics, methods used for validation, research type and software development and spotting of security loopholes from OWASP Top 10 are drawn. Also, methodologies used for evaluating security have been discussed. Various techniques of SQLi attack have been specified here. Those are first-order attack, tautology attack, piggy-backed query, inference, logically false queries, alternate coding, union query and stored procedures [15]. Also, prevention technique by using stored procedures and adaptive algorithm is described. Paper is concluded by providing comparison table of various prevention schemes and attacks (Fig. 3). The

Fig. 3 Secured applications developed by different companies [16]

8

S. Manhas

paper consists of a survey of SQL prevention techniques. Authors have mentioned various applications that are developed by various companies to provide a suitable solution of this problem [16] Prevention of SQL injection attacks contains different methods such as penetration testing and defensive coding [2]. A survey of various techniques proposed by different researchers has been conducted. Approaches based on static time, run time and dynamic time are presented. AMNESIA technique to identify and repel SQL injection attacks has elaborated along with its principle and limitations. Authors have described step by step the process of entry of malicious user in the page without knowing correct username and password based on the ways mentioned in previous line. In this paper, authors have proposed two schemes for detection and prevention of SQL injection attacks. The proposed scheme has two sections as [17]: 1. 2.

Static phase—In this phase, user-generated SQL queries are examined by using static pattern matching algorithm. Here, list of anomaly pattern is maintained. Dynamic phase—If any type of new vulnerability is found, then alert will be indicated and new anomaly design will be produced. Therefore, the newly generated anomaly pattern will be introduced to the static pattern list.

SQL injection attacks are formulated with the idea of misusing, escalating privileges, modifying or deleting the contents can be classified into following categories [18]: • ORDERWISE SQLi attacks—These problems are executed when the code is shot into user’s code immediately or indirectly to achieve unauthorized access. • BLIND SQLi attacks—This attack is performed by framing series of true and false questions via SQL statements. By asking these questions, information is extracted from an application. Attacker gets the desired data from this attack rather than getting error messages. The ML-driven plan of attack mechanically generates different attack payloads which can be accomplished into inputs of web-based applications and then transfers them to a system that is sheltered by a firewall [19]. A survey of various safeguard methods of SQL injection attack is conducted by [20]. Tautology checker checks tautology attacks in web applications. CANDID tool is used to detect SQLi. AMNESIA is a tool designed for analysing and monitoring illegal queries before their execution on a database. Other various detection and prevention techniques are CSSE, SQL IDS, SQLPrevent, SQLRand, SQLChecker, JDBC Checker, WAVES, SECURITY GATEWAY, SQL DOM and WebSSARI. This paper provides methods to prevent SQL injection attacks in stored procedures. Tokenization method is used in the process to change SQL queries into number of helpful tokens [21]. Then, encoding of all the literals, table, information and fields is done on the query by AES algorithm to avoid SQLi attacks. Firstly, the database is secured after this all the process follows. Proposed methodology includes deploying techniques on both frontend and backend phases.

An Interpretive Saga of SQL Injection Attacks

9

4 SQLi Defence Mechanism To prevent web-based attacks, IDS and NIDS, IDS uses misuse and anomalous recognition strategies to fishing tackle by means of SQL hypodermic injection attacks works found at less expensive grade tiers like transport layer and network layer [17]. Table 1 shows various methods proposed by authors to prevent SQL injection attacks.

5 Conclusion and Future Research Directions Web application vulnerabilities are becoming a matter of trouble for the users as website surfing is no safer now. SQL injection attacks are also one of the prominent web application attacks that breaches security and integrity of a website. There is no doubt about this that researchers are doing valuable work in this field but, attackers somehow manage to break the protocol and exploit the vulnerability. This paper explains various aspects of SQL injection attacks regarding its process, key factors behind the attack, work done by various researchers previously, tools and techniques to detect and protect from SQLi attacks. Future research directions provided in the paper will help researchers to propose new methodologies as well as to remove the existing flaws in the system. As we all know that there is no such thing which is 100% secure but at least one can reduce the impact of an attack up to a great extent. This section consists of the future research directions of various research papers that are reviewed in the literature review section. It provides us a path or direction, which researchers can follow to carry forward the significant work done by various research bodies. Future work includes using the proposed algorithm by combining ASCII values with it in order to prevent SQL attacks [31]. Future recommendation consists of creating procedure for space management in the pattern matching section of the proposed architecture, because due to some large set of patterns, it becomes difficult to match the pattern appropriately [17]. Reports in the paper show that second-order SQLi attack’s impact on database is way higher than the other types. Therefore, future work can be done to minimize the impact of this attack [18, 32]. Executability is a root cause of the generation of SQLi attacks. So, future work consists of paying more attention towards this problem [33].

10

S. Manhas

Table 1 SQLi defence mechanism Paper No.

Methodology/Technique

Year

Black box testing

Description

[22]

AMNESIA

2017

⊠

The technique works by gathering static and dynamic both parts together to detect web application vulnerabilities at the runtime.

[23]

Neural network-based model

2015

⊠

This model describes training phase, validating phase and testing phase

[24]

SOFIA

2016

⊠

SOFIA is a prototype known as security oracle for SQLi vulnerabilities

[25]

BIOFUZZ

2014

☑

It is a black box approach which uses search-based testing

[26]

pSigene

2014

⊠

It is used to automatically generate trespass signatures just by excavation these large sum involving court computer data entirely on attacks

[27]

Joza

2015

⊠

This prototype model combines the strength of both positive and negative taint inferences

[28]

HIPS

2014

⊠

It is known as hybrid injection prevention system that uses both pattern matching inspection engine and machine learning

[29]

SEPTIC

2016

⊠

It aims to deal with that semantic mismatch problem

[30]

µ4SQLi

2014

☑

It is a black box automated testing approach which targets SQLi vulnerabilities

[19]

ML-Driven

2015

⊠

It is a machine learning approach that efficiently detects SQL vulnerabilities in web application firewalls

An Interpretive Saga of SQL Injection Attacks

11

References 1. Qian L, Zhu Z, Liu S (2015) Research of SQL injection attack and prevention technology, No 123456, pp 303–306 2. Johari R, Sharma P (2012) A survey on web application vulnerabilities (SQLIA, XSS) exploitation and security engine for SQL injection. In: Proceedings of the international conference on communication systems and networking technologies CSNT 2012, pp 453–458 3. Henderson D et al (2016) SQL injection: a demonstration and implications for accounting students 11(1) 4. Nagpal B, Chauhan N, Singh N (2015) A viable solution to prevent SQL injection attack using SQL injection 3(3) 5. Nagpal B, Chauhan N, Singh N (2017) A survey on the detection of SQL injection attacks and their countermeasures. J Inf Process Syst 13(4):689–702 6. Jang YS, Choi JY (2014) Detecting SQL injection attacks using query result size. Comput Secur 44:104–118 7. Pathak MP, Khan NK, Tantak TC, Phata PPP (2016) Novel approach to detect and prevent web attacks, pp 504–510 8. Nehra V, Gulati N (2016) Database security against SQL injection attacks using three level security approach. Int J Eng Sci Comput 6(5):4650–4656 9. Kushwah A, Singh G (2014) SQL injection attacks: prevention for all types of attacks 2(2):37– 42 10. Patel KV, Sheth R (2017) Survey on prevention of web injection using WAF and input whitelisting, No March, pp 117–120 11. Chavda KS (2014) Int J Adv Eng Res pp 173–179 12. Ojagbule O, Wimmer H, Haddad RJ (2018) Vulnerability analysis of content management systems to SQL injection using SQLMAP. In: Conference of proceedings - IEEE SOUTHEASTCON, vol 2018–April, pp 1–7 13. Kirit CI, Kumar Chuabay V, Patel AR (2016) Secure web application: preventing application injections 1(1):143–147 14. Musa Shuaibu B, Md Norwawi N, Selamat MH, Al-Alwani A (2015) Systematic review of web application security development model. Artif Intell Rev 43(2):259–276 15. Verma N (2015) A detailed study on prevention of SQLI attacks for web security 4(4):308–311 16. Elshazly K, Fouad Y, Saleh M, Sewisy A (2014) A survey of SQL injection attack detection and prevention. J Comput Commun 02(08):1–9 17. Prabakar MA, KarthiKeyan M, Marimuthu K (2013) An efficient technique for preventing SQL injection attack using pattern matching algorithm. In: 2013 International conference on emerging trends in computing, communication and nanotechnology (ICE-CCN), No ICECCN, pp 503–506 18. Sharma C, Jain SC (2014) Analysis and classification of SQL injection vulnerabilities and attacks on web applications. In: 2014 International conference on advances in engineering and technology research, ICAETR 2014 19. Appelt D, Nguyen CD, Briand L (2015) Behind an application firewall, are we safe from SQL injection attacks? In: 2015 IEEE 8th International conference on software testing, verification, and validation, ICST 2015 - Proceedings 20. Dehariya H, Kumar Shukla P, Ahirwar M (2016) A survey on detection and prevention techniques for SQL injection attacks. Int J Wirel Microw Technol 6(6):72–79 21. Ntagwabira L, Kang SL (2010) Use of query tokenization to detect and prevent SQL injection attacks. In: 2010 3rd International conference on computer science and information technology, vol 2. IEEE, New York, pp 438–440 22. Djuric Z (2013, September) A black-box testing tool for detecting SQL injection vulnerabilities. In: 2013 Second international conference on informatics & applications (ICIA). IEEE, New York, pp 216–221

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23. Sonewar PA, Mhetre NA (2015) A novel approach for detection of SQL injection and cross site scripting attacks. In: 2015 International conference on pervasive computing (ICPC). IEEE, New York, pp 1–4 24. Ceccato M, Nguyen CD, Appelt D, Briand LC (2016) SOFIA: an automated security oracle for black-box testing of SQL-injection vulnerabilities. In: 2016 31st IEEE/ACM International conference on automated software engineering (ASE). IEEE, New York, pp 167–177 25. Thomé J, Gorla A, Zeller A (2014) Search-based security testing of web applications. In: Proceedings of the 7th International workshop on search-based software testing, pp 5–14 26. Howard GM, Gutierrez CN, Arshad FA, Bagchi S, Qi Y (2014) pSigene: Webcrawling to generalize SQL injection signatures. In: 2014 44th annual IEEE/IFIP International conference on dependable systems and networks. IEEE, New York, pp 45–56 27. Naderi-Afooshteh A, Nguyen-Tuong A, Bagheri-Marzijarani M, Hiser JD, Davidson JW (2015, June) Joza: hybrid taint inference for defeating web application SQL injection attacks. In: 2015 45th Annual IEEE/IFIP International conference on dependable systems and networks. IEEE, New York, pp 172–183 28. Makiou A, Begriche Y, Serhrouchni A (2014) Improving web application firewalls to detect advanced SQL injection attacks. In: 2014 10th International conference on information assurance and security. IEEE, New York, pp 35–40 29. Medeiros I, Beatriz M, Neves N, Correia M (2016) Hacking the DBMS to prevent injection attacks. In: Proceedings of the Sixth ACM conference on data and application security and privacy, pp 295–306 30. Appelt D, Nguyen CD, Briand LC, Alshahwan N (2014) Automated testing for SQL injection vulnerabilities: an input mutation approach. In: Proceedings of the 2014 International symposium on software testing and analysis, pp 259–269 31. Srivastava M (2014) Algorithm to prevent back end database against SQL injection attacks. In: 2014 International conference on computing for sustainable global development, INDIACom 2014, pp 754–757 32. Shahriar H, North S, Chen W (2013) Early detection of SQL injection attacks. Int J 5(4):53–65 33. Appelt D, Nguyen CD, Alshahwan N (2014) Automated testing for SQL injection vulnerabilities An.pdf

Numerical Simulation of Boundary Layer Flow of MHD Influenced Nanofluid Over an Exponentially Elongating Sheet Debasish Dey and Rupjyoti Borah

Abstract The present problem investigates the nature of simultaneous effects of thermal and mass diffusions on the MHD influenced nanofluid stream through absorbent region. The absorbent region is characterized by an exponentially stretching sheet. Similarity transformation has been used in the methodology to change the nature of differential equations. Due to the complexity introduced by nonlinearity of these coupled equations, the analytical approach does not hold suitably. Therefore, the “4th order Runge-Kutta Shooting technique” is utilized to solve these equations by developing programming code in MATLAB. Influences of various flow parameters on the motion with thermal and mass diffusions are discussed through graphs. Also, the numerical values of physical quantities are discussed in tabular form. Keywords Nanofluid · Heat and mass transfers · Chemical reaction · MHD · Exponentially stretching sheet · Porous medium · Shooting method

Nomenclature u v T C L T0 C0 υ μ Pr

Velocity along x− axis Velocity along y− axis Wall temperature Wall concentration Characteristic length Characteristic temperature Characteristic concentration Kinematic viscosity Dynamic viscosity Prandtl number

D. Dey · R. Borah (B) Department of Mathematics, Dibrugarh University, Dibrugarh, Assam 786004, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_2

13

14

Kr∗ s DB Nb K∗ k ρf ρp Cp σ B0 K M Le g ψ DT Nt

D. Dey and R. Borah

Rate of chemical reaction Suction parameter Brownian motion coefficient Brownian motion parameter Porosity Thermal conductivity Density of the base fluid Density of the nanoparticle Specific heat at constant pressure Electrical conductivity Strength of magnetic field Permeability of the porous medium Magnetic parameter Lewis number Chemical reaction parameter Dimensionless stream function Thermophoresis coefficient Thermophoresis parameter

Suffix w ∞

At wall At ambient position

1 Introduction Hydro-magnetic influenced fluid motion over an exponentially stretching/shrinking geometry has imperative appliances in manufacturing processes like MHD electrical power generation, production and extraction of rubber, paper production, medical sciences and many other fields. In the recent times, the significance of porous medium in biofluid dynamics and biomechanics has taken a great deal of interest into research areas. In the human body, a huge part of the tissues like ligaments, muscles and bones form a distortable porous media. Some examples of natural porous mediums are water flows from the soil to the atmosphere, water with inorganic nutrients flows from the root to the leaves and homemade water treatments. The nature of flow induced by stretching/shrinking surface was first analysed by Crane [1]. Kumar [2] has investigated the MHD fluid flow with porous medium by considering exponentially shrinking surface. Recently, the importance of this flow model increases and many authors influenced by this model and hence put their ideas by taking different

Numerical Simulation of Boundary Layer Flow of MHD …

15

fluids. Abbas et al. [3], Petrovic et al. [4], Dey [5] and Debnath et al. [6] have shown the nature of hydro-magnetics influenced fluid motion over stretching surface. The combined thermal and mass transfer analysis of the different fluids flow has received a huge amount of significance into research areas due to its applications in mechanical engineering sciences, medical sciences and industrial processes. The heat transfer characteristic with the stretching surface has many industrial processes such as annealing and thinning of Copper wire and extrusion of polymer. Recently, Dey and Borah [7, 8], Nayak et al. [9], Rehman et al. [10] and Dey et al. [11] have studied the both thermal and mass diffusion effects on different fluid models and concluded with significant results. The enhancement of heat transfer rate of a fluid is an essential requirement for several industrial processes. Dispersing of nano-sized materials into traditional fluids is one of the most crucial technique to enhance the heat transfer rate. In the recent time, most of the industrial processes and engineering problems are worked out by using nano-technology. Nanofluids have lots of applications in different fields such as electronic applications, transportation, medical sciences and space and defence. Choi [12] was the first author who have coined the term “Nanofluid”. He has demonstrated experimentally that addition of small amount of nanoparticles noticeably boosts up the effective thermal conductivity of the base fluids. From this era, many researchers have been developing the theory of nanofluids and importance of nano-technology. In the recent time, Das et al. [13], Ghosh and Mukhopadhyay [14], Khashi’ie et al. [15], Ali et al. [16], Ho et al. [17] and Prasannakumara [18] and so many authors have studied the flow behaviours of nanofluids with thermal and mass diffusions by considering different flow configurations with different problem solver schemes. Based on the above-mentioned literatures, we have scrutinized the nature of nanofluid flow under the influence of variable magnetic field due to an exponentially stretching sheet. The leading equations of this problem are modernized by applying suitable similarity transformations and hence solved with the support of fourth-order Runge-Kutta Shooting technique. The flow characteristics in terms of velocity, thermal and mass fractions are presented graphically for different flow parameters. To validate our works, we have collated our numerical values of skin friction coefficients with the analytical results of Kumar [2], which reflects a perfect conformity. Also, a comparative numerical values of physical quantities in between Shooting and MATLAB built in bvp4c schemes have been established.

2 Mathematical Formulation Here, we have taken the time-independent and two-dimensional boundary layer nanofluid flow induced by an exponentially ( stretching ) sheetx which is situated in porous medium. A variable magnetic B = 0, B y , 0 = B0 e 2L is applied along the normal direction of the flow. Following Kumar [2], the form of the variable porosity x of the porous medium is K ∗ = K 0 e− 2L where K 0 is the constant. The sheet is

16

D. Dey and R. Borah

Fig. 1 Schematic diagram of the present flow problem

characterized by the stretching velocity Uw (x) = ce− L where the constant c > 0 corresponds to stretch at the surface of the sheet (Fig. 1). Mathematical forms of the physical laws are shown below: x

∂v ∂u =− , ∂x ∂y

(1)

σ B y2 ∂u ∂ 2u υ ∂u +v =υ 2 − u− u, ∂x ∂y ∂y ρ K∗ ⎡ ) ⎤ ( ∂C ∂ T ∂T k ∂2T DT ∂ T 2 ∂T + τ DB +v = + , u ∂x ∂y ρC P ∂ y 2 ∂y ∂y T∞ ∂ y u

u

∂C ∂ 2C DT ∂ 2 T ∂C − Kr ∗ (C − C∞ ). +v = DB 2 + ∂x ∂y ∂y T∞ ∂ y 2

(2)

(3)

(4)

(ρc)

where τ = (ρc) pf is the fraction of the effective heat capacity of the nanoparticle material and the host fluid. The interconnected boundary conditions are: y = 0 : u − Uw (x) = 0, v − vw = 0, x

x

T = Tw (x) = T∞ + T0 e 2L , C = Cw (x) = C∞ + C0 e 2L ; y → ∞ : u → 0, T → 0, C → 0.

(5)

Numerical Simulation of Boundary Layer Flow of MHD …

17

2.1 Similarity Transformation and Similarity Equations The following similarity transformations (following Kumar [2]) are utilized to alter Eqs. (1)–(4) into a set of solvable equations: √ T − T∞ c/2υ L ye x/2L , θ (η) = , Tw − T∞ C − C∞ K0c 2LKr∗ − x σ B02 L . (6) ϕ(η) = ,K = ,g = e L, ,M = Cw − C∞ ρc υL c τ D B (Cw − C∞ ) τ DT (Tw − T∞ ) Nb = , Nt = , Le = υ/D B , Pr = μC p/k υ υT∞

ψ=

√

2υ Lce x/2L f (η), η =

&v = − ∂ψ . where ψ is characterized in the form of velocity components as u = ∂ψ ∂y ∂x The continuity Eq. (1) is clearly hold and the other Eqs. (2)–(4) become in the following form: K f ,,, + K f f ,, − 2K f ,2 − 2K M f , − 2 f , = 0

(7)

θ ,, + Pr( f θ , − f , θ ) + Pr Nb θ , ϕ , + Pr Ntθ ,2 = 0,

(8)

( ) Nt ,, θ − Le gφ = 0 φ ,, + Le f φ , − f , φ + Nb

(9)

The relevant boundary conditions (5) receive the subsequent form: η = 0 : f (η) = s, f , (η) = 1, θ (η) = 1, φ(η) = 1; η → ∞ : f , (η) → 0, θ (η) → 0, φ(η) → 0.

(10)

2.2 Physical Quantity In many physical areas, some physical quantities have played an important role to characterize impact of fluid flow at the surface of the geometries. The dimensionless numbers which are very important examined in the present problem are viscous drag coefficient (C f ), thermal diffusion rate at the surface [Nusselt number(Nu)] and concentration accumulation rate [Sherwood number-(Sh)]. Mathematical expressions of these quantities are: Cf =

) ) ( ) ( ( ∂u ∂T ∂C μ L L , Nu = − &Sh = − ρUw2 ∂ y y=0 Tw − T∞ ∂ y y=0 Cw − C∞ ∂ y y=0 (11)

18

D. Dey and R. Borah

Applying Eq. (6) into Eq. (11), we have got the following quantities: √ x C f 2Ree 2L = f ,, (0), where Re =

cL υ

/

/ 2 −x 2 −x , e 2L Nu = −θ (0)& e 2L Sh = −φ , (0). Re Re

(12)

the Reynolds number.

3 Methodology Due to the nonlinearity introduced in the set of Eqs. (7)–(9), the analytical schemes are not suitable for solving these equations. Therefore, the MATLAB 4th order Runge-Kutta Shooting scheme is employed to solve the nonlinear couple Eqs. (7)– (9) numerically along with the boundary restrictions (10). This method is one of the most widely used techniques for solving a boundary value problem of fluid dynamics. This method renovates a boundary value problems to an initial value problems. A trial and error approach is then executed to develop the solutions for the initial value version that satisfies the given boundary restrictions. Equations (7)–(9) are written in the form of following three functions in this scheme by introducing new variables as follows. Let, f , = f 1, f ,, = f 2, θ = th, θ , = th1, φ = ph, φ , = ph1. Therefore, we have got following functions which represents Eqs. (7), (8) and (9), respectively. function y = f f (K , M, f, f 1, f 2) x1 = −K ∗ f ∗ f 2 + 2 ∗ K ∗ f 1 ∗ f 1 + 2 ∗ K ∗ M ∗ f 1 + 2 ∗ f 1; x2 = K ; y = x1/x2; end function y = tt(Pr, Nb, Nt, f, f 1, th, th1, ph1) x1 = − Pr ∗ f ∗ th1 + Pr ∗ f 1 ∗ th − N b ∗ th1 ∗ ph1 − N t ∗ th1 ∗ th1; y = x1; end function y = phh(Nt, Nb, Le, g, f, f 1, th2, ph, ph1) x1 = Le ∗ ( f 1 ∗ ph − f ∗ ph1) − (Nt/Nb) ∗ th2 + Le ∗ g ∗ ph; y = x1; end The shooting method does not require linearization of the equations, which has been successfully applied to this class of problems. It adjusts the initial conditions so

Numerical Simulation of Boundary Layer Flow of MHD …

19

that the solution of the related initial value problem satisfies the required boundary conditions at the outer boundary point. In case of boundary value problems of second-order differential equations, one condition is prescribed at either of the end points. Hence, at the initial point of integration, one condition is always missing. Shooting method estimates the missing initial condition in such a way that the estimation satisfies the condition prescribed at the boundary too, to some desired accuracy. At the beginning, the missing value is guessed and refined by using an iterative technique until the desired accuracy is obtained.

4 Discussion of the Results In this study, a special emphasis is given on the characteristics of the flow behaviour with thermal and mass transmissions for different values of flow parameters M, K , g, Nt, Nb&Le on the consequent flow problem. Figures 2 and 3 are depicted to show the presence of magnetic field on the nanofluid flow. Application of magnetic field develops a resistive type force called “Lorentz force” which resists the motion of the nanofluid (see Fig. 2). That is, the motion of the fluid decelerates with enlarging values of M. Also, the application magnetic fields help to enhance the temperature of the nanofluid (see Fig. 3). It can be attributed that application of magnetic field decelerates the motion of the fluid and consequently 1

0.9

Pr = 0.71, Le = 2, s=0.23, K=0.5, Kr= 0.2,

M=0.0

Nt=0.2, Nb = 0.3

M=0.5 M=1.0

0.8

M=1.5

0.7

f'( )

0.6

0.5

0.4

0.3

0.2

0.1

0 0

Fig. 2 Velocity for M

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

20

D. Dey and R. Borah 1 M = 0.0

Pr = 0.71, Le =2, Nb = 0.5, K = 0.5,

0.9

M = 0.5

Nb = 0.5,g = 0.2, s = 0.23

M = 1.0

0.8

M = 1.5

0.7

0.5

(

)

0.6

0.4

0.3

0.2

0.1

0

0

0.5

1

1.5

2

2.5

3

3.5

4

Fig. 3 Temperature for M

a kind of friction generates which enhances the thermal energy. These results of M establish the certainty of the theory of the physics that the Lorentz force plays as a hindering force and subsequently, the motion of the fluid is controlled by applying magnetic field. Influence of the thermophoresis parameter (Nt) on the temperature and mass fraction of the nanofluid is represented in Figs. 4 and 5. From these figures, it is perceived that both the temperature and mass fractions of the nanofluid are escalating functions of N t. Figure 6 is portrayed to show the influence of Nb which is responsible for the Brownian motion of the nanofluid on the temperature field of the system. It is perceived that the enhancing values of Nb enhance the thickness of the thermal boundary layer and hence the temperature of the nanofluid grows up. But, impact of Nb reduce the thickness of the concentration boundary layer of the nanofluid and consequently, it lessens the volume fraction of the nanoparticles (see Fig. 7). Validation of the results In the nonappearance of nanoparticles, chemical reaction effects and mass diffusion equations, the governing equations of this present problem (1)–(3) are equivalent to published work of Kumar [2]. We have compared our numerical values of shear stress at the surface (see Table 1) for different values of suction parameter with the results of Kumar [2], which gives a perfect conformity of our results. Thus, the utilization of 4th order Runge- Kutta Shooting technique in this problem is acceptable.

Numerical Simulation of Boundary Layer Flow of MHD …

21

1 Nt = 0.1

Pr = 0.71, Le =2, M = 0.5, K = 0.5,

Nt = 0.3

Nb = 0.5,g = 0.2, s = 0.23

0.8

Nt = 0.5 Nt = 0.7

0.6

(

)

0.4

0.2

0

-0.2

-0.4 0

1

0.5

2

1.5

2.5

3

4

3.5

4.5

Fig. 4 Temperature for Nt

Nt = 0.2

1.6

Nt = 0.4 Nt = 0.6

1.4

Nt = 0.8

1.2

(

)

1

0.8

0.6

0.4

Pr = 0.71, Le =2, M = 0.3, K = 0.5,

0.2

Nb = 0.3,g = 0.1, s = 0.23 0 0

0.5

Fig. 5 Mass fraction for Nt

1

1.5

2

2.5

3

3.5

4

4.5

5

22

D. Dey and R. Borah 1 Nb = 0.2

Pr = 0.71, Le =2, M = 0.5, K = 0.5,

Nb = 0.4

Nt = 0.5,g = 0.2, s = 0.23

0.8

Nb = 0.6 Nb = 0.8

0.6

(

)

0.4

0.2

0

-0.2

0

1

0.5

2

1.5

2.5

3

4

3.5

4.5

Fig. 6 Temperature for Nb 1 Nb = 0.6

Pr = 0.71, Le =2, M = 0.3, K = 0.5,

0.9

Nb = 0.8

Nt = 0.3,g = 0.1, s = 0.23

Nb = 1.0

0.8

Nb = 1.2

0.7

( )

0.6

0.5

0.4

0.3

0.2

0.1

0 0

0.5

Fig. 7 Mass fraction for Nb

1

1.5

2

2.5

3

3.5

4

4.5

5

Numerical Simulation of Boundary Layer Flow of MHD …

23

Table 1 Numerical values of skin friction coefficient f ,, (0) for exponentially shrinking sheet when Sc = 0, Kr = 0& Pr = 0.71 Values of s

Value of K

Value of M

Analytical solutions (Kumar [2]) of f ,, (0)

Shooting method solution (Present results) f ,, (0)

2

0.5

1

0.5938

0.5950

1.7500

1.8520

3

Table 2 Numerical values of physical quantities for different values Nt and Nb when M = 0.2, Pr = 0.71, K = 0.5, Le = 2, g = 0.1&S = 0.23

Nt

Nb

Sherwood number (−φ , (0))

Shooting method solution

Shooting method solution

0.6879

1.0139

0.5

0.5931

0.5072

0.8

0.5239

0.2397

0.2

0.5685

1.4000

0.5

0.4416

1.5193

0.8

0.3574

1.5625

0.2

0.2

0.2

Nusselt number (−θ , (0))

Table 2 gives the influence of Nt and Nb on the Nusselt and Sherwood numbers under the fixed values of other flow parameters. From this table, it is apparent that the thermophoresis parameter (Nt) condenses the Nusselt number as well as the Sherwood number. Again, the Nusselt number of the nanofluid effects on the surface of the sheet under the influence of Nb reduces. But, it enhances the effect of Sherwood number of the nanofluid on the surface of the sheet.

5 Conclusion The overall conclusion of this investigation are made in the following way: (1)

(2) (3)

The effect of M controls the motion of the nanofluid, whereas the thermal fraction of the nanofluid enhances. The thermal and mass fractions of the nanofluid are increasing functions of Nt. Due to the effects of Nb, the thermal fraction of the nanofluid enhances and the mass fraction of the nanofluid diminishes. The thermal and mass fractions of the fluid boost up with the thermophoresis parameter.

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References 1. Crane LJ (1970) Flow past a stretching plate. Zeitschrift für Angew Math und Phys ZAMP 21(4):645–647 2. Kumar R (2015) Combined effects of variable magnetic field and porous medium on the flow of MHD fluid due to exponentially shrinking sheet. Int J Math Archive 6(6):218–226 3. Abbas Z, Hasnain J, Sajid M (2019) Effects of slip on MHD flow of a dusty fluid over a stretching sheet through porous space. J Eng Thermophys 28(1):84–102 4. Petrovi´c J, Stamenkovi´c Ž, Koci´c M (2018) MHD flow and heat transfer in porous medium with induced magnetic filed effects. Int J Eng, pp 171–174 5. Dey D (2017) Hydromagnetic Oldroyd fluid flow past a flat surface with density and electrical conductivity stratification. Lat Am Appl Res 42(2):41–45 6. Debnath K, Dey D, Borah R (2020) Thermophoresis and diffusion thermo effects on shear thickenning and shear thining cases of fluid motion past a permeable surface. J Mech Continnua Math Sci 5:68–81 7. Dey D, Borah R (2020) Dual solutions of boundary layer flow with heat and mass transfers over an exponentially shrinking cylinder: stability analysis. Lat Am Appl Res 50(4):247–253 8. Dey D, Borah R (2021) Stability analysis on dual solutions of second-grade fluid flow with heat and mass transfers over a stretching sheet. Int J Thermofluid Sci Technol 8(2) 9. Nayak MK, Mabood F, Makinde OD (2020) Heat transfer and buoyancy-driven convective MHD flow of nanofluids impinging over a thin needle moving in a parallel stram influenced by Prandtl number. Heat Transf - Asian Res 42(2):655–672 10. Rehman KU, Malik MY, Makinde OD (2018) Parabolic curve fitting study subject to Joule heating in MHD thermally stratified mixed convection stagnation point flow of Eyring-Powell fluid induced by an inclined cylindrical surface. J King Saud Univ - Sci 30:440–449 11. Dey D, Borah R, Mahanta B (2021) Boundary layer flow and its dual solutions over a stretching cylinder: stability analysis. In: Emerging technologies in data mining and information security. Advances in intelligent systems and computing, pp 27–38 12. Choi SUS (1995) Enhancing thermal conductivity of uids with nanoparticles. In: Proceedings of the 1995 ASME international mechanical engineering Congress and exposition, San Francisco, USA, ASME FED 231/MD, pp 99–105 13. Das K, Acharya N, Kundu PK (2018) Influence of variable fluid properties on nanofluid flow over a wedge with surface slip. Arab J Sci Eng 43(5):2119–2131 14. Ghosh S, Mukhopadhyay S (2018) Flow and heat transfer of nanofluid over an exponentially shrinking porous sheet with heat and mass fluxes. Propuls Power Res 7(3):268–275 15. Khashi’ie NS, Arifin NM, Pop I, Wahid NS (2020) Flow and heat transfer of hybrid nanofluid over a permeable shrinking cylinder with Joule heating: a comparative analysis. Alexandria Eng J 59(3):1787–1798 16. Ali A, Akhtar J, Anjum HJ, Awais M, Shah Z, Kumam P (2021) 3D nanofluid flow over exponentially expanding surface of Oldroyd-B fluid. Ain Shams Eng J 17. Ho CJ, Cheng CY, Yang TF, Rashidi S, Yan WM (2021) Cooling characteristics and entropy production of nanofluid flowing through tube. Alexandria Eng J 18. Prasannakumara BC (2021) Numerical simulation of heat transport in Maxwell nanofluid flow over a stretching sheet considering magnetic dipole effect. Partial Differ Equations Appl Math 4:100064

Driver Drowsiness Detection and Traffic Sign Recognition System Ruchi Pandey, Priyansha Bhasin, Saahil Popli, Mayank Sharma, and Nikhil Sharma

Abstract A humongous number of road accidents occur every year and a substantial amount of these cases are due to the drowsy condition of the driver. A driver’s wellbeing on the road decides the fate of fellow passengers. A drowsy driver is indeed a great threat to many lives. A large population is directly or indirectly affected by these situations. In case of any mishap, these vehicles cause huge damage to both life and property. Driving drowsy is as dangerous as driving drunk. In both scenarios, the driver has no control over the vehicle. The best way to avoid such accidents caused by a driver’s drowsy condition is to detect his/her drowsiness and warn him/her before he/she falls asleep. Being drowsy and skipping traffic signs due to drowsiness are a point of major concern for road accidents. To combat such hazardous situations, we have come up with an innovative idea in which we would get the complete analysis of the driver’s condition and sleep pattern and alert him with a beep sound. This innovative yet useful project is called the drowsiness detection system. Firstly, the system preprocesses the image to focus on important information. Secondly, detection, binarization, and localization are implemented. Finally, classifications are made of the traffic signs which are detected based on deep learning. This document proposes a method of detection of drowsiness and recognition of traffic signs based on image processing, combined with the convolution neural network (CNN). Being able to accurately and effectively identify road signs can improve driving safety. Traffic signs provide general and useful information on traffic rules, road conditions, and driving directions to road users whether they are passengers, drivers, or pedestrians.

R. Pandey Hanu Software Solutions, Greater Noida, India P. Bhasin Tata Consultancy Services, Gurugram, Haryana, India S. Popli · M. Sharma GGSIPU, Delhi, India N. Sharma (B) Delhi Technological University, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_3

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Neglection or skipping of traffic signs by drivers due to any reason is a threat to all road users and our project provides a solution to help drivers drive better and safer. Keywords CNN · Drowsiness · Fatigue detection · EEG

1 Introduction Thruway crashes chiefly happen due to driver’s drowsiness or fatigue. Sluggishness limits the driver’s degree of consideration and response to a particular circumstance in driving. The likelihood of crash events increases because of the absence of consideration of the driver. With the increasing demands of traveling, the number of detecting automobiles has also increased. We all can be victims of drowsiness and fatigue while driving. Taxi drivers, truck drivers, and people who travel long distances are lethargic, and this is because of too-short night sleep, altered physical condition, or continuous driving for a long time. With the extending traffic conditions, this issue will in general step up. About 20% of the accidents that happen are generally because of the weakness of the driver as assessed by the NHTSA (Public Highway Traffic Safety Administration). Due to the, whether, drowsiness of the drivers, there is a lot of attention given to self-driving cars. The ability to detect traffic signs is an important aspect for a self-driving car to keep people safe not only inside the car but also outside the car. The traffic signs on the road no matter the aspect or environment have a purpose to regulate traffic and to make sure that drivers adhere to the traffic rules to make the road safe for all parties concerned. We have focused our project on traffic signs. We used the German traffic sign benchmark. The dataset consisted of 43 different types of traffic signs. About 75% of the frames were in grayscale and the rest in color. The problem we are trying to solve has some advantages such as traffic signs being unique so the object difference is small and the traffic signs can be seen by the driver/system. On the other side, we have to contend with different types of weather conditions. The primary target of this framework is to create a vigilant system to detect whether the eyes of the driver are open or shut and also to detect the traffic signs. In our project, the approach was to use convolution neural networks by extracting traffic signs from the color information. Convolutional neural networks (CNN) were used by us to classify the traffic signs and condition of eyes, and color-based segmentation was also used by us to extract/crop signs from the image. The conduct of the driver and the driver’s deficiency is the explanation behind most of the street mishaps. Aside from death and wounds, vehicle accidents are additionally the explanation behind monetary misfortunes and low efficiency. Drowsiness is the major cause of driver’s disability to drive precisely leading to fatality [1]. Different drivers are likewise put in danger due to the imprudent conduct of the drowsy driver. Drowsy driving is a chief, but often unnoticed road safety problem [2]. To curtail such a situation, we have come up with an innovative idea in which we will design a system using deep learning concepts and later implementing it with Arduino which will

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detect drowsiness and alert the driver with a beep sound. The proposed framework can be checked and can profoundly help diminish street mishaps. The rest of the paper is designed as follows: Section 2 discussed about driver drowsiness detection techniques and its different types; Section 3 illustrated methodology related to the implementation of driver drowsiness detection techniques; Section 4 described result of implementation followed by the conclusion in Sect. 5.

2 Overview Driver drowsiness and skipping traffic signs are the primary cause of accidents, and to detect drowsiness, various techniques are there which depend on various parameters like behavioral, physiological, and vehicular parameters. For safe driving, a driver should be well versed with the traffic signs and their importance depending on the types of signs, i.e., informatory sign, mandatory sign, and cautionary sign.

2.1 Driver Drowsiness Detection Techniques Drowsiness detection techniques are used widely in different approaches to building a model for detecting the fatigue condition of the driver. Various parameters can be used to detect the drowsy state of the person driving the vehicle. Behavioral parameter covers areas like analyzing facial features, recognizing eye movements, etc.; physiological parameter covers areas like EEG recognition, pulse sensor technique, and vehicular parameter which includes areas such as real-time lane detection and the techniques that are used by autonomous vehicles. There are different techniques which are explained as follows:

2.1.1

Techniques Based on Behavioral Parameters

Behavioral parameters are non-invasive measures used to detect drowsiness. These technologies use the driver’s behavior parameters to measure the driver’s fatigue, such as closed eyes rate, facial expressions, blinking, head position, and yawning. (i)

(ii)

Eye movement and dynamic template matching To prevent traffic accidents, a vision-based real-time driver fatigue detection system is proposed [3]. Facial representation method The state of the laboratory that uses the finite element analysis is by researchers, which may be an intricate framework that includes the characteristic database represented as templates, and the result of the database detects drowsiness. Similarly, Assari and Rahmati [4] support facial figures

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(iii)

(iv)

(v)

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that admit the drowsiness detection system. System firstly uses background subtraction techniques to determine the entrance image. Then, using the horizontal projection and the template, the representation of the face is obtained. Next, the element found in the tracking step that uses the template that coincides with elements, and we use the determination of the face condition of the face to investigate the incidence of drowsiness. Yawning extraction method One of the main causes of traffic accidents is tiredness or sleepiness. To prevent this problem, Saradadevi [5] proposed an effective system that uses yawn extraction to control driver fatigue. The method suggested is used to detect the mouth, using edge detection technology to detect the face edges, and then, detect the boundary of the left and right regions and find the upper half and get the mouth and local area of the mouth the lower limit by evaluating the vertical projection of the lower half of the face and horizontal projection of the resulting area, respectively. System of tracking eye movement An eye movement tracking system was proposed by Nguyen et al. [6] to detect sleepiness in the drivers. The setup includes a capturing camera, an alarming device, and the laptop has developed (recommended) software and provides an easy-to-use GUI. First, the system receives the image from the webcam. Then afterward, a top–down model method approach is used to find the face area and the eye area alongside. Closed eyes recognition method Khunpisuth et al. [7], first, the Pi camera captures the video and detects the facial area in the image, using the Haar cascade classifier of the Viola–Jones method. After face detection, use the blink rate to calculate the level of drowsiness. Using face template matching to detect eye area, the author uses three templates to verify blink an eye area. If the driver blinks too frequently, the system prompts drowsiness. On reaching a certain level, i.e., a hundred, an alarm sound will be produced to alert the driver. Techniques Based on Vehicle Parameters

Technology based on vehicle parameters attempts to detect vehicle characteristics that support driver fatigue, such as frequent lane changes, vehicle speed variability, angle of the steering wheel, and the tip of the wheel. (i)

Real-time lane detection system Katyal et al. [8] proposed a driver drowsiness detection system. The system is divided into two stages: first, the Hough transform supported by the rail is detected. Second, the driver’s eyes are detected to detect drowsiness. The results show that the setup is helpful for drivers who travel long distances, drive late at night, and are drunk.

Driver Drowsiness Detection and Traffic Sign Recognition System

(ii)

2.1.3

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Automatic detection of driver fatigue To solve the problem of driver tiredness, a technique of utilizing yaw angle (YA) and wheel angle (SWA) data to detect fatigue is proposed for the driver in realdriving conditions [9]. The system first studies the operating characteristics of SWA and YA in different fatigue states, and then calculates the characteristics of ApEn in the time series of the pop-up window, and then uses the nonlinear characteristics to build the theoretical dynamic time series, based on fatigue characteristics. Techniques Based on Physiological Parameters

Technology-based physiological parameters detect the physical conditions of drivers who support sleepiness, such as heart rate [10], respiratory rate, and body temperature. These biological parameters are more reliable and accurate in drowsiness detection because they are related to what happens to the physical driving force. (i)

(ii)

EEG-based driver fatigue detection A system utilizing electroencephalogram (EEG) signals [11] was proposed to prevent traffic accidents normally caused by driver tiredness or sleepiness. The suggested method first detects signs associated with different levels of sleepiness. And the results show that all fatigue objects are detected by the suggested system. Pulse sensor method In most cases, previous studies have specifically studied the physical condition of the driver to detect drowsiness. For this reason, Rahim et al. [12] use infrared heart rate sensors or pulse sensors to detect drowsy drivers. The heartbeat sensor measures the pulse in the driver’s fingers or intestines and visualizes the vital signs of the heart. Experimental results show that when the driver turns from alarm to drowsiness, the low frequency to high frequency (LF/HF) ratio decreases, and if the alarm is issued in time, many traffic accidents can usually be avoided.

2.2 Traffic Sign Recognition Technique Traffic sign recognition is one of the main parts of features present in ADAS. ADAS refers to advance drivers-assistance systems. The technology is introduced to make the driving experience harmless for all automobile users [13]. Traffic sign recognition focuses on the classification and recognition of traffic signals during the drive. A good understanding of traffic signs is a vital aspect of driving but it is more important for a driver to be attentive as traffic signs act as guides for all road users [14].

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Introduction to Traffic Signs

The traffic signs are of various types based on the various information that they depict: (a)

Mandatory signs These are the signs with a red outer circular border. These signs are required to be followed vigilantly by a driver or a pedestrian, overlooking them can be a punishable offense. Examples of mandatory signs are—Stop, No entry, and Speed Limit. The figures given below are examples of mandatory signs [15] (Figs. 1, 2, and 3).

Fig. 1 No entry

Fig. 2 Pedestrians prohibited

Fig. 3 Speed limit 50 km/h

Driver Drowsiness Detection and Traffic Sign Recognition System

(b)

(c)

Fig. 4

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Cautionary signs These are the signs with red triangular borders. These signs are used to warn the drivers and the pedestrians of what is coming ahead so that they can perform the necessary actions for a safe ride ahead. Examples of cautionary signs are— Right-Hand Curve, Narrow Road Ahead, and Slippery Road. The figures given below are examples of cautionary signs (Figs. 4, 5, and 6). Informatory signs These are the signs with a blue rectangular border. These are used to provide important information and the drivers can act accordingly up to their concern. Examples of informatory signs are—School Ahead, Parking Both Sides, Hospital Nearby, etc. The figures given below are examples of informatory signs (Figs. 7, 8, and 9).

Narrow road ahead

Fig. 5 Narrow bridge

Fig. 6 Right-hand curve

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Fig. 7 Hospital

Fig. 8 Eating place

Fig. 9 Petrol pump

2.2.2

Traffic Sign Recognition

The recognition of traffic signals primarily consists of detection and classification. (a)

Detection The objective of traffic sign recognition is to find the locales of interest which are known as regions of interest (ROI) in which a traffic sign is bound to be found and confirm the theories on the sign’s presence. The early discovery stage of a traffic sign acknowledgment framework offers significant expenses because of the huge size of identification in a total single picture [16]. To decrease the space, earlier data of the sign area should be trimmed. This procedure is called ROI. ROI finds the traffic sign in the picture dependent on the shape. The traffic signs are then trimmed and pronounced as useful

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signals. The foundation picture is pronounced as an undesirable sign and eliminated by characterizing it as a dark pixel [17]. By these suspicions, an enormous bit of the picture can be overlooked. Traffic signs are planned with specific tones and shapes which make them simpler to be perceived [18]. There are some cons to using only a single frame for traffic sign detection. • It is hard to accurately identify a traffic sign when transitory obstruction happens. • The actuality of the traffic sign is difficult to check. To escalate the speed and precision of traffic sign location, resulting in pictures by utilizing data about the previous picture. Additionally, data provided by later pictures are utilized to help with confirming the right identification of traffic signs, and in this way, those recognized and followed traffic signs can decrease the load of the processor [19]. (b)

Classification Image segmentation is the route toward dividing an advanced picture into numerous bits. The objective of the division is to streamline as well as change the portrayal of a picture into something more significant and simpler to examine [20]. A binary image grouping strategy is an advanced image that has just two potential qualities for every pixel. The pixels used to address the article and foundation are white and dark individually. Given arrangement measures the procedure utilized in the grouping of traffic signs which is the parallel order technique. Each traffic sign is gathered depending on the measure of white and dark pixels. These sums are coordinated with the measure of white and dark pixels from the format information.

3 Methodology The image recognized through the camera is converted to the grayscale and after that, Haar cascade classifier is used to detect the face from the image that is recognized. After the detection of the face, the left eye and right eye are recognized by using the same method that we used for detecting the face. Training of CNN classifiers is done to foretell the status of the eye. To use the CNN model, images used for training and testing the model ought to be of the same size. This can be achieved by using the same pixel dimension for both groups of images. The testing of the status of the eye (whether eyes are closed or not) is done based on increment and decrement of the score. If the score is more than 15, then the alarm will ring and it will alert the driver. The recognition of traffic signals primarily consists of detection and classification, which is implemented using convolutional neural network algorithm. As easy-to-use hardware and software, Arduino is an open-source electronics platform. Arduino can read inputs and turn them into an output like activating an engine, turning a LED ON. Ivrea Interaction Design Institute was the birthplace of Arduino

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as a tool for fast prototyping, aimed at students who did not have enough skills in electronics and programming. As soon it reached a wider range, it started adapting to newer needs and evolving with every advancement (Fig. 10). Step by step procedure of the used technique is discussed in Tables 1 and 2.

Fig. 10 Arduino model

Table 1 Implementation steps for driver drowsiness detection system Implementation steps for driver drowsiness detection system Step 1: Take real-time input from the camera Input is taken from the camera which is attached to the system, input here is images captured from the camera. An infinite loop is used to capture each frame. The images are stored in the variable declared Step 2: Detection of Face and creating a region of interest Detection of the face from the image using a Haar cascade classifier and a boundary box is drawn for each face; this boundary box is the region of interest Step 3: Detecting eyes from the region of interest and feeding them to the CNN classifier In this step, extraction of only eye data from the full image takes place. The extracted image will be provided to the CNN classifier Step 4: CNN classifier will predict the status of the eye CNN classifiers will predict whether the eyes are open or closed Step 5: Calculation of score to check whether the person is drowsy or not A score is a value that is used to determine how long the person’s eyes are closed. If both eyes are closed, the score increases continuously and when the eyes are open, the score decreases. A fixed score is declared as a threshold, (In this model, it is 15), this means that if the score is 15 or more than 15, then the eyes of the detected person are closed for a long duration and the alarm will start beeping and will stop when the score is 0 Step 6: Implementation of Deep Learning model using Arduino The proposed model is later implemented using Arduino

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Table 2 Implementation steps for traffic sign recognition system Implementation steps for traffic sign recognition system Step 1. Image Frame Detection The target of the grayscale here is to detect an image frame of a traffic sign for performing classification Step 2. Binarization Desaturation of detected image frame for agile calculation of the algorithm Step 3. Feature Extraction Capturing distinguishing characteristics of the traffic sign board image frame Step 4. Comparison of Images Comparing the images—The image detected from the camera with the images used in training the model Step 5. Traffic Sign Recognition After comparison of the images when the model finds a match, the name of that traffic sign will be displayed on the screen along with the match probability

Figure 11 shows the flowchart that is used for building the deep learning model for detecting driver drowsiness. The workflow of the model is explained as when the model is started, it will recognize the image and then detection of face and eyes from Fig. 11 Flowchart of drowsiness detection algorithm

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that image will take place; in our model, we are using eye movement recognition technique so, region of interest is eyes and it will focus on eyes only; after the detection phase, the CNN classifier will predict the status of the eye (open or closed) and then it will calculate the score depending on the status of eyes (whether the eyes are closed or open). If the eyes are open, score will be zero and when the eyes are closed, the score will start increasing. When the score reached the fixed threshold, i.e., 15 points (5 seconds) the model will ring the alarm to alert the driver. The alarm will not stop till the score is back to 0 again. This model is implemented on an Arduino device. The above steps will run on loop till the machine is ON, so that recognition of the traffic signs will not be affected till the drive ends. Figure 12 shows the workflow of traffic sign recognition system, and it starts with the step of capturing and recognizing the image as input, next step focuses on binarization of the captured image; binarization refers to the conversion of the image to grayscale. After converting the image to grayscale, feature extraction will take place and then the captured image will be compared to the classes of the traffic sign present in the dataset. Based on the comparison, model will predict the traffic sign captured from the input. Fig. 12 Flowchart of traffic sign recognition algorithm

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4 Result and Discussion Two different datasets are used to make the models and both the models are trained using CNN. Below is the output of both models. Figure 13 shows the output screens of the CNN model for driver drowsiness detection. The first frame shows open eyes with a score = 0 which aims at a person with open eyes, when the eyes of the person in the frame closes, the score will start increasing as shown in the second frame, finally, if the person who is being monitored in the frame has his eyes closed for more than 5 seconds, i.e., score >15 then the boundary of the frame will turn red and alarm will start beeping sound and will not stop until the score again becomes 0. In this, OpenCV was used to trace faces and a Haar cascade classifier for the detection of eyes, and prediction was done by implementing a CNN model.

4.1 Traffic Sign Recognition In the traffic sign recognition model, the CNN algorithm was used to recognize the traffic sign in real time. The traffic sign recognition system is based on the classes of the traffic sign present in the dataset used. There is a total of 43 classes of traffic signs, i.e., 43 different traffic signs and outputs are based on those classes. Few examples of the output of the traffic sign recognition model are shown in Figs. 14, 15, 16, and 17. The frame in the above figures is showing recognized traffic signs and the probability of prediction.

Fig. 13 Outputs of drowsiness detection system

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Fig. 14 No passing sign

Fig. 15 Turn right ahead sign

Fig. 16 Road work

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Fig. 17 Double curve

5 Conclusion An increasing number of accidents due to drowsiness and skipping the traffic signs area is globally affecting road safety and is a major threat to human lives. Driving behavior requires the driver to be vigilant and mindful. According to various studies, the heedless driving behavior of drivers depends on two major factors: distraction and drowsiness. Our solution primarily focuses on these two factors. In the proposed solution, we used deep learning and CNN algorithm to build a driver drowsiness detection system using Arduino and traffic sign recognition system. The proposed solution is having the ability of real-time detection of drowsiness and recognition of traffic signs with high reliability. In the future, we can enhance our model by implementing traffic sign recognition along with driver drowsiness detection because as of now there is no such implementation that both the models are implemented on the same Arduino device. We can also include other behavioral factors in drowsiness detection such as yawning, facial expressions, etc., to increase the accuracy of our model. To increase road safety, we can also add an object detection method in traffic sign recognition so that if there is any object lying on the road it can be detected and the driver can drive smoothly.

References 1. Schwarz C, Gaspar J, Miller T, Yousefian R (2019) The detection of drowsiness using a driver monitoring system. In: 26th International technical conference on the enhanced safety of vehicles (ESV) 2. Kusuma Kumari BM (2014) Review on drowsy driving: becoming dangerous problem. Int J Sci Res 3. Horng W-B, Chen C-Y, Chang Y, Fan C-H (2004) Driver fatigue detection based on eyetracking and dynamic template matching. In: Proceedings of the international conference on networking, sensing and control, vol 1, Mar 2004, pp 7–12

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4. Assari MA, Rahmati M (2011) Driver drowsiness detection using face expression recognition. In: Proceedings of the IEEE international conference on signal, image processing and applications (ICSIPA), Nov 2011, pp 337–341 5. Saradadevi M, Bajaj P (2008) Driver fatigue detection using mouth and yawning analysis. Int J Comput Sci Netw Secur 8(6):183–188 6. Nguyen TP, Chew MT, Demidenko S (2015) Eye tracking system to detect driver drowsiness. In: Proceedings of the 6th International conference on automation, control and robotics and applications (ICARA), Feb 2015, pp 472–477 7. Khunpisuth O, Chotchinasri T, Koschakosai V, Hnoohom N, Driver drowsiness detection using eye-closeness detection. In: Proceedings of the 12th International conference on signal-image technology & internet-based systems (SITIS), Nov/Dec 8. Katyal Y, Alur S, Dwivedi S (2014) Safe driving by detecting lane discipline and driver drowsiness. In: Proceedings of the IEEE International conference on advanced communication, control and computing technologies, May 2014, pp 1008–1012 9. Li Z, Li SE, Li R, Cheng B, Shi J (2017) Online detection of driver fatigue using steering wheel angles for real driving conditions. Sensors 17(3):495 10. Sharma A, Sharma N, Kaushik I, Kumar S, Khatoon N (2020) Predictive analysis of type 2 diabetes using hybrid ML model and Iot. In: IoT security paradigms and applications, pp 303–320. https://doi.org/10.1201/9781003054115-14 11. AlZu’bi HS, Al-Nuaimy W, Al-Zubi NS (2013) EEG-based driver fatigue detection. In: Proceedings of the 6th International conference on the developments on systems engineering, (DESE), Dec 2013, pp 111–114 12. Rahim HA, Dalimi A, Jaafar H (2015) Detecting drowsy driver using pulse sensor. J Technol 73(3):5–8 13. Leng LB, Giin LB, Chung W-Y (2015) Wearable driver drowsiness detection system based on biomedical and motion sensors. In: Proceedings of the IEEE Sensors, Nov 2015, pp 1–4 14. Sharma N, Kaushik I, Rathi R, Kumar S (2020) Evaluation of accidental death records using hybrid genetic algorithm. SSRN Electron J. https://doi.org/10.2139/ssrn.3563084 15. Warwick B, Symons N, Chen X, Xiong K (2015) Detecting driver drowsiness using wireless wearables. In: Proceedings of the 12th International conference on mobile Ad Hoc and sensor systems, (MASS), Oct 2015, pp 585–588 16. Manchanda C, Rathi R, Sharma N (2019) Traffic density investigation & road accident analysis in India using deep learning. In: 2019 International conference on computing, communication, and intelligent systems (ICCCIS). https://doi.org/10.1109/icccis48478.2019.8974528 17. Chellappa Y, Joshi NN, Bharadwaj V (2016) Driver fatigue detection system. In: Proceedings of the IEEE International conference on signal image processing (ICSIP), Aug 2016, pp 655–660 18. Grover M, Verma B, Sharma N, Kaushik I (2019) Traffic control using V-2-V based method using reinforcement learning. In: 2019 International conference on computing, communication, and intelligent systems (ICCCIS). https://doi.org/10.1109/icccis48478.2019.8974540 19. Awais M, Badruddin N, Drieberg M (2017) A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors 17(9):1991 20. Rustagi A, Manchanda C, Sharma N, Kaushik I (2020) Depression anatomy using combinational deep neural network. In: Advances in intelligent systems and computing international conference on innovative computing and communications, pp 19–33. https://doi.org/10.1007/ 978-981-15-5148-2_3

Application of Data Visualization: Realization of Car Rental System Rajendrani Mukherjee, Drick Datta, Gargi Ganguly, Srinjini Bandopadhyay, Chirantan Chakraborty, Birabrata Pal, and Deborup Chatterjee

Abstract Travel suppliers largely depend on business travel as their main source of profit because it is fewer prices conscious compared to the pleasure travel market. The past recession has caused corporations to scale-down business related to travel drastically. Car rental companies are a major part of the travel industry. Car rental rates vary with economic ups and down. Nowadays, there are online car renting services which give much benefit to users in modern society. The manual car rental system supports service in stipulated time only. So, customers have inadequate time to make any transactions. With the help of the online car rental system, we can elongate our operational hours. In this paper, an attempt has been made to design an entire car rental analysis server using data visualization techniques. Keywords Visualization · Fuel · Revenue · Prototype · Economy · Customer

1 Introduction Car rental system enables the company to make their services available to common people and also keeps records about their services. The main target of this project is to automate the car rental system. Automation plays a very important role in reducing cost [1]. The system of renting cars existed back in the previous years, when people rented cars for their personal reasons. With enhancement of technology, many people became interested in the car rental business and hence got involved. Comfort and safety are two major concerns regarding this industry. To avoid harassment from both the sides, every agency uses monitoring devices and keeps track of the cars on road. Nowadays, car rental service is widely found all over the world. In this research, we have visualized a model which will utilize prominent technologies to facilitate effective management. We have considered several parameters like types of R. Mukherjee (B) · D. Datta · G. Ganguly · S. Bandopadhyay · C. Chakraborty · B. Pal · D. Chatterjee Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_4

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cars (SUV, Sedan, Jeep, Chevrolet Spark, Hyundai Accent, Mitsubishi Mirage, Ford Focus, Nissan Versa, and Toyota.), fuel types, number of trips taken, and customer’s reviews while designing the prototype. In the replica, the service providers will keep a record of the places a particular car travels. The no. of trips a car takes to a particular place is noted. With each trip, customers are requested to rate their experience (reviewing both cars and drivers). This helps us to find out which cars are chosen and preferred by the customers. The maintenance of the cars is a major part of the rental service. In our visualization prototype, the rental service will keep as many preferred cars in the cities to meet the demand. According to our visualization model, if customer X tries to book a car for a day, he/she will be shown the cars which frequently take trips in that particular route. Customer X can choose the car with the best reviews. Customer X can book a car specifically for his/her travel time, co-travelers, and the nature of travel. The service providers will give him/her a brief description of which cars are on high demand and will help to choose the best facility available. The entire car rental analysis is done by Power BI and SQL. Power BI is business analytics service provided by Microsoft. The project is designed to help people utilize transport effectively and safely.

2 Related Works Several researchers have implemented online car rental system in several ways using various methodologies, here are a few instances. Chit Su Mon et al. have designed a mobile application for the online car rental system and have used Google Firebase as storage [2]. This mobile car rental app offers the following modules—login or signup module, search module, payment module, and admin module. Sarkar et al. have designed an android application dedicated to car rental system [3]. Their system basically has three applications—one for the users, one for admin, and another one for the drivers. Distance and position of both the drivers and the customers are tracked through Google API and Google Maps. This app enables online invoice generation, instant reservation confirmation, or payment confirmation through SMS or email. According to a report by Ken Research (publisher of Market intelligence research reports), the American car rental market revenue is anticipated to grow at compound annual growth rate (CAGR) of 35% [4]. Moreover, this sector is expected to be worth more than 30 billion USD by 2019, which is quite an encouraging aspect for market players in this segment. Peng-Sheng You and Yi-Chih Hsiehb proposed a hybrid heuristics approach to solve the problem regarding fleet size in car rental systems [5]. In 2017, Darun Kesrarat et al. proposed a smart matching system which performs car matching using a search function to give maximum satisfaction to customers [6]. Dong Li and Zhan Pang proposed a mechanism for dynamic booking of car rental system [7]. The authors suggested two heuristics which outperform the popularly used probabilistic nonlinear programming (PNLP) heuristic.

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3 Implementation 3.1 Data Preprocessing In order to implement the automation design, a dataset from Kaggle was chosen (https://www.kaggle.com/kushleshkumar/cornell-car-rental-dataset). Prominent fields from this dataset are car owner registration number, most picked cars, review rating, year in which the car is manufactured, fuel type, trips taken, city, rates, location latitude and longitude, revenue, etc. The data were then imported to SSMS for cleaning, which included deletion of redundant data. A few rows in the dataset contained null values of some attributes; we discarded those rows to avoid discrepancies in our final result.

3.2 Application of SSMS and Power BI in Data Analytics Power BI was utilized to perform real-time stream analytics. It helped us to fetch data from multiple social media sources to get access to real-time analytics. SQL server management studio (SSMS) was leveraged to connect and work with SQL server from a graphical interface instead of using the command line. Once the server is linked with Power BI, the required data were imported. The data are fed in CSV format, and any change in data [8] (automatically or manually) is detected by the server and reflected in power BI dashboard. Power BI was linked with the SSMS server which leads to invoking the database, and a fulltime running server in the BI was established. A revenue graph (year wise) was generated (Fig. 1)

Fig. 1 Visualization of revenue

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and which kind of fuel is bringing the most revenue was determined. The graph for revenue collection tells us about the renter trips and the revenue collected over the years. Revenue is an important segment of the dashboard. The revenue graph also portrays the cities visited the most. A car performance tracker report (Fig. 2) is also made. A slicer was made to sort out the vehicle fuel type for each page (Fig. 3). Figure 4 tells us about how many cars are operating in the company and what the yearly growth of the company is.

Fig. 2 Car performance tracker

Fig. 3 Visualization of fuel type used by cars

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Fig. 4 Visualization of car count and company

4 Conclusion A car rental is a means of transportation that can be used for the time being for a fee. Even if someone does not have admittance to their own personal vehicle or don’t possess a vehicle, a rented car will suffice the purpose to reach the destination. The Indian Car Rental Industry has grown significantly over the years due to a growing demand for sustainable modes of transportation among its urban population. An insightful car rental system will increase customer retention and simplify vehicle and staff management. The purpose of our prototype car rental system is to automate the existing manual system by the help of computerized equipment and full-fledged computer software, fulfilling all the important requirements so that valuable information can be stored for a longer period with easy access. This project is automated, but the only flaw in this project is that the data can’t be entered from an application. This project can be made more complete if we can implement it using an application which will provide input to the designed prototype.

References 1. Mukherjee R, Sridhar Patnaik K (2019) Introducing a fuzzy model for cost cognizant software test case prioritization. In: IEEE TENCON 2019: recent advances in program analysis and software testing (RAPAST), pp 502–507, 17–20th October, 2019, Kerala, India 2. Mon CS et al (2020) A prototype of a mobile car rental system. J Phys: Conf Ser 1529:032023. https://doi.org/10.1088/1742-6596/1529/3/032023 3. Sarkar J, Khode Y, Jadhav S, Laddha A (2019) Car rental system for Maharashtra (Android app). Int J Res Trends Innov 4(5):36–38

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4. KenResearch, Growth in several trends of worldwide car rental market outlook, June 3, 2020 5. You P-S, Hsiehb Y-C (2014) A study on the vehicle size and transfer policy for car rental problems. Transp Res Part E: Logist Transp Rev 64:110–121 6. Kesrarat D et al (2017) Smart matching for car rental. In: ICMLC 2017: Proceedings of the 9th International conference on machine learning and computing, February 2017 7. Li D, Pang Z (2017, February) Dynamic booking control for car rental revenue management: a decomposition approach. Euro J Oper Res 256(3):850–867 8. Das A, Mallick C, Dutta S (2020) Deep learning-based automated feature engineering for rice leaf disease prediction. In: Das A, Nayak J, Naik B, Dutta S, Pelusi D (eds) Computational intelligence in pattern recognition. Advances in intelligent systems and computing, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-2449-3_11

Physical and Mental Health Problem’s Technical Resolutions Priyanshu Joshi, Samviti Bhardwaj, Abhishek Patel, and Priyanka

Abstract Various apps are available in the market that offers the necessary assistance required regarding health care to the people. Although many people use these apps, their willingness to stick to them and find them trustworthy has a pretty poor response. There is an explanation for such low engagement on these apps even if they are so useful. The user experience and the reliability of the app matter the most for any app to succeed in this world of technology. This research will focus on analyzing user reviews of the available apps on such help and mental health facilities, and it will uncover the strengths, weaknesses, and areas of improvement in the apps. The significance and the capabilities are being discussed as per the consumer reviews and comments that have helped improve the apps and bring new functionalities and features to the app. Keywords Mental health · App · Physical health · Depression · Anxiety

1 Introduction In times when we are faced with uncertainty and forced isolation, our mental health is likely to get impacted. Fear, worry, anxiety, panic, etc., are all normal reactions when we are faced with a problem like the current pandemic. It has been observed that due to the Covid-19 pandemic, the number of people suffering from mental health problems has increased and those who were already suffering from one mental health disease or the other were likely to get infected by the virus [1]. The Covid-19 virus changed everything from working at homes to being away from our loved ones. Lack of contact with people, human touch, and not being able to see the outside world can be possible triggers for mental health illness. One good thing that came out of this P. Joshi (B) · S. Bhardwaj · A. Patel · Priyanka HMRITM, Delhi, India e-mail: [email protected] Priyanka e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_5

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pandemic is everything is being done virtually. You can access facilities from the comfort of your homes. In terms of medical facilities, when faced with a problem, you are just 1 text or phone call away from getting consulted by a professional. Statistics show that there has been significant growth in the number of people using digital or e-medical and healthcare facilities [2]. Almost everyone these days has a smartphone and most people are dependent on mobile applications for various facilities. It has been seen among people that almost everyone uses 1 medical or healthcare facility application to monitor their health or get consultation virtually [3]. Since the rate of mental health problems is increasing, the number of mental health applications is also increasing. The use of electronic devices like mobile phones, laptops, or tablets is not just limited to calling, texting, or using social media, but people are using it as a virtual or digital doctor. More and more people are downloading mental health applications on their devices. Mobile applications have been one of the biggest growth areas when it comes to the diagnosis and treatment of mental health problems [4]. Smartphones can perform several different functions with just one touch on the screen. It can help you to diagnose whether you are suffering from a mental health problem or not; if you are suffering from any mental health problem, then it can help you connect with a medical professional like a therapist or psychiatrist, and it can help you to monitor your mental health. It has been seen that more and more people are willing to use mobile applications to monitor their mental health [5]. There are several benefits of using mobile applications for monitoring, diagnosing, or treating mental health problems like the ease of accessibility, portability, and availability. Even people who live in remote areas or rural areas can easily access mobile applications to monitor their mental health. Mobile application technology has shown the greatest reach in the last few years [6]. Smartphones because of their ease of use, availability, 24-h connectivity, and mobility encourage more and more people to use them. Smartphones also offer a wide range of technologies that can be or are already being used on them like virtual reality (VR), augmented reality (AR), interfaces connected to sensors, real-time interaction, social networking, geolocation, and more as shown in Fig. 1. We will discuss the app’s availability and usage in further sections in this paper. This paper aims to deliver a clear image of how the apps are affecting the health sector in this booming technological world. The following sections will discuss all the necessary details regarding the physical and mental health-related apps [7].

2 Literature Review Mental health apps include various apps that target to improve the multiple mental health problems such as anxiety, depression, and it ranges all the way to the misuse of substances too. These apps also have the options to look after your physical health problems also [8]. These apps have shown additional benefits to ongoing treatments or psychotherapy. The benefits were not massive which thus directs us to think that there is still a need for improvement in this area. The effect sizes of apps with ANXIETY

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Fig. 1 Health-tracker app

and DEPRESSION support were measured. The effect size was medium in the case of self-management of anxiety symptoms [9]. The effect sizes of depression had a lower rate. The effect size was small to medium that makes not much of a difference. The apps that fall into this category are the fastest-growing apps. More than twothirds of American adults use their smartphones to help and manage their health [10]. They are willing to use the apps that have great functionality, good response, and a great setup within the app, having everything organized and managed properly. Nearly, 60% of people owning smartphones has downloaded at least one health app. There are over 318,000 health apps live right now, and over 10,000 of these are related to mental health too [11]. It has been reflected in researches that people with mental health issues tend to own more smartphones and other mobile devices, and they are keen to use these apps to keep a track of their mental health [12]. Besides this, there is a large difference between people knowing about the app’s capability and them using it for real. Along with this, people with mental health problems can have a different and more negative attitude toward the app’s ability to control and manage their sensitive information. These points indicate that different people have different opinions about different apps available for taking care of their mental health [13]. For the time being, most individuals choose mental health apps after going through the reviews or after the comments that are made by people who have already used the app before [14]. They believe that the apps must be secure and safe for them to put their sensitive information in them. The people expect a level of trust from the app. The price of the app also plays a vital role in its success. It is witnessed that the apps with lower pricing are rated better than the ones with higher pricing [14]. More than half of those who downloaded the health apps have valued the ease of use over the credibility of the app.

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3 Mental Health Facilities Available There are a wider variety of applications available on the App Store or Google Play Store when you search with the keywords like “mental health,” “depression,” “anxiety,” and “stress” [15]. You can also search for mental health applications using combinations of different keywords. Mental health apps are designed to maximize engagement and minimize app usage burden [16]. It is seen that mobile applications whose main target is mental health have common applications backgrounds or strategies [17]. The mental health facilities that are available in these mobile applications majorly employ the following facilities into their applications (Table 1). Based on studies, users are likely to use applications that are easily accessible, offer a wide variety of facilities and features, provide a personalized experience, pocketfriendly, informative, secure, authentic, provide social and professional support, and have a visually appealing user interface [18]. Most of the mental health facilities available in these applications employ more than 1 strategy in their application which makes it difficult to comment on the effectiveness of individual performance of each mental healthcare facility. In today’s time, where most of the things are taking place virtually, healthcare facilities have also opted for the digital way. People from both rural and urban areas use smartphones and have access to mobile applications [19]. With portability, ease of use and 24-h network facility, more and more people are using mobile applications every day. In the medical field, rapid growth is seen in the number of people using mobile applications to monitor their health (both physical and mental) and seeking medical help thereafter. With advancements in technology, it becomes easier for people to access mental healthcare facilities. However, access to smartphones doesn’t guarantee people accessing mobile applications [20]. Though the rate of people using e-medical facilities is constantly increasing, the pace is very slow. Even after downloading mental health apps, the chances of it being opened and used are less as suggested by the studies. Evidence suggests that the chances of opening and using mental health applications after being downloaded are less [21]. Having access to smartphones, having an interest in mental health applications, and downloading mental health applications are all necessary but not sufficient to Table 1 Facilities available Facilities

Brief

Monitoring/Tracking

• It includes tracking of thoughts, state of mind, emotions, behaviors, and feelings.

Information/Education • It includes tips, advice, and things to do by yourself to cope up with a problem. Mindful practices

• It includes deep breathing, journaling, meditation, coping self-talk, music, relaxing sounds, distracting games, and self-care practices.

Social support

• It includes contacting a friend or a family member

Professional support

• It connects the user with a medical professional like a doctor, therapist, or psychiatrist

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guarantee regular app usage and accessibility [22]. To bridge the digital gap between access and usage of mental health applications, a requirement of understanding what the user wants in the applications and acknowledging their concerns needs to be done [23].

4 Challenges The challenges that are faced by the people while using these apps or the difficulties that arise with these apps are listed below [24].

4.1 Lack of Trust There are a lot of people that do not trust the technology at all. They find all of the facilities as a hoax and nothing but a way of fooling people [25]. They are more concerned for their privacy and believe that their privacy and security will be tampered by these physical and mental apps. This lack of trust makes them not use the apps, and then, they themselves suffer in looking for help [26]. They don’t believe that these apps can be really handy and can be of great use to them [27]. They must know that the apps offer good security and privacy along with surplus features [28]. The security is confirmed by the app developers because no app developer would want negative reviews on their app [29].

4.2 Lack of Information Many people are not aware that they can get help online [30]. They have always relied on offline help, and they continue to do so. They think that the mobile devices are not advanced enough and that they are only used for basic purposes [31]. This mindset can be changed if we increase the awareness about the physical and mental health apps which will only help such people get help through an easier way [32]. Information can also be provided by the clinics when the patients visit them. Posters can be applied at certain areas which promote the apps and display the benefits of the apps which will attract the people and get them helped just in a click [33].

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4.3 Poor UI User interface plays a major role in the success or the failure of a physical and mental health app [34]. The app developers should keep in mind that the UI must be userfriendly and not much complex. People who are not much knowledgeable about the devices find it difficult to operate these apps which ultimately leads them to quit using the apps [35]. Poor UI is one of the major reasons that the user is forced to switch to other apps [36]. If the UI is according to the needs of the user and has some extra qualities like it can be customized by the user too, then it will be a great success for the app as there are many people who like to work in their own style and the thing that hampers it or doesn’t meet their expectations will be denied by them [37].

4.4 Less Features If the physical and mental health app has fewer features in comparison to the other apps available, then the user is more likely to switch to those apps [38]. The features are something that keeps the user engaged and happy if they meet the requirements of the user. Not every app can meet every single user’s requirements but the basic ones that are common to all can be easily fulfilled [39]. The user looks out for a feature-loaded application, and the one that suits the user best is the one that is used by him the most [40].

4.5 High Pricing The apps with lower pricing are preferred by the users more than the physical and mental health apps that are highly priced [30]. High pricing is also disappointing when the app doesn’t meet the user’s requirements. The app should be moderately priced, or low pricing would be even better which will increase the ratings of the app if it offers all the required functionalities.

4.6 Bad Ratings The users also judge the physical and mental health apps through the ratings they achieve on the stores by the consumers that are already using the app. The reviews matter for every app developer in order to improve their app. The ratings will only be good if the apps meet the required facilities for the users. This fact cannot be ignored that no app can completely satisfy every user’s needs, but still, the apps can

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bring a major difference if they deliver the basic functionalities with some additional features to keep the user engaged and get treated properly without any haphazard.

5 Conclusion and Future Scope In conclusion, applications for mental health or anxiety or depression hold a great medical advantage. We have gained insights into the strengths and weaknesses of mobile applications for mental health and uncovered what the user likes and dislikes. While mental health patients have easy access to mental health applications, far fewer are actually downloading them, and even fewer are using them. Evidencebased research suggests that the right framework can help guide one to make the best application for people suffering from mental health problems. An application that is user-friendly, engaging, has a visually appealing user interface, safe and secure, and provides professional, as well as social support, needs to be developed. To ensure that the technology remains useful in the medical field, regular studies and surveys need to be conducted to provide the best and safe experience to users. Understanding the user’s insight is critical to developing and designing effective applications that will be downloaded and used by the targeted audience. It is important to provide users with a variety of features, engaging content, and personalized facilities. Despite all the limitations, mental health applications will play an important role in the future. Real-time monitoring of the user’s medical condition would ensure that all user’s experiences and symptoms are recorded. These mobile applications also offer the facility of real-time intervention and support be it medical reminders, helpful tips, social and professional support, etc. Although these applications can’t replace therapy, they are useful in providing medical support at the ease of your home and monitoring one’s medical condition. These applications when designed and developed while keeping user requirements in mind can prove to be really effective and useful. Developers should pay special attention to making these applications safe for the user while providing customer support and emergency contact.

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Entropy Generation Analysis of MHD Fluid Flow Over Stretching Surface with Heat and Mass Transfer Debasish Dey and Madhurya Hazarika

Abstract Entropy generation analysis of MHD fluid flow moves through a stretching surface with velocity slip, and chemical reaction parameter has been studied. The irreversibility ratios due to heat transfer, fluid friction, and the action of heat and mass transfer at the stretching sheet’s surface are calculated. A system of nonlinear partial differential equations with boundary conditions is translated into ordinary differential equations. The Runge–Kutta–Fehlberg technique (RKF45) is used to solve the system of nonlinear partial differential equations with appropriate boundary conditions using symbolic computer algebra software Maple 21. The impact of many physical parameters on entropy generation number is visually plotted, including magnetic parameter, velocity slip parameter, velocity ratio parameter, Prandtl number, chemical reaction parameter, and heat generation/absorption parameter; another distinguishing feature of this work is the determination of the ratios of irreversibility to the total entropy generation number at the surface. Keywords Entropy generation · MHD flow · Velocity slip · Chemical reaction · R-K Fehlberg method

1 Introduction Entropy is a highly essential rule in fluid dynamics research. The term entropy refers to the measure of irreversibilities. These irreversibilities occur as a result of heat transmission, fluid friction, momentum diffusion, magnetic fields, and the action of heat and mass transfer, among other things. This entropy production is also related to efficiency. The analysis of entropy formation aids in the reduction of energy loss and the improvement of efficiency. Entropy generation is important in the field of engineering and thermal systems. Because of the multiple uses in the real world, a number of researchers explored and tested their theories in this subject. In thermal analysis, researchers have concerned about the no wastage of energy during the heat transfer D. Dey · M. Hazarika (B) Department of Mathematics, Dibrugarh University, Dibrugarh, Assam 786004, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_6

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process. They were no happy for this wastage, so entropy generation (EG) helps to minimize this wastage production and maximizes the efficiency of the system. Bejan [1–3] established the concept of entropy generation and examined the irreversibility analysis through heat and fluid flows. Sauli and Aiboud have investigated production of entropy in a liquid firm. Abdelhameed [4] has studied the irreversibility analysis of MHD flow of water. Various researchers have considered non-Newtonian fluid flow model such as micropolar flow, Casson flow, power-law fluid flow, nano-fluid, and Jeffrey fluid to study this entropy generation. Entropy generation study along inclined channels and between concentric cylinders using spectral quasilinearization approach has been studied by Srinivacharya and Bindu [3, 6]. Govindaraju et al. [7] explored nano-fluid flow second law analysis with slip effects. Khan et al. [8] analyzed the irreversibility of unsteady flow over a vertical plate. Various works on entropy generation analysis have recently been published [9–12]. The fluid flow through a stretching surface/sheet is very important in various applications of science and engineering. Applications of stretching sheet include extraction of plastics, spinning of fibers, casting of metals, cooling of metallic plate, and rubber sheets. The MHD flow over an extended surface was investigated in the presence of radiation and partial slip [13, 14]. The investigation of exerting magnetic field effects has significant implications in the field of science and engineering. Various applications, such as MHD power generators, pumps, and bearings, rely heavily on the interaction of a conducting fluid and a magnetic field [15]. Alarifi et al. [16] demonstrated fluid flow over a vertical stretched sheet using a shootingbased R-K-Fehlerberg integration algorithm. Halim and Noor [17] examined the nano-fluid flow with mixed convection using shooting method and bvp4c solver. Ijaz Khan et al. [18] investigated the nonlinear mixed convective nano-fluid flow of Walter-B fluid with bio-convection. Noor and Hashim [19] have analyzed viscous flow over a linearly elongated sheet. Various works have been performed on MHD fluid flow over stretching/shrinking sheet [20–23]. Fluids with velocity slip on the boundary have a wide range of uses, including polishing oil, artificial heart valves, internal cavities, and so on. Fang and Wang [24] investigated viscous slip MHD flow across a moving sheet. Mukhapadhay and Mandal [25] looked at MHD flow over a vertical porous plate. Mandal and Layek [26] have discussed unsteady MHD Casson fluid flow with slip effects. Gbadeyan et al. [27] investigated Casson nano-fluid flow with velocity slip and convective heating in theoretical research. Jain et al. [28] investigated MHD fluid flow on an absorptive surface with slip and mixed convection. Recently, Sridhar et al. [24] have worked on Casson flow with slip effects on vertical extending plane. Our main aim is to analyze the entropy generation of MHD fluid flows over a stretching surface with velocity slip and chemical reaction. In this paper, we have extended the work of Bayones et al. [29] by analyzing entropy generation and various irreversibility ratios with mass transfer, velocity slip effects, and chemical reactions. Here, we have tried to find out the various irreversibility ratios (Bejan number (Be), Be1 −Be3 ) at the surface of the stretching sheet. We have compared our numerical results by employing MATLAB built in bvp4c solver and R-K-Fehlberg method with the previous work done by Bayones et al. [29], and we have seen an excellent agreement with the previous result.

Entropy Generation Analysis of MHD Fluid Flow …

59

2 Mathematical Formulation Let us consider steady 2D MHD boundary layer flow with mixed convection past through a stretching surface. Let Uw (x) = a ' x (where a ' > 0) be the stretching velocity and U∞ (x) = b' x be the stream velocity. Let Tw , Cw be the temperature and concentration at the surface. T∞ and C∞ be the free stream temperature and concentration. An induced magnetic field of strength B0 has been applied perpendicular to the surface of the stretching sheet. Under these approximations, the governing equations of the current flow model can be written as [29]: Governing equations u

dUn σ B02 ∂u ν∂ 2 u ∂u − +v = Un + (u − Uw ) ∂x ∂y dy ∂ y2 ρ + gβT (T − T∞ ) + gβc (C − C∞ )

(1)

u

∂T Q0 ∂T ∂ 2u +v =α 2 + (T − T∞ ) ∂x ∂y ∂y ρC p

(2)

u

∂C ∂ 2C ∂C +v = Dm 2 − k1 (C − C∞ ) ∂x ∂y ∂y

(3)

Boundary conditions y = 0; u = Uw + u slip , v = 0, T = Tw , C = Cw

(4)

y → ∞ : u → U∞ , T → T∞ , C → C∞

(5)

Similarity transformation: ψ ,η = f (η) = √ Uw νx u=

/

Uw T − T∞ (C − C∞ ) , φ(η) = y, θ (η) = νx Tw − T∞ Cw − C∞

∂ψ ∂ψ ,v = − , Tw = T∞ + c' x, Cw = C∞ + c' x ∂y ∂x

(6) (7)

Using the similarity transformation, we have the transformed equations f ''' + f f '' − f

'

2

) ( + M 1 − f ' + 1 + λ1 θ + λ2 φ = 0

(8)

1 '' θ − f ' θ + f θ ' + Qθ = 0 Pr

(9)

1 '' φ − f 'φ + f φ' − K1φ = 0 Sc

(10)

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Boundary conditions η = 0 : f = 0, f ' = e + e1 f '' , θ = 1, φ = 1

(11)

n → ∞ : f ' → 1, θ → 0, φ → 0

(12)

σ B2

'

Grx Gmx , Pr = αν , Sc = Dνm , K 1 , e = ab' are the where M = νρ0 , λ1 = Re 2 , λ2 = Re2x x magnetic parameter, bouncy parameter for temperature and concentration, Prandtl number, Schmidt number, chemical reaction parameter, and velocity ratio parameter, respectively.

3 Entropy Generation The volumetric entropy generation rate can be defined as [2, 11, 15]: ( ( ) ( ( ) ) )( ) Rd ∂C 2 Rd ∂ T ∂C k ∂T 2 μ ∂u 2 σ B02 u 2 + EG = 2 + + + T∞ ∂ y T∞ ∂ y ρ C∞ ∂ y T∞ ∂ y ∂y (13) where the first term is the irreversibility due to heat transfer, 2nd term signifies the fluid friction, and 3rd and 4th term define the combined effect of heat and mass transfer. Using Eqs. (6) and (7), we have the entropy generation number (Ns) Ns =

( χ )(( χ ) ' ) EG Br '' 2 MBr ' 2 ' f + f + l1 φ 2 + θ 'φ' =θ2+ EG0 Ω Ω Ω Ω

∞) , Br = where E G 0 = k (TwL−T 2T 2 2

∞

(

Br Ω

'' 2

+ MBr Ω f Ns

'2

=

C∞ ,l Cw −C∞ 1

= Rd Ck∞

to heat transfer), Be1 )= ( ' χ χ l1 ( Ω φ ) ( Ω ) 2 +θ ' φ ' (Irreversibility due to viscous dissipation), Be2 = . Ns

Bejan number (Be) = ) f

μUw2 ∞ , Ω = TwT−T ,χ k(Tw −T∞ ) ∞ '2 θ (Irreversibility due Ns

(14)

4 Results and Discussions Table 1 portrays the skin friction co-efficient and Nusselt number for the different values of magnetic field parameter. In Table 1, we have compared the result of f '' (0) and −θ ' (0) with the existing results of Bayones et al. by setting Pr = 1, λ = 0.5, e = 0.5, Sc = 0, K 1 = 0, λ1 = 0, λ2 = 0, Q = 1. As shown in Table 1,

Entropy Generation Analysis of MHD Fluid Flow …

61

Table 1 Skin friction co-efficient and Nusselt number for various values of M when Pr = 1, Q = 1, λ1 = 0.5, e = 0.5, Sc = 0, K 1 = 0, λ2 = 0, M

f '' (0) Bayones et al. [29]

−θ ' (0) Present result Present result Bayones (Bvp4c solver) (R-K Fehlberg et al. [29] Method)

Present result Present result (bvp4c solver) (R-k Fehleberg)

0

0.94096

0.9409

0.94088549

0.57333

0.5736

0.57359896

1

1.06964

1.0695

1.06953725

0.58860

0.5890

0.58897527

2

1.18340

1.1834

1.18340685

−

0.6008

0.60084236

16

2.22152

2.2216

2.22157972

0.66733

0.6673

0.66733265

the results show greater agreement. Table 2 shows the numerical values of Be, Be1 , and Be2 at the surface for M (magnetic field parameter), λ1 , λ2 (Bouncy parameter) and heat generation/absorption parameter. From Table 2, it is seen that viscosity along with the magnetic field plays a significant role on the irreversibility ratios (Be1 ). Irreversibility due to momentum diffusion produces more wastage production than others. Bouncy parameter for temperature and concentration (λ1 , λ2 ) helps to increase the Bejan number (Be) at the surface but reduces for increasing values of M and Q. Irreversibility ratios due to fluid friction along with magnetic field decrease as rising values of M. But, in case of λ1 , λ2 and Q, there is a reduction in values of Be1 . Figures 1, 2, and 3 portray the entropy generation number for various values of e, e1 and λ1 . From these figures, we have seen that entropy generation enhances for rising values of e and λ1 . At steady state, enhancing values of bouncy parameter will increase dominancy bouncy force over magnetic field due to application of Lorentz force. As a consequence, for a fixed values of M, generation of entropy increases. Table 2 Irreversibility ratios at the surface for different values of M, λ1 , λ2 , Q M

λ1

λ2

Q

Be

Be1

Be2

1

0.22389795

0.50413266

0.27196938

2

0.22284823

0.50210255

0.27504921

3

0.22203427

0.50071424

0.27725147

0.1

0.26362656

0.39455357

0.34181986

0.3

0.26537856

0.39922145

0.33539998

0.5

0.26687651

0.40373260

0.32939088

0.1

0.26375522

0.38671967

0.34952510

0.2

0.26456082

0.38898214

0.34645703

0.3

0.26531661

0.39121368

0.34346970

0.5

0.36262856

0.33658857

0.30078286

1.0

0.26687651

0.40373260

0.32939088

1.5

0.14275239

0.49841917

0.35882843

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Fig. 1 Entropy generation profile against η for various values of e

Fig. 2 Entropy generation against η for values of e1 (velocity slip)

This notifies that to reduce the wastage production, we have to control the values of e and λ1 . Figure 3 indicates there is a reduction in production of entropy for increasing values of slip parameter which may lead to enhance the efficiency and reduce the wastage production.

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63

Fig. 3 Entropy generation against η for various values of λ1

Figures 4, 5, 6, and 7 show the effect M, chemical reaction parameter (K 1 ), bouncy parameter for species concentration (λ2 ), Prandtl (Pr) on entropy generation. There is a fast decrease of entropy generation for rising values of M. This is due to magnetic field trends to suppress the convection and retards the fluid motion and reduce in heat transfer, so production of entropy decreases. Chemical reaction parameter helps to increase the entropy generation near the surface after the critical point it decreases. Fig. 4 Entropy generation against η for various values of M

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Fig. 5 Entropy generation against η for different values of K 1

Fig. 6 Entropy generation against η for different values of λ2

Figure 7 shows that entropy generation rises gradually from the neighborhood of the surface, and then, it reduces to the zero value beyond the critical point. Increase of Prandtl number reduces the diffusion of heat at the boundary layer which will lead to rise in temperature distribution. So, entropy generation number also increases at the surface.

Entropy Generation Analysis of MHD Fluid Flow …

65

Fig. 7 Entropy generation profile against η for various values of Pr

5 Conclusions In our present study, the entropy generation analysis for the steady 2D boundary layer flow over a stretching surface has been studied. R-K-Fehlberg method using symbolic computer algebraic software Maple 21 has been employed to analyze the entropy generation number and various irreversibility ratios under the effect of physical parameters. The results indicate that. • The entropy generation enhances with increase of velocity ratio parameter, bouncy parameter for temperature and concentration, but opposite trend is observed in case of e and M. • Prandtl number helps to enhance the Ns near the surface of the stretching surface, but it reduces outside the boundary layer. • The irreversibility due to viscous dissipation along with magnetic field produces more wastage production than others. • Bejan number at the surface rises with the increasing values of bouncy parameters λ1 , λ2 , but in case of M and Q, it decreases.

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A Stacking Ensemble Framework for Android Malware Prediction Abhishek Bhattacharya, Soumi Dutta, Salahddine Krit, Wen Cheng Lai, Nadjet Azzaoui, and Adriana Burlea-Schiopoiu

Abstract Every Android application needs the collection of permissions during installation time, and these can be used in permission-based malware detection. Different ensemble strategies for categorising Android malware have recently received much more attention than traditional methodologies. In this paper, classification performance of one of the primary ensemble approach (Stacking) in R libraries in context of for Android malware is proposed. The presented technique reserves both the desirable qualities of an ensemble technique, diversity, and accuracy. The proposed technique produced significantly better results in terms of categorisation accuracy. Keywords Stacking · Ensemble · Classification · Voting · Android malwares

1 Introduction Android gadgets are now susceptible to threats such as malicious service activation without the user’s knowledge, service denial, and so on. Furthermore, Android apps are easy to decompile and reverse engineer—an explicit feature of Java applications A. Bhattacharya (B) · S. Dutta Institute of Engineering and Management, Kolkata, India e-mail: [email protected] S. Krit Ibn Zohr University, Agadir, Morocco W. C. Lai National Taiwan University of Science and Technology, Taipei, Taiwan e-mail: [email protected] N. Azzaoui University Kasdi Merbah Ouargla, Ouargla, Algeria A. Burlea-Schiopoiu University of Craiova, Craiova, Romania e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_7

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that is frequently exploited by malicious attackers. In contrast to other mobile operating systems, Android preserves openness and does not place many restrictions on its users when it comes to using apps. Each Android app needs a set of permissions that are required during installation time. As a result, identifying malware based on the permission sets it requires during installation is quite possible. However, because of the dimensionality of these massive datasets, learning may be slowed and learning efficiency may be reduced [1, 2]. Ensembles are predictive models that combine predictions from two or more models to produce a single forecast. Ensemble learning approaches are popular and the go-to strategy when the best performance on a predictive modelling assignment is the most important outcome. There are two main reasons to choose an ensemble model over a single model, all of which are related: an ensemble can outperform any single contributing model in terms of prediction and performance, and employing an ensemble reduces the spread or dispersion of predictions and model performance. The objective of supervised learning algorithms is to search through a hypothesis space for an appropriate hypothesis that will generate good predictions for a specific problem. Finding a suitable hypothesis, even if the hypothesis space contains hypotheses that are well-suited to a particular situation, can be difficult. To generate a superior hypothesis, ensembles combine many hypotheses. The term “ensemble” refers to approaches that use the same underlying learner to create several hypotheses. Multiple classifier systems is a larger phrase that includes hybridisation of hypotheses that are not driven by the same base learner. In the R environment, there are a few Ensemble models are implemented. In Stacking, multiple layers of machine learning models are stacked one on top of the other, with each layer passing its predictions to the one above it, and the top layer model making decisions based on the outputs of the layers below it. First, works on malware detection and ensemble classification techniques are looked at. The proposed ensemble technique’s specific phases are then discussed. Following that, there will be experimental validations and comparisons to existing methodologies.

2 Related Work As smart phones with the Android operating system gain popularity around the world, mobile malware has grown in size. Diverse dynamic and static malware detection solutions have been offered to combat this explosion of Android malware. Permission-based malware detection via the AndroidManifest.xml file by means of machine learning classifiers is a common static detection methodology [3]. Selection of significant features has become an unavoidable step in mining huge dimensional data in recent years, and the use of heuristic attribute selection algorithms is one of the primary research topics in this subject. Rough set theory inspired the “quality of classification” measure, which can be used with bio-inspired heuristic search approaches (Genetic Algorithm, Particle Swarm Optimisation, etc.) to pick optimal or near optimal subsets of features [4]. Previous research has revealed that

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one attribute selection approach can have certain biases, whereas an ensemble method is better suited to enhancing and compensating these biases [5]. The goal of ensemble approach is to combine multiple models to generate a prediction model. The ability of ensemble approaches to increase prediction performance is well-known [6–8]. The results show that in the classification of Android malware, stacking ensemble approaches beat Bagging, Boosting, and basic classifiers [9]. In prostate cancer detection, the authors present a stacking-based interpretable selective ensemble learning algorithm [10].

3 Proposed Methodologies This section discusses proposed Stacking-based ensemble techniques for Android malware classification.

3.1 Dataset Preparation The dataset includes 2500 benign and 1150 malicious samples that were taken from Wang’s repository [11] since the research community prefers to use standardised datasets.

3.2 Ensemble Methods Ensemble approaches boost predicting performance by mixing learning algorithms. Model over fitting is often reduced, and model resilience is improved. Ensembling is a strategy for mixing two or more base learners, which are algorithms of similar or dissimilar sorts. There are an endless number of methods to mix distinct models in making different ensemble models. Stacking is the process of teaching a new learning algorithm to conglomerate the forecasts of numerous base learners into a single prediction. Those base learners are trained first using the existing training data, and then the super learner, is trained to create a final prediction based on the base learners’ predictions [11–14]. Several layers of machine learning models are piled on top of each other, with each model transmitting its predictions to the layer above it, and the top layer model making decisions depending on the outputs of the layers below it (Fig. 1). The dataset’s original input features are given to the bottom layer models (d1, d2, d3) (x). The top layer model, f (), forecasts the ultimate output using the output of the lower layer models (d1, d2, d3) as input. In this work it is shown that this top layer model can also be replaced by many other simpler formulas like: Average voting, Majority voting, and Weighted average voting in prediction of Android malwares.

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Fig. 1 Block diagram of stacking

Stacking is experimented in this work by creating 3 sub-models for the dataset, specifically using Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) (Fig. 2). In Majority voting, for each test case, each model provides a forecast (vote), and the final output prediction is the one that obtains more than half of the votes. If no prediction obtains more than half of the votes, we can conclude that the ensemble approach was unable to create a stable prediction in this case. In weighted average voting, the prominence of one or more models is boosted, unlike majority voting, where each model has the same privileges. In weighted voting, the better models’ predictions are counted many times. Intuitive knowledge may help to decide reasonable set of weights. The average predictions are computed for each occurrence of the test dataset in the simple averaging approach. This strategy is frequently used to reduce over fitting. Top Layer Model

SVM

LR

Fig. 2 The hierarchy of proposed stacking model

RF

A Stacking Ensemble Framework for Android Malware Prediction 1

0.9952

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0.9937

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TRR

0.985 0.98

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0.975 0.9689

0.97

0.972

0.9704

0.965 0.96 0.955 RF

SVM

LR

Avg

Weighted Avg

Majority

Fig. 3 Comparison of TPR of three stacking approaches

4 Result and Discussion The proposed work has been evaluated using True Positive Ratio (TPR), Accuracy, F1 score, and False Positive Ratio (FPR). Individually random forest model generates TPR 0.9952 which is quite close to TPR value 0.9937 that is generated by Weighted Average model (Fig. 3). Prediction weights are often higher for more accurate models. So, 0.5 is assigned to RF and 0.25 to SVM and LR each. Average voting and Weighted average voting both produce better results than both SVM and LR. Weighted average voting generates lowest FPR value (0.0001) which is better than any individual Models (Fig. 4). Weighted average voting also produces improved F1 score (weighted average of the precision and recall scores) than any individual models that is shown (0.9968) in Fig. 5. In term of Accuracy, Weighted average voting (0.9956) outperforms all individual models (Fig. 6).

5 Conclusions In this work, a comparative analysis of the performance of different ensemble Stacking approaches (Average voting, Majority voting, and Weighted average voting) for Android malware classification is presented. The proposed predictive model mostly consists of R bundle-based algorithms and ensemble approaches, which are infrequently used in the perspective of permission-based detection and categorisation of Android malware. The proposed technique has shown to improve classification performance. Improvements to the stacking ensemble problem, including statistical features to assist inference, will be considered in future work.

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0.006 0.004 0.002 0.0001 0 RF

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Fig. 4 Comparison of FPR of three stacking approaches 1 0.9968

0.996

F1 Score

0.995 0.99 0.9858 0.9842

0.985

0.9827

0.9826

SVM

LR

0.98 0.975 RF

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Fig. 5 Comparison of F1 score of three stacking approaches

Weighted Avg

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1 0.995

0.9956

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Fig. 6 Comparison of accuracy of three stacking approaches

References 1. Bhattacharya A, Goswami RT (2017) DMDAM: data mining based detection of Android malware. In: Mandal J, Satapathy S, Sanyal M, Bhateja V (eds) Proceedings of the first international conference on intelligent computing and communication. Advances in intelligent systems and computing, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-2035-3_20 2. Bhattacharya A, Goswami RT (2017) Comparative analysis of different feature ranking techniques in data mining-based Android malware detection. In: Satapathy S, Bhateja V, Udgata S, Pattnaik P (eds) Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications. Advances in intelligent systems and computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_5 3. Bhattacharya A, Goswami RT (2018) A hybrid community based rough set feature selection technique in Android malware detection. In: Yang XS, Nagar A, Joshi A (eds) Smart trends in systems, security and sustainability. Lecture notes in networks and systems, vol 18. Springer, Singapore. https://doi.org/10.1007/978-981-10-6916-1_23 4. Bhattacharya A, Goswami RT, Mukherjee K (2019) A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of Android malwares. Int J Mach Learn Cyber 10:1893–1907 5. Neumann U, Genze N, Heider D (2017) EFS: an ensemble feature selection tool implemented as R-package and web-application. BioData Min 10(1):21. https://doi.org/10.1186/s13040-0170142-8 6. Rokach L (2005) Ensemble methods for classifiers. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, Boston, MA. https://doi.org/10.1007/0387-25465-X_45 7. Dutta S, Das AK, Dutta G, Gupta M (2019) A comparative study on cluster analysis of microblogging data. In: Abraham A, Dutta P, Mandal J, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Advances in intelligent systems and computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_77 8. Dutta S, Ghatak S, Das AK, Gupta M, Dasgupta S (2019) Feature selection-based clustering on micro-blogging data. In: Behera H, Nayak J, Naik B, Abraham A (eds) Computational intelligence in data mining. Advances in intelligent systems and computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_78 9. Dhalaria M, Gandotra E, Saha S (2019) Comparative analysis of ensemble methods for classification of android malicious applications. In: Singh M, Gupta P, Tyagi V, Flusser J, Ören T,

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A. Bhattacharya et al. Kashyap R (eds) Advances in computing and data sciences. ICACDS 2019. Communications in computer and information science, vol 1045. Springer, Singapore. https://doi.org/10.1007/ 978-981-13-9939-8_33 Wang Y, Wang D, Geng N, Wang Y, Yin Y, Jin Y (2019) Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection. Appl Soft Comput 77:188–204. https://doi.org/10.1016/j.asoc.2019.01.015 Dutta S, Chandra V, Mehra K, Das AK, Chakraborty T, Ghosh S (2018) Ensemble algorithms for microblog summarization. IEEE Intell Syst 33(3):4–14. https://doi.org/10.1109/MIS.2018. 033001411 Mohammed M, Mwambi H, Mboya IB et al (2021) A stacking ensemble deep learning approach to cancer type classification based on TCGA data. Sci Rep 11:15626. https://doi.org/10.1038/ s41598-021-95128-x Dutta S, Saha N, Das AK, Ghosh S (2019) Clustering model for microblogging sites using dimension reduction techniques. Int J Inf Syst Model Des (IJISMD) 10(2):26–45. https://doi. org/10.4018/IJISMD.2019040102 Dutta S, Ghatak S, Dey R et al (2018) Attribute selection for improving spam classification in online social networks: a rough set theory-based approach. Soc Netw Anal Min 8:7. https:// doi.org/10.1007/s13278-017-0484-8

A Comparative Analysis of Performances of Different Ensemble Approaches for Classification of Android Malwares Abhishek Bhattacharya, Soumi Dutta, Mohammad Kamrul Hasan, Kusum Yadav, Dac-Nhuong Le, and Pastor Arguelles Jr

Abstract All Android applications require the accumulation of permissions during installation, and these are considered features that can be used in permission-based malware detection. Different ensemble strategies for categorisation of Android malware have recently gotten a lot more attention in comparison with traditional methodologies. In this work, comparative analysis of performances of different ensemble approaches (boosting, bagging and stacking) in R libraries for classification of Android malwares is projected. Both the desirable qualities of an ensemble technique, accuracy and diversity are preserved by the presented methods. In terms of categorisation accuracy, the proposed techniques have produced significantly superior results. Keywords Boosting · Bagging · Stacking · Ensemble · Classification

A. Bhattacharya (B) · S. Dutta Institute of Engineering and Management, Kolkata, India e-mail: [email protected] M. K. Hasan Universiti Kebangsaan Malaysia, Bangi, Malaysia e-mail: [email protected] K. Yadav College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia e-mail: [email protected] D.-N. Le Haiphong University, Haiphong, Vietnam e-mail: [email protected] P. Arguelles Jr University of Perpetual Help System DALTA, Las Piñas, Philippines e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_8

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1 Introduction As Android devices become more widely used, the amount of security risks associated with those phones has increased. Modern mobile phones are prone to threats such as the theft of client information, the execution of malicious administrations without the client’s knowledge, the rejection of services and so on. Assailants see the Android operating system as easy prey since Android applications are vulnerable to detection, and it is commonly abused by vengeful attackers who attempt to blend malignant elements into thoughtful applications [1]. Reverse engineering is a threat to Android applications. Malevolent attackers have routinely exploited this weakness, attempting to weave malignant traits into benign applications [2, 3]. In contrast to other mobile operating systems, Android maintains openness and does not place many restrictions on its users’ ability to download and upload apps. Regrettably, because of deficiency of safety awareness, the user is not the best person to review the intents of applications. The most frequent approach to static analysis is source code review, and however„ certain obfuscation strategies have been developed in recent years to make it less operative. Dynamic analysis is a technique for analysing apk execution logs that involves simulating the application in an isolated environment [4]. Ensembles are predictive models that aggregate predictions from two or more different models to create a single forecast. When the best performance on a predictive modelling assignment is the most essential outcome, ensemble learning approaches are popular and the go-to strategy. But generally the computational cost and complexity of ensemble methods are greatly increased. This rise is due to the expertise and time required to train and maintains multiple models as opposed to a single model. There are two primary reasons to prefer an ensemble model over a single model, and they are related—an ensemble can outperform any single contributing model in terms of prediction and performance and the spread or dispersion of predictions and model performance is reduced by using an ensemble. Ensemble techniques optimise predictive performance by combining learning algorithms. They typically reduce over fitting in models and improve model robustness. The large number of high-quality statistical algorithms available in R is one of the main reasons for using it. As statistics researchers advanced to the forefront of statistical learning, they created R packages that included their most recent techniques. Works pertaining to malware detection and ensemble classification strategies are surveyed at first. Formerly the detail steps of the proposed ensemble strategies have been discussed. Experiential validations and comparisons with present strategies follow.

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2 Related Work In recent years, various ensemble techniques for permission-based detection of Android malware have been proposed. To choose the optimal combination of classifiers, a comparison of alternative ensemble approaches is presented [5]. The experimental findings of the ensemble features selection approach are related to different features selection strategies in [6], and the ensemble one enhances classification accuracy with bi-normal separation. In [7], a community-based methodology for permission-based categorisation of Android malware is described, which uses an ensemble-voted feature selection process. The ensemble technique is a powerful tool for combining several learning algorithms to increase overall prediction precision [8]. Previous research has revealed that a particular attribute selection strategy can have certain biases, whereas an ensemble strategy is better suited to enhancing and compensating these biases [9]. The objective of ensemble approach is to combine multiple models to generate a prediction model. Ensemble methods are well-known for their ability to improve prediction performance [10]. The results [11] reveal that stacking ensemble techniques outperformed bagging, boosting and base classifiers in classification of Android malwares. Supervised ensemble approaches build a set of base learners and employ their weighted results to forecast new data. Several empirical studies show that ensemble techniques frequently perform better than any individual base learner [12].

3 Proposed Methodologies This section deals with proposed ensemble strategies in classification of Android malware.

3.1 Dataset Preparation The dataset contains 2500 benign and 1150 malicious samples that were taken from Wang’s repository [13] since the scientific community prefers to use standardised datasets.

3.2 Ensemble Methods By combining learning algorithms, ensemble techniques improve predictive performance. They typically reduce model over fitting and improve model robustness. Ensembling is a strategy for mixing two or more base learners, which are algorithms

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of similar or dissimilar sorts. This is done in order to build a more reliable system that takes into account all of the base learners’ predictions. Practically speaking, there are an infinite number of ways to combine different models. However, the following are the most commonly used techniques: boosting, stacking and bagging. Bagging is represented as making multiple models are constructed from different subsamples of the training dataset. Boosting is creating multiple models where every model learns to correct the forecast errors of a preceding model in the chain. Stacking can be represented as creating multiple different types models and a supervisor model that studies how to best conglomerate the primary models’ forecasts [14–17].

3.2.1

Boosting

Boosting is a successive procedure where the first algorithm has been trained on the whole dataset, and succeeding algorithms are formed by fitting the first algorithm’s residuals, weighting observations that the previous model failed to predict. It is based on the creation of a sequence of weak learners, each of which may not be appropriate for the whole dataset but is suitable for a subset of it. As a result, each model improves the ensemble’s performance. C5.0 and stochastic gradient boosting have been implemented in this work.

3.2.2

Bagging

Bootstrap aggregation is another name for bagging. Bootstrapping is a sampling technique in which ‘n’ observations have been chosen from a dataset having ‘n’ rows. The key point is that each row is chosen with replacement from the actual dataset, so that each row has an equal chance of being chosen in every iteration. In this work, two bragging algorithms—random forest and bagged CART algorithms of R—have been used.

3.2.3

Stacking

Several layers of machine learning models are stacked one on top of the other, with every model passing its forecasts to the model in the layer above it, and the top layer model making decisions based on the outputs of the models in the layers below it (Fig. 1). The dataset’s original input features (x) are given to the bottom layer models (d1, d2, d3). The top layer model, f (), forecasts the ultimate output using the output of the lower layer models (d1, d2, d3) which are assumed as input. The forecasts of separate models are not greatly correlated with those of other models. Classification and regression trees (CART), logistic regression (GLM) and support vector machine (SVM) have been used as bottom level models.

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Fig. 1 Block diagram of stacking

4 Result and Discussion C5.0 and stochastic gradient boosting have been implemented in this work (Table 1). Experimental result shows that C5.0 yields an accuracy of 99.59%. Figure 2 demonstrates the visualisation of accuracy percentages generated by C5.0 and stochastic gradient boosting. Two ensemble (bagging) methods—bagged CART and random forest—have been used in this work for the classification of Android malwares. Result shows that random forest generates an accuracy of 99.59% (Table 2). The visualisation of accuracy and kappa statistic is shown in Fig. 3. Another ensemble technique, i.e. stacking, is implemented in this work by creating three sub-models for the dataset, specifically using support vector machine (SVM), logistic regression and classification and regression trees (CART) (Fig. 4). It can be seen that the SVM individually produces the most correct model with an accuracy of 99.61% (Table 3). It is advantageous if the predictions generated by the sub-models have low correlation when stacking is used to integrate the predictions of several models. This would imply that the models are talented, but in different ways, allowing a new classifier to figure out how to get the most out of each model for a higher score. If the sub-model predictions were highly corrected (>0.75), they would most of the time make the same or quite related predictions, reducing the benefit of combining the predictions. From Table 4, it is observed that the correlation between all pairs of predictions is Table 1 Accuracy percentage of different boosting algorithms

Name of boosting algorithms

Accuracy percentage

C5.0

99.589

Stochastic gradient boosting (gbm)

99.059

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Fig. 2 Boosting ensemble methods in R Table 2 Accuracy percentage of different bagging algorithms

Name of boosting algorithms

Accuracy percentage

Bagged CART (treebag)

99.35

Random forest (RF)

99.59

Fig. 3 Bagging ensemble methods in R

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Top Layer Model

CART

SVM

Logistic

Fig. 4 Architecture of proposed stacking model

Table 3 Accuracy percentage of different base algorithms in stacking

Name of base algorithms

Accuracy percentage

Regression trees (CART)

96.28

Support vector machine (SVM)

99.61

Logistic regression (GLM)

98.60

Table 4 Correlation of different base models

Regression trees (CART)

Logistic regression (GLM)

Support vector machine (SVM)

Regression 1.00000000 trees (CART)

0.1805323

0.01745181

Logistic regression (GLM)

0.18053228

1.00000000

0.61233178

Support vector machine (SVM)

0.01745181

0.6123318

1.00000000

often poor. In the stacking ensemble approach, Fig. 6 shows correlations between predictions made by sub-models (Figs. 5 and 6). When predictions of the classifiers are combined using a simple linear model, it is observed that the accuracy has been lifted to 99.67% which is a small improvement over using SVM alone and of course over regression trees (CART) and logistic regression (GLM) too (Table 5).

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Fig. 5 Accuracy of multiple base models in stacking Fig. 6 Correlations between sub-model predictions in stacking ensemble

Table 5 Accuracy and Kappa statistic of proposed stacking model

Accuracy

Kappa statistic

0.9967

0.9923

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5 Conclusions In this work, a comparative study of the performance of several ensemble approaches (boosting, bagging and stacking) in R libraries for Android malware classification is planned. The proposed predictive model mostly consists of R bundle-based algorithms and ensemble approaches, which are rarely used in the context of permissionbased detection and categorisation of Android malware. The proposed technique has shown to improve classification performance. More sophisticated algorithms can be used to combine predictions in an attempt to determine when the various methods should be used.

References 1. Yang Z, Jin M, Zhang Z, Lu J, Hao K (2017) Classification based on feature extraction for hepatocellular carcinoma diagnosis using high throughput DNA methylation sequencing data. Proc Comput Sci 107:412–417. https://doi.org/10.1016/j.procs.2017.03.130 2. Bhattacharya A, Goswami RT (2017) Comparative analysis of different feature ranking techniques in data mining-based android malware detection. In: Satapathy S, Bhateja V, Udgata S, Pattnaik P (eds) Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications. Springer. https://doi.org/10.1007/978-981-10-3153-3_5 3. Bhattacharya A, Goswami RT (2017) DMDAM: data mining based detection of android malware. In: Mandal J, Satapathy S, Sanyal M, Bhateja V (eds) Proceedings of the first international conference on intelligent computing and communication. Advances in intelligent systems and computing, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-2035-3_20 4. Bhattacharya A, Goswami RT (2017) Comparative analysis of different feature ranking techniques in data mining-based android malware detection. In: Satapathy S, Bhateja V, Udgata S, Pattnaik P (eds) Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications. Advances in intelligent systems and computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_5 5. Coronado-De-Alba LD, Rodriguez-Mota A, Escamilla-Ambrosio PJ (2016) Feature selection and ensemble of classifiers for Android malware detection. In: Proceedings of the 8th IEEE Latin-American conference on communications. LATINCOM, pp 1–6. https://doi.org/10.1109/ LATINCOM.2016.7811605 6. Aswini AM, Vinod P (2014) Android malware analysis using ensemble features. In: Chakraborty RS, Matyas V, Schaumont P (eds) Security, privacy, and applied cryptography engineering. SPACE 7. Bhattacharya A, Goswami RT, Mukherjee K, Nguyen NG (2019) An ensemble voted feature selection technique for predictive modeling of malwares of android. Int J Inf Syst Model Des (IJISMD) 10(2):46–69. https://doi.org/10.4018/IJISMD.2019040103 8. Dietterich T (2000) Ensemble methods in machine learning. In: Proceedings of the Multiple Classifier System conference, pp 1–15 9. Neumann U, Genze N, Heider D (2017) EFS: an ensemble feature selection tool implemented as R-package and web-application. BioData Min 10(1):21. https://doi.org/10.1186/s13040-0170142-8 10. Rokach L (2005) Ensemble methods for classifiers. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, Boston, MA. https://doi.org/10.1007/0387-25465-X_45 11. Dhalaria M, Gandotra E, Saha S (2019) Comparative analysis of ensemble methods for classification of android malicious applications. In: Singh M, Gupta P, Tyagi V, Flusser J, Ören T,

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A. Bhattacharya et al. Kashyap R (eds) Advances in computing and data sciences. ICACDS 2019. Communications in computer and information science, vol 1045. Springer, Singapore. https://doi.org/10.1007/ 978-981-13-9939-8_33 Tuv E (2006) Ensemble learning. In: Guyon I, Nikravesh M, Gunn S, Zadeh LA (eds) Feature extraction. Studies in fuzziness and soft computing, vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_8 Wei Wang’s Home Page (2016). http://infosec.bjtu.edu.cn/wangwei/?page_id=85. Accessed 31 Oct 2022 Dutta S, Chandra V, Mehra K, Das AK, Chakraborty T, Ghosh S (2018) Ensemble algorithms for microblog summarization. IEEE Intell Syst 33(3):4–14. https://doi.org/10.1109/MIS.2018. 033001411 Dutta S, Ghatak S, Dey R et al (2018) Attribute selection for improving spam classification in online social networks: a rough set theory-based approach. Soc Netw Anal Min 8:7. https:// doi.org/10.1007/s13278-017-0484-8 Dutta S, Das AK, Dutta G, Gupta M (2019) A comparative study on cluster analysis of microblogging data. In: Abraham A, Dutta P, Mandal J, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Advances in intelligent systems and computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_77 Dutta S, Ghatak S, Das AK, Gupta M, Dasgupta S (2019) Feature selection-based clustering on micro-blogging data. In: Behera H, Nayak J, Naik B, Abraham A (eds) Computational intelligence in data mining. Advances in intelligent systems and computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_78

Natural Language Processing in Chatbots D. Sai Pranav, Mehar Mutreja, Devansh Punj, and Pronika Chawla

Abstract Natural language processing (NLP) was utilized to include for the most part mysterious corpora with the objective of improving phonetic examination and was hence improbable to raise ethical concerns. As NLP gets to be progressively widespread and uses more information from social media. The main goal is to develop a fully useful voice-based mechanization framework for the home that uses the “Internet of Things, artificial intelligence, and natural language processing” (NLP) to provide an inexpensive and efficient way to connect to home appliances for work. Chatbots could be virtual individuals who can successfully make conversation with any human being utilizing intuitively literary abilities. As of now, there are numerous cloud base chatbots administrations that are accessible for the advancement and change of the chatbot segment such as “IBM Watson, Microsoft bot, AWS Lambda, Heroku,” and many others. We displayed useful engineering that we propose to construct a brilliant chatbot for wellbeing care help. Our paper provides an outline of cloud-based chatbots advances together with the programming of chatbots and the challenges of programming within the current and upcoming period of chatbots. Keywords Artificial intelligence · Automatic speech recognition · Chatbot · Jennifer chatbot · Natural language processing

1 Introduction Natural language processing may be a hypothetically persuaded range of computational procedures for analyzing and speaking to normally happening writings at one or more levels of etymological analysis for the reason of accomplishing human-like language processing for a run of assignments or applications. A few components of D. Sai Pranav (B) · M. Mutreja · D. Punj · P. Chawla Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India e-mail: [email protected] P. Chawla e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_9

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Fig. 1 Chatbots in different domains

this definition can assist in detail. Firstly the uncertain idea of “range of computational techniques” is vital since there are different strategies or strategies from which to select to achieve a specific sort of language analysis [1]. A chatbot is an immediate informing technology that is able to supply offerings utilizing immediate messaging systems with the point of giving conversational administrations to clients in an effective way. A chatbot is quick with very low confounding and versatile applications that are simple to introduce as there are no different installation packages to install. These bundles are simple to oversee and distribute. Chatbots are completely unique as human debts in that they do now no longer have online fame or exact timestamps, nor do they provoke discussions and calls with different accounts. Figure 1 indicates a few kinds of chatbots in different fields. A large “chatbot architecture” is shown in Fig. 2. The aim is to distinguish the aim of the client message. The entity acknowledgment module extricates organized bits of data from the message. The domain-specific calculations which are used to process the user’s request were done by the candidate response generator. The reaction selector fair scores all the reaction candidates and chooses a reaction that ought to work superior for the client [2]. A chatbot might offer assistance to “doctors, medical caretakers, patients, or their families.” Better corporation of data such as “patient information, drug management, crisis management, promotion of a regulation for superficial medical problems.” These are all conceivable circumstances for “chatbots” to relieve medical experts.

2 Literature Review Mishra et al. [3] depict that medical chatbot will carry on as a virtual specialist, which can be permitted to be in touch with the patients. The above chatbot is created using python language design coordinating calculations and “natural language processing” strategies. Concurring to the study, which was conducted to check the execution of the

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Fig. 2 Chatbots architecture

above chatbot, 80% answers that are given by chatbot are right, whereas inaccurate 20% of the answers that the chatbot gives are inaccurate. Therefore, the outcome of this survey revealed that this chatbot can be basically utilized not only for virtual specialist for care but also for mindfulness and educating students who are practicing medicine. Madhu et al. [4] said artificial intelligence is utilized to anticipate any illness, and for the supply, the list of conceivable medicines based on the symptoms showed by the patient. Also, in case intermittently investigation is done on a human’s body, Artificial intelligence could offer assistance to foresee any conceivable illness prior to any harm that can happen to the human body. Kazi et al. [5] proposed a thought of creating a medical chatbot for medical students. The medical chatbot employs a non-proprietary software AIMML-based chatbot, and chatbot is capable enough to precisely change over normal human dialect inquiries into the significant questions of (structured query language). Ninety-seven interrogatory problems were gathered, and thereafter, these situations were isolated into distinctive bunches on the basis of their sorts. On the basis of entire number of issues displayed with each bunch, the equivalent bunches were positioned in like manner. Agreeing to the inquiries, questions were asked; inside of which 47% of questions are posed questions, whereas other bunches have lower than 7% questions. Wu et al. [6] analyzed issue of response selection for long discussions on repossession-based chatbot. The tests of the model’s execution are then made on the open datasets. The outcome uncovered that one and the other models could outperform the state-of-art coordinating methods.

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3 Chatbot Using Python Chatbots are primarily built as a conversational speech engine built into Python and allow you to reply based on collections of all well-known conversations. Currently, a chatbot is planned in completely other languages, such as “English, Spanish, and French” [7]. Basic Python Bot Code is shown in Fig. 3.

3.1 An Overview of Typical Entries Would Be Something like | **user:** Hello, Good morning! How are you? | **bot:** I am fine, thank you and you. | **user:** Thank you and Your welcome.

3.2 Basic Python Code import time import random name = input(“Hi! I am Jarvis. What is your name?\n”) time.sleep(2) print(“Hello” + name) feeling = input(“Are you feeling well?\n”) time.sleep(2) if “Yes” in feeling: print(“That is fantastic!”) else: print(“May I recommend a doctor?”) time.sleep(2) favbooks = input(“What is your favorite book?\n”) books = [“HarryPotter”, “PercyJackson”, “LordOfTheRings”] time.sleep(2) print(“My favorite book is” + random.choice(books)) Fig. 3 Basic python bot code

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4 Chatbot Programming Challenges There is part of challenges that are related to chatbots. A few of them are as follows.

4.1 Natural Language Processing The basic and best assignment of the “chatbot” is to manage NLP issues by acing their dialect structure. Inside the event that we inquire them that what’s the climate? [8]. You’ll get a reply but inside the event that we inquire “Might you check the climate?” you may not get the correct reply. Such kind of programming problems by and large fall beneath the characteristic language handling category which may be a fundamental key centers like “Facebook, Google” with Significant Substance, and Language structure independently.

4.2 Machine Learning Getting natural language processing (NLP) is one point of organizing and making of “Chatbots” in spite of the fact that “machine learning” is another perspective on Chatbot orchestrate and upgrade. “Python was mainly developed to read codes easily. Python supports various libraries such as Pandas, NumPy, SciPy, and Matplotlib” [9]. Our computer frameworks ought to be able to memorize the proper reaction that can be wrapped up with effective programming with concepts of artificial intelligence (AI) [10].

5 Improving Chatbots • Chatbots will be more human alike Natural language processing (NLP) bots are used presumption examination and prescient analytics to scholarly people get the dialogs and the point of the questions and requests. Looking at the previous stats of chatbot generally on how companies are getting a handle on AI improvement, there is still one or two of challenges to execute a chatbot best hone reasonably [11]. Error organizations have encountered using intelligent assistants/chatbots in workplace (Fig. 4).

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Fig. 4 Errors in chatbot

6 Proposed Architecture As we can see in Fig. 5 this can be the proposed design for our chatbot, and we will clarify each portion of it within the underneath segment.

Fig. 5 Architecture of smart chatbot

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6.1 Environment In this place, the primary natural language processing (NLP) motor happens. • NLP Engine A vital component gets what a customer says at a certain point in time and after that substitute the language with well-defined input that the system can organize. An interactive agent ought to back different highlights as they are domain-specific. The natural processing machine comprises the new calculations of “machine learning” that are utilized to recognize the desire of the client and after that arrange them into list of bury that is saved by the bots [12]. Components of NLP Engine: Intent Classifier: In this, we take input from the user and translate it to meet the expectations of the bot so that it can understand the information. Entity Extractor: Mainly used to get information from a user’s query. • Agent for Dialogue Management “It can manage the veritable setting of the client saying.” For instance, on the off chance that a user said “He should call a heart specialist” and later the AI makes the call. Now just in case the user said that “Change my inquiry to the chest specialist,” here the user is insinuating to that query, which he has inquired before, the “chatbot” must decode it precisely and need to do changes some time recently asserting from the client-side. For this reason, talk organization plug-ins are useful [13].

6.2 Question and Answer System It may be a vital part to provide a solution to the clients routinely asked queries. “At first the system takes questions asked by other users and responds to these queries with similar and related answers present in the database.” Manual Training: Through this process, the space pros make a record of as frequently as conceivable asked doubts and after that draft the solutions. Automated Training: In this planning, particular sorts of organization records such as question and answer files and course of action reports are sent to the bot and ask the bot to schedule such reports. This arrangement comes around in a list containing a few questions and answers are given in records. This bot is capable of answering such questions to questions with absolute certainty.

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6.3 Plug-ins/Components The plug-ins offer nifty chatbot robotization components and chatbot action APIs for chatbots used in businesses such as field workers and HR organization chatbots.

6.4 Node Server/Traffic Server A server is careful about managing the user’s inquiries and then forwards them to the appropriate components. This server additionally makes it easy for the incoming component to respond to the front-end system.

6.5 Front-End Systems Some systems that have a client-side layout may be candidates to form the front-end. “These systems can be the chatbot meddle that exists in various stages such as” • • • • •

Microsoft Teams Facebook Google Hangouts Slack Skype for Business.

7 Chatbots and AI Concepts 7.1 How a Chatbot Works Ready to say that choosing “the right natural language processing (NLP) engine is the most important step in creating a chatbot” [14]. When one connects to a voice “chatbot” for viewing, you want a speech recognition engine to turn speech into content. Programmers have to additionally pick out whether or not they want prepared or unorganized discussions. Chatbots, which are designed for organized discussion, are highly written, unraveling programming but reducing the number of questions a customer can ask. Today, chatbots have turn out to be much extra superior via the usage of “artificial intelligence (AI)” innovations, which includes “deep learning, natural language processing, and machine learning (ML)” calculations, and calls for a splendid deal of meaningful data to supply accurate outcomes. “The extra you associate with the bot, the higher the accuracy.”

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7.2 The Limitations of Chatbots There are many researches seeking to create such a splendid chatbot that it has natural conversations which might be indistinguishable from humans. But it isn’t viable to set up one of these chatbots. Based on previous studies, the following are the main drawbacks of having a viable and competent discussion with a “chatbot.” • Fixed Rule-based: Chatbots that already exist are built with the help of simple “machine learning” methods, a set of rules, and design-based coordination [15]. • Semantics: It involves words or phrases in a “human natural language format.” Today’s chatbots may not be able to handle “natural language processing,” regardless of the fact that those chatbots were answering or investigating questions. • Sentiment Analysis: Chatbots of the past cannot feel the feelings of a topic that people are talking about. A chatbot should be able to tell if a person is happy, pathetic, or angry when presented with a speech or content design. • Recommender Systems: The above chatbots cannot advise or clarify a human point. Indeed they cannot inquire any questions. Chatbots handiest collect customer statistics and cultivate a response from the information database. “A chatbot must be able to make inquiries on the basis of questions that have already been answered [16].” • Accuracy: Chatbots need to be profiled so that their discussion is human-like to do any piece of work. Currently, “chatbots” are bad at suddenly switching a topic and giving unusual responses. Sometimes chatbots react out of context. Therefore, we are not able to reach an adequate stage of precision. • Self-learning: Administered “machine learning” strategies are not utilized in past “chatbots.” They are terrible at learning the newest forms of words or languages. You may not find a framework for coherent thought and interaction. Most chatbots cannot put together a classifier to sketch from sentence to goal and application grouping at the spatial channel. • Data Processing: Currently, chatbots cannot easily handle formatted data, and therefore, no relational database exists. That said, datasets are complicated to plan, and substance and expression mapping are vital. • User Interface: The user interface of current chatbots is sparse; it is not easy to use, and the documentation is terrifying too.

7.3 Improvements To overcome all the specified limitations, an idle chatbot that has all the deep learning features must be created. When analyzing human input, you are also responsible for creating an appropriate response. Given that we prepare “chatbots” accordingly, they will identify people’s “natural language” and react appropriately to the situation. The big disadvantage, however, is that these intrinsic reactions require an impressive amount of time and information for all the gigantic amounts of conceivable inputs to

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be learned. Training proves to be when artificial intelligence chatbots are competent to take care of the most difficult questions that are an obstacle to less complex chatbots.

8 Comparison Between Chatbots Comparison of different chatbots is demonstrated in Table 1. Table 1 Comparison of different chatbots Facebook’s chatbot

Microsoft’s chatbot

Amazon’s chatbot

Google’s chatbot

• Facebook is the • Microsoft CEO • In April of this year, • Google has made its dominant player in Satya Nadella Amazon made its way into the chatbot messaging services believes that Amazon Lex space much more through its chat-based preservation slowly. The messaging platform interfaces will interface tools company only and its proprietary replace applications generally available launched its Google WhatsApp, which is as the primary way to businesses. Using Allo smart instant why it has invested people use the the same technology messaging so much in Internet and is as Amazon’s Alexa, application in late communication bots investing heavily in businesses can 2016 [17] this vision of the create text or voice • Allo integrates the Google Assistant, • Zuckerberg has future bots which grew out of followed the chatbot • Microsoft sees an • It seems that Google Now. Allo market with advantage in Amazon configured allows users to chat enthusiasm and has artificial intelligence its chips to echo and directly with the placed itself in the technology and the voice interfaces Google Assistant to strongest position to development of instead of chat get answers to basic become the chatbots with interfaces. With questions ubiquitous chat preservation over 10 million echo • One manner Google interface for capabilities devices sold, we is making an attempt everyday consumers • It introduced the cannot blame to enhance its role Xiaoice chatbot in • What Facebook sees Amazon for striving within side the China in 2014 and because the destiny to maintain chatbot area is with the Rinna chatbot in is M, its AI-powered momentum and the latest release of Japan in 2015. They digital assistant. A awareness in the its chat base. It is an have been effective small variety of beta region where its ROI analytics device this not only in testers have to get is strongest [20] is supporting obtaining basic admission to • Currently, chatbot different businesses information for complete M that is technology cannot enhance their people, but also as supported via way become a major personal chatbots beings that people of means of humans focus for Amazon which can be enjoy interacting [18] [21] presently utilized in with on an locations like emotional level [19] Facebook Messenger [22]

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9 Conclusion and Future Scope Chatbots still have a long way to go before they reach their full potential. Nevertheless, with billions of dollars of investment every year and a large amount of human capital invested in its development, chatbots will eventually generate huge future value in the enterprise and consumer environment. In short, a chatbot is an ecosystem that is evolving very fast, and over time, new features will be added to the existing platform. The latest advances in “machine learning” technology may be able to correctly handle complex dialogue issues, such as payments. We concluded that the use of NLP pattern recognition and training data in chatbots is not enough; the inclusion of domain knowledge and its use to contextualize user input improves the chatbot’s ability to generate accurate information and conversational dialogue. Therefore, future work will focus on using structured domain knowledge bases and integrating them into deep learning models. The terms speech-to-text, visual recognition, and text analysis all fall into the category of NLP. These three areas of artificial intelligence are in a period of great prosperity and will have a single function at some point. “And this is not over yet, NLP has been discontinued, and it plays an important role in the AI field through computing power, scalability, and the current digital wave.” However, the results of NLP will only be as good as the underlying data pipeline to help the model perform more training, detection, summary, and accuracy.

References 1. Li X, Niu J, Karuppiah M, Kumari S, Wu F (2016) Secure and efficient two-factor user authentication scheme with user anonymity for network based e-health care applications. J Med Syst 40(12):1–12 2. Karuppiah M, Kumari S, Li X, Wu F, Das AK, Khan MK, Saravanan R, Basu S (2017) A dynamic ID-based generic framework for anonymous authentication scheme for roaming service in global mobility networks. Wirel Pers Commun 93(2):383–407 3. Mishra SK, Bharti D, Mishra N (2017) Dr. Vdoc: a medical chatbot that acts as a virtual doctor. J Med Sci Technol 6(3) 4. Madhu D, Jain CN, Sebastain E, Shaji S, Ajayakumar A (2017) A novel approach for medical assistance using trained chatbot. In: 2017 international conference on inventive communication and computational technologies (ICICCT). IEEE, pp 243–246 5. Abbasi S, Kazi H, Hussaini NN (2019) Effect of chatbot systems on students learning outcomes. Sylwan, English edn, vol 163(10), pp 49–63 6. Wu Y, Wu W, Xing C, Xu C, Li Z, Zhou M (2019) A sequential matching framework for multi-turn response selection in retrieval-based chatbots. Comput Linguist 45(1):163–197 7. Rahman AM, Al Mamun A, Islam A (2017) Programming challenges of chatbot: current and future prospective. In: 2017 IEEE region 10 humanitarian technology conference (R10-HTC). IEEE, pp 75–78 8. Tyagi SS (2021) Secure data storage in cloud using encryption algorithm. In: 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV). IEEE, pp 136–141 9. Jain M, Rajput H, Garg N, Chawla P (2020) Prediction of house pricing using machine learning with Python. In: 2020 international conference on electronics and sustainable communication systems (ICESC). IEEE, pp 570–574

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10. Tyagi SS (2021) Enhancing security of cloud data through encryption with AES and Fernet algorithm through convolutional-neural-networks (CNN). Int J Comput Netw Appl 8(4):288–299 11. Niculescu AI, Banchs RE (2015) Strategies to cope with errors in human-machine spoken interactions: using chatbots as back-off mechanism for task-oriented dialogues. In: Proceedings of ERRARE. Sinaia, Romania 12. Sharma S, Aayushya D, Kathuria M, Chawla P (2020) Park assist: a deep convolution neural network based parking assistant system 13. Pranav DS, Punj D, Dubey T, Chawla P (2021) Data mining in cloud computing. In: 2021 5th international conference on computing methodologies and communication (ICCMC). IEEE, pp 71–78 14. SAbdul-Kader SA, Woods JC (2015) Survey on chatbot design techniques in speech conversation systems. Int J Adv Comput Sci Appl 6(7) 15. Io HN, Lee CB (2017) Chatbots and conversational agents: a bibliometric analysis. In: 2017 IEEE international conference on industrial engineering and engineering management (IEEM). IEEE, pp 215–219 16. Setiaji B, Wibowo FW (2016) Chatbot using a knowledge in database: human-to-machine conversation modeling. In: 2016 7th international conference on intelligent systems, modelling and simulation (ISMS). IEEE, pp 72–77 17. De Cicco R, e Silva SC, Alparone FR (2020) Millennials’ attitude toward chatbots: an experimental study in a social relationship perspective. Int J Retail Distrib Manage 18. Bamba I, Singh J, Chawla P, Soni K (2021) Big social data and modelling frameworks. In: 2021 international conference on artificial intelligence and smart systems (ICAIS). IEEE, pp 724–727 19. Abdellatif A, Costa D, Badran K, Abdalkareem R, Shihab E (2020) Challenges in chatbot development: a study of stack overflow posts. In: Proceedings of the 17th international conference on mining software repositories, pp 174–185 20. Mutreja M, Khandelwal K, Dham H, Chawla P (2021) Perception of IOT: application and challenges. In: 2021 6th international conference on communication and electronics systems (ICCES). IEEE, pp 597–603 21. Samuel I, Ogunkeye FA, Olajube A, Awelewa A (2020) Development of a voice chatbot for payment using Amazon Lex service with Eyowo as the payment platform. In: 2020 international conference on decision aid sciences and application (DASA). IEEE, pp 104–108 22. Anwarulloh TP, Agustia RD (2021) Development of the chatbot Einstein application as a virtual teacher of physical learning in the house using android based google dialogflow API. Elibrary Unikom

CT Image Denoising Using Bilateral Filter and Method Noise Thresholding in Shearlet Domain Rashmita Sehgal and Vandana Dixit Kaushik

Abstract Computed tomography (CT) proves to be one of the important apparatuses to diagnose disease in clinical science. The X-beam intensity decides the nature of CT images, if the X-beam portion is higher, the nature of CT picture is improved yet it might create bad effect on the patients. Low portion CT pictures are noisy because of some significant reasons, for example, measurable vulnerability in all physical estimations. The major problem arises that if we lower the CT dose the quality of our image deteriorates and if we increase the dosage the risk on the patient increases. Thus in this paper, a technique is proposed in which bilateral filter and shearletbased thresholding are used. For better edge safeguarding, we have used the concept of method noise thresholding. From different output metrics such as PSNR, IQI, the accuracy of the resulting CT images is checked and evaluated. The findings are also contrasted with the related state of the art and some recent works as well. After review of the findings, it is noted that our proposed algorithm is better compared to existing algorithms. Keywords CT imaging · Denoising · Image quality index

1 Introduction X-ray CT images are widely used in medical fields to diagnose many types of cancer. The density of radiographs is greatly reduced, due to its harmful and adverse effects on the human body (damaging DNA and giving rise to cancer), but the use of less ionized radiation leads to degradation in the quality of medical images who produce mottle noise. To suppress the noise, many techniques have been explored so far [1]. However, due to the unequal distribution in low-dose CT images, it is not easy to denoise the R. Sehgal (B) · V. D. Kaushik Department of Computer Science and Engineering, HBTU, Kanpur, India e-mail: [email protected] V. D. Kaushik e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_10

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images using traditional algorithms and techniques. Also, these approaches involved at a very high cost of calculation. Therefore, the authors suggested many new methods to denoise medical images. In modern medical science, computed tomography is the most used technology, [2] Therefore, it is important to have a CT image without noise to know the exact information about the disease [3]. It is natural that said CT image contains the noise due to some software problems or hardware of the machines, since the X-rays pass through the body to give the desired output. Therefore, to help doctors perfectly detect the disease we try to remove the noise as much as possible [4]. We can use high X-ray intensity to capture high quality or transparent images, but because of the high radiation dose [5], those rays can be harmful for the human body. Lower X-ray intensity does not affect human bodies, but the CT images produced are of less resolution and contrast and thus include noise in all physical measurements owing to the variability of statistical methods [6]. Acquiring such pictures with low quality can be hazardous for the patient as the evaluator (specialist) may not recognize or see what he needed to and subsequently the CT pictures will not fill its need. It is clear that extraordinary specialists having high experience may not draw the outcomes from such pictures. Accordingly, we need to work on the nature of the pictures without losing any significant information from the picture. One of the famous techniques to smother commotion is edge protectionbased noise reduction strategy [7–9]. In applying this technique, interestingly, the clinical data like edges, corners, or interior data of designs ought not to be lost [10– 14]. Wavelet-based denoising is most commonly used days. But wavelet transform is not very efficient in preserving the minor edges and small details, so we make use of the Shearlet transform in order to preserve the edges well and denoise the image. Tomasi and Manduchi [15] suggested a strategy to reduce the noise limit in lowdose computed tomography (CT) images using a modified smooth patch ordering algorithm (MSPO). The author of this paper proposed a method for minimizing noise in low-dose CT images by altering the method of smooth patch ordering (SMO) and the algorithm of non-local means (NLM). The NLM algorithm in the MSPO approach is updated by replacing the robust Leclerc function with the robust bisquare function. The modified NLM then operates to decrease the mottle noise in LDCT images along with the smooth ordering of the pixels, patch classification, and sub-image averaging scheme. The iterative reconstruction and post-processing methods were previously used to increase the image denoising but in this method, the author replaces both the approaches with the Pre-whitening and TV filters, respectively, as they produce better images and have less computation cost. This method resulted out to be successful in both noise suppression and image preservation. One of the major advantages of using MSPO method is that it can be directly applied on any system without the CT raw data. Even after suppressing the noises largely the author was not able to fully eliminate the noises because of the uneven distribution in LDCT images. This method was modified by dividing the patches into different groups along with reducing the calculation cost. Wang et al. [16] proposed a novel 3D noised reduction method, for X-ray computed tomography (CT), called structurally sensitive multiscale generative adversarial net. This method was directly targeted at low-density CT images (LDCT). Density of X-rays is kept very truncated, due to its harmful and

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adverse effects on the human body (i.e., giving rise to cancerous cells, and genetic bodily damage), but this reduction in the radiation dosage leads to an escalation in image noise, compromising significant diagnosable information. In CT images, Poisson or Gaussian noise is majorly seen [14–16]. To enhance the quality of the CT images, different methods are used such as nonlinear method and linear method. These methods are used for image denoising. The major problems while image denoising are edge preservation [10–14]. Smoothening filters are not sufficient, mainly for higher noise, and it results in losing the key parts of the images such as edges and the corners. Sharpening filters result in sharpening the edges which are not satisfied to keep the smaller edges safe. This is the most prominent issue of the medical image denoising as losing the medical information is most harmful for patient which may even lead to lose of a human life [15, 16]. In recent times, bilateral filter [5] is widely used for noise reduction. This method is used for sharpening and smoothening the images while identifying the resemblance of neighborhood pixels. Soft thresholding in wavelet transformation measures mean square error (MSE) to give satisfactory denoised images [16]. Thresholding concept not only reduces the noises from the CT images but also helps in preserving the edges and corners of the image, and hence, the critical information in the image is preserved [15]. In softthresholding method, the values below the threshold value, coefficients are estimated and replaced by modified coefficients and keep the rest of the values same. With the merits of bilateral filter and wavelet thresholding, we proposed a scheme based on bilateral filter and method noise-based thresholding in shearlet domain. Bilateral Filter: Bilateral filter is a very efficient nonlinear, local, and non-iterative noise reduction and edge preserving filter, introduced by Diwakar et al. [13]. It primarily consists of two filter kernels. One is a spatial filter kernel which behaves like a classical low-pass filter and the other one is an edge preserving function which attenuates the filter kernel when the intensity difference between the pixels is large. Shearlet Transform: Shearlet transform is an excellent tool which has the capability of representing the directional information, unlike the traditional wavelet system which was only associated with two parameters—the scaling parameter a, and the translation parameter t, it also includes shearing parameter s. Traditional multiscale techniques are not very capable of gathering information about minute edges and other anisotropic features, but shearlets have the capability to efficiently represent these anisotropic features.

2 Proposed Algorithm Let, CT image be noisy that can be denoted as: X ( p, q) = Y ( p, q) + η( p, q)

(1)

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where X(p, q) is a noisy CT image, Y is a clean CT image, where η(p, q) is an additive Gaussian noise. Our major aim is to remove this white Gaussian noise while preserving minute edges and details. In this proposed method, we take the noisy image and pass it through the bilateral filter to obtain a noise-free image, but in the process of removal of noise from the image some important delicate details are removed from the image. In order to cater with this problem, we use the concept of method noise thresholding, where the filtered image is subtracted from the noisy image, by performing this task we expect to obtain only the noise component but we also observe some finer details also. In order to restore these finer but important details, we pass the image through shearlet transform and obtain the high-frequency and the low-frequency components, the high-frequency components are passed through bilateral filter and the low-frequency components are thresholded using circular shift approach. In circular shifting-based approach, firstly image is circularly shifted in horizontal direction then denoised using Bayes thresholding and lastly performs inverse shifting so that denoised pixel can move to their respective positions. Finally, inverse shearlet transform is performed to get the intermediate CT image. This image is added to the filtered image to obtain a noise-free image with all the minor details intact. Below are the steps of the proposed algorithm: 1. 2. 3. 4. 5.

6. 7. 8. 9.

We take the input CT image (I). We apply the bilateral filter over the image and obtain (R1). We subtract image R1 from I and obtain R2, i.e., R2 = I−R1. Then we apply shearlet transform over this subtracted image R2. We obtain the high-frequency and the low-frequency components, the lowfrequency components represent the approximation part while the highfrequency component represents the detailed part. Bilateral filter is applied over the approximation part or the low-frequency subband. Perform thresholding on high-frequency subbands which was obtained from Step 5 using Bayes thresholding with circular shift. Perform inverse shearlet transform and obtain R3. Perform addition of image obtain in step 2 and step 8, i.e., R4 = R1 + R3.

3 Results and Discussions The experimental results are tested on given dataset in public domain (http://www. via.cornell.edu/databases) which contains CT images. For understanding, Fig. 1a–d represents as CT (1–4). The proposed algorithm is testing over the noisy CT images which are suffering from Gaussian noise. These noisy images are obtained with different noise levels: 10, 15, 20. Figure 2 shows noisy CT image dataset over the noise level 20. In our results, several parameters for bilateral filtering are used such as patch size is 11 × 11, σ s is 1.3, and σ r is 0.14. Similarly in shearlet transform, decomposition level is set as 4. To compare the proposed method, some similar and

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Fig. 1 Original CT image dataset

Fig. 2 Noisy input CT image dataset (noise level = 20)

state-of-the-art methods are used such as [5, 7, 13, 14]. Figure 3 shows the results of bilateral filtering. The advantages of bilateral filter are to provide the sharp and smooth results, but the minor edges and small details of the image are not very wellpreserved, to tackle this problem, shearlet-based method noise thresholding is used with bilateral filter in our proposed method so that these missing details can be wellpreserved. Figure 4 clearly shows the difference between the noiseless image and the bilateral filtered image, resultant image displays some missing details. So we are incorporating the concept of method noise in order to retain these minor structures. Figure 3 shows the results of bilateral filter, and Fig. 4 shows the difference between the noiseless image and bilateral filtered image. Visual results of algorithms [5, 7, 13, 14] and proposed algorithm, respectively, are displayed from Figs. 4, 5, 6, 7, 8, and 9; it can be well seen from the results that the most texture preserving

Fig. 3 Outcomes of bilateral filtering [5]

Fig. 4 Difference between noiseless CT image and bilateral filtered image

Fig. 5 Outcomes of [7]

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Fig. 6 Outcomes of [13]

Fig. 7 Outcomes of [14]

Fig. 8 Outcomes of proposed algorithm

Fig. 9 Difference between noiseless image and outcome of proposed algorithm

results are obtained from our proposed methodology; however, [7] is proving good results in terms of sharpness and smoothness image but our proposed work gives better preservation of edges. Similarly, in Figs. 6, 7, and 8, it can also observed that due to the effect of bilateral filter, we are obtaining smooth and sharp images, while proposed methodology results are showing sharp and smooth image as well as edges are also well-preserved. Visual inspection clearly shows that our proposed technique is working better in terms of both edge preservation and noise removal. When we obtain the discrepancy between the clean picture and the proposed algorithm, as shown in Fig. 9, it can be observed that there are no missing structures or details. Our naked eyes analysis is not sufficient to provide accurate results. Hence, performance metrics like peak signal-to-noise ratio (PSNR) and image quality index (IQI) are also used to analyze the outcomes. PSNR value is evaluated by comparing the noiseless image and the image obtained by proposed technique, higher the PSNR value better the result. IQI is also used to compare clean and noise-free images, higher IQI value represents better results. Tables 1 and 2 represent the PSNR and IQI values, and our proposed method gives better results in both metrics.

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Table 1 PSNR values of denoised images CT 1 image

CT 2 image

CT 3 image

CT 4 image

σ

[5]

[7]

[13]

[14]

Proposed method

10

32.15

32.13

31.51

31.20

33.38

15

30.96

30.92

29.97

29.26

31.43

20

29.45

29.43

28.22

28.11

30.06

10

31.55

31.52

30.99

30.28

32.19

15

30.88

30.18

29.43

29.22

30.05

20

28.96

28.26

28.48

28.27

29.54

10

32.10

32.44

31.99

31.28

33.13

15

31.28

31.30

30.68

30.37

31.88

20

29.85

29.15

28.69

28.38

30.92

10

32.66

32.46

31.64

31.13

33.62

15

31.36

31.34

29.27

29.12

31.30

20

29.65

29.63

28.32

28.11

30.19

Table 2 IQI values of denoised images CT 1 image

CT 2 image

CT 3 image

CT 4 image

σ

[5]

[7]

[13]

[14]

Proposed method

10

0.9931

0.9911

0.9924

0.9914

0.9976

15

0.9534

0.9514

0.9762

0.9712

0.9865

20

0.9312

0.9312

0.9365

0.9315

0.9597

10

0.9817

0.9814

0.9751

0.9721

0.9889

15

0.9789

0.9782

0.9745

0.9725

0.9831

20

0.9421

0.9411

0.9241

0.9221

0.9521

10

0.9874

0.9872

0.9914

0.9911

0.9965

15

0.9514

0.9512

0.9762

0.9732

0.9893

20

0.9423

0.9421

0.9432

0.9422

0.9614

10

0.9871

0.9821

0.9954

0.9944

0.9964

15

0.9642

0.9632

0.9645

0.9641

0.9846

20

0.9409

0.9309

0.9469

0.9461

0.9698

4 Conclusions Method noise-based Bayes thresholding in shearlet domain and bilateral filter is followed in this paper. Good results were gathered using the proposed scheme for image denoising and edge retention. Several existing techniques are compared with our proposed scheme. The results of our proposed method are better when compared with the results of the existing methods. Our technique is not only giving better results visually, but also in terms of PSNR and IQI. Hence, our proposed method works well in terms of visual analysis and performance metrics.

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References 1. Hyder SA, Sukanesh R (2011) An efficient algorithm for denoising MR and CT images using digital curvelet transform. Adv Exp Med Biol 696:471–480 2. Boone J, Geraghty EM, Seibert JA, Wootton-Gorges SL (2003) Dose reduction in pediatric CT: a rational approach. J Radiol 228(2):352–360 3. Borsdorf A, Raupach R, Flohr T, Hornegger J (2008) Wavelet based noise reduction in CTimages using correlation analysis. IEEE Trans Med Imag 27(12):1685–1703 4. Borsdorf A, Raupach R, Hornegger J (2008) Multiple CT-reconstructions for locally adaptive anisotropic wavelet denoising. Int J CARS 2(5):255–264 5. Paris S (2007) A gentle introduction to bilateral filtering and its applications. In: ACM SIGGRAPH 2007 courses, pp 3-es 6. Chang SG, Yu B, Vetterli M (2000) Spatially adaptive thresholding with context modeling for image denoising. IEEE Trans Image Process 9(9):1522–1531 7. Zhang M, Gunturk BK (2008) Multiresolution bilateral filtering for image denoising. IEEE Trans Image Process 17(12):2324–2333 8. Diwakar M, Kumar M (2018) CT image denoising using NLM and correlation based wavelet packet thresholding. IET Image Process 12(5):708–715 9. Diwakar M, Kumar M (2018) A review on CT image noise and its denoising. Biomed Sig Process Control 42:73–88 10. Shi W, Li J, Wu M (2010) An image denoising method based on multiscale wavelet thresholding and bilateral filtering. Wuhan Univ J Nat Sci 15(2):148–152 11. Zhao L, Bai H, Liang J, Wang A, Zeng B, Zhao Y (2019) Local activity-driven structuralpreserving filtering for noise removal and image smoothing. Signal Process 157:62–72 12. Wu H, Zhang W, Gao D, Yin X, Chen Y, Wang W (2011) Fast CT image processing using parallelized non-local means. J Med Biol Eng 31(6):437–441 13. Diwakar M, Lamba S, Gupta H (2018) CT image denoising based on thresholding in Shearlet domain. Biomed Pharmacol J 11(2):671–677 14. Diwakar M, Kumar P (2019) Wavelet packet based ct image denoising using bilateral method and bayes shrinkage rule. In: Handbook of multimedia information security: techniques and applications. Springer, Cham, pp 501–511 15. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of the 6th international conference on computer vision. New Delhi, India, pp 839–846 16. Wang Y, Shao Y, Zhang Q, Liu Y, Chen Y, Chen W, Gui Z (2017) Noise removal of low-dose CT images using modified smooth patch ordering. IEEE Access 5:26092–26103

Reactive Mass Diffusion in Viscoelastic Fluid Past a Stretchable Exponential Sheet Due to Variation in Wall Concentration Kamal Debnath and Sankar Singha

Abstract An investigation is initiated to study the solute diffusion with chemical reaction of the first order in non-Newtonian viscoelastic fluid through boundary layer over a stretchable exponential sheet due to variation in wall concentration. Walter Liquid (Model B/), a model of non-Newtonian fluid, exhibits the fluid’s viscoelastic nature. The chemical reaction rate and distribution of wall concentration for the species are taken in exponential form. Utilizing suitable similarity variables, the equations guiding fluid motion and relevant satisfying boundary conditions are simplified to self-similar forms. The MATLAB solver ‘bvp4c’ is used to evaluate the resultant equations. The concentration profiles computed numerically for different involved flow parameters are plotted. The impact of flow feature factors on the concentration profiles is analyzed from graphs from a physical point of view. The mass diffusion process due to chemical reaction in viscoelastic flow through boundary layer past a stretchable exponential sheet affected noticeably with the variation of wall concentration. Keywords Boundary layer · Chemical reaction · Mass diffusion · Variable wall concentration · Viscoelastic fluid

1 Introduction The viscous layer flow that extends to the expandable surface is an important problem arising from fluid mechanics as a result of its extensive applications in industrial manufacturing, such as polymer sheet extraction, paper production, fiber glass manufacturing, metal processing, wire drawing. Crane [1] firstly studied the exact solution in the closed-form of a steady flow boundary of the membrane of viscous fluid over a simple stretchable plate. Many scientists later built on Crane’s work, including Pavlov [2], Gupta and Gupta [3], Chen and Char [4], who studied thermal and mass K. Debnath · S. Singha (B) Department of Mathematics, The Assam Royal Global University, Guwahati, Assam 781035, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_11

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transport mechanisms with magnetic field influence of different physical phenomena. Andersson [5] observed the hydromagnetic viscoelastic movement of fluid flow past an expandable sheet. The hydromagnetic heat transport flow past a stretchable layer was explained by Char [6]. El-Aziz [7] investigated the heat transition and mass transport of temperature dependent viscous fluid along with thermal conductivity as it passed through a continuously stretching sheet with Ohmic heating. The mass diffusion caused by chemical reactions in the boundary layer has numerous uses in chemical engineering, fibrous insulation, and atmospheric flows. The reactive diffusion process of species through the boundary layer was defined by Chambre and Young [8]. Stan [9] studied boundary layer flow and chemical surface interactions together. In addition, the impact of chemical reaction on fluid passing over a flat surface was also examined in many articles [10–14]. The flow through a stretchable surface is induced by the flat surface’s linear stretching velocity. However, despite being a very critical and constructive flow that occurs often in many manufacturing industries, such flow is rarely studied. Magyari and Keller [15] examined the heat transport through the boundary layer of an expandable sheet when the wall temperature varied exponentially. Khan and Sanjayanand [16] explored viscoelastic flow patterns and heat transmission along with a stretchable layer with viscous dissipation. Elbashbeshy and Sedki [17] studied numerically the fluid motion and heat transmission of stretchable exponential sheets considering wall suction. Banerjee et al. [18] examined mass dissipation in the boundary layer with chemical reaction across an expandable exponential sheet with variable wall concentration. Nayak et al. [19] demonstrated the transition of thermal energy and mass transport process for chemically reactive hydromagnetic viscoelastic fluid through boundary layer with source/sink. According to Singh and Kumar [20], the heat transition and mass transport process is affected by chemical reactions in a micropolar fluid past a porous channel with thermal radiation and heat generation. Mjankwi et al. [21] studied the impact of varying fluid characteristics on heat flux and mass absorption coefficient. Misra and Govardhan [22] investigated the impact of chemical reactions on the boundary layer flow of nanofluids, as well as fluctuations in heat transition and mass transfer. The mass diffusion in viscoelastic fluid through boundary layer past a stretchable exponential sheet with first order chemical reaction modeled by Walter Liquid (Model B/) [23, 24] is investigated in this paper. The species reaction rate and the concentration variation at the wall are taken as exponential forms. Employing similarity variables, equations guided fluid motion are reduced to self-similar type and then evaluated by solver ‘bvp4c’ of MATLAB. The numerically obtained results are graphically presented. The influence of flow feature factors on the concentration profiles is analyzed from graphs from a physical standpoint.

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2 Mathematical Formulation The mass diffusion due to chemical reaction in non-Newtonian viscoelastic fluid through boundary layer past a stretchable exponential sheet with deviation in wall concentration is considered. The fluid motion, taking appropriate approximation of boundary layer, is guided by the below mentioned equations. ∂v ∂u + =0 ∂x ∂y ⎤ ⎡ ∂u ∂ 2u ∂ 3u ∂u ∂ 2 u ∂u k0 ∂ 3u ∂u ∂ 2 u +v =ν 2 − u + u +v 3 − ∂x ∂y ∂y ρ ∂ x∂ y 2 ∂y ∂ y ∂ x∂ y ∂ x ∂ y2 u

∂C ∂ 2C ∂C +v = D 2 − R(C − C∞ ) ∂x ∂y ∂y

(1)

(2)

(3)

where u: ) velocity components along x-axis, v: velocity components along y-axis, ( μ ν = ρ : kinematic viscosity, μ: fluid viscosity coefficient, ρ: fluid density, k0 : viscoelastic parameter, C: concentration of species, D: diffusion coefficient, R: variable reaction rate, C∞ : free stream concentration. The corresponding boundary conditions are: u = Uw (x), v = 0 at y = 0; u → 0 as y → ∞

(4)

C = Cw = C∞ + C0 e( 2L ) at y = 0; C → C∞ as y → ∞

(5)

λx

where Cw : variable concentration of the sheet, C0 : constant measuring increasing rate of concentration along the sheet, λ: non-zero parameter controlling the exponential increment of surface concentration, L: reference length. The velocity expansion of the sheet Uw (x) is given by Uw (x) = ae( L ) x

(6)

To obtain self-similar forms of above equation, the following similarity transformation are introduced: ψ=

√

x

2ν La f (η)e 2L , C = C∞ + (Cw − C∞ )φ(η)

√ x where η = y 2νaL e 2L denotes similarity variable. The stream function ψ connected with velocity components as u = . = − ∂ψ ∂x

(7)

∂ψ ∂y

and

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Using relation (7) in (2) and (3), the following set of self-similar equations are obtained: ( )2 f ''' (η) + f (η) f '' (η) − 2 f ' (η) ⎡ ⎤ ( '' )2 1 ' v ' ''' − k1 2 f (η) f (η) − f (η) f (η) − f (η) =0 3 ( ) φ '' (η) + Sc f (η)φ ' (η) − λ f ' (η)φ(η) − βφ(η) = 0 x 0a ( L ) is the modified viscoelastic parameter, Sc = e where k1 = 3k 2μL 2L R0 number, β = a is the reaction rate parameter. Transformed condition at the boundary are:

f (η) = 0, f ' (η) = 1 at η = 0;

ν D

(8) (9) is the Schmidt

f ' (η) = 0, f '' (η) = 0 as η → ∞

φ(η) = 1 at η = 0; φ(η) = 0 as η → ∞

(10) (11)

3 Solution of the Problem The numerical method ‘bvp4c’ of Matlab is a collocation method used to solve differdy = g(x, y, q), x ∈ [a, b] with nonlinear boundary ential equation of the form dx conditions h(y(a), y(b), q) = 0, where vector q is a unknown parameter. This method is an effective solver different from the shooting method and it is based on an algorithm. It can compute inexpensively the approximate value of y(x) for any x in [a, b] taking boundary conditions at every step. In this method, the infinity conditions at the boundary are replaced with a finite point which reasonably satisfies the given problem. The self-similar differential Eqs. (8) and (9) are transformed to first order differential equations and then solved by using ‘bvp4c’ MATLAB solver as follows: f = f 1 , f ' = f 2 , f '' = f 3 , f ''' = f 4 , φ = f 5 , φ ' = f 6

(12)

From (4.1), we can write '

'

'

'

f1 = f2 , f2 = f3 , f3 = f4 , f5 = f6

(13)

Making use of (12) and (13), the Eqs. (8) and (9) can be written as: '

f4 =

⎤ ( ) ⎡ } 3 1 { 2 f 2 f 4 − ( f 3 )2 − f 4 + f 1 f 3 − 2( f 2 )2 f1 k1

(14)

Reactive Mass Diffusion in Viscoelastic Fluid Past … '

f 6 = −Sc ( f 1 f 6 − λ f 2 f 5 − β f 5 )

111

(15)

and the applicable boundary conditions (10) and (11) reduces as follows: f 1 (0) = 0, f 2 (0) = 1, (0) and f 2 (∞) = 0, f 3 (∞) = 0

(16)

f 5 (0) = 1 and f 5 (∞) = 0

(17)

4 Results and Discussion To find the impact of flow feature factors viz., the viscoelastic parameter k1 , the Schmidt number Sc , the reaction rate parameter β and the parameter λ, computation is carried out numerically using ‘bvp4c’ solver. To understand the behavior of diffusion owing to chemical reaction on the viscoelastic fluid through boundary layer past a stretchable exponential sheet, the computed results are plotted in Figs. 1, 2, 3, 4, 5, 6, 7 and 8. To assess the precision of the numerically acquired findings produced by ‘bvp4c’ and to validate the present work, the skin friction coefficients f '' (0) and f (∞) are computed without taking into account viscoelastic characteristics. The present work yields f '' (0) = 1.28180838 and f (∞) = 0.90564328, which are well accord with the results found by Magyari and Keller [15] ( f '' (0) = 1.281808 and f (∞) = 0.905639) and Banerjee et al. [18] ( f '' (0) = 1.281833 and f (∞) = 0.90564328). At first, our focus is concentrated on the impact of viscoelastic factor k1 on chemically reactive mass diffusion. The reactive concentration curves φ(η) for variation of

Fig. 1 Concentration curves φ(η) for variation of k1 with λ > 0

112

Fig. 2 Concentration curves φ(η) for variation of k1 with λ < 0

Fig. 3 Concentration curves φ(η) for variation of Sc with λ > 0

Fig. 4 Concentration curves φ(η) for variation of Sc with λ < 0

K. Debnath and S. Singha

Reactive Mass Diffusion in Viscoelastic Fluid Past …

Fig. 5 Concentration curves φ(η) for variation of β with λ > 0

Fig. 6 Concentration curves φ(η) for variation of β with λ < 0

Fig. 7 Concentration curves φ(η) for variation of λ > 0

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Fig. 8 Concentration curves φ(η) for variation of λ < 0

k1 are presented in Figs. 1 and 2 for λ = 1 and λ = −1. From the figures, it is observed that for both direct and inverse variations of exponential surface concentration, the dimensionless concentration at a location and the thickness of solute boundary layer drop gradually with growing η but slightly enhancing pattern is observed for rising values of k1 . Secondly, we look into the effect of Schmidt number Sc on chemically reactive mass diffusion. The reactive concentration curves φ(η) for variation of Sc are presented in Figs. 3 and 4 for λ = 1 and λ = −1. It is noticed that for both direct and inverse variations of exponential surface concentration, the concentration of the fluid reduces rapidly at every point and the boundary layer thickness diminishes as Sc enhances. The growth of Schmidt number diminishes the diffusion coefficient and thus enhances the mass transfer rate. As a result, the thickness of the species boundary layer is reduced. Next, the changes in concentration curves φ(η) for variation of reaction rate parameter β are discussed. The concentration curves φ(η) for different values of β are exhibited in Figs. 5 and 6 for first order chemical reaction for direct and inverse variations of exponential wall concentration distribution. In both situations, the concentration at a point diminishes with the growth of reaction rate. Also, the rising value of β, reduces the thickness of species boundary layer. The impact of the parameter λ which is related to wall concentration distribution is very significant in controlling the reactive mass diffusion. In Figs. 7 and 8, the reactive species profiles φ(η) are depicted for several values of λ. Out of which, in Fig. 7 all values of λ are non-negative, whereas in Fig. 8 all are non-positive. From Fig. 7, it is noticed that the concentration slightly enhancing as λ enhances and the thickness species boundary layer slightly increased with λ. But the concentration of the fluid diminishing with increasing η. On the other hand, for negative variation of λ, the concentration at fixed point diminishes for higher negative values of λ.

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Hence, it can be understood that below a certain value of λ < 0, absorption of mass occurs at the stretchable sheet and it increases with the growing magnitude of λ < 0. As a consequence, the thickness of species boundary layer enhances with the rising magnitude of λ < 0.

5 Conclusion Mass diffusion with chemical reaction in viscoelastic fluid flow through boundary layer induced by a stretchable exponential sheet is investigated. The chemical reaction rate and distribution of wall concentration for the species are taken as variables. Utilizing similarity variables, the fluid guided equations are reduced into nonlinear self-similar forms and then evaluated by employing MATLAB solver ‘bvp4c’. The present investigation brings out the fact that growing viscoelastic parameter enhances the fluid concentration. Also, as the Schmidt number rises, the fluid concentration and the thickness of species boundary layer significantly reduce and the reaction rate factor has a comparable effect to the Schmidt number. The variable wall concentration, characterized by the parameter λ, controls the mass transfer. Most importantly, below a certain value of λ, mass absorption occurs at the sheet. This model study is intended to serve as inspiration for future experimental studies. Fluid simulation can assist in the visualization of a flow problem.

References 1. Crane LJ (1970) Flow past a stretching plate. Zeitschrift für angewandte Mathematik und Physik ZAMP 21(4):645–647 2. Pavlov KB (1974) Magnetohydrodynamic flow of an incompressible viscous fluid caused by deformation of a surface. Magnitnaya Gidrodinamika 10(4):146–147 3. Gupta PS, Gupta AS (1977) Heat and mass transfer on a stretching sheet with suction and blowing. Can J Chem Eng 55(6):744–746 4. Chen CK, Char MI (1988) Heat transfer of a continuous, stretching surface with suction or blowing. J Math Anal Appl 135(2):568–580 5. Andersson HI (1992) MHD flow of a viscous fluid past a stretching surface. Acta Mech 95(4):227–230 6. Char MI (1993) Heat transfer in a hydromagnetic flow over a stretching sheet. Wärme-und Stoffübertragung 29(8):495–500 7. El-Aziz MA (2007) Temperature dependent viscosity and thermal conductivity effects on combined heat and mass transfer in MHD three-dimensional flow over a stretching surface with Ohmic heating. Meccanica 42:375–386 8. Chambre PL, Young JD (1958) On diffusion of a chemically reactive species in a laminar boundary layer flow. Phys Fluids 1:48–54 9. Stan II (1972) On boundary layer flow with chemical surface reaction. Meccanica 7:72–76 10. Andersson HI, Hansen OR, Holmedal B (1994) Diffusion of a chemically reactive species from a stretching sheet. Int J Heat Mass Transf 37:659–664 11. El-Aziz MA (2010) Unsteady fluid and heat flow induced by a stretching sheet with mass transfer and chemical reaction. Chem Eng Commun 197(10):1261–1272

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12. Chamkha AJ, Mohamed RA, Ahmed SE (2011) Unsteady MHD natural convection from a heated vertical porous plate in a micropolar fluid with Joule heating chemical reaction and radiation effects. Meccanica 46(2):399–411 13. Bhattacharyya K, Uddin MS, Layek GC, Ali PK (2013) Diffusion of chemically reactive species in boundary layer flow over a porous plate in a porous medium. Chem Eng Commun 200(12):1701–1710 14. Bhattacharyya K (2015) Reactive solute transfer in stagnation-point flow over a shrinking sheet with diffusive mass flux. J Appl Mech Tech Phys 56(3):464–470 15. Magyari E, Keller B (1999) Heat and mass transfer in the boundary layers on an exponentially stretching continuous surface. J Phys D Appl Phys 32(5):577–585 16. Khan SK, Sanjayanand E (2005) Viscoelastic boundary layer flow and heat transfer over an exponential stretching sheet. Int J Heat Mass Transf 48(8):1534–1542 17. Elbashbeshy E, Sedki AM (2014) Effect of chemical reaction on mass transfer over a stretching surface embedded in a porous medium. Int J Comput Eng Res 4(2):20–28 18. Banerjee A, Mahato SK, Bhattacharyya K (2018) Mass diffusion with chemical reaction in boundary layer flow due to an exponentially expanding sheet with variable wall concentration. Acta Tech 63(2):157–168 19. Nayak KM, Dash GD, Singh LP (2016) Heat and mass transfer effects on MHD viscoelastic fluid over a stretching sheet through porous medium in presence of chemical reaction. Propul Power Res 5(1):70–80 20. Singh K, Kumar M (2016) Influence of chemical reaction on heat and mass transfer flow of a micro-polar fluid over a permeable channel with radiation and heat generation. J Thermodyn. Article ID 8307980. 10 pages. https://doi.org/10.1155/2016/8307980 21. Mjankwi MA, Masanja VG, Mureithi EW, James MN (2019) Unsteady MHD flow of nanofluid with variable properties over a stretching sheet in the presence of thermal radiation and chemical reaction. Int J Math Math Sci. Article ID 7392459. 14 pages. https://doi.org/10.1155/2019/739 2459 22. Misra S, Govardhan K (2020) Influence of chemical reaction on the heat and mass transfer of nanofluid flow over a nonlinear stretching sheet. A numerical study. Int J Appl Mech Eng 25(2):103–121. https://doi.org/10.2478/ijame-2020-0023 23. Walters K (1960) The motion of an elastico-viscous liquid contained between co-axial cylinders. Q J Mech Appl Math 13:444–456 24. Walters K (1962) The solutions of flow problems in the case of materials with memories. J Mec 1:473–478

Technology Adoption for Facilitating Knowledge Management Practices in Firms Arpana Kumari and Arun Kumar Singh

Abstract This paper aims at highlighting technological dimensions for knowledge management (KM) practices in firms with the review of the potential conception of activities adopted by organizations to utilize the knowledge resources. The study systematically reviews the literatures available on KM practices with excluding articles based on keywords, abstracts, and full text. KM practices were incorporated in Heisig’s (J Knowl Manage 13(4):4–31, 2009 [1]) categorization format of human, technology, organization, and management process-oriented practices. The findings described that learning and sharing attitude in human are effectively directing adoption of technological tools for KM in firms. Top management support and strategy are also key KM practices supporting technology usage. In terms of originality and value, the paper reviews all the latest technologies and their integration for supporting KM in firms. The study promotes for faster and accurate business ideas with the accumulation of advanced technological tools for knowledge storing, sharing, and application. The famous Nonaka and Takeuchi (The knowledge-creating company—how Japanese companies create the dynamics of innovation. Oxford University Press, New York/Oxford, 1995 [2]) KM Model is adopted to integrate the technology and KM practices. Keywords Knowledge management · Knowledge management practices · Management process · Information and communication technology

A. Kumari (B) Symbiosis Centre for Management Studies, Noida, India e-mail: [email protected] A. K. Singh Institute of Management Studies, Ghaziabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_12

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1 Introduction In light of the constantly changing business environment, firms are focusing on enhancing knowledge base and adopting strategies supporting knowledge activities to achieve organizational goals [3]. The discussions of scholars in previous literatures are centered to the concept and process of knowledge management (KM). KM is described as the process and activities of creating storing, sharing, and applying knowledge [4] in organization’s functions for improving efficiency of firms. The aim of KM is to explore the ways of managing knowledge resources of firms to accelerate organizational performance. The activities to manage knowledge resources are majorly focused on processes and practices [3, 5]. Knowledge process is intended to demonstrate that how knowledge resources of the organizations are developed and utilized through knowledge acquisition, knowledge assimilation, knowledge dissemination, and knowledge implementation which may be placed in firms. Some scholars have discussed different mechanisms and ways to successfully follow the KM process [4]. The other schools of thoughts discussed the organizational activities and management practices [3] to support KM process in firms. Also, literatures have highlighted technological [6], cultural [7], and strategic [8], support for managing knowledge [9]. In this paper, all such activities and supports are considered as KM practices which are in line with previous scholars [10]. This paper is focused on understanding the technological aspects that are important for KM practices in the firms. One of the prominent studies is by Heisig [1] who compiled the study of 160 empirical researches on KM and categorized KM supporting activities in a framework. Also, other studies have focused on clarifying the concepts of KM, KM culture [11, 12]. Famous scholars discussed KM mechanism, whereas some [13] talked about knowledge network in firms, [11] few showcased the KM implementation for organizational befits, similarly others [14] pointed on the importance of KM unit and KM strategy [5, 6, 15, 16]. Few literatures [17] have concentrated on developing firm-specific cases of different industries. The studies have surveyed about KM importance and based on which the benefits that organizations are extracting are discussed [3]. In order to understand the technologies adopted by firms to manage knowledge resources in organizations, it is required to specify the framework and categorize the practices that support the KM and may be called KM practices of firms.

2 Review of Literature Knowledge management is the process of creating, storing, sharing, and reutilizing the organizational knowledge [13, 18, 19]. Organizations focus on two types of knowledge—explicit and tacit [20, 21]. Information data or knowledge that may be

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recorded, documented, and retrieved conveniently are termed as explicit knowledge [20]. This is tangible in nature and can exist in the form of language, codes, symbols, and pictures considered easy to understand. Other side tacit knowledge is intangible in nature. This is developed as employees gain experiences and execute projects [22]. They understand the workflow and interpret the situation to work upon. Such understanding resides in their mind and becomes difficult to transfer to other people for further usage [23]. Such mental models and interpretations are termed as tacit knowledge. This is intangible in nature and cannot be coded or documented though can be shared through storytelling, collaborative works, gossips, and social interactions [24]. Knowledge management promotes conversion of such knowledge into explicit form for benefit of other employees and effective decision-making process [15]. Technology Initially, knowledge management practices were used to be associated with technical aspects [8]. Firms having sufficient technological and software tools were applying the knowledge management practices [3, 25]. Technology was one of the elements which were considered a crucial source to store and share knowledge within firms [5, 26]. Earlier only computers, printers, telephones were means of knowledge processes. However, fast-paced world provided us with advanced version of technology and now gadgets such as smart phones, smart watch, tabs with the incorporation of artificial intelligence, Internet of things augmented reality and virtual reality and data analytics. Such advancements not only accelerated the information exchange but with video conferencing and instant messages, involvement of stakeholder in decision-making process became smooth. Thus, it can be said that earlier knowledge management practices were considered for mere storing and retrieving the information [18, 19]. Gradually researchers dwelled more into the concept of utilizing knowledge for innovations. Multiple school of thoughts popped up and later it was realized that only following the process of accumulating information and sharing among stakeholders are not enough for achieving organizational performance [18]. Knowledge management practices KM practices refer to the activities of the organization that can be processed by conscious management activities to enhance knowledge processes of the firms [10, 27]. Seven knowledge management practices are highlighted [28]: (1) Strategic knowledge management practices [29], (2) organizational structural arrangements [30], (3) knowledge management friendly culture, (4) knowledge storing and sharing promoting information and communication technology (ICT) [2], (5) learning mechanisms and (6) human resource management (HRM) practices focused on knowledge [31] practices, and (7) knowledge protection practices and mechanisms. According to some scholars [13], it is important to unlock the firms’ capabilities for advancing the organization’s efficiency to manage its knowledge resources. A researcher [10] emphasized that knowledge management practices may include organization’s activities aiming optimum knowledge management for organizational excellence. Taking

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existing research into consideration this paper conceptualize knowledge management practices The paper adopted KM categorization given by Heisig’s [1] and model of knowledge management by Nonaka and Takeuchi [32]. The categorization is based on four categories: human-oriented factors; organization-oriented factors; technology-oriented factors; and management processes-oriented factors.

3 Research Methodologies This paper conducts a systematic review (Fig. 1) of KM literature on explaining various KM practices in organizations. The systematic review approach is influenced by Bakker [33], Crossan and Apaydin [34]. This approach has been preferred to summarizing and framing the crux of existing literature of related field. For this study, only those papers have been included that are published in peer-reviewed journals and written in English, as it is assumed that they will provide the sufficient coverage on this study. The literature selection process involved specific process. The abstract and citation database Scopus (www.elsevier.com/online-tools/Scopus) was the preferred tool to carry out the search because of its touted accuracy and excellent coverage of academic literature. Several stages have been adopted to exclude articles based on titles in Stage 2, abstracts in Stage 3, and full texts in Stage 4. Consequently, suitable articles providing relevant information and understanding for the present study could be shortlisted.

Fig. 1 Systematic literature review process

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The central objective of the literature search was to gather a wide variety of KM literature instead of delimiting the search too much, as the terminology related to the targeted articles was considered not yet established. Taking into account all the predetermined criteria, the first search produced 2343 potentially relevant articles. The stages of systematic literature article are as under.

3.1 The First Stage—Keywords Search At the first stage of this study, the keywords were put to search literature took place till 2019. To find articles related to KM practices, the following search terms were used in the literature search: • “knowledge management practices and technology” • “knowledge management practices and technology” “Questionnaire” and • “knowledge management and technology” and “case study.” The main objective of this search was to access the technology-associated KM articles. During this search, only those articles which were peer-reviewed journal and written in English language were selected.

3.2 The Second Stage—Title Consideration for Article Reduction During the second stage of the article selection process, the literatures were shortlisted based on the title. Articles that failed fulfilling the title criteria were eliminated from further inclusion in potential article category, and this reduced the number of relevant articles to 745.

3.3 The Third Stage—Exclusion of Articles Based on Abstracts The third stage screened the articles based on abstracts. The articles could be analyzed based on its relevance for the study. The number of relevant articles was reduced to 248.

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3.4 The Fourth Stage—Publications Elimination Based on Full Text In the fourth stage, the shortlisted articles were read to analyze and summarize the findings of the literatures this resulted in finalizing the potential and relevant articles for this study, and finally, this eliminated big number of articles as the excluded literatures in this stage focused on knowledge concepts (definitions), knowledge models using technology and technology adoption for knowledge process (e.g., knowledge creation and sharing). This final stage of article exclusion resulted in the number of relevant articles reduced to 25.

4 Findings 4.1 Review Findings Table 1 indicates the outline of all peer-reviewed research papers based on Heisig’s [1] distribution of human-oriented, organization-oriented, technology-oriented, and management process-oriented as knowledge management practices. It is observed that majorly all articles discussed the elements of knowledge management practices that could be constituted in categorizations of Heisig [1] model. Few terms are tricky to be adjusted under one category such as strategy that talks about codification (explicit knowledge) which scholars considered under knowledge management process that comes under category of management process-oriented on the other hand personalization strategy talks about converting tacit knowledge into explicit which is more of part of individual approach. However, researcher preferred to keep this process under management responsibility so the term strategy is kept under management process-oriented category. Based on the above review, certain interpretation can be made about the knowledge management practices followed by firms to manage their knowledge resources.

Table 1 Knowledge management practices Category

Author and year

Human-oriented Leadership

[7, 35, 36]

Commitment-based HR practices

[8, 37]

Organizational learning, continuous learning via training and workshops

[38–40]

Transfer of learning

[16, 2, 36] (continued)

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Table 1 (continued) Category

Author and year

Supervisory, mentoring

[5, 9]

HRM

[5, 6, 40–42]

Top management support

[5]

Knowledge environment—Reward system, Top management encouragement, [5, 16] motivation for continuous improvement, incentive, acknowledgment Human creativity

[6]

Technology-oriented IT support

[5, 25]

Information technology

[5, 26]

Codification practices

[36, 43]

KM supportive IT practices

[18, 19]

IT tools

[5, 16, 36]

ICT-based knowledge management system

[6]

Organization-oriented knowledge strategies

[8, 14, 20]

Development of an innovative culture

[5, 16, 21, 2]

Knowledge and information flow directions

[9]

Design of the organizational structure for cooperation and coordination among employees

[9, 13]

Team work

[6, 9, 36]

Management processes-oriented Knowledge development-oriented process

[8]

KM strategy

[5, 6, 15, 16, 36]

KM procedure

[13]

Knowledge-based marketing—knowledge transformation

[11]

4.2 Knowledge Management Practices Through systematic literature review, KM practices were fit into the Heisig categorization model purview with the following four categories. 4.2.1 Human-oriented knowledge management practices. The study identified that under human-oriented approach of knowledge management practices are mainly focused on leadership, learning, and sharing the knowledge. With social and community activity knowledge is created and shared now with technological devices.

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4.2.1.1 Leadership—Leadership has been observed as a significant element for KM. Organizations appointed managers, supervisors to administer KM activities that ensure the proper execution of KM process [5]. 4.2.1.2 Knowledge-oriented human resource management practices—In previous studies, it is explicit that HRM practices promote knowledge-based activities. Acquiring intellectual employees, upskilling and reskilling employees, and providing learning opportunities for continuous performance and excellence [40, 41]. 4.2.1.3 Top Management Support—the Support of Management Plays Crucial Role in Encouraging and Empowering Employees for Knowledge Exploration, Sharing and Application [5, 9]. 4.2.1.4 Employee motivation—In a knowledge environment employees are motivated for knowledge receiving and sharing with others. Employees are influenced for continuous improvement for professional excellence [36] and later acknowledged for the contributions [5, 14]. 4.2.1.5 Provisions of Reward System—Incentives for Employees Outlined with KM Activities Promote Enhanced Knowledge Communication to Others for Retaining Knowledge for Future Usage [36]. 4.2.1.6 Supervisory and Mentoring—Providing a mentor to employees is also considered effective as it promotes knowledge sharing and application [36]. Supervisory significantly affects the application part [9] on the other hand formal staff mentoring promotes attitude among workers for managing knowledge [5]. 4.2.1.7 Knowledge-based creativity—According to Omotayo [6], human mind uses its creative capabilities that result in knowledge creation and innovation. Creative thinking brings opportunity to thinking out of the box [5]. 4.2.2 Technology-oriented knowledge management practices. Technology is pivotal in making or breaking the organizations [36]. According to Razali et al. [5], technology infrastructure and technology awareness both are equally important. Technology adds value to the organization while promoting innovation and systematic work process. Adoption of IT practices supports KM activities in an organization [18]. 4.2.2.1 IT tools—With socialization process mentioned in Fig. 2, demonstrating conceptual framework for technology integration with KM practices adopting famous model of Nonaka and Takeuchi [32], organizations must focus on advanced usage of technologies [13] like intranets, video conferencing, social media platforms, LAN, expert systems, artificial intelligence, palmtops, electronic data exchange, for bringing innovation, usage of internal social network architecture, as channel for dissemination of knowledge in organization by sharing

Technology Adoption for Facilitating Knowledge …

Tacit Knowledge Tacit Knowledge

From

Explicit Knowledge

125

To

Explicit Knowledge

Socialization IT platforms for Sharing of experience

Externalisation IT tools for Codification of Tacit knowledge

Internalisation Knowledge Application with IT tools

Combination Analytical tools for Combination of Knowledge

Fig. 2 Conceptual framework: technology platform for knowledge management practices. Source Nonaka and Takeuchi [2, p. 19)

task success report [19]. The role of IT should be on connecting and not just collecting knowledge. Also, tools like web-based geographical information system provide information of location and other features of the firm. 4.2.2.2 KM supportive IT practices—The research further explains the importance of amalgamation of infrastructure and process capabilities, including technology, culture, and organizational structure for combining new knowledge with existing ones. Referring to Fig. 2, in externalization, informal or tacit knowledge is codified into explicit knowledge with coding, symbols, images in storage devices. In internalization process, the codified or stored knowledge is incorporated in existing organizational system, and thus, those are customized with help of software. Finally under combination stage, knowledge is applied for innovations and decision making, here also technological support for data analysis and interpretations will be required. 4.2.3 Organization-Oriented Knowledge Management Practices. Limited studies are found on organizations’ orientation toward knowledge management activities. The business activity of firms is processed in an organizational setup. Roles and responsibility are deputed to individuals based on their expertise, and tasks are performed in departments and units. 4.2.3.1 Communication Channel—The channel used for communication may be of formal and informal in nature that affects the knowledgesharing interpretation process. Also, effective flow of communication like horizontal, diagonal, lateral impacts the freedom and speed of knowledge flow as employee comfort with communication is more effective with various KM activities specifically for knowledge creation and sharing [8].

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4.2.3.2 Culture—Scholars have emphasized that maintaining suitable organizational culture is very crucial for successful KM activities which consist of employee willingness to accumulate, share and apply knowledge for organizational benefit, removing fear of knowledge or competency loss [5]. Cooperation, trust, and learning are major detriments of knowledge management that integrates with knowledge management activities. 4.2.3.3 Team—Creating team for project executions, enabling employees for experience sharing through discussions and bringing insights for new knowledge creation [9, 36]. Delegation of authority, leadership, and decentralization are the significant factors over knowledge management activities [18]. 4.2.4 Management processes-oriented practices for knowledge management. Under this category, the decision and strategy by management for managing knowledge is considered. The concept of knowledge management consists of the process of knowledge creation, storage, sharing, and applying, wherein implementation of this process depends on the management initiatives and how efforts for continuous improvement of the process are put forward [8]. 4.2.4.1 KM procedure—Consists of the KM flow and operations that are adopted for KM applications [13] for firms KM effectiveness and profitability.

5 Discussion This paper intended to summarize the KM literature review, to understand the adoption of technology for knowledge management practices. The paper explained the design and the process of the literature selection which was systematic literature review. Thereafter, the result of the study was presented based on the articles reviewed. The findings of the study highlighted that technology adoption is highly important for quality KM practices. This study supported the arguments of scholars about the utilization of technology for KM not merely for storing information but more for knowledge sharing and usage [13, 25], enhancing employee’s expertise and indepth knowledgeable each day for accelerating innovation [44]. Thus, technology is considered crucial KM enabler while enhancing firm’s capability and competency it is in line with the view that technology support enables organizations in information tracking and analysis with communication tools [5, 14, 36]. Organizations integrate their technological operations for managing knowledge resources to extract benefits such as knowledge protection, creativity, innovation, and better decision making [14]. Also while discussing the significant role of communication tools, it is highlighted that well-connected employees can be engaged in regular communication leveraging

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knowledge sharing and clarity [8]. In addition to the above, the review emphasized upon the importance of organizational elements and its setup. Task divided into teams promotes knowledge creation and sharing, works delegated as per specialization creates knowledge development scope in existing projects and requires tools for virtual collaboration and communication. The present review suggested that a separate KM unit can ensure implementation KM.

6 Implications for Management Practices and Academician This paper presented analysis of all possible relevant literature. A conscious management practices may enable firms effectively and efficiently manage its knowledge resources. This study adds value to the researcher’s ongoing studies about knowledgebased practices of firms. The present study also has managerial implications. Firms are mostly in the lack of confidence in investing in managing knowledge in terms of return on investment. This study strongly provides an optimistic view about the benefits of adopting technologies for KM practices. Particularly organizations should seek leadership, HRM, technology, culture and strategy supporting knowledge creation, storage, sharing and application for better decision making, innovation, and problem solving.

7 Conclusion, Limitation, and Scope for Future Research The accumulated the literature on KM practices of high relevance and analyzed all organizational, management, human- and technology-related practices as KM practices. The discussions of previous literature were smoothly incorporated into this category wherever those fit. In this process, management process support, KM-based strategy, technology support, communication flow, KM leadership, KM supportive culture were the most common and significant KM practices adopted by the most of the organizations. Technology provides much more scope for knowledge utilization and interpretation with its analytical expertise. The methodology of this paper is its very first limitation. The study has systematically analyzed and presented the work of previous scholar, wherein some literatures may have been missed during literature selection process. Further studies may focus on other methodologies to understand about KM practices. This study adopted grounded theory of Heisig’s categorization of KM practices; in the future, scholars may conduct general study to get wider perspective of KM practices. Future studies may also conduct comparative analysis of KM practices based on industry or country. Future scholars may also focus on empirical research. Case studies may also be developed on technological tools supporting KM, KM enabling culture.

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25. Foss N, Michailova S (eds) (2009) Knowledge governance: processes and perspectives. Oxford University Press, Oxford 26. Kianto A, Andreeva T (2014) Knowledge management practices and results in service-oriented versus product-oriented companies. Knowl Process Manage 21(4):221–230 27. Zack MH (1999) Managing codified knowledge. Sloan Manage Rev 40(4):45–58 28. Hedlund G (1994) A model of knowledge management and the N-form corporation. Strateg Manage J 15(2):73–90 29. Alavi M, Kayworth TR, Leidner DE (2005) An empirical examination of the influence of organizational culture on knowledge management practices. J Manage Inf Syst 22(3):191–224 30. Scarbrough H (2003) Knowledge management, HRM and the innovation process. Int J Manpow 24(5):501–516 31. Bakker RM (2010) Taking stock of temporary organizational forms: a systematic review and research agenda. Int J Manage Rev 12(4):466–486 32. Crossan MM, Apaydin M (2010) A multi-dimensional framework of organizational innovation: a systematic review of the literature. J Manage Stud 47(6):1154–1191 33. Sarin S, McDermott C (2003) The effect of team leader characteristics on learning, knowledge application, and performance of cross-functional new product development teams. Decis Sci 34(4):707–739 34. Smith AD (2004) Knowledge management strategies: a multi-case study. J Knowl Manage 8(3):6–16 35. Fani AA, Fard HD, Yakhkeshi H (2015) The relationship between knowledge management and organizational learning within middle and senior managers of Iranian public organizations. Inf Knowl Manage 5(6):102–112 36. Chien SY, Tsai CH (2012) Dynamic capability, knowledge, learning, and firm performance. J Organ Chang Manage 25(3):434–444 37. Migdadi M (2009) Knowledge management enablers and outcomes in the small-and-medium sized enterprises. Ind Manage Data Syst 109(6):840–858 38. Liao YS (2011) The effect of human resource management control systems on the relationship between knowledge management strategy and firm performance. Int J Manpow 32(5/6):494– 511 39. Camelo-Ordaz C, García-Cruz J, Sousa-Ginel E, Valle-Cabrera R (2011) The influence of human resource management on knowledge sharing and innovation in Spain: the mediating role of affective commitment. Int J Human Resour Manage 22(7):1442–1463 40. Cohen JF, Olsen K (2015) Knowledge management capabilities and firm performance: a test of universalistic, contingency and complementarily perspectives. Expert Syst Appl 42(3):1178– 1188 41. Chen CJ, Huang JW (2009) Strategic human resource practices and innovation performance— the mediating role of knowledge management capacity. J Bus Res 62(1):104–114 42. Chua A, Lam W (2005) Why KM projects fail: a multi-case analysis. J Knowl Manage 9(3):6–17 43. Riege A (2005) Three-dozen knowledge-sharing barriers managers must consider. J Knowl Manage 9(3):18–35 44. Soto-Acosta P, Colomo-Palacios R, Popa S (2014) Web knowledge sharing and its effect on innovation: an empirical investigation in SMEs. Knowl Manage Res Pract 12(1):103–113

Slip Flow and Heat Transition for Hydromagnetic Elastico-viscous Fluid Past a Flat Moving Plate Kamal Debnath and Bikash Koli Saha

Abstract A theoretical approach has been made to investigate the hydromagnetic and slip impact on heat transport for elastico-viscous boundary layer fluid flow past a flat moving plate considering non-Newtonian fluid model Walters Liquid (Model B/). To transform equations governing fluid motion to solvable form, similarity variables are introduced to obtain the self-similar resulting equations. The specially designed solver ‘bvp4c’ of MATLAB for solving boundary value problems is used for numerical computation. To study the influence of hydromagnetic and slip parameters on the elastico-viscous fluid together with other flow feature parameters, the computed results are plotted for discussion purpose from physical standpoint. Keywords Boundary layer · Elastico-viscous · Heat transfer · Hydromagnetic · Similarity variables · Velocity slip

1 Introduction Heat transfer mechanism on moving solid surface plays a very important role in modern technology and in manufacturing industry such as production of paper, liquid film, plastic sheets extrusion, growth of crystal, polymer industry, and many more. Sakaidis [1] studied the flow behavior of boundary layer for solid moving plate with constant velocity. The transport of heat and mass under applied suction/blowing in moving surface investigated by Erickson et al. [2]. The theoretical study of above problems for continuous moving plate followed by experiment performed by Tsou et al. [3]. The hydromagnetic mixed convective fluid flow with partial slip boundary over a moving plate presented by Rashidi et al. [4]. Some connected problems on moving boundary with diversified physical conditions presented by Bhatti et al. [5].

K. Debnath · B. K. Saha (B) Department of Mathematics, The Assam Royal Global University, Guwahati, Assam 781035, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_13

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The fluid problems considering no-slip condition over boundary of solid surface investigated by many researchers under different physical conditions. But later on, it was found that in many practical problems, such condition is inappropriate and thus slip condition taken into consideration. The fluid flow with slip condition often noticed in the chemical purification and polishing process of medical treatment. Martin and Boyd [6] demonstrated the heat transport on boundary layer fluid past flat surface with partial slip impact. Pal and Shivakumara [7] investigated the radiation effect with momentum slip for unsteady hydromagnetic boundary flow. Bhattacharyya et al. [8] demonstrated the hydromagnetic and slip impact on boundary layer fluid flow over a flat porous plate. Moreover, in the modern field of engineering, there are wide application based on magnetohydrodynamic fluid flow and heat transition past a moving flat surface, example includes petroleum engineering, geothermal energy, plasma studies, aerodynamics, and many more. More importantly, to regulate the behavior or study the boundary layer, there are vast number of applications where many artificial approaches have been made. Sparrow and Cess [9] investigated the free convective heat transfer for isothermal plate induced by magnetic field. Gupta [10] studied the heat flux initiated by magnetic field over the electrically conducting vertical flow for moving wall. Andersson [11] and Wang [12] described the Newtonian fluid flow over stretched surface under the control of momentum slip boundary condition. The existence of hall impact for hydromagnetic boundary fluid flow over moving flat plate studied by many research scholars [13, 14]. Taking inspiration from the above-mentioned works, the present study aims to analyze the hydromagnetic and slip impact on heat transport for elastic-viscous fluid past a flat moving plate taking Walters Liquid (Model B ' ) [15, 16]. Utilizing similarity variables, the partial differential equations that regulate fluid motion are reduced to solvable form and then solved using the MATLAB ‘bvp4c’ inbuilt solver. The numerically evaluated results are plotted for illustration and discussion for different values of flow dominant parameters along with other involved flow feature parameters.

2 Mathematical Formulation The steady two-dimensional elastico-viscous boundary layer fluid flow past a flat moving plate with hydromagnetic and slip effects is considered. The flow geometry of the model is displayed in Fig. 1. The fluid governing equations of motion considering approximation theory of boundary layer given by ∂v ∂u + =0 ∂x ∂y ⎡ ⎤ ∂u k0 ∂ 3u ∂u ∂ 2 u ∂u ∂ 2u ∂ 3u ∂v ∂ 2 u u +v 3 − +v =ν 2 − u − ∂x ∂y ∂y ρ ∂ x∂ y 2 ∂y ∂ y ∂ x∂ y ∂ y ∂ y2

(1)

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Fig. 1 Flow geometry of the problem

−

σ B02 u ρ u

∂T ∂T K ∂2T +v = ∂x ∂y ρC p ∂ y 2

(2)

(3)

where u: velocity toward x-axis, v: velocity toward y-axis, ν = μρ : kinematic viscosity, μ: fluid viscosity coefficient, ρ: fluid density, σ : fluid’s electrical conductivity. B0 : magnetic field, k0 : elastic-viscous parameter, T: temperature, K: fluid thermal conductivity, C p specific heat. The appropriate conditions imposed at the boundary: (

) ∂u , v = 0 at y = 0; u → U∞ as y → ∞ u = Uw + A ∂y

(4)

T = Tw at y = 0; T → T∞ as y → ∞

(5)

where Uw : constant free velocity of the plate, U∞ : constant free stream velocity, A: slip length, Tw : constant plate temperature, T∞ : constant free stream temperature. Stream functions along with similarity variables taken as ϕ=

√

/ 2U∞ νxh(η), η =

∂ψ ∂ψ U∞ y, u = , v=− , T = Tw + (Tw − T∞ )θ (η) 2νx ∂y ∂x

(6) Using the relation (6) in Eqs. (2) and (3), we finally obtain the following sets of equation: ⎡ ( )2 ⎤ h ''' (η) + h(η)h '' (η) + k1 2h ' (η)h ''' (η) + h(η)h 'v (η) − h '' (η) + Mh ' (η) = 0 (7)

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θ '' (η) + Prh(η)θ ' (η) = 0 where M = μC p : K

2xσB02 : ρU∞

hydromagnetic parameter, k1 =

(8)

k 0 U∞ : 2μx

elastic-viscous parameter,

Prandtl number, Pr = The final form of conditions imposed at the boundary obtained from Eqs. (4) and (5) is ''

h(η) = 0, h ' (η) = λ+αh '' (η) at η = 0; h ' (η) = 1, h (η) = 0 as η → ∞ (9) θ (η) = 1 at η = 0; θ (η) = 0 as η → ∞ where λ =

velocity ratio at the plate, α=

Uw : U∞

/

U∞ : 2μx

(10)

velocity slip parameter.

3 Solution of the Problem The numerical method ‘bvp4c’ of MATLAB is a collocation method used to solve = g(x, y, q), x ∈ [a, b] with non-linear differential equation of the form dy dx boundary conditions h(y(a), y(b), q) = 0, where vector q is a unknown parameter. This method is an effective solver different from the shooting method, and it is based on an algorithm. It can compute inexpensively the approximate value of y(x) for any x in [a, b] taking boundary conditions at every step. In this method, the infinity conditions at the boundary are replaced with a finite point which reasonably satisfies the given problem. The self-similar differential Eqs. (7) and (8) are transformed to first order differential equations as ''

h = h 1 , h ' = h 2 , h = h 3 , h ''' = h 4 , θ = h 5 , θ ' = h 6

(11)

From (3.1), we can write '

'

'

'

h1 = h2, h2 = h3, h3 = h4, h5 = h6

(12)

Making use of Eqs. (11) and (12), the Eqs. (7) and (8) can be written as ⎡ ⎤ ( ) 1 1 2 {h 4 + h 1 h 3 + Mh 2 } h4 = (h 3 ) − 2h 2 h 4 − h1 k1 '

'

f 6 = −Prh 1 h 6

(13) (14)

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and the applicable boundary conditions (9) and (10) reduces as follows: h 1 (0) = 0, h 2 (0) = λ+αh 3 (0) and h 2 (∞) = 1, h 3 (∞) = 0

(15)

h 5 (0) = 1 and h 5 (∞) = 0

(16)

4 Results and Discussion To extract the physical significance of the flow pattern for various of involved flow parameters, the numerical computation of the results of velocity profile and the temperature profiles is plotted by using MATLAB inbuilt software ‘bvp4c’. Figures 2, 3, 4, 5, 6, 7, 8 and 9 are the graphical representation of the results drawn with variation of parameters like elastico-viscous k1 , hydromagnetic M, velocity ratio λ, slip δ, and the Prandtl number Pr. The velocity ratio parameter throughout the study considered less than one. Figures 2, 3, and 4 indicate the velocity profile h ' (η) for variation of k1 , M, λ, and α against η. Figure 2 shows that the transport of fluid enhances with the growth of k1 initially and ultimately reduced and finally settled down with increasing distance. Temperature in the fluid helps to deform the polymers of elastic-viscous material, and thus, fluid flow enhances initially but with distance, effects of temperature gradually diminish and retard the fluid motion. From Fig. 3, it is observed that the fluid motion reduces initially with the rise of M, but at η = 2 onwards, it starts accelerating for some distance and then settled down gradually with uniform flow. Magnetic field produces a resistive force termed as Lorentz force which resists the fluid motion initially but with distance, its effect becomes less, and thus, fluid motion enhances

Fig. 2 Velocity h ' (η) versus η for k1 with M = 0.1, λ = 0.1, α = 0.3, Pr = 0.4

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Fig. 3 Velocity h ' (η) versus η for M with k1 = 0.4, λ = 0.1, α = 0.3, Pr = 0.4

Fig. 4 Velocity h ' (η) versus η for α with k1 = 0.4, M= 0.1, λ = 0.1, Pr = 0.4

Fig. 5 Velocity h ' (η) versus η for λ with k1 = 0.4, M= 0.1, α = 0.3, Pr = 0.4

Slip Flow and Heat Transition for Hydromagnetic Elastico-viscous…

Fig. 6 Temperature θ (η) versus η for k1 with M= 0.1, α = 0.3, λ = 0.1, Pr = 0.4

Fig. 7 Temperature θ (η) versus η for M with k1 = 0.4, α = 0.3, λ = 0.1, Pr = 0.4

Fig. 8 Temperature θ (η) versus η for Pr with k1 = 0.4, M= 0.1, α = 0.3, λ = 0.1

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Fig. 9 Temperature θ (η) versus η for λ with k1 = 0.4, M= 0.1, α = 0.3, Pr = 0.4

for a while and finally settled down. From Fig. 4, it is noticed that the velocity rises with the large variation in the curves initially with the increasing values of α, and then, velocity drops and finally settled down showing no variations. The result is quite justified as slip parameter helps in free movement of fluid at the plate but with distance more fluid accumulate at the plate, and the effects of slip factor reduce. Figure 5 illustrates that with the rise of λ, the velocity decelerates initially but gradually enhances for a while and finally settle down with distance. Physically, it can be interpreted as the velocity ratio parameter enhances, the free stream velocity decreases, and thus, the fluid velocity diminishes and soon settled down. The temperature profile θ (η) with the variation of k1 , M, Pr, and λ against η is depicted in Figs. 6, 7, 8, and 9. Figure 6 signifies that the fluid temperature reduces with the growth of k1 , and it indicates that ss fluid becomes more viscous, it hinders transition of thermal energy to the fluid easily, and thus, temperature curves decrease. Figure 7 indicates that the fluid temperature does not significantly affect with the increasing values of M. From Figs. 8 and 9, it is clear that the temperature of the fluid reduced with growth of Pr and λ. The result is justified as the growth of Pr diminishes the thermal conductivity of the fluid. The growth of velocity ratio parameter reduces the free stream velocity, and thus, transition of heat from the plate to the fluid decreases. The results discussed signifies that the fluid’s temperature depends on the flow parameters and the medium.

5 Conclusion The study concerned with the hydromagnetic and slip impact on heat transition for elastic-viscous boundary layer fluid flow past a flat moving plate. The finite difference method-based MATLAB package ‘bvp4c’ is applied to solve the resultant equations of fluid motion. There is enough scope to extend this work. The different analytical

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and numerical methods can be employed to compare the obtained results. To visualize the clear picture of the obtained results, flow simulation of the problem can be done. The following conclusions of the study can be jotted: • The fluid temperature helps to deform the polymers of elastico-viscous material, and thus, fluid flow enhances initially but with distance, the temperature gradually diminishes and retard the fluid motion. • The magnetic field produces a resistive force which resists the fluid motion initially but with distance, its effect becomes less, and thus, fluid motion enhances for a while and finally settled down. • The slip parameter helps in free movement of fluid at the plate, but with distance, more fluid accumulates at the plate, and the effects of slip factor reduce. • The velocity ratio parameter enhances, and thus, the free stream velocity decreases, and thus, the fluid velocity diminishes and soon settled down. • The growth of elastic-viscosity makes the fluid more viscous, and thus, it hinders transition of thermal energy to the fluid easily, and thus, temperature curves decrease. • The growth of Prandtl number diminishes the thermal conductivity of the fluid. • The velocity ratio parameter growth reduces the free stream velocity, and thus, transition of heat diminishes from the plate to the fluid.

References 1. Sakiadis BC (1961) Boundary-layer behavior on continuous solid surfaces. J AICHE 7:26–28 2. Erickson LE, Fan LT, Fox VG (1966) Heat and mass transfer on a moving continuous flat plate with suction or injection. Int Eng Chem 5:19–25 3. Tsou F, Sparrow E, Goldstein R (1967) Flow and heat transfer in the boundary layer on a continuous moving surface. Int J Heat Mas Transfer 10:219–235 4. Rashidi MM, Kavyani N, Shirley A (2014) Double diffusive magneto-hydrodynamic (MHD) mixed convective slip flow along a radiating moving vertical flat plate with convective boundary condition. PLOS One 9(10) 5. Bhatti MM, Rashidi MM, Pop I (2017) Entropy generation with nonlinear heat and mass transfer on MHD boundary layer over a moving surface using SLM. Nonlinear Eng 6(1):43–52 6. Martin MJ, Boyd ID (2006) Momentum and heat transfer in laminar boundary layer with slip flow. J Thermophys Heat Transfer 52:710–719 7. Pal D, Shivakumara IS (2008) Mixed convection heat transfer from a vertical heated plate embedded in a sparsely packed porous medium. Int J Appl Mech Eng 11:929–939 8. Bhattacharyya K, Layek GC, Reddy RS (2012) Slip effect on boundary layer flow on a moving flat plate in a parallel free stream. Int J Fluid Mech Res 39:438–447 9. Sparrow EM, Cess RD (1961) The effect of a magnetic field on free convection heat transfer. Int J Heat Mass Trans 3(4):267–274 10. Gupta AS (1962) Laminar free convection flow of an electrically conducting fluid from a vertical plate with uniform surface heat flux and variable wall temperature in the presence of a magnetic field. Z Angew Math Phys ZAMP 13(4):324–333 11. Andersson HI (2002) Slip flow past a stretching surface. Acta Mech 158(1):121–125 12. Wang CY (2002) Flow due to a stretching boundary with partial slip: an exact solution of the Navier-Stokes equations. Chem Eng Sci 57:3745–3747

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13. Riley N (1964) Magnetohydrodynamic free convection. J Fluid Mech 18:577–586 14. Watanabe T, Pop I (1995) Hall effects on magnetohydrodynamic boundary layer flow over a continuous moving flat plate. Acta Mech 108:35–47 15. Walters K (1960) The motion of an elastico-viscous liquid contained between co-axial cylinders. Q J Mech Appl Math 13:444–456 16. Walters K (1962) The solutions of flow problems in the case of materials with memories. J Mech 1:473–478

Comprehensive Analysis of Various Distance Metrics on Colour-Based CBIR System Shaheen Fatima

Abstract In the recent decades, lots of images have been added to the database and is growing rapidly. Since the database is very huge, hence retrieval and querying of these images become difficult. The content-based image retrieval (CBIR) system provides an efficient option, that is based on extraction of image features and compare. The primary method of feature extraction in CBIR system is based on the colour content of images. Finding the similarity among image features is mainly based on distance metrics, and it plays crucial role in image retrieval. There are many such similarity metrics found from the literature, few of them perform well on some specific cases. Thus, it is important to know, the appropriate metrics for optimal image retrieval. This article presents a comprehensive survey on various popular distance metrics on wide range of image database. The survey is based on finding the similarity of image features, that are based on colour content of an image. The survey gives good insight into the similarity measuring metrices. The popular set of large image database is used for analysis purpose. The results show, superiority of Canberra and Bray–Curtis distances over other distance metrics.

1 Introduction The recent past decades have shown tremendous growth in digital computers, multimedia applications and storage, which resulted into the large image database. Since the databases are quite large, hence there are lots of new research and applications areas which have taken birth viz. efficient multimedia (image) storage, transmission, processing and retrieving. One of such important area is image retrieval system. The image retrieval system basically searches the similar image from large set of image database. The most basic and traditional system retrieve image based on keywords and image description. Though these were very simple methods, but they had a serious problem like time-consuming, laborious and expensive. These problems S. Fatima (B) Department of Applied Electronics, Gulbarga University, Kalaburagi, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_14

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were overcome by content-based image retrieval (CBIR) system. The CBIR system searches the images based on content of the images such as colours, shapes and textures derived from the image itself. The CBIR system is active and growing ever since 1990s [4, 5, 7]. The performance of a CBIR system mainly depends on the selected image features [2, 4] and similarity measuring metrics [11]. The most widely used similarity measuring metrics are distance metrics like Euclidean distance [11]. Since similarity measuring metrics plays a vital role in CBIR system [11], hence this article carries out a comprehensive survey on various such metrics and presents the results. The experiments were conducted on well-known image database [10, 20]. The rest of article is arranged as follows: The architecture of CBIR system and state-of-the-art is presented in Sect. 2, and Sect. 3 provides related work and methodologies. The experimental results are shown and discussed in Sect. 4, and the article is concluded in Sect. 5.

2 CBIR System: State-of-the-Art This section presents the detailed content-based image retrieval (CBIR) system and state-of-the-art.

2.1 CBIR System The present information era generates and deals with huge data transactions in the form of text, image and videos. The major contributing domain are satellite data, medical field, image and video repositories, digital libraries, historical research, digital forensics, biometrics and much more. The important field that has grown in the recent past is image storage and searching. The traditional method for searching (retrieving) image from database is text-based image retrieval (TBIR) [17]. Though TBIR does well with few cases, but pose very serious problems like heavy computational effort and sometime results into irrelevant image retrieval. To overcome the disadvantages of TBIR, the CBIR was developed to retrieve the relevant images [1]. The CBIR uses visual contents present in the image such as texture, colour and shape. The CBIR system seems to be robust compared to TBIR, as the former system is based on the content of the image itself than some description. The basic CBIR system architecture is shown in Fig. 1. The visual features (colour, texture and shape) of the images from the database are extracted and stored in the feature database. Similarly, the features of query image are also extracted and compared with the feature database for matching and retrieval. The similarity measures are based on distance metrics. The lesser the distance between selected features of images better is the similarity and match. Consequently, the distance between features of query image and itself from the database is null.

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Fig. 1 Basic content-based image retrieval system

2.2 CBIR State-of-the-Art The detailed survey on the CBIR system classification is presented in [13]. The authors presented colour feature-based method, viz. histogram based and statistical features, continued with texture-based methods viz. spectral and statistical-based approaches, further they have presented shape-based descriptors viz. model boundary and region-based methods [13]. The authors in [13] have concluded that the hybrid feature extraction phase (such as integrating the colour, shape and texture features) resulted in the improved retrieval rate of the CBIR Systems. The work presented in [6] has categorized the CBIR methods into three levels viz. (1) the first approach by using colour and or shape features, (2) logical features such as object identity and (3) finally, the abstract features (using significant retrieval scenes). The work presented in [12] describes efficient extraction of features and efficient matching of images from the image database. The input RGB colour image was converted to int the grayscale and then discrete cosine transform (DCT) was applied. The other texture features which are statistical like std, mean, smoothness, skewness and entropy were computed from the quantized DCT histogram. Hence, the analogy measurement of the queried image was compared with other database images by calculating the distance metrics such as sum of the absolute difference (SAD), the sum of the squared absolute differences (SSAD), the Euclidean distance, city block distance, Canberra distance, maximum value distance and Minkowski distance.

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The work presented in [15] describes detailed CBIR system using colour coherence vector (CCV) method [15]. In CCV approach, both the coherent and incoherent vectors were considered for adequate representation of image features, so that the clear distinction between the objects in the images was accomplished. The research in [9] demonstrates that there is a lots of difference between human understanding of the image by visual and representation of the image features. The detailed feature extraction process through edge orientation auto correlogram (EOAC) is presented in [18]. The article described in [19] proposed an enhanced CBIR system based on averaging colour technique. Here, [19] the authors have compared two techniques viz. mean based and central tendency regarding recall and precision values. The complete and detailed survey can be found in [3]. The readers, who are interested in further literature, may refer [3].

3 Related Work and Methodologies This section describes various features of images in CBIR, emphasizing colour feature of image. Further, this section presents the distance metrics that are widely used in CBIR system for comparison.

3.1 Image Features The most important step in CBIR system is feature extraction. The basic and low level image features viz. colour, texture and shape are being used and proposed in CBIR systems [16]. The histogram of an image, which is occupancy of colours in pixel format, is used in CBIR algorithms. The major advantage of using histogram is: it is easy to compute and does not take special information about the image into consideration [14]. Following are different strategies of feature extraction in an image for CBIR system. 1. Texture: This is based on visual patterns and spatial information of an image. The textures are being represented by texel, unlike pixel, and stored into sets. The sets basically give information about the texture and its location in an image. These features are based on spatial information of the image and hence can be changed to frequency domain for further analysis. 2. Shape: The shape of an image refers to the specific region that is being sought, and it is not the actual image shape. This can be extracted by segmenting an image or by edge detection. Further, they can be extracted using filters also. These features are invariant of scale, rotation and translation of the image. 3. Colour: The colour feature is one of the simple and most widely used in image retrieval methods. The most widely used method for colour feature extraction is histogram of an image. The image retrieval strategies based on colour histogram

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are generally fast, take less memory and insensitive to size and rotation of the images. Due such advantages, this feature is popular in CBIR system. The work presented in the article is based on colour histogram feature. The colour histogram construction is based on calculating colour intensity of the actual image into different resolution bins. The various information that can be drawn from such histogram: colours like red, green and blue, similarly hue, saturation and value. The author has used colour histogram based evaluated for RGB-based methods.

3.2 Similarity Measures The similarity measures are used to compare the features of query image with images from the database. Based on the optimality of similarity measures, the similar images to query image are retrieved from database. The most popular and widely used similarity measures are distance metrics. The CBIR system calculates the distances between the features of query image and images from database. The lesser the distance between query image and image from database better is the similarity and hence are retrieved. Thus, this metrics play an important role on the performance of the CBIR system. From the literature, it is found that there are lots different distance metrics used for comparison and image retrieval [8]. The different distance metrics: (1) Euclidean distance, (2) Manhattan distance, (3) Canberra distance, (4) Bray– Curtis distance, (5) square chord distance and (6) chi squared distance. The author has used all the above-mentioned distances for comprehensive survey to find out which is better for image retrieval systems.

4 Experimental Results This article presents exhaustive survey on various distance metrics that are used in similarity measure in the CBIR system. The simulation for this survey is carried out on MATLAB 2021 on Windows 10 operating system over I3 processor of 2.5 GHz. The large image set of heterogeneous categories is taken from [10, 20] for image retrieval. The author has taken thirty (30) images each from seventeen (17) categories from [10, 20]. The popular similarity measuring distance metrics viz. Euclidean (Eq. 1), Manhattan (2), Canberra (3), Bray–Curtis (4), square chord (5) and chi squared (6) are used for this survey. We have taken first image from each category as query image and searched for similar images from database based on above said similarity metrics, and results are recorded. The recorded results are presented in two forms: (1) graphical representation and (2) numerical forms. Since the graphical results recorded were huge, hence we have presented here only of two categories for all the distance metrics for visual comparison. The selected image categories are quite different from each other. In Figs. 2, 3, 4, 5, 6 and 7, the first image is query image

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Fig. 2 Flower images retrieved based on Bray–Curtis and Canberra metrics

Fig. 3 Flower images retrieved based on Euclidean and Manhattan metrics

Fig. 4 Flower images retrieved based on square chi and square chord metrics

and second image is also a query image retrieved from database. The subsequent images from 2 (two) to 15 (fifteen) are retrieved images from the database based on various distance metrics. Similarly, the numerical results are presented in Table 1. The first row in the table shows all the distance metrics, and first column shows all the image categories in

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Fig. 5 Sailboats images retrieved based on Bray–Curtis and Canberra metrics

Fig. 6 Sailboats images retrieved based on Euclidean and Manhattan metrics

Fig. 7 Sailboats images retrieved based on square chi and square chord metrics

Table 1. The distance between query image and the second best image (first is query image itself) of each category for said distance metrics is recorded. Table 1 shows that Canberra and Bray–Curtis show better results as compared to other distance metrics. The square chord distance metric has shown constant distance irrespective the query image and image from the database. The worst performance is shown by Euclidean and Manhattan distance metrics. The chi squared distance metric

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has shown the promising results, but not as good as Canberra and Bray–Curtis. The Canberra and Bray–Curtis distance metrics, both have shown same results, which can owed to the similarity in their distance calculation methods, can be seen from Eqs. 3 and 4, respectively. Euclidean Distance: ⎡ | d | ∑ DE (x, y) = √ (xi − yi )2

(1)

i=1

Manhattan Distance: DMAN (x, y) =

d ∑

|xi − yi |

(2)

d ∑ |xi − yi | |x i | + |yi | i=1

(3)

d ∑ |xi − yi | xi + yi i=1

(4)

i=1

Canberra Distance: DCAN (x, y) = Bray–Curtis Distance: DBRAY (x, y) = Square Chord Distance: DSQR (x, y) =

d ∑ √ √ ( xi − yi )2

(5)

i=1

Chi Squared Distance: DCHI (x, y) =

d ∑ (xi − yi )2 xi + yi i=1

(6)

Figures 2, 3 and 4 are retrieved images of flower category, and similarly, Figs. 5, 6 and 7 show the retrieved images of sailboats. These figures also support the results presented in Table 1. The superior performance of Canberra and Bray–Curtis distance metrics is also seen from Figs. 2, 3 and 4 and from Figs. 5, 6 and 7.

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Table 1 Smallest distances between query and retrieved images Euclidean Manhattan Canberra Bray–Curtis Square chord Horses Aviation Bears Buses Cars Castles Cats Dogs Flowers Lakes Roses Sail boats Sunsets Tigers Trees Tulips Water falls

27.54 207.09 11.22 15.04 46.92 49.44 360.54 1 5.30 151.90 29.33 182.87 7.02 235.18 42.77 30.376 178.77 17.36

8.28 22.19 5.26 5.99 11.58 10.73 27.14 5.38 21.08 8.36 17.63 4.12 20.11 9.04 7.84 20.73 6.96

0.03 0.07 0.02 0.04 0.05 0.06 0.27 0.04 0.19 0.05 0.15 0.02 0.12 0.03 0.05 0.17 0.05

0.03 0.07 0.02 0.04 0.05 0.06 0.27 0.04 0.19 0.05 0.15 0.02 0.12 0.03 0.05 0.17 0.05

3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00

Chi squared 0.10 0.67 0.04 0.18 0.10 0.26 3.09 0.10 1.57 0.16 1.53 0.04 1.09 0.15 0.20 1.26 0.11

5 Conclusions The content-based image retrieval (CBIR) system is used to retrieve the similar images from huge image database. It is based on extraction of image features and similarity measure comparison. The primary method of feature extraction in CBIR is based on the colour content of image. The similarity measure between query image and images from database is based on some distance metrics, which itself plays an important role in CBIR system. This article has presented a comprehensive survey on various popular distance metrics on wide range of image database. The survey gives good insight into the similarity measuring metrices and presented results in numerical and graphical form. The inferior performance of Euclidean distance maybe concluded. Experimental results show the superiority of Canberra and Bray–Curtis distances over other distance metrics. Further, the similar performance of Canberra and Bray–Curtis owes to its similarity in their distance measuring methods. Authors are working towards designing an efficient CBIR systems.

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References 1. Ashok Kumar PM, Subha Mastan Rao T, Arun Raj L, Pugazhendi E (2021) An efficient text-based image retrieval using natural language processing (NLP) techniques. In: Intelligent system design. Springer Singapore, Singapore, pp 505–519 2. Atlam H, Attiya G, El-Fishawy N (2017) Integration of color and texture features in CBIR system. Int J Comput Appl 164:23–29 3. Bharathi K, Mohan MC (2017) Content based image retrieval: an overview of architecture, challenges and issues. Int J Eng Res Comput Sci Eng 4(12) 4. Birgale L, Kokare M, Doye D (2006) Colour and texture features for content based image retrieval. In: Third international conference on computer graphics, imaging and visualization (CGIV 2006), Sydney, 26–28 July 2006, pp 146–149 5. Gudivada V, Raghavan V (1995) Content-based image retrieval systems. Computer 28:18–22 6. Hirwane R (2012) Fundamental of content based image retrieval. Int J Comput Sci Inf Technol 3(1):3260–3263 7. Kokare M, Chatterji BN, Biswas PK (2002) A survey on current content based image retrieval methods. IETE J Res 48(3–4):261–271 8. Kokare M, Chatterji B, Biswas P (2003) Comparison of similarity metrics for texture image retrieval. In: TENCON 2003. Conference on convergent technologies for Asia-Pacific region, vol 2, pp 571–575 9. Latif A, Rasheed A, Sajid U, Ahmed J, Ali N, Ratyal NI, Zafar B, Dar SH, Sajid M, Khalil T (2019) Content-based image retrieval and feature extraction: a comprehensive review. In: Mathematical problems in engineering, vol 2019, pp 1–21 10. Li J, Wang J (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Anal Mach Intell 25(9):1075–1088. https://doi.org/10.1109/TPAMI.2003. 1227984 11. Long F, Zhang H, Feng DD (2003) Fundamentals of content-based image retrieval. In: Feng DD, Siu WC, Zhang HJ (eds) Multimedia information retrieval and management: technological fundamentals and applications. Springer, Berlin, Heidelberg, pp 1–26 12. Malik F, Baharudin B (2013) Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain. J King Saud Univ Comput Inf Sci 25(2):207–218 13. Mistry Y, Ingole D (2013) Survey on content based image retrieval systems. Int J Innov Res Comput Commun Eng 2:1827–1836 14. Rashno A, Rashno E (2019) Content-based image retrieval system with most relevant features among wavelet and color features 15. Saxena P, Shefali (2018) Content based image retrieval system by fusion of color, texture and edge features with SVM classifier and relevance feedback. Int J Res Granthaalayah 6(9):259– 273 16. Singhal N, Shandilya SK (2010) A survey on: content based image retrieval systems. Int J Comput Appl 4 17. Sumathy P, Shanmugavadivu P, Vadivel A (2018) Image retrieval and analysis using text and fuzzy shape features: emerging research and opportunities, 1st edn. IGI Global 18. Tiwari A, Bansal V (2004) PATSEEK: content based image retrieval system for patent database. In: The fourth international conference on electronic business—shaping business strategy in a networked world. Tsinghua University, Beijing, pp 1167–1171 19. Vasanthanayaki C, Malini R (2013) An enhanced content based image retrieval system using color features. Int J Eng Comput Sci 2(12) 20. Wang J, Li J, Wiederhold G (2001) Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963. https://doi.org/10.1109/ 34.955109

Numerical Simulation of MHD Viscous Fluid Flow Over a Porous Stretching Surface with the Effects of Power-Law Heat and Mass Flux Ashim Jyoti Baruah and Rupjyoti Borah

Abstract A numerical investigation has been made to analyze the simultaneous effects of both thermal and mass transmissions of viscous fluid flow caused due to an extending surface that is situated at a porous medium. A homogeneous magnetic field is utilized in the vertical direction of the flow. The non-linear supported equations are modernized into solvable form by employing similarity transformation. The MATLAB routine bvp4c scheme is taken up to carry out the results of the problem. The results are discussed in terms of pictorial mode with different novel flow parameters. From the results, it is perceived that the temperature and mass fraction of the fluid enhance from water to oil and hydrogen to ethanol, respectively. Keywords MHD · Heat transfer · Mass transfer · Heat flux · Mass flux · Radiation · Porous medium · Chemical reaction · bvp4c

Nomenclature u, v ρ B0 K M k∗ C B>0 ψ qr

Velocity components in x and y directions Density of the fluid Magnetic field strength Thermal conductivity Coefficient of fluid viscosity Mean absorption coefficient Proportionality constant Temperature coefficient Similarity variable Radiative heat flux

A. J. Baruah (B) Department of Mathematics, Namrup College, Namrup, Assam, India e-mail: [email protected] R. Borah Department of Mathematics, Dibrugarh University, Dibrugarh, Assam 786004, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_15

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λ R Ec Q τw Cf uw x, y σ N CP σ∗ Kp Q0 T v0 M S Pr θ qw Rex Nux f

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Permeability parameter Radiation parameter Eckert number Heat generation parameter Wall shearing stress Skin friction coefficient Stretching velocity Axial and normal coordinates Electrical conductivity Kinematic viscosity Specific heat at constant pressure Stefan–Boltzman constant Permeability of porous medium Heat source coefficient Fluid temperature Velocity of suction/injection Magnetic parameter Suction/injection parameter Prandtl number Dimensionless temperature Wall heat flux Local Reynolds number Nusselt number Dimensionless velocity

Subscripts w ∞

Condition at the surface Condition at the free stream

1 Introduction Study of viscous fluids flowing through different media with varied geometries and assumptions gives numerous mechanical applications which are used in processing industries, biotechnology, and also in heat and mass transfer processes. The study of flows through a porous medium and stretching sheets is also gaining attention for researchers due to their applications in biomedical sciences. The study of nanofluids is also very important as their presence is specially related to the enhancement of heat, increasing the efficiency of the thermal diffusivity of the fluid. It also enhances the viscosity of the base fluid. Dey and Baruah [1] have investigated the importance of

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the thermal transmission by the free convective method in the magnetized fluid flow caused due to an absorbent medium by using the visco-elastic fluid model. Dey and Borah [2] have analyzed the characteristic of simultaneous effects of both thermal and mass diffusions on the viscous fluid streaming above a contracting cylinder. Dey and Borah [3] have investigated the importance of the magnetized fluid flow with the simultaneous impacts of both thermal and mass transmissions by using the secondgrade fluid model. Dey et al. [4] have scrutinized the non-uniqueness solutions of the viscous fluid streaming above an elongating cylinder with the impacts of non-uniform thermal conductivity and mass transference. Dey and Chutia [5] have investigated the nature of the flow with bioconvective heat transference by using the dusty nanofluid model, and the flow behavior is observed near the vertical elongating surface. The aforesaid literature has implemented the numerical method called the “three-stage Lobatto IIIa formula” by developing a bvp4c code in MATLAB for solving their problems. Fiza et al. [6] have examined the magnetized fluid flow under the influence of Hall current in a parallel plate by considering the Jeffrey fluid model. Ibrahim et al. [7] have studied the influence of Joule heating and heat source on the radiative fluid flow caused due to a stretching sheet which is placed at magnetite absorbent medium with power-law heat flux by analytical approach. Kumar et al. [8] have presented an analytical investigation of the effects of Hall current on magnetohydrodynamics fluid flow in between two vertical walls under the influence of heat source/sink. Mishra and Rauta [9] have analyzed the importance of time-dependent dusty fluid flow with the effects of heat transmission by employing the present considering surface. Rasheed et al. [10] have examined the importance of the fluid streaming above an elongating type surface by considering the visco-elastic fluid model. Raju [11] and Suresh et al. [12] have explored the idea and importance of homogeneous chemical reaction with the combined effects of heat and mass transference on the time-dependent convective fluid flow caused due to a vertical plate. Turkyilmazoglu [13] has analyzed the effects of flow factors such as heat generation/absorption, thermal radiation, and free convective heat transmission on the fluid flow caused due to a porous medium by employing an induced magnetic field. Yasmin et al. [14] have explored the importance of both heat and mass transmissions in an electrically conducting fluid flow due to a curved stretching surface by considering a micropolar fluid model. They have also investigated the physical applications of the considering fluid model and its flow manners. In recent times, the fluid streaming above an elongation/contracting geometries under the influences of the different flow factors has multifarious applications. The importance of the present considering surface in the real life is discussed in the aforementioned literature. The main goal of this study is to analyze the simultaneous effects of both thermal and mass transmissions of viscous fluid flow caused due to an extending surface that is situated at a porous medium. The flow is governed by the inertia force, viscous force, and the Lorentz force which is responsible for applying a magnetic field. The leading equations which support the present problem are modernized into a solvable form by using similarity variables. To carry out the results of the problem, the MATLAB routine bvp4c solver scheme is adopted. The influences of parameters are shown graphically and discussed. Again, a comparison has been made for the validation

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of our results with the previously published paper (Ibrahim et al. [7]) and achieved good conformity. The present importance of this fluid model and the aforementioned research papers are motivated us to finalize this paper. We believe that this paper contains lots of novelty and physical significance.

2 Formulation of the Problem The following suppositions are formulated to establish the formulation of this problem. I. II. III.

IV. V.

The time-independent, 2D, incompressible, and radiative flow of viscous fluid due to an extending surface. The flow is induced by extending surface with velocity u w (x) = cx, c > 0, placed at porous medium. Heat and mass transfers through prescribed wall temperature Tw = T∞ + Ax 2 and concentration Cw = C∞ + Bx 2 with the consequences of Joule heating and viscous dissipation are imposed. The flow is taken in the x-direction, and an invariable magnetic field is implemented in the y-direction of the system. Keeping origin is fixed, two identical and opposite forces are employed along with the flow direction, so that the surface is elongated.

Following the above assumptions with boundary layer theory and Rosselands approximations, the governing equations can be made in the following form: ∂v ∂u + = 0, ∂x ∂y

(1)

σ B02 ∂u ∂ 2u ν ∂u u, +v =ν 2 − u− ∂x ∂y ∂y ρ Kp   3 2 ∂T ∂ T ν ∂u 2 1 16σ ∗ T∞ ∂T k ∂2T u + + +v = 2 ∗ ∂x ∂ y ρC p ∂ y Cp ∂y ρC p 3k ∂ y2 u

+ u

σ B02 2 Q0 u + (T∞ − T ), ρC p ρC p

(3)

∂C ∂C ∂ 2C +v = D 2 − Kr(C∞ − C). ∂x ∂y ∂y

The relevant surface restrictions are y = 0 : u = cx, v = −v0 ,

∂T = Ax 2 , ∂y

(2)

∂C = Bx 2 ; ∂y

(4)

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y → ∞ : u → 0, T →, C → C∞

155

(5)

To convert the nature of governing equations into a solvable form, the following similarity scheme (referring Ibrahim et al. [7]) is adopted which satisfies the continuity Eq. (1). 

√ c T − T∞ , , u = cx f  (η), v = − cv f (η), θ (η) = v Tw − T∞ kp c ρcp v σ B02 c − c∞ v , M= φ(η) = ,K = , Pr = , Sc = , cw − c∞ ρc v k D η=y

5

c2 T3 Kr Q0 v0 Ec = √ , R = 4σ ∗ ∞ , Q = , Kr∗ = ,s = , k∗k ρCp c c ρc v ACp     3  μ ∂u x 16σ ∗ T∞ ∂T Cf = , Nux = − , 1+ ρu 2w ∂ y y=0 Tw − T∞ kk ∗ ∂ y y=0   uw x x ∂c , Rex = Shx = − cw − c∞ ∂ y y=0 v

(6)

After applying the scheme Eq. (6), the Eqs. (2), (3), and (4) become f  + f f  − f  1+



2

  1 − M+ f  = 0, K

 4   R θ  + Pr f θ  − 2Pr f  θ + PrEc f 2 + PrEcM f 2 − PrQθ = 0, 3 φ  + Sc f φ  − 2Sc f  φ − ScKr∗ φ = 0.

(7) (8) (9)

The converted surface restriction becomes f (0) − s = 0,

f  (0) − 1 = 0, θ  (0) − 1 = 0, φ  (0) − 1 = 0;

f  (∞) → 0, θ (∞) → 0, φ(∞) → 0

(10)

The following quantities are needed to evaluate the drag force, heat transfer rate, and mass accumulation rate at the surface of the system. Expression of these quantities is as follows:   1 4 −1 −1 Rex2 C f = f  (0), Rex 2 Nux = − 1 + R θ  (0), Rex 2 Shx = −φ  (0) (11) 3

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3 Result and Discussion In this study, the non-linear coupled Eqs. (7), (8), and (9) along with Eq. (10) are solved by developing MATLAB bvp4c code which is a finite difference code that implements the three-stage Lobatto IIIa formula. The results are discussed in terms of graphical mode with different novel flow parameters such as M, K, R, Q, Pr, Sc, and Kr, and the values of flow parameters are taken as M = 0.5, K = 2, R = 0.5, Q = 0.5, Pr = 0.71, Sc = 0.22, S = 0.23 and Kr∗ = 0.5 otherwise, it will be stated. Table 1 shows that the values of the drag force (C f ) of the fluid at the surface of the geometry increase with M during the injection of the fluid. Whereas, opposite characteristics of the drag force of the fluid are developed under the suction environment of the fluid by using the bvp4c scheme. From this consequence, it has been observed that this result is the same as observed by Ibrahim et al. [7] in his study using HAM solution. Figures 1, 2, and 3 are depicted to show the influence of M on the motion, thermal, and mass fractions of the fluid. Application of M in the transverse direction of the flow on the electrically conducting fluid develops Lorentz force which generates energy in the system. The Lorentz force enhances the viscosity of the fluid, and hence, it decelerates the motion of the fluid. Due to this nature of flow, one kind of friction during motion of the fluid develops which generates heat energy in the system, and hence, the thermal fraction increases. Due to the above phenomena, we have achieved from Figs. 1 and 2 that the developing amount of M decelerates the motion of the fluid and enhances the thermal fraction of the fluid in the vicinity of the surface. Also, from Fig. 3, it is perceived that the enlarging amount of M reducing the mass fraction of the fluid. Generally, the mass fraction of the fluid enhances when the motion of the fluid falls with the effects of the same parameter. But, a conflicting characteristic is faced in this case as because of the appearance of mass = Bx 2 at the surface of the system. flux ∂C ∂y Figures 4, 5, and 6 show the influence of permeability of porous medium (K ) on the motion, temperature, and mass fractions of the fluid, respectively. It is seen from Figs. 4 and 6 that more the permeability in the porous medium more will be the velocity and mass fractions of the fluid. Figure 5 illustrates that the thermal fraction Table 1 Comparison of drag force (Cf ) of the fluid at the surface of the sheet when K = 10, R = 0.1, Pr = 0.72, Ec = 0.2, and Q = 0.2 M

s = −0.5 Ibrahim et al. [7] work (HAM solution)

s = 0.5 Proposed work (bvp4c solution)

Ibrahim et al. [7] work (HAM solution)

Proposed work (bvp4c solution)

0.0

−0.828193

−0.8506

−1.328193

−1.3476

0.5

−1.039380

−1.0490

−1.539380

−1.5477

0.7

−1.114734

−1.1217

−1.614734

−1.6208

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Fig. 1 Velocity distribution with M

Fig. 2 Thermal fraction of the fluid with M

of the fluid falls repeatedly in the vicinity of the surface with the rising values of K . From this result, it is concluded that we can control the system by reducing the heat energy with the help of a porous medium. Also, the speed of the system may enhance by developing the amount of porosity of the porous medium. The influence of R and Q on the present problem is demonstrated in Figs. 7 and 8, respectively. It is found that the thermal patterns dwindle with the enriches of R and Q. The radiation parameter characterizes the comparative involvement of thermal transmission by conduction mode to thermal radiation transference. In

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Fig. 3 Mass fraction of the fluid with M

Fig. 4 Velocity distribution with K

general, enhancement of radiation parameter increases the temperature of the fluid. But, in this study, the temperature of the fluid reduces only because of power-law heat flux at the surface. The same physical phenomena are observed during the effects of Q on the thermal patterns. The effects of Pr and Sc on the fluid motion are shown graphically in Figs. 9 and 10. It is observed from Fig. 9 that the thermal patterns of the fluid enrich with the developing amount of Pr. It is also perceived that the temperature distribution of water is smaller than the light organic fluids and oils. Whereas from Fig. 10, it is noticed that the mass fraction increases with the augmenting values of Sc. From the results, it is perceived that the thermal and mass fraction of the fluid enhance from water to oil and hydrogen to ethanol, respectively. Figure 11 is depicted to show the influence of Kr on the mass fraction during the fluid motion. It is observed that the mass fraction of the fluid enhances with developing values of Kr. The effects of this flow parameter in the fluid flow are very

Numerical Simulation of MHD Viscous Fluid Flow Over a Porous…

159

Fig. 5 Temperature of the fluid with K

Fig. 6 Mass fraction of the fluid with K

important as it has lots of physical applications in modern time. This flow parameter arises only because of the deposition and withdrawing of the fluid in the considering system. It is noticed that all the figures satisfy the surface restrictions of this problem.

160 Fig. 7 Temperature of the fluid with R

Fig. 8 Temperature of the fluid with Q

Fig. 9 Temperature of the fluid with Pr

A. J. Baruah and R. Borah

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Fig. 10 Mass fraction of the fluid Sc

Fig. 11 Mass fraction of the fluid with Kr∗

4 Conclusions A numerical exploration has been formulated to investigate the simultaneous consequences of both thermal and mass transmissions of viscous fluid flow caused due to an extending surface that is situated at a porous medium. From this study, we make the following conclusions: • The increasing values of M decelerate the motion of the fluid and enhance the thermal fraction of the fluid in the vicinity of the surface. Also, the increasing amount of M reducing the mass fraction of the fluid. • Due to the influence of K , the motion and mass fractions of the fluid increase, whereas the thermal fraction of the fluid decreases. • The thermal transmission lessens with the increasing amount of R and Q. • It is perceived that the rising amount of Pr and Sc enhance the thermal and mass fraction patterns of the fluid from water to oil and hydrogen to ethanol, respectively. • The mass fractions during the fluid motion increase with Kr.

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References 1. Dey D, Baruah AJ (2019) Visco-elastic effects on nano-fluid flow in a rotating system in presence of hall current effect. In: Emerging technologies in data mining and information security, Springer, Singapore, pp 575–585 2. Dey D, Borah R (2020) Dual solutions of boundary layer flow wth heat and mass transfers over an exponentially shrinking cylinder: stability analysis. Lat Am Appl Res 50(4):247–253 3. Dey D, Borah R (2021) Stability analysis on dual solutions of second-grade fluid flow with heat and mass transfers over a stretching sheet. Int J Thermofluid Sci Technol 8(2) 4. Dey D, Borah R, Mahanta B (2021) Boundary layer flow and its dual solutions over a stretching cylinder: stability analysis. In: Hassanien AE, Bhattacharya A, Dutta S. (eds) Emerging technologies in data mining and information security. Advances in intelligent systems and computing, vol 1286. Springure, Singapore, pp 27–38 5. Dey D, Chutia D (2020) Dusty nanofluid flow with bioconvection past a vertical stretching surface. J. King Saud Univ Eng, Sci 6. Fiza M, Alsubie A, Ullah H, Hamadneh NN, Islam S, Khan I (2021) Three-dimensional rotating flow of MHD jeffrey fluid flow between two parallel plates with impact of hall current. Math Prob Eng 7. Ibrahim SM, Kumar PV, Lorenzini G, Lorenzini E (2019) Influence of Joule heating and heat source on radiative MHD flow over a stretching porous sheet with power-law heat flux. J Eng Thermophys 28(3):332–344 8. Kumar D, Singh AK, Bhattacharyya K, Banerjee A (2021) Effects of hall current on MHD natural convection in between two vertical flat walls with induced magnetic field and heat source/sink. Int J Ambient Energy 1–14. 9. Mishra SK, Rauta AK (2015) Boundary layer flow & heat transfer of an unsteady dusty fluid over a stretching sheet. Int J Sci Eng Res 6:182–189 10. Rasheed HU, Khan Z, Islam S, Khan I, Guirao JL, Khan W (2019) Investigation of twodimensional viscoelastic fluid with nonuniform heat generation over permeable stretching sheet with slip condition. Complexity 11. Raju RS (2017) Transfer effects on an unsteady MHD mixed convective flow past a vertical plate with chemical reaction. Eng Trans 65(2):221–249 12. Suresh P, Krishna YH, Rao RS, Reddy PJ (2019) Effect of chemical reaction and radiation on MHD flow along a moving vertical porous plate with heat source and suction. Int J Appl Eng Res 14(4):869–876 13. Turkyilmazoglu M (2019) MHD natural convection in saturated porous media with heat generation/absorption of thermal radiation: closed-form solutions. Arch Mech 71(1) 14. Yasmin A, Ali K, Ashraf M (2020) Study of heat and mass transfer in MHD flow of micropolar fluid over a curved stretching sheet. Sci Rep 10(1):1–11

Free Convective Oscillatory Flow of Visco-Elastic Dusty Fluid in a Channel with Inclined Magnetic Field Hridi Ranjan Deb

Abstract The time dependent oscillatory flow of a conducting visco-elastic dusty fluid with inclined magnetic field and radiation through a vertical channel filled with a saturated porous medium is under consideration. In the Cartesian coordinate system, x-axis lies along the centre of the channel, and y-axis is the distance measured in the normal direction. The motion of visco-elastic fluid with dust is governed by secondorder fluid model and Saffman model. The partial differential equations governing the motion of fluid and dust are obtained and solved by analytical method. The analytical expression for velocity field, temperature field, shearing stress, and volume rate of flow is obtained. Velocity profile, shearing stress, and volume rate of flow of the fluid motion and dust particles have been represented graphically for different values of flow parameters involved in this study. Keywords Second-order fluid · Saffman model · Visco-elastic · Heat transfer · Dusty fluid · Shearing stress · Flow flux

1 Introduction The inefficiency of the hypothesis of Newtonian fluid to elucidate the conduct of synthetic materials and many other physical situation such as stress relaxation and memory effects governed to the need of generalizing the linear relationship between stress and strain rate tensors. This generalization of one dimensional constitutive equation gives rise to the development of non-Newtonian fluid flow. There are different variety of non-Newtonian fluid model out of which second-order fluid model characterizes the visco-elastic property along with normal stress effects. Visco-elastic fluid is a well-developed fluid which displays both elastic and viscous properties which are the characteristics of fluids and solids, respectively. Visco-elastic materials have many applications in industrial and medical sciences because they are very good shock absorber. For example, flow of blood in our body H. R. Deb (B) Silchar Collegiate School, Silchar, Assam 788003, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_16

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is visco-elastic in nature as elastic energy which is accumulated in the circulatory system and then utilized to generate heat energy. The residual part of the energy is used in the mobility of the body and other functions. Due to the growing demand in industry and technology, the flow of visco-elastic fluid has been studied under different conditions by several authors [1–7]. In nature, multiphase flow can be observed in rivers in the process of sediment transportation. Here, water acts as liquid phase, and sediments act as the solid phase. Also, in human bodies, multiphase flow occurs in case of blood flow where plasma acts as the liquid flow, and red blood cells are designated as solid. Multiphase flows in the presence of magnetic field are of significance due to their practical implementations like (i) fluidization, (ii) MHD generators, (iii) dusty plasma devices, (iv) use of dust in cooling systems, (v) nuclear reactors. Saffman [8] has proposed a dusty fluid model to analyze the stability of laminar flow of dusty gas. The flow of viscous fluid with dust finds extensive use in different branches of science and technology, and various researchers [9–13] have contributed research work under different geometries. The intermingling of visco-elastic fluid and dust particles is a subject of interest because of it is occurrence in powder technology, transport of liquid slurries in chemical processing, nuclear processing, and in different geophysical situations. Due to large scale of implementation in different fields of industry, substantial interest has been developed on its usefulness in modern years, and immense work has been contributed by various researchers [14–19]. In the present analysis, flow of visco-elastic fluid in a channel with dust in presence of inclined magnetic field has been considered. In this analysis, second-order fluid model has been considered as it characterizes the visco-elastic fluid. The constitutive equation of second-order fluid was put forward by Coleman and Markovitz [20] and Coleman and Noll [21] and is associated with dust as proposed by Saffman [8].

2 Formulation of Problem Consider the time dependent flow of dusty visco-elastic fluid with radiative heat transfer in vertical channel. In the Cartesian coordinate system, x-axis lies along the mid-way between the channel walls, and y-axis is perpendicular to the channel walls. The left and right walls are represented as y = 0 and y = a1 . An external uniform magnetic field of strength B1 makes an angle α 1 in the direction of the flow. Our investigation is restricted to the following assumptions • The dust particles are compact, globular, non-conducting uniform in size and evenly distributed in the flow region. • Throughout the motion, the number density (N 0 ) is considered as constant.

Free Convective Oscillatory Flow of Visco-Elastic Dusty Fluid…

165

• The interactions between the particles and chemical reactions have not been considered. • The flow region has constant temperature. The governing equations are as follows: ∂ 3u ∂ 2u v1 N0 K 0 1 ∂ P1 ∂u − u+ (u p − u) =− + v1 2 + v2 ⇀ 2 ∂t ρ∂x K1 ρ ∂t∂ y ∂y σe B12 sin2 α1 u + gβ1 (T − T0 ) − ρ

(2.1)

∂u p − K 0 (u − u p ) = 0 ∂t

(2.2)

∂T 1 ∂q k ∂2T + − =0 ∂t ρc p ∂ y 2 ρc p ∂ y

(2.3)

The initial boundary conditions are ⎫ u(y, 0) = 0, u p (y, 0) = 0, T (y, 0) = T1 ,⎪ ⎪ ⎪ ⎬ u(a1 , t) = 0, u p (y, 0) = 0, T (a1 , t) = T2 = T0 + (T1 − T0 )eiωt , u(0, t) = 0, u p (0, t) = 0, T (0, t) = T0 ,

⎪ ⎪ ⎪ ⎭

(2.4)

where u—velocity of fluid, up —velocity of dust particles in the x—direction, ω— frequency of oscillation, t—time, T —fluid temperature and T 1 —initial fluid temperature, T 0 —left wall temperature and T 2 —right wall temperature, P1 —fluid pressure, g—acceleration due to gravity, q—radiative heat flux, β 1 —coefficient of volume expansion, K 0 —stokes constant, cp —specific heat at constant pressure, k—thermal conductivity, K 1 —permeability porous medium, σ e —conductivity of the fluid, ρ— fluid density, νi = μρi where i = 1, 2, μe is the magnetic permeability and H o is the intensity of magnetic field (Fig. 1). The fluid is considered to be optically thin with a relatively low density. The expression for radiative heat flux is given by Cogley et al. [22], ∂q = 4α02 (T0 − T ) ∂t where α 0 is the constant representing mean radiation absorption. We introduce the following dimensionless variables:

(2.5)

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g

x

u=0

u=0

up=0

up=0 T=T2

T=T0 B0 α1 y y y=0

y=a

Fig. 1 Geometry of the problem

x=

x , a1

y=

tU1 y u T − T0 K1 , u= , θ= , t= , Da = 2 , a1 U1 T f − T0 a1 a1

ν N0 K 0 a12 U 1 a1 , l= , Re = , 2 ρν ν K 0 a1 vρcp up 4α 2 a12 gβ(T − T0 )a12 ν2 U1 , Pr = , Gr = , , N2 = , up = d= a1 ν1 k k U1 vU1 a1 P1 a 2 σe B02 1 P1 = , H2 = 1 , s2 = vρU1 vρ Da M1 =

where U 1 is the velocity of the mean flow and d is the visco-elastic parameter. The governing equations are reduces to Re

∂u ∂ 3u ∂ P1 ∂ 2u − (s 2 + H 2 sin2 α1 + l)u + lu p + Gr θ (2.6) =− + 2 +d ∂t ∂x ∂y ∂t∂ y 2 ∂u p = u − up ∂t

(2.7)

∂θ ∂ 2θ = 2 + N 2θ ∂t ∂y

(2.8)

ReM1 Re Pr

Free Convective Oscillatory Flow of Visco-Elastic Dusty Fluid…

167

The relevant boundary conditions are u(y, 0) = u p (y, 0) = 0, θ (y, 0) = 1,

⎞

⎟ u(1, t) = u p (1, t) = 0, θ (1, t) = eiωt ,⎠ u(0, t) = u p (0, t) = 0, θ (0, t) = 0

(2.9)

where s—porous medium shape factor parameter, H—magnetic parameter, N—radiation parameter, Pr—Prandtl number, Da—Darcy number, Gr—Grashof number, l— particle concentration parameter, M 1 — particle mass parameter, Re—flow Reynolds number.

3 Method of Solution Considering the flow of fluid with dust as purely fluctuating, we assume ∂P = L 1 eiωt , u(y, t) = u 0 eiωt , ∂x u p = u p0 eiωt , θ (y, t) = θ0 eiωt

−

where L 1 –is a constant representing oscillation amplitude for pressure gradient. The Eqs. (2.6), (2.7) and (2.8) become (1 + di ω)

d2 u 0 − n 22 u 0 = −L 1 − Gr θ0 dy 2

u p0 =

(3.1)

u0 (1 + i MReω)

(3.2)

d2 θ0 + n 21 θ0 = 0 dy 2

(3.3)

The relevant boundary conditions are u 0 = u p0 = 0, θ 0 = 0 on y = 0 u 0 = u p0 = 0, θ 0 = 1 on y = 1 √ n 1 = N 2 − i ωRe Pr

) (3.4)

and ( n 2 = s 2 + H 2 sin2 α1 + i ωRe +

1 (1 + iωReM1 )

)

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The expression for shearing stress (Cf) for the fluid phase at y = 1 is Cf =

∂ 3u ∂ 2u + d ∂ y2 ∂t∂ y 2

and The expression for shearing stress (Cf − 1) for the dust phase at y = 1 is Cf − 1 =

∂ 3u p ∂ 2u p + d ∂ y2 ∂t∂ y 2

The expression for rate of heat transfer across the channel’s wall at y = 1 is Nu = −n 1 cot(n 1 )eiωt .

4 Results and Discussion In this analysis, the main purpose is to study the effect of visco-elastic parameter in combination of other flow parameters have been studied. In this investigation, d = 0 represents Newtonian fluid, otherwise it exhibits visco-elastic fluid. For numerical calculations, the values of the parameters are mentioned below, and variations in values are shown in particular figure H = 3, Gr = 4, Pr = 4, Re = .2, N = 1, l = 1, s = .2, ω = 1, λ = .5, t = 1, M 1 = .2, α 1 = π /4, d = 0, −.2. The effect of magnetic parameter (H) on the velocity of dusty fluid and dust particles is shown in Figs. 2 and 3. The application of transverse magnetic field generates Lorentz force a type of resistive force. Also, Lorentz force increases the viscosity of fluid; hence, velocity of dusty fluid and dust particles experiences a diminishing trend due to growth of magnetic parameter (H). Figures 4 and 5 depict the velocity profile of dusty fluid and dust particles with the variation of Grashof number (Gr). When the magnitude of Grashof number is magnified, then the strength of buoyancy force also increases, so the velocity of dusty fluid (Fig. 4) and dust particles (Fig. 5) also amplified. Figures 6 and 7 depict the velocity profile of dusty fluid and dust particles with the variation of Reynolds number (Re). It is apparent that for the ascending values of Reynolds number, there is reduction of velocity in dusty fluid (Fig. 6) and dust particles (Fig. 7). The effects of porous medium shape factor parameter (s) are exhibited in Figures 8 and 9. The permeability of porous medium is reduced with of porous medium shape factor parameter (s), and hence, the velocity of both dusty fluid and dust particles experiences a retarding trend. Also velocity of both dusty fluid (Fig. 10) and dust particles (Fig. 11) gradually decreases with the rise of inclination parameter (α 1 = π /3, π /2).

Free Convective Oscillatory Flow of Visco-Elastic Dusty Fluid…

169

0.3 d=0, H=3 d=0, H=5 d=-.2, H=3 d=-.2, H=5

0.25 0.2

u

0.15 0.1 0.05 0 -0.05 0

0.2

0.1

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.6

0.7

0.8

0.9

1

0.6

0.7

0.8

0.9

1

y

Fig. 2 Effect of d and H on velocity profile u against y 0.35 d=0, d=0, d=-.2, d=-.2,

0.3 0.25

H=3 H=5 H=3 H=5

up

0.2 0.15 0.1 0.05 0 -0.05

0

0.1

0.2

0.3

0.4

0.5

y

Fig. 3 Effect of d and H on velocity profile up against y 0.45 d=0, Gr=4 d=0, Gr=6 d=-.2, Gr=4 d=-.2, Gr=6

0.4 0.35 0.3

u

0.25 0.2 0.15 0.1 0.05 0 -0.05 0

0.1

0.2

0.3

0.4

0.5

y

Fig. 4 Effect of d and Gr on velocity profile u against y

170

H. R. Deb 0.6 d-0, d=0, d=-.2, d=-.2,

0.5

Gr=4 Gr=6 Gr=4 Gr=6

0.4

up

0.3 0.2 0.1 0 -0.1 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.6

0.7

0.8

0.9

1

y

Fig. 5 Effect of d and Gr on velocity profile up against y 0.3 d=0, d=0, d=-.2, d=-.2,

0.25 0.2

Re=.5 Re=2 Re=.5 Re=2

u

0.15 0.1 0.05 0 -0.05

0

0.1

0.2

0.3

0.4

0.5

y

Fig. 6 Effect of d and Re on velocity profile u against y 0.35 d=0 d=0 d=-.2 d=-.2

0.3

Re=.5 Re=.2 Re=.5 Re=.2

0.25

up

0.2 0.15 0.1 0.05 0 -0.05

0

0.1

0.2

0.3

0.4

0.5

y

Fig. 7 Effect of d and Re on velocity profile up against y

0.6

0.7

0.8

0.9

1

Free Convective Oscillatory Flow of Visco-Elastic Dusty Fluid…

171

0.3 d=0, d=0, d=-.2, d=-.2,

0.25

s=.2 s=1 s=.2 s=1

0.2

u

0.15 0.1 0.05 0 -0.05

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.6

0.7

0.8

0.9

1

y

Fig. 8 Effect of d and s on velocity profile u against y 0.35 d=0, d=0, d=-.2, d=-.2,

0.3

s=.2 s=1 s=.2 s=1

0.25

up

0.2

0.15

0.1

0.05

0

-0.05

0

0.1

0.2

0.3

0.4

0.5

y

Fig. 9 Effect of d and s on velocity profile up against y

From these Figures 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11, it can be concluded that the velocities of both dusty fluid and dust particle are maximum at the centre of the channel. Also, it is noticed in Figures 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11, that an accelerated flow is observed in Newtonian fluid as compared to visco-elastic fluid. Figures 12, 13, 14, and 15 represent the shearing stress of dusty fluid and dust particles at the channel with the variation of visco-elastic parameter in combination with other flow parameters involved in this study. Here, two types of flow are considered 1. The flow through cooled channel wall for Gr > 0. 2. The flow through heated channel wall for Gr < 0.

172

H. R. Deb 0.4 d=0

0.35

= /3

1

d=0

= /2

1

d=-.2

0.3

d= -.2

= /3

1

= /2

1

0.25

u

0.2 0.15 0.1 0.05 0 -0.05

0

0.2

0.1

0.3

0.4

0.6

0.5

y

0.7

0.8

1

0.9

Fig. 10 Effect of d and α 1 on velocity profile u against y 0.4 d=0 0.35

d=0 d=-.2

0.3 d=-.2

= /3

1

= /2

1

= /3

1

= /2

1

0.25

up

0.2 0.15 0.1 0.05 0 -0.05 0

0.2

0.1

0.3

0.4

0.6

0.7

0.8

0.9

1

4.2

4.4

4.6

4.8

5

0.5

y

Fig11 Effect of d and α 1 on velocity profile up against y 5

d=0, d=-.2, d=0, d=-.2,

Cf

0

Gr=4 Gr=4 Gr=-4 Gr=-4

-5

-10

3

3.2

3.4

3.6

3.8

4

H

Fig. 12 Effect of d and Gr on shearing stress (cf) at right wall against H

Free Convective Oscillatory Flow of Visco-Elastic Dusty Fluid…

173

6 4 2 0 d-0, Gr=4 d=-.2, Gr=4 d=0, Gr=-4 d=-.2 Gr=-4

Cf

-2 -4 -6 -8 -10 -12

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

4.6

4.8

5

s

Fig13 Effect of d and Gr shearing stress (cf) at right wall against s 8 6 4 d=0, Gr=4 d=-.2, Gr=4 d=0, Gr=-4 d=-.2, Gr=-4

Cf1

2 0 -2 -4 -6 -8

3

3.2

3.4

3.6

3.8

4

4.2

4.4

H

Fig14 Effect of d and Gr shearing (cf − 1) at right wall against H

Figures 12 and 13 reveal the behaviour of shearing stress of dusty fluid on the right wall against magnetic parameter (H) and porous medium shape factor parameter (s). From both these figures, it is noticed that the intensity of shearing stress shows a rising trend in case of flow through a heated channel wall and declining in cold channel wall with the development of magnetic parameter (H) and porous medium shape factor parameter (s). Also, the magnitude of shearing stress of visco-elastic fluid is more as compared to Newtonian fluid in cooled channel wall, but on the other hand, reverse phenomenon is noticed in case of heated plate.

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H. R. Deb 6

4

2

d=0, d=-.2, d=0, d=-.2,

Cf1

0

Gr=4 Gr=4 Gr=-4 Gr=-4

-2

-4

-6

-8 0.2

0.4

0.6

0.8

1.6

1.4

1.2

1

2

1.8

s

Fig15 Effect of d and Gr shearing stress (cf − 1) at right wall against s

The effect magnetic parameter (H) and porous medium shape factor parameter (s) on shearing stress of dust particles on the right wall are revealed in Figures 14 and 15. It is noticed that the shearing stress of dust particles rises in case of heated channel wall, but diminishing trend is observed in case of cooled channel wall with the development of magnetic parameter (H), porous medium shape factor parameter (s), and visco-elastic parameter (d). Flow flux of both dusty fluid and dust particles is depicted in Figures 16 and 17. From these figures, it may be concluded that flow flux of both dusty fluid and dust particles for both Newtonian flow and non-Newtonian gradually decreases with time, and magnitude of flow flux of non-Newtonian flow (d = −.2, −.4) is of lower order in comparison with Newtonian flow (d = 0). It is clear from the expression visco-elastic parameter has no effect on Nusselt number. 0.023 d=0 d=-.2 d=-.4

0.0225 0.022

vf

0.0215 0.021 0.0205 0.02 0.0195 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

t

Fig.16 Effect of d on flow flux of dusty fluid (vf ) against time (t)

0.8

0.9

1

Free Convective Oscillatory Flow of Visco-Elastic Dusty Fluid…

175

0.023 d=0 d=-.2 d=-.4

0.0225

0.022

vd

0.0215

0.021

0.0205

0.02

0.0195 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

t

Fig.17 Effect of d on flow flux of dust particles (vd ) against time (t)

In this present investigation, considering visco-elastic parameter (d) = 0 and angle of inclination (θ ) = π /2, the obtained results are in good agreement with results obtained by Prakash et al. [11].

5 Conclusions In this investigation, free convective oscillatory flow of visco-elastic dusty fluid in a channel with inclined magnetic field has been studied. The analytical method is employed to solve governing equations. Some of the important points from the present investigation are listed below: 1. The effect of visco-elastic parameter is dominant in velocity of dusty fluid, velocity of dust particles and skin friction 2. Growth in magnetic parameter (H), Reynolds number (Re), inclination of magnetic parameter (α 1 ), and permeability of porous medium (s) values leads to deaccelerating of velocity in both dusty fluid and dust particles, but the trend is reversed for Grashof number (Gr). 3. Accelerated flow is observed in Newtonian fluid as compared to visco-elastic fluid. 4. The potency of shearing stress of visco-elastic fluid is more as compared to Newtonian fluid in cooled plate, but on the other hand, reverse phenomenon is noticed for the heated plate. 5. The shearing stress of dust particles rises in case of heated channel wall, but diminishing trend is observed in case of cooled channel wall with the development of magnetic parameter (H), porous medium shape factor parameter (s), and viscoelastic parameter (d).

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6. Flow flux of in case of dusty fluid and dust particles for Newtonian flow and non-Newtonian gradually decreases with time, and magnitude of flow flux of non-Newtonian flow is of lower order in comparison with Newtonian flow. 7. The visco-elastic parameter has no impact on rate of heat transfer.

References 1. Luo L, Shah NAIM, Alarifi IM, Vieru D (2019) Two layer flows of generalized immiscible second grade fluids in a rectangular channel. Math Methods Appl Sci 43(3):1–12 2. Nisa ZU, Nazar M (2019) Natural convection flow of second grade fluid with thermal radiation and damped thermal flux between vertical channels. Alexandria Eng J 58(4):1119–1125 3. Rana MA, Ali Y, Shoaib M, Numan M (2019) Magneto hydrodynamic three dimensional couette flow of a second grade fluid with sinusoidal injection/ suction. J Eng Thermodyn 28:138–162 4. Ali F, Khan M, Gohar M (2021) Magnetohydrodynamic fluctuating free convection flow of second-grade fluid flow in a porous medium hindawi math. Probl Eng. Article ID 6648281. https://doi.org/10.1155/2021/6648281 5. Deka B, Choudhury R (2018) on hydromagnetic flow of a second-grade fluid induced by an inclined plate. Int J Heat Technol 36(1):325–331 6. Choudhury R, Das B (2016) Influence of viscoelasticity on MHD heat and mass transfer flow through a porous medium bounded by an inclined surface with chemical reaction. Int J Heat Tech 34:332–338 7. Dey D (2018) Viscoelastic fluid flow through an annulus with relaxation, retardation effects and external heat source/sink. Alex Eng J 57(2):995–1001 8. Saffman PG (1962) On the stability of laminar flow of a dusty gas. J Fluid Mech 13(1):120–129 9. Manjunatha S, Gireesha BJ, Bagewadi CS (2017) Series solutions for an unsteady flow and heat transfer of a rotating dusty fuid with radiation effect. Acta Math Univ Comenianae 86(1):111– 126 10. Saidu I, Yusuf MW, Uwanta IJ, Iguda A (2010) MHD effects on convective flow of dusty viscous fluid with fraction in a porous medium. Aust J Basic Appl Sci 4(12):6094–6105 11. Prakash OM, Makinde OD, Kumar D, Dwivedi YK (2015) Heat transfer to MHD oscillatory dusty fluid flow in a channel filled with a porous medium. Sadhana 40(4):1273–1282 12. Prakash O, Makinde D (2015) MHD oscillatory couette flow of dusty fluid in a channel filled with a porous medium with radiative heat and buoyancy force. Latin Am Appl Res 45:185–191 13. Singh NP, Singh AK (2002) MHD effects on convective flow of dusty viscous fluid with volume fraction. Bul Inst Math Aca Sin 30:141–151 14. Ali F, Bilal M, Sheikh NA, Khan I, Nisar KS (2019) Two-phase fluctuating flow of dusty viscoelastic fluid between nonconducting rigid plates with heat transfer. IEEE Access 35 15. Sivraj R, Kumar BR (2012) Unsteady MHD dusty visco-elastic fluid couette flow in an irregular channel with varying mass diffusion. Int J Heat Mass Transf 55:3076–3089 16. Khan AA, Tariq H (2020) Peristaltic flow of second-grade dusty fluid through a porous medium in an asymmetric channel. J Porous Media 23:883–905 17. Gupta RK, Gupta K (1990) Unsteady flow of a dusty visco-elastic fluid through channel with volume fraction. Indian J Pure Appl Math 21(7):677–690 18. Deb R (2018) Second-order fluid through porous medium in a rotating channel with Hall current. Proc IEMIS 1:369–377. https://doi.org/10.1007/978-981-13-1951-8_33 19. Dey D (2016) Non-Newtonian effects on hydromagnetic dusty stratified fluid flow through a porous medium with volume fraction. Proc Natl Acad Sci India Sect A 86(1):47–56

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20. Coleman BD, Markovitz H (1964) Incompressible second order fluid. Adv Appl Mech 8:69 21. Coleman BD, Noll W (1960) An approximation theorem for functional with applications in continuum mechanics, archs ration. MechAnal 6:355 22. Cogley ACL, Vinvent WC, Gilees SE (1968) Am Inst Aeronaut Astronaut 6:551

Study of Power Law Fluid Flow Through a Stretched Vertical Surface with Viscous Dissipation and Its Rheology Debasish Dey and Bhagyashree Mahanta

Abstract An analysis on steady flow of Hydromagnetic power Law fluid past a stretched surface which is vertical together with viscous dissipation has been carried out. The governing equations pertaining to velocity curve, temperature and concentration curves has been derived and further elucidated by bvp4c method which is built-in the MATLAB software. The above mentioned profiles for various fluidic parameters are shown graphically in this paper. Also, the tabulation for skin friction for different parameters is also shown. It is witnessed that Magnetic parameter (M) aids in lowering the shear stress in shear thickening fluid. Also, a fall in temperature is noted as Prandtl Number (Pr) is increased. Keywords Power law fluid · Stretched vertical surface · Viscous dissipation and bvp4c method

Abbreviations u, v υ g β T T∞ β∗ C C∞ σ B0 ρ

Along x and y axis (components of the velocity of fluid) Kinematic viscosity Acceleration due to gravity Volumetric coefficient (thermal expansion) Temperature of fluid Temperature of fluid at the surface Volumetric coefficient (mass expansion) Concentration of the fluid Concentration of the fluid at the surface Electrical conductivity Strength of magnetic field Fluid density

D. Dey · B. Mahanta (B) Department of Mathematics, Dibrugarh University, Dibrugarh, Assam 786004, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_17

179

180

n k Cp Q D λ Gr Gc M E Pr A Sc Kc

D. Dey and B. Mahanta

Flow behaviour index Thermal conductivity of the fluid Specific heat at constant pressure Heat generation/absorption parameter Diffusion coefficient Rate of chemical reaction Thermal Grashof number Mass Grashof number Magnetic parameter Power law parameter Prandtl number Energy dissipation number Schmidt number Chemical reaction parameter

1 Introduction A report/analysis on the flow on a stretching sheet which is flowing hydro magnetically and vertically in nature has picked up a massive growth over the years for its unavoidable applications in many processes involving industrial and technological processes. Cooling of glass fibres, hardening of copper wires, thinning and cooling of metallic plates, etc. are the very important applications of stretched surface. The rate of cooling can be administered by drawing the stretched sheet in a viscous fluid and finally the required product can be attained. Also, there are innumerable applications of MHD fluids pertaining to power law fluids in manufacturing industries such as oil–water emulsion, cosmetics, jam. The influence of flow and heat transmission on the flow (non-Newtonian) through a surface which is stretched and vertical has fascinated numerable investigators. Taking this kind of flow into consideration there are many such examples in day to day life such as in energy storage, geophysics. one of such investigators, Agbaje et al. [1] has looked into a flow (stretching sheet) which has a stagnation point in a porous medium along with heat transfer. Aladdin et al. [2] has used a very effective MATLAB method known popularly as bvp4c technique to discuss the radiation parameter and its influences on the governing flow devouring the stagnation point in it. In the manifestation of induced magnetic field, one of the renowned researcher Ali et al. [3] has beautifully comprehended the work of Agbaje et al. The outcomes of heat transfer on a Hydromagnetic flow which is visco-elastic in nature over a wavy channel in presence of slip velocity are surveyed exquisitely by Choudhury et al. [4, 5]. Also, they have deliberately studied the flow of mass and heat transfer in a channel which is vertical. Also, a study on hydromagnetic flow which has a stagnation point and is porous in nature has been reviewed by Chaudhury et al. [6] by ways of the shooting method. Furthermore, Dey and Deb [7] studied the stimulus effects of Lorentz force on the flow pertaining to the visco-elastic nature.

Study of Power Law Fluid Flow Through a Stretched Vertical…

181

An intensive study by numerous researchers on the topic of Power law fluid flow has been done due to its frequent use of the shear stresses in our day to day activities [8–11]. Charted by the surveying of this, Dey and Deb [7] stunningly studied the flow which is magneto hydrodynamic in nature and also comprising dissipation of energy over a medium with porous nature taking the help of the famous bvp4c technique. Trailed by the constant method, Dey and Nath [12] carried an investigation on binary mixture flow past a porous 2-D stretching surface. An investigation on thermal Maxwell power law nanofluid which is unsteady in nature is done by Jawad et al. [13]. Sanni et al. [14] studied magnificently on MHD cross fluid past a stretched sheet which is curved and non-linear which is done along with the flow transfer of heat and mass. Analysis on power law fluid past a deformable plate in a hybrid nanofluid flow along with heat transfer is done beautifully by Khashi et al. [15]. In view of the above wonderful works of the researchers and driven by their investigations, this paper aims to look upon the hydromagnetic flow of power law fluid over a stretched sheet which is vertical with viscous dissipation. The defined boundary conditions and similarity transformation of the specialised PDEs are first transformed to ODEs and solved them additionally by numerical method by taking the help of MATLAB built-in bvp4c methodology. The biggest implication of this model is that it aids in defining its performance and links various data that are investigated widely throughout the diverse scope of shear stress. The uniqueness of this paper aims in studying the rheology of power law fluid flow through a stretched sheet which is vertical with viscous dissipation which is different as compared to Nayak [16]. The analysis of the flow is described in the next section. Followed by Sect. 3, the approach used for this problem is clearly expounded. After the procedure, the final graphs and tables that are obtained is depicted and shown in Sect. 4. In the next section, brief discussions were done to concisely wrap up the problem. Finally, we have the reference section which assisted us to complete this paper suitably.

2 Mathematical Construction of the Flow Analysis In this paper, we assume a 2-D incompressible MHD fluid flow which is steady, laminar and electrically conduction viscous fluid which passes through a stretched vertical sheet via viscous dissipation. Following are the assumptions taken to formulate our problem: (i) The axis taken along the surface is x-axis which is directed upwards. (ii) Magnetic field which is uniform is applied in the path (normal) to the flow. (iii) Magnetic Reynolds number is expected to be very small (Fig. 1). Keeping in mind these assumptions, the constitutive equations are governed by the power law fluid. The respective equation which administers the above flow is pointed as follows:

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Fig. 1 Prototype of the flow analysis

∂v =0 ∂y ( ) ∂ 2u K ∂ ∂u n ∂u σ B02 ∗ = υ 2 + gβ(T − T∞ ) + gβ (C − C∞ ) − u+ v ∂y ∂y ρ ρ ∂y ∂y ( ) k ∂2T ∂T Q0 μ ∂u n+1 = v + (T − T∞ ) + ∂y ρC p ∂ y 2 ρC p ρC p ∂ y v

∂ 2C ∂C = D 2 − λ(C − C∞ ) ∂y ∂y

(1)

(2)

(3)

(4)

with the following initial and boundary conditions as follows: u = −u w , v = vw , T = Tw , C = Cw at y = 0

(5)

u → 0, T → Tw , C → Cw as y → ∞ We present the following set of similarity transformations: / uw uwυ T − T∞ , , u = −u w f ' (η), v = − f (η), θ (η) = υx x Tw − T∞ C − C∞ ϕ(η) = , Cw − C∞ /

η=y

(6)

Study of Power Law Fluid Flow Through a Stretched Vertical…

183

Applying the data given in Eqs. (5) and (6) in Eqs. (2), (3) and (4) we have the resulting dimensionless set of equations: f '' f + f ''' − G r θ − G c ϕ − M f ' + E f ''' f '' n−1 = 0 f '' f + f ''' − G r θ − G c ϕ − M f ' + E f ''' f '' n−1 = 0

(7)

θ '' + Pr θ ' f + Pr Qθ − A f '' n f ''' = 0

(8)

ϕ '' + Sc f ϕ ' − K c Scϕ = 0

(9)

f ' = 1, f (0) = f w , θ = 1, ϕ = 1 at η = 0

(10)

f ' → 0, θ → 0, ϕ → 0 as η → ∞ where f w = Q=

Q0 x , ρCp u w

√−vw , vu w /x

Kc =

λx , uw

Gc = Sc =

gβ ∗ (Cw −C∞ )x , u 2w υ , D

E=

nK ρ

M =

3 (n−1) u w2 n+1 n−1 υ 2 x 2

/

σ B02 x , k

,A=

Kp =

(n+1)μ k

u w K ∗p , υx

Pr =

υρC p , k

1 (2n−1) u w3 n−1 (υ x) 2 (Tw −T∞ )

3 Method of Solution The above leading equations are solved by a scheme called bvp4c method which is built-in MATLAB software. Here, we have used this method in the form of y ' = f (x, y, c) with some valid boundary conditions θ (y(a), y(b), c) where a ≤ x ≤ b. We attain the following set of non-linear ODEs as mention below: ' Let f = y1 , f ' = y2 = y1 , f '' = y3 y1' = f ' = y2 y2' = f '' = y3 y3' = f ''' =

G r y4 + G c y6 + M y2 − y3 y1 1 + E y3n−1

This is obtained by isolating f ''' from Eq. (7). Let θ = y4 , θ ' = y5 , y4' = θ ' = y5

(11)

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y5' = θ '' = Ay3n

G r y4 + G c y6 + M y2 − y3 y1 1 + E y3n−1

− Pr y5 y1 − Pr Qy4

(12)

This is obtained by extracting θ '' from Eq. (8). Let ϕ = y6 , ϕ ' = y7 y6' = ϕ ' = y7 y7' = ϕ '' = K c Sc y6 − Sc y1 y7

(13)

This is obtained by extracting ϕ '' from Eq. (9). Taking in account the above suppositions, we have the following formulated boundary conditions: y2 (0) = 1,

y1 (0) = λ,

y4 (0) = 1,

y6 (0) = 1,

y2 (1) = 0,

y4 (1) = 0,

y6 (1) = 0

(14) With the use of the above boundary conditions along with Eqs. (11), (12) and (13) in MATLAB software we get some satisfying results which are structured in graphs and tables in the subsequent section.

4 Results and Discussion The discussion on study of rheology on fluid flow characterising the power law over a stretched vertical surface together with viscous dissipation has been exhibited and presented using bvp4c method. Also, for assorted number of parameters that are involved in our problem, numerical calculations were done to solve them. The parameters include Prandltl number, power law parameter, magnetic parameter, etc. The effects of these factors highlighted our work and thus their conclusions on velocity, temperature and concentration profiles are shown as under Fig. 2a depicts the velocity curve with respect to η for diverse values of E (power law parameter) when n < 1. From the figure, we can clearly state that as there is an increment in E, the thickness of the boundary layer drops. As a result, there is a decrement in the viscosity of the flow which finally turns into the enhancement of the velocity. A similar variation can also be seen in Fig. 2b when n > 1. The consequences of Prandtl Number (Pr) on the temperature curve have been depicted in Fig. 3a, b when n < 1 and n > 1, respectively. It is apparent from the figures that as the Prandtl number builds up there is a gradual fall of temperature from the surface. This is due to the increment of thickness of velocity boundary layer whilst decrement in thermal boundary layer. So, high viscosity is observed when there is hike in momentum diffusivity. As a result, there is lowering of the flow speed and so the temperature. Further, Fig. 4a, b delineates the result of consequences

Study of Power Law Fluid Flow Through a Stretched Vertical…

(a)

185

(b)

Fig. 2 Velocity curve versus η for values of E when M = 1.0, Gr = 1.0, Gc = 1.0, A = 0.1, K = 2.0, Pr = 0.71, s = 0.6 (n < 1 and n > 1, respectively)

on concentration curve by the parameter Schmidt Number (Sc) of the fluid. It can be noted that as Sc increases, the fluid concentration decreases gradually with the addition of distance between them. The fall of the concentration curve is due to the weakening of the concentration boundary layer. Table 1 enlists the values of velocity gradient past a stretched vertical sheet with viscous dissipation for diverse values of Magnetic field (M) and Prandtl number (Pr). Velocity gradient plays the characteristic role of the drag formation and this is an important part of the fluid dynamics to study upon. The flow remains undisturbed when the velocity gradient is zero and gradually gets disturbed as there is a hike in the displacement of the flow. From the table we can get an insight that the velocity gradient increases as the Magnetic parameter and Prandtl number increases. The rate of change in the velocity is known as the shear stress. It is very important to study the shear stress as it has wide applications in the research field especially during the study of fluids that are non-Newtonian in nature. Since, shear stress is comparative to the fluid viscosity and also due to more friction shear stress is liable for the damages that happens in the surface. Hence, our aim to lower the

(a)

(b)

Fig. 3 Temperature curve versus η for values of Pr when E = 1.0, Gr = 1.0, Gc = 1.0, A = 0.1, K c = 2.0, M = 1.0, Sc = 0.6 (n < 1 and n > 1, respectively)

186

D. Dey and B. Mahanta

(a)

(b)

Fig. 4 Concentration curve versus η for values of sc when E = 1.0, M = 1.0, Gr = 1.0, Gc = 1.0, A = 0.1, K c = 2.0, Pr = 0.71 (n < 1, n > 1, respectively)

stress so as to have a minimal damage by studying and analysing the factors which can help to achieve the same. In Table 2, we can infer that Pr benefits to diminish the shear stress whereas a contradictory behaviour can be seen with the magnetic parameter (M). Table 1 Values of velocity gradient f '' (n < 1) M

Q

Pr

Gr

Gc

A

E

f '' (Choudhury et al. [6])

f '' (Present work)

1.0

−1

0.71

1.0

1.0

0.1

1.0

1.67031

1.6700

2.0

–

–

–

–

–

–

1.39749

1.3972

3.0

–

–

–

–

–

–

0.98815

0.9871

1.0

−1

0.71

1.0

1.0

0.1

1.0

1.74282

1.7723

–

–

1.0

–

–

–

–

2.33510

2.3452

–

–

2.0

–

–

–

–

2.98427

2.8876

Table 2 Values of shear stress f

'' n

M

Q

Pr

Gr

Gc

A

E

f '' (n < 1)

f '' (n>1)

1.0

−1

0.71

1.0

1.0

0.1

1.0

1.4296

2.9214

2.0

–

–

–

–

–

–

1.5106

3.4466

3.0

–

–

–

–

–

–

1.5811

3.9522

1.0

−1

0.71

1.0

1.0

0.1

1.0

1.4296

2.9214

–

–

1.0

–

–

–

–

1.4266

2.9034

–

–

2.0

–

–

–

–

1.4176

2.8290

Study of Power Law Fluid Flow Through a Stretched Vertical…

187

5 Conclusion An exploration on rheology of fluid flow pertaining to power law past a sheet which is stretched and vertical along with viscous dissipation has been carried out numerically in this present paper taking the help of variety of parameters that affects the different curves of the fluid. From the present study it is beautifully noted that the Magnetic parameter (M) reduces the velocity of the fluid due to the induced Lorentz force. Also, there is an increment in the fluid velocity when the power law parameter (E) upsurges. The Prandtl number lowers the temperature as its layer gets narrowed for both the cases of shear thinning and shear thickening. It can be also perceived that as the concentration boundary layer gets weakened, there is a sharp fall in the concentration as the Schmidt Number (Sc) increases. It is important to mention that the velocity gradient lower as the magnetic parameter increases and rises in the case of Prandtl number. Additionally, enhancement of both these parameters lowers the shear stress.

References 1. Agbaje TM, Modal S, Makukula ZG, Motsa SS, Sibanda P (2018) A numerical approach to MHD stagnation point flow and heat transfer towards a stretching sheet. Ains Shams Eng J 9(2):233–243 2. Aladdin N, Bachok N, Nasir N (2019). Effect of thermal radiation on MHD stagnation point flow over a shrinking sheet. J Phys 1366 3. Ali F, NazarR ANM, Pop T (2011) MHD stagnation point flow and heat transfer towards stretching sheet with induced magnetic field. Appl Math Mech 32(4):409–418. https://doi.org/ 10.1007/s10483-011-1426-6 4. Choudhury R, Deb HR, Dey D (2011) Effects of MHD flow and heat transfer of a visco-elastic fluid in a vertical channel with slip velocity. Int J Appl Eng Res 6(3):331–343 5. Choudhury R, Mahanta M, Dey D (2014) Visco-elastic fluid flow with heat and mass transfer in a vertical channel through porous medium. J Global Res Math Arch 2(1):22–33 6. Chaudhary S, Singh S, Chaudhary S (2016) Numerical solution for magnetohydrodynamic stagnation point flow towards a stretching or shrinking surface in a saturated porous medium. Int J Pure Appl Math 106(1):141–155. https://doi.org/10.1273/ijpam.v106i1.11 7. Dey D, Deb HR (2018) Hydromagnetic flow of power law fluid in a porous medium with energy dissipation: a numerical approach. Italian J Eng Sci Tech Itali 61+1:130–134 8. Dey D, Hazarika M (2020) Entropy generation of hydro-magnetic stagnation point flow of micropolar fluid with energy transfer under the effect of uniform suction/injection. Latin Amn Appl Res Int J 50(3):209–214 9. Dey D (2017) Hydromagnetic oldroyd fluid flow past a flat surface with density and electrical conductivity stratification. Latin Am Appl Res Int J 47(2):s41–45 10. Dey D, Borah R (2020) Dual solutions of boundary layer flow with heat and mass transfers over an exponentially shrinking cylinder: stability analysis. Latin Am Appl Res Int J 50(4):247–253 11. Dey D, Chutia B (2020) Dusty nanofluid flow with bioconvection past a vertical stretching surface. J King Saud Univ Eng Sci 12. Dey D, Nath K (2018) Hydromagnetic binary mixture flow of visco-elastic fluid past a stretching porous surface with energy transfer. Model Meas Cont B 86(3). https://doi.org/10.18280/mmc_ b.860310

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13. Jawad M, Saeed A, Gul T, Ghah Z, Kumam P (2021) Unsteady thermal Maxwell power law nanofluid flow subject to forced thermal marangoni convection. Sci Rep 11 14. Sanni KM, Asghar S, Rashid S, Chu YM (2021) Nonlinear radiative treatment of hydromagnetic non-Newtonian fluid flow induced by a non–linear convective boundary driven curved sheets with dissipations and chemical reaction effects. Front Phys 15. Khashi NS, Arifin NM, Pop I, Nazae R, Hafidzuddin EH, Wahi N (2020) Flow and heat transfer past a permeabl power law deformable plate with orthogonal shear in a hybrid nanofluid. Alexandria Eng J 59(3):1869–1879 16. Nayak MK (2016) Steady MHD flow and heat transfer on a stretched vertical permeable surface in presence of heat generation/absorption, thermal radiation and chemical reaction. AMSE J Ser 85:91–104

A Simulation of Nanofluid Flow with Variable Viscosity and Thermal Conductivity Over a Vertical Stretching Surface Debasish Dey, Rajesh Kumar Das, and Rupjyoti Borah

Abstract An effort has been put numerically to explore the nanofluid flow in porous medium past a vertical elongating surface with thermal and mass transportations by considering the non-homogeneous flow factors. In methodology, adopting some pertinent similarity conversions, the nonlinear governing PDE’s are converted into its corresponding ODE’s, and this system of ODE’s are solved numerically utilizing MATLAB built in bvp4c solver scheme. The results are displayed through pictorial mode in terms of velocity, thermal, and mass fractions of the nanofluid. This fluid model has imperative applications in the modern times due to the presence of nonhomogeneous flow factors and nanoparticles. Due to the presence of nanoparticles, the Brownian motion and thermophoresis parameter have played a great rule during the flow. Keywords Nanofluid · Variable viscosity · Variable thermal conductivity · MHD · Heat transfer · Mass transfer

Nomenclature a, b, c B0 C Cp DB DT M Nb

Constant Strength of magnetic field Concentration of the fluid Specific heat at constant pressure Brownian diffusion parameter Thermophoretic diffusion parameter Magnetic parameter Brownian motion parameter

D. Dey · R. K. Das (B) · R. Borah Department of Mathematics, Dibrugarh University, Dibrugarh, Assam 786004, India e-mail: [email protected] D. Dey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_18

189

190

Nt Pr qr T C g θr θk u v

D. Dey et al.

Thermophoresis parameter Prandtl number Heat flux Temperature of the fluid Concentration of the fluid Acceleration due to gravity Viscosity parameter Thermal conductivity parameter Velocity along x− direction Velocity along y− direction

Greek Symbols βT βC μ ν σ κ1 ρ λ

Coefficient of thermal expansion Volumetric coefficient of expansion Dynamic viscosity Kinematic viscosity Electric conductivity Mean absorption Density of fluid Thermal conductivity

Subscripts w ∞

At wall At ambient situation

1 Introduction The nanofluid is one of the most recent technique for enhancing thermal transmission rate. The nanofluid is the colloidal form of nanoparticles and base fluid. Choi [1] was the first author who have developed the term ‘nanofluid’ in 1995 experimentally. The main advantage of this kind of fluid is that the thermal conductivity of the nanofluids is comparatively larger than the traditional base fluids. This enhance of thermal conductivity is predicted to be reason of Brownian motion, inter-facial layer, and volume fraction of the nanoparticles. The study of nanofluids flow caused due to

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different geometries has multifarious applications in different fields like engineering sciences, medical sciences, electronics, heat transfer devices, etc. Also, heating and cooling capacity of nanofluid is one of the major processes used in industries such as chemical, refrigeration, air-conditioning, etc. The combined effects of heat and mass transfers of nanofluids have taken a great deal of interest into the recent research trends. Jana et al. [2] have investigated the enhancement of thermal transmission of fluid by adding single and hybrid nanoparticles. Gosh and Mukhopadhyay [3] have studied the flow behaviors of nanofluid by considering exponential form of retarding surface. In the recent time, many researchers Dey and Chutia [4], Bakar et al. [5], Anuar et al. [6], Alzahrani et al. [7], and Ali et al. [8] have investigated the enhancement of thermal conductivity and flow natures of nanofluids by taking different base fluids. They have also scrutinized the application of nanofluids in different purposes. The fluid flow around stretching/shrinking surfaces has industrial applications such as annealing and thickening of copper wire, blow molding, paper production, etc. Crane [9] was the foremost author who has analyzed the fluid flow nature by considering stretching surface. Again, the impact of magnetic field in the fluid flow environment is one of the major key requirement for industries. Because, we can control the motion of the fluid as well as thermal transmission by employing good amount of magnetic field. The study of nanofluids flow under the impact of magnetic field has covered important areas in the recent research. Many authors Adnan et al. [10], Khan et al. [11], Khashi’ie et al. [12], Lanjwani et al. [13], Dey and Borah [14], and Dey et al. [15] have investigated the stimulus of magnetic field in the nanofluid flow and their important physical significance by employing stretching/shrinking geometries. The viscosity and thermal conductivity of the fluid is important thermos-physical properties. These properties have non-ignoble characteristic in the fluid flow environment because the physical laws are completely depend on these properties. Many researchers have considered these quantities as a constant. But, we have achieved great advantages by considering the thermal conductivity and variable viscosity in the fluid flow problems. Lai and Kulacki [16] have studied the variable viscosity effects on the free heat transfer phenomena by considering vertical surface which is places at porous medium. Recently, the researchers Hazarika et al. [17], Dey et al. [15], and Manjunatha and Gireesha [18] have investigated the effects of variable viscosity and thermal conductivity of the different fluids by considering vertical and horizontal surfaces and given importance conclusions. Here, we have investigated the characteristics of nanofluid flow caused due to a vertical elongating sheet with non-homogeneous thermo-physical properties under the influence of a variable magnetic field. The formulation of this problem is made based on the physical laws of physics such as principles of conservation of mass, momentum, energy, and species. In the methodology section, a new dimensionless variables are adopted to modernize the leading equations of this study into a set

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of solvable equations along with the surface restrictions. The fourth-order threestage Lobatto IIIa formula is executed to solve this set of equations by developing MATLAB bvp4c code. Based on the aforementioned literature, we have believed that this investigation has lots of novelties and may help the other researchers and scientists.

2 Mathematical Formulation Here, we have assumed time-independent, two-dimensional, and incompressible boundary layer viscous nanofluid streaming above a vertical elongating sheet which is placed at porous medium. Also, a variable magnetic field of strength B(x) = −1 B0 (x) / 3 has been applied along the normal direction of the flow. Considering fixed origin, two identical and conflicting forces are supplied along x-axis such that 1 stretching velocity is u w (x) = c(x) / 3 where c is a positive constant. Assuming all fluid properties constant, except thermal conductivity and viscosity. Also, we have ignored the persuaded magnetic field in compare to the imposed magnetic field and viscous dissipation. The flow diagram of this study and its coordinate system is presented in Fig. 1. Utilizing the above considerations together with boundary layer and Boussinesq approximations, the supported equations of this present problem become as follows: ∂v ∂u + = 0, ∂x ∂y

Fig. 1 Schematic diagram of the flow model

(1)

A Simulation of Nanofluid Flow with Variable Viscosity …

u

u

1 ∂μ ∂u ∂u ∂u ∂ 2u σ B 2 (x)u +v =ν 2 + − ∂x ∂y ∂y ρ ∂y ∂y ρ + g{βT (T − T∞ ) + βC (C − C∞ )},

3 2 ∂ T ∂T 16σ ∗ T∞ ∂T 1 ∂λ ∂ T λ ∂2T + +v = + 2 ∂x ∂y ρC p ∂ y ∂ y ρC p ∂ y 3ρC p κ1 ∂ y 2     ∂C ∂ T DT ∂ T 2 + τ DB + , ∂y ∂y T∞ ∂ y   ∂ 2C ∂C DT ∂ 2 T ∂C u +v = DB 2 + . ∂x ∂y ∂y T∞ ∂ y 2

193

(2)

(3)

(4)

The boundary conditions are as follows: At, y = 0 : u = u w (x), v = vw , −λ

∂T = h f (x)(Tw − T ), C = Cw , ∂y

As, y → ∞ : u → 0, T → T∞ , C → C∞ .

(5)

Above physical variables, quantities are welldefined in nomenclature section. Utilizing Rosseland approximation model, the radiative heat flux qr is specified ∗ ∂T 4 as qr = − 4σ . Assume the term T 4 is stated as linear combination of T . So, 3κ1 ∂ y 4 expanding T using Taylor’s series expansion and avoiding the superior order terms, 3 4 T − 3T∞ . Where T 3 is considered as ambient temperature. we have T 4 ∼ = 4T∞ Using the following similarity transformations, the set of leading Eqs. (2) to (5) can be modernized into a set of solvable equations. We also presumed the temperature and concentration vary along x− axis such that Tw = T∞ + ax and Cw = C∞ + bx. 1 −1 −1/ η = yc / 2 x / 3 υ∞ 2

(6)

2 1 1 ψ = x / 3 c / 2 υ / 2 f (η)

(7)

∴u=

∂ψ ∂ψ and v = − . ∂y ∂x

Introducing non-dimensional variables for temperature and concentration as: θ (η) =

C − C∞ T − T∞ and φ(η) = Tw − T∞ C − C∞

(8)

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Following Hazarika et al. [16] and Lai et al. [15], the formal expression of variable viscosity and thermal conductivity are defined as μ=−

μ∞ θr θ − θr

(9)

λ=−

λ∞ θk θ − θk

(10)

Using these substitutions governing Eqs. (2) to (5) becomes: 2 θr 1 θr f  − f f  − f  θ  + f 2 + M f  − (λT θ + λC φ) = 0, (11) 2 θ − θr 3 3 (θ − θr )    θk Pr  θk θ 2 + 2 f θ  − 3 f θ θ  + Kr − 2 θ − θk 3 (θ − θk ) + Pr Nbθ  φ + Pr Ntθ 2 = 0, φ  +

Nt  2 θ + Le f φ  − Le f  φ  = 0. Nb 3

(12) (13)

The corresponding boundary conditions (5) transform to: η = 0 : f (η) = s, f  (η) = 1, θ  (η) = −Bi[1 − θ (η)], φ(η) = 1 η → ∞ : f  (η) = 0, θ (η) = 0, φ(η) = 0. where the non-dimensional parameters in (11–14) are: 3 σ B02 gβT (Tw − T∞ ) gβC (Cw − C∞ ) 16σ ∗ T∞ , λT = , λ = , Kr = , C ρC p c2 c2 3ρC p κ1 μC p τ D B ((Cw − C∞ )) τ DT ((Tw − T∞ )) ν∞ Pr = Nb = , Nt = , Le = λ∞ ν∞ T∞ ν∞ DB

M=

(14)

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3 Results and Discussion In this section, we have thoroughly discussed the numerical solution of the assumed problem. The set of resulting Eqs. (11)–(13) along with the surface restriction (14) is evaluated numerically by employing the MATLAB built in bvp4c solver scheme. Here, we have studied the effects of pertinent flow parameters such as M, Nb, Nt, θr &θk on the different flow patterns in terms of motion, thermal, and mass transmissions of the nanofluid. To visualize the numerical solutions obtained by using the MATLAB built in bvp4c solver scheme, Figs. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13 are plotted. Here by default, the flow parameter values are considered as the Pr = 0.71, M = 0.5, θk = 6, θr = 6, Nb = 0.3, Nt = 0.3, Le = 2, Kr = 0.05, Bi = 0.5, and S = 0.23 unless it is specified. Figures 2, 3, 4, and 5 illustrate the effect of different pertinent parameters on velocity profile. Figures 2 and 3 illustrate the impact of Nb and Nt on velocity distribution. It is observed that in both the cases with increasing value of Nb and Nt, the viscosity of nanofluids falls and hence velocity increases. Effect of θr on velocity profile is expressed by Fig. 4. Figure 5 shows that with the intensification of thermal conductivity parameter declines the velocity profile.

Fig. 2 Sketch of velocity patterns for Nb

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Fig. 3 Sketch of velocity patterns for Nt

Fig. 4 Sketch of velocity patterns for θr

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Fig. 5 Sketch of velocity patterns for θk

Fig. 6 Sketch of thermal fraction for Nb

Figures 6, 7, 8, and 9 depicts temperature profile for various pertinent parameters. Figure 6 indicates that with increment of Nb, the temperature of the nanofluids increases. Brownian motion is the arbitrary movement of nanoparticles as they are suspended in a fluid due to collision of them. Hence, the increasing kinetic energy of the molecules due to this effect helps in raising the temperature. The temperature

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Fig. 7 Sketch of thermal fraction for Nt

Fig. 8 Sketch of thermal fraction for θr

profile for several values of Nt is exhibit by Fig. 7. It is found that with increment of Nt, the temperature increases. This is because with increment of thermophoresis forces, the nanofluid particles start movement from warm section to cooler section. Figure 8 shows that there is a small drop in temperature with increasing of θr . With increment of θk , the temperature profile decreases which is given by Fig. 9.

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Fig. 9 Sketch of thermal fraction for θk

Fig. 10 Sketch of mass fraction for Nb

Figures 10, 11, 12, and 13 display the concentration profiles for various pertinent parameters. As rising of Brownian motion, parameter Nb causes rise of kinetic energy of the fluid particles and hence concentration falls (see Fig. 10). Figure 11 shows the impact of thermophoresis parameter on concentration profile. Which gives that with enhancement of Nt, the nanoparticles start moving from hotter region to cooler

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Fig. 11 Sketch of mass fraction for Nt

Fig. 12 Sketch of mass fraction for θr

region and so the boundary layer thickness rises. So, concentration profile increases. Figures 12 and 13 depict the effect of θr and θk on concentration distribution, and it is noticed that the mass fraction of the nanofluid reduces for developing amount of θr , but an opposite characteristics is observed for θk .

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Fig. 13 Sketch of mass fraction for θk

4 Conclusion From our study we can conclude as follows: (1) (2) (3) (4) (5)

The velocity decreases while temperature and concentration enhance with increasing of magnetic field parameter. Mutually, velocity and temperature rise with rising of Brownian motion parameter while concentration falls. In all three distributions, viz velocity, temperature, and concentration increases in increasing of thermophoresis parameter. Both temperature and concentration decreases with enhancement of viscosity parameter. Both velocity and temperature decreases with increment of thermal conductivity parameter while concentration decreases.

References 1. Choi SUS (1995) Enhancing thermal conductivity of uids with nanoparticles. In: Proceedings of the 1995 ASME international mechanical engineering Congress & exposition, San Francisco, USA, ASME FED 231/MD, pp 99–105 2. Jana S, Salehi-Khojin A, Zhong WH (2007) Enhancement of fluid thermal conductivity by the addition of single and hybrid nano-additives. Thermochim Acta 462:45–55 3. Ghosh S, Mukhopadhyay S (2018) Flow and heat transfer of nanofluid over an exponentially shrinking porous sheet with heat and mass fluxes. Propuls Power Res 7(3):268–275

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4. Dey D, Chutia B (2020) Dusty nanofluid flow with bioconvection past a vertical stretching surface. J King Saud Univ - Eng Sci 5. Bakar NAA, Bachok N, Arifin NM (2018) Stability analysis on the flow and heat transfer of nanofluid past a stretching/shrinking cylinder with suction effect. Results Phys 9:1335–1344 6. Anuar NS, Bachok N, Arifin NM, Rosali H (2019) Effect of suction/injection on stagnation point flow of hybrid nanofluid over an exponentially shrinking sheet with stability analysis. CFD Lett 11(12):21–33 7. Alzahrani AK, Ullah MZ, Alshomrani AS, Gul T (2021) Hybrid nanofluid flow in a DarcyForchheimer permeable medium over a flat plate due to solar radiation. Case Stud Therm Eng 26:100955 8. Ali A, Akhtar J, Anjum HJ, Awais M, Shah Z, Kumam P (2021) 3D nanofluid flow over exponentially expanding surface of Oldroyd-B fluid. Ain Shams Eng J 9. Crane LJ (1970) Flow past a stretching sheet. J App Math Phys (ZAMP) 21:645–647 10. Adnan NSM, Arifin NM, Bachok N, Ali FM (2019) Stability analysis of MHD flow and heat transfer passing a permeable exponentially shrinking sheet with partial slip and thermal radiation. CFD Lett 11(12):34–42 11. Khan WA, Makinde OD, Khan ZH (2016) Non-aligned MHD stagnation point flow of variable viscosity nanofluids past a stretching sheet with radiative heat. Int J Heat Mass Transf 96:525– 534 12. Khashi’ie NS, Arifin NM, Pop I, Wahid NS (2020) Flow and heat transfer of hybrid nanofluid over a permeable shrinking cylinder with Joule heating: a comparative analysis. Alexandria Eng J 59(3):1787–1798 13. Lanjwani HB, Chandio MS, Anwar MI (2021) Dual solutions of time-dependent magnetohydrodynamic stagnation point boundary layer micropolar nanofluid flow over shrinking/stretching surface. Appl Math Mech-Engl Ed 42:1013–1028 14. Dey D, Borah R (2021) Stability analysis on dual solutions of second-grade fluid flow with heat and mass transfers over a stretching sheet. Int J Thermofluid Sci Technol 8(2) 15. Dey D, Borah R, Mahanta B (2021) Boundary layer flow and its dual solutions over a stretching cylinder: stability analysis. In: Hassanien AE, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Advances in intelligent systems and computing, vol 1286, pp 27–38. Springer, Singapore 16. Lai F, Kulacki F (1990) The effect of variable viscosity on convective heat transfer along a vertical surface in a saturated porous medium. Int J Heat Mass Transf 33(5):1028–1031 17. Hazarika GC, Borah J, Konch J (2019) Effects of variable viscosity and thermal conductivity on free convective MHD fluid flow over a stretching sheet. Math Forum 27:2015–2019 18. Manjunatha S, Gireesha BJ (2015) Effects of variable viscosity and thermal conductivity on MHD flow and heat transfer of a dusty fluid. Eng Phys Math. https://doi.org/10.1016/j.asej. 2015.01.006

Soret and Dufour Effects on MHD Micropolar Fluid Flow with Heat and Mass Transfer Past a Horizontal Plate in Porous Medium Krishnandan Verma

Abstract In the present study, efforts have been made to investigate numerically the impact of Soret effect along with its reverse effect, i.e. Dufour effect on steady, MHD micropolar fluid flow over a horizontal plate which is semi-infinite in extent with the transmission of heat along with mass in a medium taken to be porous. The effect of chemical reaction and heat source is further considered. Appropriate dimensionless transformations are used to reduce the equations governing the problem to nondimensional form. The numerical solution of the problem is obtained in graphs for velocity, angular velocity, temperature and concentration distributions for important parameters affecting the problem using bvp4c, which is inbuilt solver in MATLAB. Coefficient of skin friction, surface couple stress, Nusselt number as well as Sherwood number are determined. The validity of our solution is established when the current outcomes are compared with some other published work. Numerical findings show that Soret and Dufour number boost the heat transmission rate but slow the mass transfer rate. Keywords Dufour effect · Micropolar fluid · Porous medium · Soret effect · bvp4c

1 Introduction The problems on the transport of heat and mass on micropolar fluids have several engineering and industrial applications, viz. polymer sheet production, microemulsions, extrusion of plastic sheets, cooling of metal plates, coating of wires as well as fibres, etc. Pioneering work on micropolar fluid was done by Eringen [1–3], and his proposed model gave a new dimension for the study of fluids having non-symmetric stress tensor like blood, polymer fluids, etc. Gorla [4] investigated micropolar fluid flow in a steady state with thermal transfer past an erect plate which is semi-infinite in

K. Verma (B) Department of Mathematics, Dibrugarh, Assam 786004, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_19

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extent with mixed convection and uniform heat source. Chaudhury and Jha [5] investigated on unsteady flow of hydromagnetic micropolar fluid, the process of thermal and mass transport past a horizontal surface in porous medium. The analysis of micropolar fluids in porous medium on MHD flow with the transfer of heat along with mass is crucial for numerous applications, namely oil exploration, MHD generators, etc. Mohanty et al. [6] examined the flow, thermal and mass transport phenomenon of MHD micropolar fluid due an extending plane with the effect of viscous dissipation in a medium taken to be porous. Kiran Kumar et al. [7] examined the consequences of Dufour effect on thermal and solutal transport process of MHD micropolar fluid on a pervious plate which is infinite in extent. Hiremath and Patil [8] used perturbation method to study the influence of natural convection currents of micropolar fluid flow past an upright plate in porous environment. Rahman and Sultana [9] investigated numerically Darcy–Forchheimer model of porous media past a vertical pervious pate on micropolar fluid flow in a steady two-dimensional system. Their findings reveal that the Darcy parameter diminishes the skin-friction coefficient and fluid velocity besides enhancing the fluid temperature. Aurangziab et al. [10] investigated the effect of thermal diffusion and diffusion-thermo effect, Hall effect and heat source on hydromagnetic micropolar fluid flow in porous medium using Keller Box method. Mishra et al. [11] examined the consequences of radiation and heat source on hydromagnetic micropolar fluid flow with thermal exchange past an impervious plate in a medium taken to be porous. Yashmin et al. [12] examined numerically the transport of heat along with mass past a curved extending surface on MHD micropolar fluid where the flow is motivated by stretched curved surface. They noticed hike in fluid temperature and concentration with the increase of radius of curvature but micro-rotations as well as fluid velocity diminishes. Srinivasacharya and Shiferaw [13] used HAM to investigate the impact of Soret and Dufour effects on a steady micropolar fluid flow through two plates placed parallel to each other with cross diffusion effects. Verma et al. [14] examined the consequences of heat source and chemical effect on steady hydromagnetic micropolar nanofluid fluid flow past a shrinking surface. Some other important works on micropolar fluid with the transmission of heat together with mass can be looked in the literature [15–19]. Motivated by the above works, we aim to investigate the Soret effect along with its reverse effect, i.e. Dufour effect on MHD micropolar fluid flow over a horizontal plate which is semi-infinite in extent in porous medium with heat and mass transfer. A first-order chemical reaction is also considered in the problem. The results are obtained numerically using MATLAB solver bvp4c.

2 Mathematical Construction of the Problem Consider MHD steady flow of micropolar fluid past a horizontal plate which is semiinfinite in extent in a medium taken to be porous in a two-dimensional system. The flow is confined in the region y > 0. The plate is placed along the x-axis, while the y-axis is perpendicular to the plate. A uniform weak magnetic field whose strength

Soret and Dufour Effects on MHD Micropolar …

205

Fig. 1 Geometric model

is B0 is applied in perpendicular direction. The induced magnetic field is ignored because of small magnetic Reynolds number. Let the temperature and concentration of the fluid near the plate be Tw and Cw , respectively, and the temperature and concentration far away be T∞ and C∞ . The flow diagram is shown in Fig. 1. The mathematical equations in accordance with our assumptions are ∂v ∂u + = 0, ∂x ∂y u

∂N ∂u ∂ 2u σ B02 ϕ ∂u +v = υ 2 + k1 + (U0 − u) ∂x ∂y ∂y ∂y ρ   υϕ + ∗ (U0 − u) + Aϕ U02 − u 2 , Kp G1

u

∂2 N ∂u − 2N − = 0, 2 ∂y ∂y

(1)

(2)

(3)

∂T 1 ∂qr Q ∂T k ∂2T D K T ∂ 2C − + +v = + (T − T∞ ), 2 ∂x ∂y ρc p ∂ y ρc p ∂ y cs c p ∂ y 2 ρc p

(4)

∂C D KT ∂2T ∂C ∂ 2C − K 1∗ (C − C∞ ). +v =D 2 + ∂x ∂y ∂y Tm ∂ y 2

(5)

u

, T , k, ρ, cs , Q, D, K T , c p and K 1∗ represents where u, v, N , σ, k1 = ρκ, υ = (μ+κ) ρ x-direction velocity, y-direction velocity, micro-rotations, electrical conductivity, vortex viscosity, apparent kinematic viscosity, fluid temperature, thermal conductivity, density, concentration susceptibility, heat source, mass diffusion rate, thermal diffusion rate, specific heat at constant pressure and reaction rate, respectively.

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The restrictions imposed on the boundary are u = 0, v = 0, N = 0, T = Tw , C = Cw at y = 0,

(6)

u = U0 , N = 0, T = T∞ , C = C∞ at y → ∞.

(7)

The heat flux resulting from radiation in accordance with Rosseland approximation is qr = −

4σ ∗ ∂ T 4 , 3k ∗ ∂ y

(8)

3 4 T − 3T∞ . where T 4 ≈ 4T∞ The given non-dimensional transformations are implemented

 1

ψ(x, y) = (2υU0 x) 2 f (η), N =  η=

U0 2υx

 21

y, θ =

U0 2υx

 21 U0 g(η),

T − T∞ C − C∞ ,φ = . Tw − T∞ Cw − C∞

(9)

The dimensionless form of Eqs. (1)–(5) using Eq. (9) is f





  1  + f f + g + F 1 − f + M + 1 − f  = 0, Kp   Gg  − 2 2g + f  = 0, 





2



(10) (11)

(3R + 4)θ  + 3RP r δθ + 3RP r f θ  + 3RP r D f φ  = 0,

(12)

φ  + Sc Sr θ  + 2Sc kc φ + Sc f φ  = 0,

(13)

where μc p D K T (Tw − T∞ ) k1 G 1 U0 k∗k , Sr = , = ,G = ,R = , ∗ 3 k υ υx 4σ T∞ υTm (Cw − C∞ ) D K T (Cw − C∞ ) 2ϕυx , F = 2ϕ Ax Kp = ∗ , Df = K p U0 υcs c p (Tw − T∞ ) Pr =

M=

K ∗x 2Qx σ B02 ϕx and kc = 1 ,δ = ρ ρc p U0 U0

(14)

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207

represents Prandtl number, coupling constant, micro-rotation parameter, radiation parameter, Soret number, permeability parameter, Dufour number, inertia coefficient parameter, magnetic parameter, heat source parameter and chemical parameter. The modified form of Eqs. (6) and (7) is f = 0, f  = 0, g = 0, θ = 1, φ = 1 at η = 0,

(15)

f  → 1, g → 0, θ → 0, φ → 0 at η → ∞.

(16)

Local skin friction,   2U0 x  ∂u C f = ρU 2 , τw = (μ + κ) = + κN f (0), 0 ∂y υ y=0 τw 2

Local wall couple stress,  Mwx = G 1

∂N ∂y

 =

U0 x  g (0), υ

Nusselt number,   υ  χqw ∂T θ (0), qw = −k =− Nux = , k(Tw − T∞ ) U0 x ∂ y y=0 Sherwood number,   υ  ∂C χ mw φ (0), m w = −D =− . Shx = D(Cw − C∞ ) U0 x ∂ y y=0

3 Results and Discussions The solution is obtained numerically in graphical form by solving Eq. (10)– (13) together with the imposed boundary conditions (15) and (16) using bvp4c, a MATLAB solver. It obtains numerical solution to two point boundary value problems. The algorithm of bvp4c is an iterated scheme and is quite handy in solving system of nonlinear equations. We set the following value of the param = 0.5, K p = 0.5, F = R = δ = 0.1, Sr = 0.3, in order to carry out the eters D f = 0.2, Pr = 0.7, Sc = 0.24, kc = 0.5 and G = 2 numerical computation.

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Table 1 Numerical data of f  (0), g  (0), −θ  (0) and − φ  (0) Sr

Df

kc

f  (0)

g  (0)

−θ  (0)

−φ  (0)

0.3

0.2

0.5

1.7745

−0.5755

0.2687

0.0354

0.6

1.7745

−0.5755

0.2688

0.0332

0.9

1.7745

−0.5755

0.2689

0.0310

1.2 0.3

0.05

0.5

0.3

0.3

1.7745

−0.5755

0.2691

0.0289

1.7745

−0.5755

0.2671

0.0355

1.7745

−0.5755

0.2697

0.0353

0.7

1.7745

−0.5755

0.2738

0.0350

1

1.7745

−0.5755

0.2770

0.0348

1.7745

−0.5755

0.2661

0.3049

0.2

0.1 0.2

1.7745

−0.5755

0.2666

0.2472

0.4

1.7745

−0.5755

0.2679

0.1139

0.5

1.7745

−0.5755

0.2687

0.0354

Numerical data are computed in Table 1, for f  (0), g  (0), −θ  (0) and −φ  (0). The validity of our present solution is confirmed by comparing the present result of f  (0), g  (0) and −θ  (0) with Mishra et al. [12]. The variations caused due to Soret number on concentration distribution and Dufour number on temperature distribution are illustrated in Figs. 2 and 3. It can be observed that the rise of Soret number enhances the concentration of the fluid. Figure 3 illustrates that increase in Dufour number also augments the temperature of the fluid. The change in Dufour number does not show any variation on fluid temperature till η = 2.5 but in between 2.5 < η < 8, the fluid temperature increases. Since η is a length taken normal to the plate, so it can be said that the impact of Dufour Fig. 2 Change in φ(η) caused by Sr

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Fig. 3 Change in θ (η) caused by D f

number on fluid temperature near the immediate surface is negligible but as we move apart normal to the surface, the temperature enhances (Table 2). Magnetic parameter’s influence on velocity and angular velocity distribution in the presence ( = 1) and absence ( = 0) of coupling parameter has been analysed in Figs. 4 and 5. It is distinctly seen that magnetic parameter enhances the velocity as well as angular velocity in the absence as well as the presence of coupling constant. Although the growth of magnetic parameter enhances the velocity distribution, the thickness of the velocity boundary layer decreases. It can be seen that the velocity distribution shows no change after η > 2. The velocity is affected by Lorentz force, which is an opposing force, arising due to magnetic field. Figure 6 shows that species Table 2 Numerical data of the current result and previous result for f  (0), g  (0) and − θ  (0) when R = 0.1, G = 2, Pr = 0.7, Sc = 0.24, Sr = D f = kc = δ = = 0 M

Kp

F

f  (0)

g  (0)

−θ  (0) − θ  (0)

Present result 0

0.5

0.1

1.5208

−0.5348

0.2716

1

0.5

0.1

1.8191

−0.5752

0.2728

1

0.6

0.1

1.7254

−0.5633

0.2734

1

0.5

0.2

1.8554

−0.5804

0.2730

Mishra et al. [12] 0

0.5

0.1

1.520775

−0.53477

0.271877

1

0.5

0.1

1.819147

−0.57523

0.273121

1

0.6

0.1

1.725367

−0.56327

0.272758

1

0.5

0.2

1.855385

−0.58043

0.27328

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Fig. 4 Impact of M on f  (η)

Fig. 5 Impact of M on g(η)

concentration of the fluid is enhanced quite drastically with the increase in chemical reaction parameter. In Figs. 7 and 8, the influence of permeability parameter on fluid as well as angular velocity is illustrated. Permeability parameter in the study varies indirectly to permeability of the porous medium, and since permeability gives the measure of porous media opposition against the flow, so for porous media flows, analysis of permeability parameter on fluid velocity is very crucial to acquire deeper insight into the flow process. The graph shows that rise in permeability parameter decreases the velocity as well as the angular velocity profiles. The influence of heat source on fluid

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Fig. 6 Change in φ(η) caused by kc

Fig. 7 Change in f  (η) caused by K p

temperature has been analysed considering M as well as neglecting M. In Fig. 9, it is observed that the influence of heat source (both by considering as well as neglecting M) is such that the fluid temperature enhances quite drastically. The positive values of δ imply heat generation which plays a key role in raising the fluid temperature, and the result is evident in Fig. 9.

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Fig. 8 Change in g(η) caused by K p

Fig. 9 Change in θ (η) caused by δ

4 Conclusion The key results are highlighted below. (i)

(ii)

Fluid velocity is enhanced with the rise of M, but it declines with the increase of K p . Rise of M boosts the angular velocity profile, while K p contributes to the decline of angular velocity. Fluid temperature rises with the increase of D f and δ. Fluid concentration increases for larger values of Sr and kc .

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(iii)

213

Skin-friction coefficient and wall couple stress remain invariant and kc , Sr, D f and kc . But on the other hand, for Sr, D f Sr, D f and kc , Sr, D f and kc contribute to the enhancement of heat transport rate but decrease the mass transport rate.

References 1. 2. 3. 4. 5. 6.

7.

8. 9. 10.

11.

12. 13. 14.

15.

16. 17. 18. 19.

Eringen AC (1964) Simple microfluids. Int J Eng Sci 2:205–217 Eringen AC (1966) Theory of micropolar fluids. J Math Mech 16:1–18 Eringen AC (1972) Theory of termomicrofluids. J Math Anal Appl 38:480–496 Gorla RSR (1992) Mixed convection in a micropolar fluid from a vertical surface with uniform heat flux. Int J Eng Sci 30:349–358 Choudary RC, Jha AK (2008) Effects of chemical reaction on MHD micropolarfluid flow past a vertical plate in slip-flow regime. Appl Math Mech Engl Ed 29:1179–1194 Mohanty B, Mishra SR, Pattanayak HB (2015) Numerical investigation of heat and mass transfer effect of micropolar fluid over a stretching sheet through porous media. Alex Eng J 54:223–232 Kiran Kumar RVMSS, Raju VCC, Prasad PD, Varma SVK (2016) Heat and mass transfer in MHD micropolar fluid in presence of diffusion thermo and chemical reaction. Appl Math 11(2):704–721 Hireman PS, Patil PM (1993) Free convection effects on the oscillatory flow of a couple stress fluid through a porous medium. Acta Mech 98:143–158 Rahman MM, Sultana Y (2008) Radiative heat transfer flow of micropolar fluid with variable heat flux in a porous medium. Nonlinear Anal Model Control 13:71–87 Aurangzaib A, Kasim ARM, Mohammad NF, Sharidan S (2013) Unsteady MHD mixed convection flow with heat and mass transfer over a vertical plate in a micropolar fluid-saturated porous medium. J Appl Sci Eng 16:141–150 Mishra SR, Hoque MM, Mohanty B, Anika NN (2019) Heat transfer effect on MHD flow of a micropolar fluid through porous medium with uniform heat source and radiation. Nonlinear Eng 8:65–73 Yashmin A, Ali K, Ashraf M (2020) Study of heat and mass transfer in MHD flow of micropolar fluid over a curved stretching sheet. Sci Rep 10, Article no. 4581 Srinivasacharya D, Shiferaw M (2014) Flow of micropolar fluid between parallel plates with Soret and Dufour effects. Arab J Sci Eng 39:5085–5093 Verma K, Borgohain D, Sharma BR (2021) Analysis of chemical reaction on MHD micropolar fluid flow near stagnation point with nanoparticles and external heat. Int J Heat and Tech 39(1):262–268 El-Hakiem MA, Mohammadein AA, El-Kabeir SMM, Gorla RSR (1999) Joule heating effects on magnetohydrodynamic free convection flow of a micropolar fluid. Int Comm Heat Mass Tran 26(2):219–227 Kim YJ (2004) Heat and mass transfer in MHD micropolar flow over a vertical moving plate in a porous medium. Trans Porous Media 56:17–37 Si X, Zheng L, Lin P, Zhang X, Zhang Y (2013) Flow and heat transfer of a micropolar fluid in a porous channel with expanding or contracting walls. Int J Heat Mass Transf 67:885–895 Fakour M, Vahabzadeh A, Ganjib DD, Hatami M (2015) Analytical study of micropolar fluid flow and heat transfer in a channel with permeable walls. J Mol Liq 204:198–204 Bakier AY (2011) Natural convection heat and mass transfer in a micropolar fluid-saturated non-Darcy porous regime with radiation and thermophoresis effects. Therm Sci 15:317–326

Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus Jayashree S. Shetty, Nisha P. Shetty, Vedant Rishi Das, Vaibhav, and Diana Olivia Abstract Gestational diabetes mellitus (GDM) is a condition often seen during pregnancies in which a hormone made by the placenta prevents the body from using insulin effectively. Women with GDM are at an increased risk of complications during pregnancy and during delivery. The offspring and the mother are also at an increased risk of getting diabetes in the future. Therefore, careful screening is necessary to avoid further complications. The objective of this research is to facilitate proper prediction of the presence of GDM in women so that timely intervention can help prevent future adversities. Multiple machine learning algorithms with data analysis methods are employed to investigate the probability of GDM and reach an optimal solution. The methodology makes use of the latent factor model and stochastic gradient descent to account for the missing data. Information entropy is used to calculate the amount of information each variable presents. The final classification is done and compared using three methods. These include ensemble method, hidden Markov model, and association analysis. Experiments reveal that the ensemble method involving decision trees, k-nearest neighbors, and logistic regression with weighted averaging delivers promising performance. Test data accuracy of 80% was recorded on the ensemble method. Keywords GDM · Association rule mining · Ensemble method · HMM · Latent factor

J. S. Shetty · N. P. Shetty (B) · V. R. Das · Vaibhav · D. Olivia Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India e-mail: [email protected] J. S. Shetty e-mail: [email protected] D. Olivia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_20

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1 Introduction Gestational diabetes mellitus (GDM) is emerging as a global public health concern. It is a form of diabetes that is diagnosed in the second or third trimester of pregnancy [1]. It is defined as any degree of glucose intolerance with onset of first recognition during pregnancy. The definition applies whether insulin or only diet modification is used for treatment and whether the condition persists after pregnancy [2]. The prevalence of GDM in lower or upper middle-income countries has been observed to be 64% higher than in their high-income counterparts [3]. Women who have been diagnosed with GDM are at a higher risk of developing diabetes mellitus later in life. The offspring of mothers with diabetes mellitus have a higher risk of developing obesity than the offspring of fathers with diabetes mellitus. Studies also show that an offspring born after a mother developed diabetes mellitus had a higher BMI and a higher risk of developing diabetes mellitus than an offspring born before their mother developed diabetes mellitus [4]. Early detection and treatment are imperative in the treatment of GDM, which would help reduce the risk for the mother and the offspring. The ensuing work is on the detection of GDM using multiple machine learning algorithms and data analysis algorithms. Work has been previously done for the detection of GDM using different algorithms. This paper, however, proposes to use a different set of algorithms to find the performance of the models on the detection of GDM, which is of paramount importance. The given dataset is also treated with data analysis methods to fill in the missing datapoints. Algorithms such as ensemble methods with weighted average technique, hidden Markov model, and association analysis have been used. These are implemented after the missing data is filled in using the latent factor model and stochastic gradient descent.

2 Proposed Methodology Figure 1 displays the methodology applied to the UCI repository dataset. The details are as follows: 1. Gestational diabetes dataset from UCI repository is preprocessed, and missing values are computed using latent factor and stochastic gradient descent algorithm. 2. Calculation of information entropy is done. Ensemble method (weighted average involving a decision tree, k-nearest neighbor, and logistic regression), association rule mining, and hidden Markov model are employed, and their accuracies are compared.

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Fig. 1 Process for UCI dataset with ensemble method, HMM, and association mining

3 Experimental Section 3.1 Implementation Details Any negative data values resulting from the data latent factorization step have been replaced with zero. The configuration for latent factorization had settings of alpha = 0.0001, beta = 0.01, and 50 steps. The train-test split ratio is 75:25. The entropy of the dataset was calculated to be 0.67. The Gaussian hidden Markov model has been trained on the dataset with components as two, covariance type as spherical, and 1000 iterations. The ensemble learning model consists of decision trees, k-nearest neighbors classifier, and a logistic regression model. The decision tree was pruned to a max depth of two and max-leaf nodes as two. The KNN classifier used a k value of eight for the number of neighbors.

3.2 Latent Factor Model Latent Factor [5] models are a state-of-the-art methodology for model-based collaborative filtering. Here, we take a Rm * n (Rm * n = Pm * k FTn * k) data matrix

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comprising of m number of patients having n features. Each element value in the domain can be given as r a b = paT f b ; where pa is the patient’s affinity to a factor, and f b is the feature attribute vector. ⎛ min ⎝ p, f

∑

⎞ (rab − paT f b )2 + λ1 || pa ||2 + λ2 || f b ||2 ⎠

(1)

(a,b)

To solve the values of pa and f b stochastic gradient descent algorithm given below is employed. l1 and l2 as regularization constant to prevent over fitting. The primary aim of SGD is to select a real ra,b , then search the corresponding factor vector pa from the patient factor matrix P, f b from feature matrix Q, computes the predicted score paT f b and updates parameters according to the following two rules: pa ← pa + γ (m ab f b − λ1 pa )

(2)

f b ← f b + γ (m ab pa − λ2 f b )

(3)

where m ab = ra,b paT f b which shows the difference between predicted score and actual score with learning rate γ .

3.3 Association Rule Mining Association rule mining [6] is one of the descriptive mining techniques which aims to extract the hidden relationship between items in databases with humongous quantities of data. One of the very good examples of association is a person who buys a computer must a buy relevant software with it, i.e., Windows or Linux. Association rule finding occurs in two stages: 1. Finding the most frequent item sets whose occurrences exceed a certain preordained threshold. 2. Applying minimum confidence constraint to generate strong association rules for the found frequent item sets. Let I be the total set of items represented by I = i 1 , i 2 , i 3 . . .. A transaction T is a subset of total item set I which is purchased by a customer at one go. Every transaction has a unique ID known as transaction ID. An association rule can be given in the form X ⇒ Y where X, Y ⊆ I , and X ∩ Y = ∅. It implies if X occurs, then Y does too, with a certain probability determined by some support confidence value. Support implies how often the item appears in a database. Confidence determines how frequently the rule is used.

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3.4 Apriori Algorithm Agrawal and Srikant [7] proposed this algorithm which is widely used in many commercial products. It is an iterative approach, and there are two steps in each iteration. • The first step generates a set of candidate item sets by combining the frequent elements. • Then, in the second step, we count the occurrence of each candidate set in database and prune all disqualified candidates (i.e., all infrequent item sets). • Then, beget strong rules which satisfy minimum confidence and support threshold from the resulting frequent item set. Apriori algorithm uses the principle that all the proper subsets of a frequent item set is frequent.

3.5 Information Entropy Information entropy [8] states the measure of information produced in a message. H = −K

n ∑

pi log pi

(4)

i=1

where K is a positive constant and pi are a set of probabilities.

3.6 Ensemble Methods Ensemble methods [9] combine the learning and predictions of myriad base models, which includes the machine learning classifiers like logistic regression, decision tree, etc., to breed one optimal model. Some of the most common ensemble methods include bagging, voting, averaging, boosting, etc. The implementation in the paper uses weighted averaging on a combination of decision tree, KNN, and logistic regression.

3.7 Gaussian Hidden Markov The hidden Markov model (HMM) [10] is a statistical model that interprets the (nonobservable) process by analyzing the pattern of a sequence of observed symbols. Predicting the weather (sunny, rainy, or cloudy) based on past data, say that deciding

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if it will rain on dn depends on whether it had rained on dn − 1, dn − 2, and so on. Such predictions can easily be made using Markov model formulae as given below: For a sequence d1 , . . . , dn P(q1 , . . . , qn ) =

n ∏

P(qi |qi − 1)

(5)

i=1

However, if you are locked in a room, the only way to decide is based on if a person entering the room is carrying a wet umbrella or not. Thus, for a sequence of weather D = d1 , . . . , dn and umbrella sequence U = u 1 , . . . , u n , hidden Markov model (HMM) can be expressed as L(d1 , . . . , dn |u 1 , . . . , u n ) =

n ∏ i=1

P(xi |qi ) ·

n ∏

P(qi |qi − 1)

(6)

i=1

4 Result Analysis The training accuracy for HMM is 69.83%, and the test accuracy is 69.04%. We also tried predicting the outcome using association rules, but test accuracy was 39.88% only. This is mainly because this dataset does not have enough samples of one particular trend. The data sample size is too less. Association rules could have given better results if more data was available. An ensemble learning model with weighted average gave a train and test accuracy of 78.1% and 80%, respectively. The ensemble learning model consists of a decision tree, K-nearest neighbor classifier, and a logistic regression model. The decision tree was overfitting the data when default parameters were used. Therefore, it was pruned to a max depth of two and max-leaf nodes as two. This gave much better results. The decision tree alone gave a training accuracy of 71.83% and test accuracy of 73.21%. The KNN classifier classified on the eight nearest neighbors alone gave a training accuracy of 77.6% and test accuracy of 74.4%. The logistic regression model has a train and test accuracy of 76.3% and73.2%, respectively. The ensemble model performed the best. The closer the F1-score is to one, the better is it’s performance on the dataset. Observing the F1-score for all the models, it is observed that the model with association rules has the worst performance of all the models that have been tried on the dataset. It reports an F1-score of 0.4. The best performing model in this category is again the ensemble method reporting an F1-score of 0.66. Sensitivity, also called true positive rate, is ratio of number of true positives to total number consisting true positives and false negatives. Specificity, also called the true negative rate, is the ratio of true negatives to the total number consisting of true negatives and false positives. Sensitivity and specificity values of all models show a similar trend with the worst performance by association rule mining method with a sensitivity of 0.28 and a specificity of 0.6. The sensitivity of 0.28 indicates a terrible

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Fig. 2 Accuracy of the models

Fig. 3 F1-score of the models

performance of the model for detecting the true positives in the sample. The best performance was reported by the ensemble method with a sensitivity of 0.66 and a specificity of 0.85. It can be observed that classical methods like decision trees, KNN, and logistic regression perform decently in comparison with the ensemble method which is a combination of all. The HMM model did not perform well since HMM works best on sequences of data and lack of data resulted in a performance weaker than those by the classical machine learning methods. Association rule mining method also required the availability of more data for it’s training, in absence of which it has reported extremely low scores making it unsuitable for the given dataset (Figs. 2, 3, 4, 5; Table 1).

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Fig. 4 Sensitivity of the models

Fig. 5 Specificity of the models Table 1 Results Classifier HMM Association rules Decision tree K-nearest neighbor Logistic regression Ensemble method

Accuracy

F1-score

Sensitivity

Specificity

69.04 39.88 73.21 74.4 73.2 80

0.57 0.4 0.59 0.54 0.59 0.66

0.5 0.28 0.5405 0.6 0.5405 0.66

0.833 0.6 0.8413 0.8 0.8413 0.85

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5 Conclusion Gestational diabetes mellitus is a problem prevalent all around the world. Early diagnosis is critical in avoiding any complications for the mother and the offspring. With the access to hospitals increasing for the general population, the patient caseload is also increasing. Hence, an automated, fast diagnosis will be useful in the detection of GDM. Multiple models were trained on the dataset with the aim to aid early clinical diagnosis of GDM. Under-performance could be observed in association rule mining due to the scarcity of the amount of data that need to be trained for it. Observations show failure in the hidden Markov model as it is suited for sequential data. A good performance was reported by the ensemble method consisting of weighted averaging on decision trees, KNN, and logistic regression.

References 1. American Diabetes Association (2015) Classification and diagnosis of diabetes. Diabetes Care 38:S8–S16 2. Gestational diabetes mellitus (2003) Diabetes Care 26:s103–s105 3. Nguyen CL, Pham NM, Binns CW, Duong DV, Lee AH (2018) Prevalence of gestational diabetes mellitus in eastern and southeastern Asia: a systematic review and meta-analysis. J Diabetes Res 2018:6536974 4. Buchanan TA, Xiang AH, Page KA (2012) Gestational diabetes mellitus: risks and management during and after pregnancy. Nat Rev Endocrinol 8(11):639–649 5. Latent factor models and matrix factorizations (2010) In: Encyclopedia of machine learning. Springer US, Boston, MA, p 571 6. Toivonen H (2010) Association rule. In: Encyclopedia of machine learning. Springer US, Boston, MA, pp 48–49 7. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases. Morgan Kaufmann Publishers Inc., San Francisco, CA, pp 487–499 8. Núñez J, Cincotta P, Wachlin F (1996) Information entropy. In: Chaos in gravitational N-body systems. Springer, Netherlands, Dordrecht, pp 43–53 9. Dietterich TG (2000) Ensemble methods in machine learning. In: Multiple classifier systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 1–15 10. Awad M, Khanna R (2015) Hidden Markov model, pp 81–104

From the Perspective of Digital Transformation: Amazon’s Tryst with Competition Law Sunishi Tiwari

Abstract With the unprecedented shift to the online space, as a catalyst for digital transformation, the dynamic e-commerce industry finds itself presented with the best opportunity to grow and cater to a large demographic. Having taken interest in the industry, the CCI, apart from conducting studies and meetings along the subject lines, initiated an investigation into the actions of e-commerce retailers which include Amazon. This paper makes a comparative analysis of Amazon’s practices and its interaction with the antitrust laws in the USA and addresses the same in the Indian context, whether the amazon.in would be classified as an enterprise following unfair trade practices. Keywords Amazon · E-commerce · Competition · Abuse of dominance

1 Introduction E-commerce has been considered to be one of those industries that has created a dent in the typical brick-and-mortar system of retail trade and offline market, and it has led to positive effects like creating jobs, increasing the probability of consumerism by providing accessibility to those situated in remote areas by creating infrastructure and logistics arrangements [1], where the typical market stores did not have any approach, since such investment always comes with a heavier risk of minimal to non-existent profit. As one of the pioneers in the e-commerce industry, Amazon originally started out as an online marketplace to sell books in 1994 to become the world’s largest online retail Website, the third-largest streaming media company, and largest cloudcomputing provider [2]. Apart from this diverse portfolio of services they have to offer, the company has acquired various subsidiaries [3], most of which are well known, clear that Amazon would not settle for less, would adapt to any change demanded by the dynamic society, it wishes to thrive in. S. Tiwari (B) School of Law, University of Petroleum & Energy Studies, Dehradun, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_21

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The emergence of e-commerce has resulted in an increased competition in trading, specifically organized retailing, due to the growth in the usage of the Internet and its popularity, the substantial change in the mode of purchase, which will gain momentum in becoming much frequently used in the upcoming years with increase in Internet accessibility and the hectic lifestyles of consumers [4]. The year 2020 ended up witnessing a shift in the consumers basket, directed towards essential products and a significant change in shopping modality inclined to the usage of e-commerce platforms, which the companies and brands have taken notice of and have taken appropriate steps to redirect their focus to acquire digital media, as a method to tap the target audience [5].

2 Research Methodology The research adopted by the author would be doctrinal in nature. The material referred to would be from pre-existing sources. Apart from legal commentaries, the researcher would make use of the articles, journals, and reports available on the Internet. These extracts would help the author justify the dominance of the ecommerce giant, the techniques of predatory pricing employed, and other practices which may be anticompetitive, whether the legal framework can prevent the MNC from exploiting the benefit of economies of scale, while the same is instrumental for digital transformation.

3 Research Gap While the secondary material would assist in delineating the structure of the paper, the factors needed to establish dominance, possible predatory pricing; it is limited to the legal scenario of the e-commerce giant in the American subcontinent. Since the CCI had initiated an investigation into the actions of e-commerce companies which included Amazon, an analysis of the company in question would help a reader understand whether there exists an anticompetitive effect on the market, while the same promotes digital transformation. Therefore, the paper wishes to address the gap in the Indian literature, with regard to Amazon’s practices in Indian scenario.

4 A Background on Antitrust Regulation Antitrust laws are considered to be the “Magna Carta of free enterprise” [6]. These laws ensure that there exists legislative support to maintain a free economy, one which emphasizes on public wealth: where goods must withstand objective competition; the public shall act upon the impartial result of the market’s judgement, to allocate

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the nation’s resources and in conclusion pave the way of economic development and its subsequent direction [7]. In the United States of America, the antitrust regulation comprises the Sherman Act 1890, supplemented by the enactment of the Clayton Act in 1914 and the Robert Patman Act in 1936. It needs to be understood that the interpretation [8] is to prevent acts of monopolization and does not oppose the existence of monopolies as entities. It is with the enactment of the monopolies and Restrictive Trade Practices Act, 1969, that competition law in India originated, which focused primarily on curbing monopolies. During the 1990s economic reforms, this act was made obsolete, and it was the enactment of the Competition Act, 2002 that the Raghavan committee of 1999 brought about the required change in the Indian competition regulation scenario.

5 Amazon’s Practices from the Antitrust Perspective It may be understood that the online competition can influence retail markets, where a study of the pricing by multichannel retailers can increase the frequency and extent of uniform prices in the various locations [9]. The unprecedented growth of Amazon as a company has been marvelled at, along with the extensive criticism and various instances of being subject to scrutiny by the various regulatory authorities. One such instance would be the e-books case [10], where Amazon was accused of employing predatory pricing, but without any substantial entry barriers, there is no opportunity of sustaining a power of monopoly since significant evidence portrayed that Amazon engaged in loss leading, which cannot be considered to be predatory pricing [11], although the former may come off as anticompetitive. A determined practice of loss leading usually leads to the death of small firms, those who offer limited product lines, and such elimination of these firms does cause a negative impact on the few consumers they catered to, in case these firms provided distinctive services that would unlikely be replaced by the market [12]. But, in this instance, Amazon’s pricing strategy displayed a lack of a negative effect on the range of choices and quality e-books available to consumers, was saved from antitrust charges, unlike Apple and the other publishers who ended up gaining advantage in the downstream market, which is harmful to competition. Keeping in mind the extensive services offered by the conglomerate, Amazon’s relevant product market would be considered on a large scale: retail e-commerce, but the same cannot be said in cases where the antitrust issue is limited to a defined submarket [13]. Also, considering the recent changes, where the traditional retailing stores have been creating their online platforms, it may be observed that e-commerce retailers are opening brick-and-mortar stores, like Amazon [14]. Hence, the convergence of both forms: online and traditional, it is possible that there may no longer be a stark difference in delineating Amazon’s reach to be purely electronic retail commerce.

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As far as defining the relevant geographic market is concerned, even though the brick-and-mortar stores can be considered substitutes, and the courts will include the same to account for the locations the enterprise caters to [15]. But, thankfully, due to the separate operations in most countries, Amazon’s geographic would be limited to the respective service area. In the last decade, Amazon Inc. has acquired a dominant position in retail ecommerce owing the same to the implementation of two elements in its business strategy: a willingness to sustain losses along with aggressive investment of their profits and the integration of their multiple business lines [16]. Forgoing profits to keep up the promise of acquiring scale, it may be understood from the introduction of a loyalty program, i.e. Prime, the company ends up withholding its acquisition of the customers, thereby increasing their market share certainty. In spite of the losses, which may also be the reason why, Prime can be considered the crucial factor contributing to Amazon’s growth as an online retailer, and this factor may be the retailer’s single biggest driver of growth [17]. Even though Amazon portrays its staggering growth, by indicating double-digit increases in their annual net sales, they choose to show minimal profits, while investing the same aggressively instead [18]. One such practice that has been observed in the instance of Amazon’s low prices: negotiating for low prices from their suppliers cannot be considered illegal, since “a firm that has substantial power on the buy side of the market (i.e. monopsony power) is generally free to bargain aggressively when negotiating the price, it will pay for goods and services” [19]. Upon analysing this practice within the factors required to prove predatory pricing, it would be found that Amazon’s strategy would not be considered sufficient evidence as short-term losses with the intent to make long-term gains, irrational within the ambit of interpretation of predatory pricing. While any enterprise chooses to forgo their profit for growth, this undercuts a central premise of contemporary predatory pricing doctrine, and it would be irrational to consider the practice to be predation [20]. Another aspect that needs to be addressed is the exclusive distribution agreements, between e-commerce retail platforms and the sellers engaging their services. Apart from the elements [21] that need to be fulfilled in order to be anticompetitive, furthermore, when a particular product is limited to being available on a single online retail platform and the same cannot be found on any other online retail platform or traditional brick-and-mortar setups, the substitutability of that product would be determined vis-à-vis the other products which can fall within the category of the same relevant product market [22].

6 Focusing on Amazon.in and the Competition Act, 2002 With respect to the standard procedure, it is fair to assume that amazon.in would pursue the parent company’s set-up. Apart from this, the additional responsibilities

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such as: making necessary adjustments to cater to the Indian public, cooperating with the relevant authorities for the expansion of the e-commerce business in the Indian market. Where the relevant market has been discussed in the previous part, it may be understood that the relevant product market may depend on the particular product and its substitute availability, whereas the relevant geographic market would extend to include the area served by amazon.in, i.e. India. In fact, while determining the relevant market in e-commerce sector, the Commission, in its note submitted to OECD, mentioned that “in many cases dealing with e-commerce sector, CCI considered both online and offline market as two alternate sources of distribution of the same products and not two separate relevant product markets” [23]. Mentioned in the Annual Report 2019 [24], the operations in India are limited by the restriction on the ownership and control of Indian companies by foreign entities and that the responsibilities like marketing tools and logistics services are given to third-party vendors, while they hold a minority interest in these third-party sellers on the Amazon platform in India. But even with such limitations, the renewed FDI policy now allows 100% in the marketplace model of B2C e-commerce [25], i.e. Amazon, it may be observed that in the growing popularity of this mode of retail trading, the funding of such ecommerce companies stems from their primary sources or venture capital and private equity investors, like Amazon Incorp. which is the former, in case of the company being scrutinized by the regulatory authorities. In fact, most recently, the parent company has infused a heavy dose of capital into its Indian counterpart [26]. With this funding backing such companies, it is fair to believe that they are capable of withstanding losses to a certain extent, unlike their local independent competitors. There exists the problem of denial of access to the data of the customers [27] which becomes a major issue due to the vitality of the same for the sellers or business users who could use the same, rather these e-commerce sites analyse this information to launch and promote their in-house products. It is only when the exclusion of information from the market that may have a negative impact on the underlying product market, in a sufficiently direct and significant way, would such an exclusion be considered to be anticompetitive [28]. But the issue that persists with regard to this is that antitrust laws lack a well-developed approach to information power and its effect on the competition in the market [29]. Yet it was found that the CCI has stated that online distribution channels by these e-commerce platforms provide consumers the option to make a comparison of the features in a manner of balancing the pros and cons, apart from the prices and the convenience of delivery at their door step, and hence on the basis of these factors, the Commission held that such arrangements, specifically referring to the online retail mode, are unlikely to create any entry barrier since most products sold through their exclusive e-partners, end up facing competitive constraints [30]. Therefore, it cannot be alleged that amazon.in has a dominant market position and the same does not employ any unfair techniques for gaining a monopoly status.

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7 The Digital Transformation, Courtesy Amazon The CCI would be unable to hold Amazon responsible due to the model of functioning followed by the same, with the focus on long-term market share growth, unlike the required factors found in cases of predatory pricing. From the perspective of digital transformation, Amazon has been one of the most significant contributors and keeping in mind the transition to the same. The digital integration of local shops with e-commerce platforms ends up providing insightful data on consumer trends and choices, apart from which the companies grasp a better understanding of their product’s performance, and such proximity affords the same the opportunity to strategize or modify their products to cater to the needs of the consumer for better performance [31]. Certainly, the benefits afforded to buyers (from these platforms) include the comfort of shopping from home, relatively saving their time, cost and energy of commuting, apart from the range of options available to compare multiple products without expending much energy or resources. But where the decision in All India Online Vendors Association v. Flipkart India Private Limited and Others [32], was governed by the previous FDI policy, which did not permit 100% automatic investment in the market place model of e-commerce at the time, the change in this particular aspect may give the CCI a fair reason to investigate these e-commerce platforms. After all, the CCI has acknowledged that there exists a need to bring forth a complementary innovative regulatory architecture, along with a suitable code of conduct to be duly followed by digital platforms, and apart from these, the occasional requisite tweaks to the existing antitrust regulatory framework [33], but with changes to the draft National e-commerce regulation policy, India might be able to avoid ambiguities in the adjudication of such matters as well as uphold the objectives of all these laws involved. Even after the passage of many years, since the opening of the trading sector to FDI: general and specifically referring to the e-commerce sector, the Indian FDI policy remains vague and has grave implementation gaps [34]. The challenges in framing and amending competition law for a dynamic industry like e-commerce are technical and economical, from the potential of such platforms to gain dominance and the same gaining steady momentum due to many factors, for instance: network effects, complementary products providers having access to users of the platform and dependence of competitors, to name a few [35]. With respect to the company in question, Amazon has portrayed that the same has had an incredible influence on the other businesses, be it the ones they have interacted with or the ones that have been affected by its operations, directly or indirectly, but evidence of such influence has not resulted in the same acquiring market power [36]. Instead it has charged prices low that end up benefitting the consumers, but not the extent of limiting competition. Other competitors have the capacity to compete and charge prices along similar lines [38], and thereby, amazon’s practices may have contributed to improving consumer welfare in the market.

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8 Conclusion Keeping in mind the COVID-19 pandemic, the rapidly paced and extensive shift of business from the physical space to the digital space, as observed by CCI [37], would offer these e-commerce retailers a major boost causing a rise to their market shares, so far, the present scenario along with amazon’s practices does not prove to considerably anticompetitive, and thereby, an antitrust analysis is limited to the present and should not be influenced by the probabilities that may arise in the future [39]. While the e-commerce platform did approach the Karnataka High Court [40] to prevent the CCI from investigating, the Supreme Court [41] upheld the former’s decision to allow CCI to investigate. But upon analyzing the practices, it may be understood that until any negative impact is witnessed, Amazon’s practices would not be considered to be acting in violation of antitrust principles.

References 1. Bailay R (2019, August) Session–II: Mall Crawl: online retail shopping, CCI Workshop on Ecommerce changing competition landscape in India. Competition Commission of India. https:// www.cci.gov.in/sites/default/files/whats_newdocument/proceedings.pdf 2. Tiku N (2018, January 5) “How to curb Silicon Valley power—even with weak antitrust laws.” Wired. https://www.wired.com/story/how-to-curb-silicon-valley-powereven-with-weak-antitr ust-laws/ 3. Prior K (2016, May 23) A history of Amazon’s acquisitions. TECHCO. http://tech.co/historyamazon-acquisitions-2016-05 4. Saraswathy B (2019) The Flipkart-Walmart deal in India: a look into competition and other related issues. Antitrust Bull 64(1):136–147 5. Majumder S (2016) GST and E-commerce. Natl Law School India Rev 28(2):123–133 6. U.S.A. v. Topco Assocs., Inc., 405 U.S. 596, 610 (1972) 7. Times-Picayune Publishing Co. v. United States, 345 U.S. 594 (1953) 8. Sherman Act 15 U.S.C. §2 (1890) 9. Cavallo AF (2018, October) More Amazon effects: online competition and pricing behaviors, National Bureau of Economic Research. https://www.nber.org/papers/w25138 10. United States v. Apple Inc., 952 F. Supp. 2d 638 (2013) 11. Kirkwood JB (2014) Collusion to control powerful customer: Amazon, e-books, and antitrust policy. Univ Miami Law Rev 69(1):1–64 12. Ledgerwood SD, Heath WJ (2012) Rummaging through the bottom of Pandora’s Box: funding predatory pricing through contemporaneous recoupment. Virginia Law Bus Rev 6(3):511–566. https://doi.org/10.2139/ssrn.1906062 13. Myers A (2019) Amazon doesn’t have an antitrust problem: an antitrust analysis of amazon’s business practices. Houston J Int Law 41(2):387–412 14. Morris D (2017, March 26) Amazon is exploring more brick-and-mortar retail concepts. Fortune. http://fortune.com/2017/03/26/amazon-retail-concepts/ 15. Kagan J (2010, November) Bricks, Mortar, and Google: defining the relevant antitrust market for internet-based companies. New York Law School Law Rev 55(1):271–292 16. Khan LM (2017) Amazon’s antitrust paradox. Yale Law J 126(3):710–805 17. DiChristopher T (2015, September 11) Prime will grow Amazon revenue longer than you think: Analyst. CNBC.https://www.cnbc.com/2015/09/11/prime-will-grow-amazon-revenue-longerthan-you-think-analyst.html

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18. Supra note 16. 19. W. Penn Allegheny Health Sys., Inc. v. UPMC, 627 F.3d 85, 103 (3rd Cir. 2010) 20. Matsushita Elec. Indus. Co. v. Zenith Radio Corp., 475 U.S. 574, 604 (1986) (White, J., dissenting) 21. Tata Engineering and Locomotive Co. Ltd. (Telco) v. The Registrar of Restrictive Trade Agreement, AIR 1977 SC 973, 693 22. Sharma D (2015, May 27) India: competition law and e-commerce: a concern for the future. Mondaq. https://www.mondaq.com/india/antitrust-eu-competition-/400368/competition-lawand-e-commerce-a-concern-for-the-future 23. Org. for Econ. Cooperation & Dev, & Capobianco, A (2018, June) Implications of E-commerce for competition policy: a note by India 129th OECD competition committee meeting (No. JT03431274). Organisation for economic co-operation and development. https://one.oecd.org/ document/DAF/COMP/WD(2018)52/en/pdf 24. Amazon Inc. (2019) Annual report 2019. https://www.annualreports.com/HostedData/Annual ReportArchive/a/NASDAQ_AMZN_2019.pdf 25. India Brand Equity Foundation (2021, January) Indian E-commerce industry report. https:// www.ibef.org/industry/ecommerce-presentation 26. Nandy M, Bhalla T (9 July 2020) Amazon parent invests |2,310 cr in Indian unit, Mint https://www.livemint.com/companies/news/amazon-parent-invests-2-310-cr-in-ind ian-unit-11594261167551.html 27. Competition Commission of India (2020, January) Market study on E-commerce in India, key findings & observations. https://www.cci.gov.in/sites/default/files/whats_newdocument/ Market-study-on-e-Commerce-in-India.pdf 28. Streetmap.EU Limited v. Google Inc., [2016] EWHC 253 (Ch.), 96 29. Patterson MR (2017) Antitrust, consumer protection, and the new information platforms. Antitrust 31(3):97–106 30. Mohit Manglani v. Flipkart India Pvt. Ltd., 2015 SCC OnLine CCI 61 31. Indiaretailing Bureau (2020, October 10) India’s retail sector to witness next wave of growth in unorganised segment driven by technology. Indiaretailing.com. https://www.indiaretailing. com/2020/10/05/retail/indias-retail-sector-to-witness-next-wave-of-growth-in-unorganizedsegment-driven-by-technology/ 32. All India Online Vendors Association v. Flipkart India Private Limited and Others, Case No. 20/2018 (CCI) 33. Uberoi NK, Nanda A, Verma T (2020) India, E-commerce competition enforcement guide. Global Competition Review, USA 3(1) https://globalcompetitionreview.com/guide/e-com merce-competition-enforcement-guide/third-edition/article/india 34. Saraswathy B (2019) The Flipkart-Walmart Deal in India: a look into competition and other related issues. Antitrust Bull 64:136 35. Fox E (2013) Against goals, 81 FORDHAM L. REV. 2158, 2158 36. Supra note 16, at 412. 37. Flipkart India (P.) Ltd. v. ACIT (2018) 170 ITD 751 38. Patterson MR (2017) Antitrust, consumer protection, and the new information platforms, 31 Antitrust 97 39. Summers v. Earth Island Inst., 555 U.S. 488, 493 (2009S) 40. Amazon Seller Services Private Limited and Others v. Competition Commission of India (2021) 4 Kant LJ 504 41. Amazon Seller Services Private Limited & Others v Competition Commission of India SLP(c) No.11615/2021

Secured Quantum Key Distribution Encircling Profuse Attacks and Countermeasures Veerraju Gampala, Balajee Maram, and A. Suja Alphonse

Abstract To enhance security in today’s computerized environment, communication must go above the boundaries of protocols. On the one extreme, improvements in encryption technology have been achieved, while the integrity of traditional techniques has indeed been repeatedly violated on another. In an online shopping, financial activity, or exchanging message communication via networks, safe transmission or activity should be considered at all times. The traditional cryptography’s integrity is frequently reliant on computational limitations. The safety of the RSA system, one of most frequently used public-key encryption technique, is predicated on factoring’s claimed difficulty. As a result, traditional encryption is followed more by possibility of unanticipated developments that may be hacked or attacked in quantum codebreaking technology and techniques. In the proposed system of quantum key distribution, the Trojan horse attack and time-shift attack are discussed. Quantum key distribution (QKD) is often recognized as a way to provide encrypted systems or communication services that assure communication security and reliability. Algorithms based on the concepts of quantum key distribution have showed promise in the pursuit for a higher security approach. So, the proposed quantum key distribution (QKD), the best aspect of quantum cryptography, decides to make cryptography unconditionally secure in communication of the networks. This suggested system also addresses quantum key distribution protocols in the context of significant network loss, as well as different threats and countermeasures that are taken into concern. They produce a safe key by using traditional post-processing techniques

V. Gampala Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur 522502, Andhra Pradesh, India e-mail: [email protected] B. Maram (B) Chitkara University Institute of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh, India e-mail: [email protected] A. Suja Alphonse Karunya Institute of Technology and Sciences, Coimbatore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_22

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like error checking and safety amplification. By using protocols, these keys can be utilized for making transmission completely trustworthy. Keywords Encryption · Quantum cryptography · Quantum key distribution (QFD) · Distribution protocols · Countermeasures · Trojan horse attack · Time-shift attack

1 Introduction Cryptography is essential for network communication security since it enables only valid users to access the original data. It accomplishes these tasks using traditional symmetric key methods as well as some sophisticated asymmetric techniques. However, they are not completely reliable in terms of safety. With the discovery of quantum computing, it is now possible to break through traditional cryptographic systems with ease [1]. Every public-key encryption is based on the assumption that computing a safe key will take quite some time. However, considering the fast rate by which quantum algorithms operate, this will no longer be an issue. As a result, anything that will be communicated or has previously been transferred is subject to public exposure or eavesdropping without quantum safe encryption [2]. Cryptography is becoming increasingly important as the Internet grows in popularity. It should really be worried about safe communication whenever we undertake an Internet purchase with our credit cards or perform financial transactions utilizing Internet banking. Unfortunately, traditional cryptography’s safety is frequently reliant on theoretical limitations. The safety of the RSA system, the most frequently used common key method, is predicated on factoring’s supposed difficulty [3]. The delivering of data that is encrypted in any of the quantum systems, and even the tiniest effort to retrieve it would move freely or, to put it another way, trash it, and concentrates on actual concerns in QKD in this analysis. It points out that practical concerns in QKD have traditionally resulted in field breakthroughs [4, 5]. The necessity to combat the photon-number-splitting threat, for example, prompted the development of the decoy-state protocol, which permits effective extraction of safe key utilizing previously susceptible low coherently pulse-based QKD systems. Other example is the development of measuring device-independent (MDI) QKD to combat detector end attacks [6]. Quantum hacking has lately received a lot of technical and public concern. However, this is an intercept-resend attack, which is difficult to execute in real life. As a result, no effective demonstration of this assault has yet been made in tests. Furthermore, because this attack has previously been included in existing security proofs, it tends to illustrate the safety of QKD methods rather than their weakness [7]. The first actual example of effective hacking over a professional QKD system is shown here. It is remarkable that a well professional QKD system can be broken with just today’s technology. This research demonstrates the slick aspect of QKD and challenges us to reconsider the safety of actual QKD techniques and their real

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implementations. The time-shift assault that we outlined in is what we employ [8]. The time-shift attack is straightforward to execute though it should not need Eve to perform any measurements or information set-up. The notion of a Trojan-horse attack against QKD systems is not novel, nor is the threat it poses. Like most QKD systems, the Alice subsystem is also associated with susceptibility to this type of attack because it produces the physical phenomenon. If a QKD system is in use, including the BB84 protocol, Eve might get knowledge on the setting of the device, namely the polarizer or phase modulator (PM), required for encrypting the hidden bit, by transmitting a suitably prepared light pulse into Alice and analysing its back-reflections [9]. Installing a passive tracking device is a straightforward approach for Alice to identify a Trojan-horse attack in the environment. This is generally accomplished by the use of an appropriate detector or an array of detectors that monitors the received signals and sends an alert when pre-defined thresholds are exceeded [10]. Such countermeasure, meanwhile, cannot be easily implemented for the Bob subsystem because a passive measurement device would produce undesired absorption in the already-weak states of light flowing from the quantum channel, further lowering the secret key levels. Furthermore, it might be unable to deliver the desired amount of safety. Attacks that take advantage of the more serious flaws are typically technically impossible with today’s capabilities. The photon count splitting attack, for instance, necessitates the eavesdropper Eve doing a quantum non-demolition assessment of Alice’s photon count. The assault is still impractical, and better QKD methods have defeated it [11]. The approach remains impractical, and better QKD methods have defeated it. The time-shift attack focused on sensor efficacy mismatch and on the other hand is a better application approach. But, when tested on a customized instance of a commercialized QKD system, this approach only provided Eve with a little information-theoretical edge. Eve was able to enhance her randomized (brute-force) search across all potential keys by capturing limited details regarding the key in 4% of her efforts during the hack [12, 13]. In the proposed method of quantum key distribution, it uses Photonics units, countermeasures of a quantum key distribution system are used in the quantum variant of a Trojan-horse attack, and time-shift attack of quantum cryptography extracts data directly from the encoding devices. The usage of passively optical elements has indeed been proposed as a way to avoid attacks. These attacks have resulted in a data leaking concern. This enables us to quantify network security and link it to the optical element specifications. The approach is backed up by practical evaluation of reflection and propagation of the most important optical components for security inside the functioning range.

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2 Various Attacks of Quantum Key Distribution Technique 2.1 Trojan-Horse Attack of Quantum Key Distribution Trojan-horse attacks on quantum key distribution networks have been identified as a danger to safe key exchange between two parties (Alice and Bob). A number of direct and indirect optical devices integrated in Alice’s and Bob’s configuration are frequently employed to defend a QKD system from such threats. To assure the uncompromising safety of a QKD system, though, a safety study against the Trojanhorse attack is necessary. On a feasible fibre-based QKD phase-coding technology in the 400–1700 nm spectral region, offer a security evaluation over Trojan-horse threats. Trojan horse is also known as a light code injection in which the accent is on the devices being used, as opposed to earlier assaults that concentrate on the data obtained through photons. The plan is to transmit light signals towards Alice’s or Bob’s set-up that will be made visible and reach Eve’s detecting device. Eve gets the knowledge about Alice’s base by seeing the reflected signal. Eve can execute an unencrypted attack and discover the precise flow of quantum bits if she has this knowledge before Bob. This implies that Eve may acquire enough about the encryption key without been noticed by Alice or Bob. Figure 1 depicts the main concept of this sort of assault. Eve uses the quantum stream to examine Alice’s device using temporal, spectral, and space–time modes. An additional source is selected, manipulated, and the backdispersed signal is examined. Eve’s tracking technology can be based on the supplemental source’s specifications. Eve should also deal with the legitimately missing transmission. Protocol parameters To identify the authentic digital signals from Alice, Bob employs a set of diodes. Eve’s strong photon tends to load transmission nets in the photon diode, especially if they are scheduled to arrive beyond the detection. This causes a post phenomenon, in which traps degrade gradually, exposing charge carriers that might drive present pulses, in the forward gateways. Eve may prefer to attack with the lightest slots after pulsing is strongly dependent on the intensity. The successful chance of identifying Bob’s variation as it drops into the few photons determines the lower threshold. Lowering the rate of attacks reduces the number of photons that Eve

Fig. 1 Trojan-horse attacks on quantum key distribution networks

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receives, but it also means Eve only explores a portion of the slots, giving her just a partial understanding of the actual key. This may be strong sufficient to assure positive data leaks at the conclusion of the protocol, i.e. after Alice and Bob have extracted the cryptographic keys by predicting Eve’s knowledge and discarding it through safety amplification. The dark counting rate will be increased by all these following pulses, resulting in a greater amount of erroneous alarms in the photons. As a result, the quantum bit error rate (QBER) that Alice and Bob would experience at the end of the key transfer will be greater. Eve’s goal is to keep the QBER from crossing the “abort threshold,” which would result in her eavesdropping efforts failing. Furthermore, if the intensity surpasses a specific level, the photons may recognize a click with a strong possibility for that specific slot, as described. (i)

(ii)

(iii) (iv)

(v)

(vi) (vii) (viii)

The amount of specific quantum theory linked by Alice and Bob, and therefore accessible for parameterization and key retrieval, is determined by the plaintext block, p e q. The amount of quantum systems used for parameterization is determined by the equation Ne = M, where r is the amount of quantum systems employed for parameterization, and (0, 1 2) is the allowed error rate. Let us additionally define x: = qr to denote the number of quantum systems utilized for key creation for later reference. Input: Alice and Bob are assigned the current state AB, in which each made up of q quantum systems. Measuring system: Alice and Bob use the set-up to measure the q quantum systems. The digital measured values are recorded in separate strings, the basic keys. For Alice and Bob, they are (A, B) and (E, S), and accordingly, B, S are of length q and relate to the values in π, while A, E are of length n and relate to values that are not in π. Prediction of parameters: Alice transmits B to Bob, and the transcript is labelled KB. Bob makes a comparison between B and S. Bob assigns the flag Gie = Ϩ , and they reject if the proportion of faults exceeds Ϩ. If not, he puts F pe = ϕ X, and they continue. Correction of an error: Alice provides Bob the condition L = synd (A) with the thread SA. Bob calculates A = corr (E, S). Security amplification: They construct keys of range JA = T pe (A) and KB = T pe (^A) of length l. Output: The keys KA and KB, the seeds S, the text, and the flags are all part of the system’s result. It assumes that all entries are initialized to a pre-defined value in the event of an abort.

Fault correction: The protocol’s fault reduction section is divided into two parts. The exact fault correction method is specified by two functions, synd and corr, which are run by Alice and Bob, respectively. We do not make any assumptions about these functions and instead evaluate hash functions in the second section to see if they work. Alice first calculates a condition J = synd(A) and delivers it to Bob over the

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public network. Bob then calculates an approximation X = corr (E, J), excluding E from the equation. By evaluating hash values of their particular sequences A and aborting the process if they differ, Alice and Bob may ensure that the decryption operation performed with a strong possibility. Alice generates a hash of A of size t (in bits) and transmits it to Bob, who generates the matching value for A.

2.2 Time-Shift Attack of Quantum Key Distribution In a QKD system, the time-shift attack takes advantage of a detection effectiveness mismatch between both the two devices in the time domain. The detection effectiveness for the bits “0” and “1” is assumed to be equivalent in QKD security demonstrations. At period A, the detection effectiveness of the bit “0” is significantly greater than that of the bit “1,” but at time B, the reverse is true. If the detection efficiencies are consistent in time domain, then the detection efficiency mismatch be safely eliminated. Only when the detection efficiencies are consistent in time domain can the detection performance variance be safely eliminated. It points out that due to inherent dead-time, especially non-gated single-photon detection efficacy mismatches. The time-shift attack has a basic concept. Eve can change each signal’s start time to either A or B at irregular intervals, with probability of pX = 1 pY and pY = 1 pX, accordingly. Eve can carefully select pX to ensure that Bob’s detection occurrences of “0”s and “1”s are equivalent. Eve can “steal” knowledge without notifying Alice or Bob even though Bob’s obtained data will be skewed towards “0” or “1” regarding the time shift (X or Y ). For launching the time-shift attack, Eve connects Alice and Bob with either a lengthened arm, shifting the signal all over time X a negative shift, or just around time Y a positive shift. The coefficient of determination strategy is as follows: (1) (2)

(3)

Eve detects Alice’s quantum states and analyses each one in an independently selected basis; Eve introduces a new quantum state (falsified state) in a separate basis with a variable bit value based on her measured values. For comparison purposes, if she analyses in Z basis and receives bit “0” (labelled as Z0), she needs to organize bit “1” in X basis (X1); for comparison purposes, if she analyses in Z basis and receives bit “0” (labelled as Z0), she needs to organize bit “1” in X basis (X1); Eve transmits out her falsified state at various times depending on her measured data, so it appears to Bob’s single-photon detector (SPD) at either t0 according to her evaluation outcome “0” or t1 relating to her evaluation outcome “1”.

The demonstration’s result is surprising since Eve cannot make a quantum nondemolition (QND) measurement on the quantum amount or adjust for just any loss caused by the threat in the demonstration, despite Eve’s ability to have infinitely

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sophisticated knowledge in safety proofs. In another words, in safety proofs, Eve is far weaker than the external observer. It is interesting to observe how an existing approach for example the time-shift attack can outperform the QND attacker, which is far beyond technological advancements. In this strategy, Eve detects Alice’s qubits and does a full von Neumann evaluation on each one. She subsequently uses her measured values to make a new time-shifted signal. Eve may collect complete facts on the finalized key without adding any fault in the extreme scenario when there is total detector efficacy imbalance; that is, there is a temporal frame where the device for bit “0” is functional, while the device for bit “1” is fully dormant and inversely.

3 Countermeasures The following are preventive techniques for the attacks listed previously.

3.1 Hardware Compatibility Alice and Bob consider respective machines as two “black boxes” in this scenario. This is known as device-independent (DI). That really is, it is not required to completely define each of its components. DI-safety QKDs are based on the rejection of a Bell inequality, which verifies the existence of quantum correlations. Consequently, a loophole-free Bell test is currently unattainable owing to the detector frequency loophole which demands a detection efficiency of about 80% or greater. Indeed, DI-QKD is very unfeasible with today’s technology due to significant decoupling and communication loss, as well as the restricted considered an important part of single-photon devices. Therefore, Eve uses strong light pulses in a substantial series of attacks on QKD implementations, as mentioned above. The following are few of the alterations that may be applied to the machine: ● In the event of an assault, a genuine single-photon source is favoured over an affected light source. ● Rather than merely fixing the established QKD procedures, it is proposed that approaches are devised that are resistant to many types of hacking threats. ● Optical segregation is the primary technique of protection against these attacks. ● Photon must only enter the device at a specified frequency to prevent Trojan-horse attacks. The laser encrypting function should also be available for a limited time. ● Placing a monitoring detector at Bob’s entry that sends a relatively small percentage of all input signal towards this detector in a randomized manner. ● Allowing Eve to enter for a short period of time.

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3.2 Safety Enhancement Safety enhancement can be used to erase the compromised details that were released to Eve. This would restrict the range of the key that may be used for encryption even more, while also ensuring that the Eve is unaware of it. Because of considering the loophole in this safety proof, that are limiting the theoretical to actual errors, this would immediately help the effective application of QKD. Moreover, if a security flaw is discovered, an appropriate countermeasure is typically readily available. As a result, security against known threats may be guaranteed. However, hackers may be able to bypass the system in the future.

3.3 Protocol Encryption There are a variety of ways to generate the basic secret key required to validate commuters, such as using a third party approach or applying any of the chaos theories. However, it is important to keep in mind that only an “almost universal” set of hash methods may be utilized to provide safe authenticity.

4 Conclusion Nonetheless, before QKD can be widely adopted, it faces a number of important challenges, and thus, the key challenges are the approaches that are currently being taken to provide secured transmission against various networks. The QKD system was created with this objective in mind, to guarantee not only intellectual but also real encryption. It aimed at ensuring security for traditional QKD, which can function consistently at significant key rates using existing network, against different channel attacks like time stamp and Trojan-horse attack. It is really worth mentioning that the safety precautions established for this reason apply to the QKD transmitter entities Alice and Bob, which provide an identical design and are used for secured transmission of information through various quantum channels. As a result, it offers extra information on the hardware, calibration, secure countermeasures, and thread of the QKD technology. Initiatives to standardize QKD are presently progress, addressing application stability and countermeasures against channel threats. Consequently, it is hoped that in the future, potential solutions will be developed towards the objective of future application guidelines that are immediately required to support for robust security monitoring and analysis.

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References 1. Campagna M et al (2015, June) Quantum safe cryptography and security. In: European Telecommunications Standards Institute (ETSI), ISBN No. 97910-92620-03-0 2. Rivest RL, Shamir A, Adleman LM (1978) A method of obtaining digital signatures and public-key cryptosystems. Commun ACM 21:120–126 3. Shor PW (1994) Algorithms for quantum computation: discrete logarithms and factoring. In: Goldwasser S (ed) Proceedings of 35th annual symposium on foundations of computer science. IEEE Computer Society Press, pp 124–134 4. Huttner B, Imoto N, Gisin N, Mor T (1995) Quantum cryptography with coherent states. Phys Rev A 51:1863–1869 5. Hwang W-Y (2003) Quantum key distribution with high loss: toward global secure communication. Phys Rev Lett 91:057901 6. Wang X-B (2005) Beating photon-number-splitting attack in practical quantum cryptography. Phys Rev Lett 94:230503 7. Makarov V, Anisimov A, Skaar J (2006) Effects of detector efficiency mismatch on security of quantum cryptosystems. Phys Rev A 74(2):022313 8. Makarov V, Skaar J (2007) Faked states attack using detector efficiency mismatch on SARG04, phase-time, DPSK, and Ekert protocols. arXiv preprint quant-ph/0702262 9. Vinay SE, Kok P (2018) Extended analysis of the Trojan-horse attack in quantum key distribution. Phys Rev A 97(4):042335 10. Lucamarini M, Choi I, Ward MB, Dynes JF, Yuan ZL, Shields AJ (2015) Practical security bounds against the trojan-horse attack in quantum key distribution. Phys Rev X 5(3):031030 11. Lutkenhaus N (2000) Security against individual attacks for realistic quantum key distribution. Phys Rev A 61:052304 12. Scarani V, Acin A, Ribordy G, Gisin N (2004) Quantum cryptography protocols robust against photon number splitting attacks for weak laser pulse implementations. Phys Rev Lett 92:057901 13. Qi B, Fung C-HF, Lo H-K, Ma X (2007) Time-shift attack in practical quantum cryptosystems. Quant Inf Comp 7:73–82

Early Parkinson Disease Detection Using Audio Signal Processing Mohit Bansal, Satya Jeet Raj Upali, and Sukesha Sharma

Abstract Parkinson disease is a long terms degenerative disorder and it has been found in the studies that a person diagnosed with PD suffers several speech impairments and this problem in patients can be used to differentiate them from a healthy person. In this paper different machine learning algorithms have been used such as, Random Forest Classifier, Xgb Classifier, Naive-Bayes, K-NN, Decision Tree Classifier. The data set used in this paper was divided in the ratio of 70:30, 70 for training dataset and 30 for testing dataset and then hyperparameter tuning was done to select the best of the hyperparameters to be used for the models and to get good accuracies from implemented algorithms and for the evaluation of model accuracies and f1-score has been used to evaluate the models. The XgbClassifier gave the best accuracy of 96.61% and f-1 score of 98.00. Keywords Parkinson disease · Voice recordings · RandomForest classifier · Xgb classifier · Decision tree classifier · Naive-Bayes · K-NN · Hyperparameter tuning · f-1 score

1 Introduction Parkinson disease is one of the prevalent neurodegenerative disorders, which has affected more than 10 million people worldwide. The lack of dopamine in the human brain is the cause of PD among citizens and it affects primarily the motor symptoms and the most obvious symptoms of PD in early stages are rigidity, tremor, bradykinesia, abnormality in the posture of walking and many more. There is no precise cure of PD available as of now but the detection of Parkinson disease at the start of it can help in reducing the effects of it using pharmacological or physical therapy. The research towards early detection of PD has so many challenges such as: small dataset size, class imbalance, overfitting etc. The motor symptoms of PD results from death of cells that are present in brain’s substantia nigra. Recently, the development in PD M. Bansal · S. J. R. Upali · S. Sharma (B) UIET, Panjab University, Chandigarh, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_23

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studies shows that PD increases steadily with age from 40 to 80 years increasing the number of cases from 41 to 1903 per 100,000 individuals [1]. The probable causes of PD are inheritance from the complex interaction of genes or exposure to certain chemicals or any type of prior head injury. Although there is no precise cure of PD, early detection of PD can help in minimizing the symptoms of it using pharmacological or physical therapy [2]. UPDRS is generally used a rating tool to keep track of advancement of PD [3, 4]. Previous studies on early diagnosis of PD have resulted in different methods to detect early PD in patients using motor data [5], SPECT (Single -photon emission computerized tomography) imaging data or PET (Positron emission tomography) imaging data and voice recordings of different patients. Junior et al. proposed a method based on voice signals in which experiments were conducted using five different ML algorithms based on linear SVM [6, 7]. This is implemented on a dataset consisting of 40 individuals out of which 20 were diagnosed with PD and other 20 were healthy, resulting in accuracy of 72.5% and with a false negative rate of 10%. Urcuqui et al. used the e-Motion Capture System, a software that measures the distance of a body in the corridor using Kinect [8, 9]. The author harvested data from 60 individuals out of which 30 were healthy and 30 were diagnosed with PD. The model achieved an accuracy of 82% with a false negative rate of 23% and false positive rate of 12%. Segovia et al. used neuroimages and applied multiple kernel learning approaches [10]. The author implemented two kernels: one for voxels in the striatum and another one for voxels in the remaining region, on the recorded data from 87 patients and achieved an accuracy of 78%. Javed et al. applied different feature selection methods such as Lasso Regression, Principal Component Analysis (PCA) [11–13]. These techniques were used to enhance the accuracies of various machine learning algorithms, which were applied on 88 features that were extracted from motion sensors during hand rotation test and resulted with highest accuracy of 89% among all the models. In this paper, various machine learning algorithms such as Random Forest Classifier, Xgb Classifier, K-NN, Naive-Bayes, Decision Tree Classifier have been applied to perform telediagnosis. The main aim of all the applied techniques in this paper is to create a model which will be beneficial in detecting PD at its earlier stage in patients which will catalyze the efforts to provide effective medical supervisions to minimize the symptoms at early stages which will make the life of patients a little easier. Dataset used in this paper is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson’s disease (PD). The best results are obtained from Xgb Classifier which gives an accuracy of 96.61%.

2 Proposed Approach In this paper, the Oxford Parkinson’s Disease Detection Dataset has been used [14]. The information for the dataset was gathered from 31 different people out of which 23 were found positive with PD and the other 8 were healthy. The dataset consists of

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195 voice recordings from which 147 were PD and the other 48 were healthy voice recordings (Fig. 1). From each of the specific recordings several information’s related to voice were extracted such as absolute average distance between two periods (jitter value), minimum vocal frequency, maximum vocal frequency and Multi-Dimensional Voice Program (MDVP) for percentage measurement of variations of amplitude (Shimmer) and frequency (Jitter) [15], harmonicity measurement (HNR and NHR) [16]. These factors have been used to distinguish between healthy and patients diagnosed with PD. As shown in Fig. 2. The average vocal fundamental frequency in healthy people is much higher than that in sick people. The average vocal fundamental frequency in healthy people comes out to be 181.93 with 28 over this and 20 below this whereas for the sick people the average fundamental frequency decreases drastically and reaches 145.18 with 73 over and 74 below this. In this figure, it is clear that not only does the average vocal fundamental frequency decrease for the sick but also the minimum vocal fundamental frequency and the maximum vocal fundamental frequency decreases for the sick which proves that the patients suffering from PD find it difficult to communicate properly. In Fig. 3, the incidence of the measures of ratio of noise to tonal components in the voice on the health status of the individuals has been described and it gives us an idea that the people suffering from the disease have higher levels of Noise to Harmonic Ratio (NHR). Looking at the second box plot, the HNR ratio for people who have PD are lower levels than healthy individuals. Figure 4, depicts the relationship between all parameters and people’s health status and it gives a fair idea about how components are related to each other, like, MVDP Jitter (%) has a very high correlation with MDVP Jitter (Abs), MDVP RAP, MDVP

Fig. 1 Total number of healthy and sick recordings

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Fig. 2 Fundamental voice frequency parameters on people’s health status

Fig. 3 Measure of NHR and HNR components in voice

PPQ and Jitter DDP. Several algorithms that led to accurate models like Random Forest Classifier, Xgb classifier, K-Nearest Neighbor, Naïve Bayes and Decision Tree Classifiers have been used in this paper to calculate the accuracy and F-1 score of each of the models.

3 Materials and Methods The Oxford Parkinson Detection Data was divided into ratio of 70:30 for training and testing of models. Random Forest Classification Model has been fed with various hyperparameters such as maximum depth with range [1–5, 10], n-estimators with range [1–5, 10], maximum features with range [1–5, 10], minimum samples leaf with

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Fig. 4 Relation between all parameters and people’s health status

range [1–4] out of which the best hyperparameter were found using Grid Search CV [17], which were 5 for maximum depth, 10 for maximum features, 2 for minimum samples leaf, 10 for n estimators which were used in the rest of the five models proposed to get the high accuracy and F1-score. A.

B.

Random Forest Classification RF Classifier is an AI technique which could be used for either Classification or Regression [18]. RF contains combination of many decision trees for various different subsets of the dataset to calculate the average and to enhance the accuracy of the dataset. Random Forest takes the prediction from each tree and based on that it predicts the eventual output. Xgb Classifier XGBoost Classifier works good on regression, classification, ranking and userdefined prediction problems [19]. The basic purpose of the algorithm is to enhance the speed and accuracy of model. In this algorithm, decision trees

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D.

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are created in sequential form, weights are assigned to all independent variables which are afterward fed into the decision tree which predicts results. The individual classifiers ensemble to give a strong and more precise model. K-NN K-NN is a data classifier algorithm which works properly for multiclass classification problems that determine what group of data it represent by looking at k nearest neighbor data points encircling it [20]. K-NN is a “lazy learner” algorithm because if the training data is biased than the K-NN algorithm ends up being biased as well. Naive-Bayes Naive-Bayes is a classification technique which is used for problems having large datasets [21]. Naïve Bayes model can be easily implemented and hence works perfectly for large datasets. Naive-Bayes classifier assumes that the presence of a particular feature of a class is unrelated to the presence of other features. Naive-Bayes can easily outperform highly sophisticated classification models. Decision Tree Classifier Decision Tree is a Supervised learning approach that could be used for either classification or Regression problem. Decision Tree implements a divide and conquer approach to classify similar elements [22]. DTs is a tree-structure classifier consisting of two nodes namely Decision Node and Leaf Node in which decision nodes have multiple branches and make any decision whereas leaf nodes do not possess any further branches and are the output of the decision.

4 Results and Evaluation In the experiment, the Oxford Parkinson’s Disease Detection Dataset is used with all 23 features to predict and splitted our data in the ratio of 70:30 for training vs testing of our model. Initially, the data preprocessing and hyperparameter tuning has been done with the help of GridSearchCV which gives us the best hyperparameters which has been used ahead in all our models to get the best of the accuracies from them on Oxford Parkinson’s Disease Detection Dataset. For evaluation of the model the two-evaluation metrics have been chosen are accuracy and f1-score which gives us an idea about how our model is performing and what are accuracies of the various algorithms which have been employed. In Table 1, the accuracies of the algorithms used in this paper are given such as, Random Forest Classifier, Xgb Classifier, K-NN, Naive-Bayes, Decision Tree. It can be clearly observed from the table that the Xgb Classifier Algorithm gives the best accuracy of 96.610169% among all with F1-Score of 98.00% and the second best is K-NN with accuracy score of 94.915254% and F1-Score of 97.029703%.

Early Parkinson Disease Detection Using Audio … Table 1 Accuracy and F-1 score of each model

Model/Algorithm

249 F1-score (%)

Accuracy (%)

Random forest classifier

92.783505

88.135593

Xgb classifier

98.000000

96.610169

K-NN

97.029703

94.915254

Naive-Bayes

77.647059

67.796610

Decision tree classifier

91.666667

86.440678

5 Conclusion The results obtained from the models suggested in this paper gives us an idea that Machine Learning approaches could always be used to exponentially improve the examination of parkinsonism in patients. As this paper has been purely focussed on the early detection of PD in patients to degrade the symptoms in early stages to make the lives of patients a little easier ahead, the algorithm suggested in this can be proved significantly beneficial in early diagnosis of PD. Usually the time at which the disease is examined, 60% of the nigrostriatal neurons already gets degraded, and nearly 80% dopamine also gets depleted. But the Machine Learning approaches can significantly bring great improvements which will give a chance of early treatment and much of this degradation could be slowed and treated in future several different models as well the same models can be employed with different data preprocessing and hyperparameter tuning methods to improve the accuracies and predict PD more precisely. In this work, Random Forest, K-NN, Xgb, Naïve Bayes classifiers are implemented and results are compared. Xgb Classifier gives best accuracy (96.61%) as compared to the other classifiers.

References 1. Pringsheim T, Jette N, Frolkis A, Steeves TDL (2014) The prevalence of Parkinson’s disease: a systematic review and meta-analysis. Mov Disord 29(13):1583–1590. https://doi.org/10.1002/ mds.25945 2. Mak MK, Wong-Yu IS, Shen X, Chung CL (2017) Long-term effects of exercise and physical therapy in people with Parkinson disease. Nat Rev Neurol 13(11):689–703. https://doi.org/10. 1038/nrneurol.2017.128 3. Ramaker C, Marinus J, Stiggelbout AM, van Hilten BJ (2002) Systematic evaluation of rating scales for impairment and disability in Parkinson’s disease. Mov Disord 17(5):867–876. https:// doi.org/10.1002/mds.10248 4. Siderowf A, McDermott M, Kieburtz K, Blindauer K, Plumb S, Shoulson I (2002) Test-retest reliability of the unified Parkinson’s disease rating scale in patients with early Parkinson’s disease: results from a multicenter clinical trial. Mov Disord 17(4):758–763. https://doi.org/ 10.1002/mds.10011 5. Espay AJ et al (2016) Technology in Parkinson’s disease: challenges and opportunities. Mov Disord 31(9):1272–1282. https://doi.org/10.1002/mds.26642

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6. Junior SB, Turrisi da Costa VG, Chen SH, Guido RC (2019) U-healthcare system for prediagnosis of Parkinson’s disease from voice signal. In: Proceedings - 2018 IEEE International symposium on multimedia, ISM 2018, pp 271–274. https://doi.org/10.1109/ISM.2018.00039 7. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297 8. Urcuqui C et al (2018) Exploring machine learning to analyze Parkinson’s disease patients. In: Proceedings - 2018 14th International conference on semantic knowledge grids, SKG 2018, pp 160–166. https://doi.org/10.1109/SKG.2018.00029 9. Arango Paredes JD, Muñoz B, Agredo W, Ariza-Araújo Y, Orozco JL, Navarro A (2015) A reliability assessment software using kinect to complement the clinical evaluation of Parkinson’s disease. In: Proceedings annual international conference of the IEEE engineering in medicine and biology society. EMBS, vol 2015-November, pp 6860–6863. https://doi.org/10.1109/ EMBC.2015.7319969 10. Segovia F et al (2016) Analysis of 18F-DMFP PET data using multikernel classification in order to assist the diagnosis of Parkinsonism. In: 2015 IEEE nuclear science symposium, medical imaging conference, NSS/MIC 2015, pp 1–4. https://doi.org/10.1109/NSSMIC.2015.7582227 11. Javed F, Thomas I, Memedi M (2018) A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease. In: 2018 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), vol 2018, pp 5426–5429. https://doi.org/10.1109/EMBC.2018.8513683 12. Li F, Yang Y, Xing EP (2005) From Lasso regression to feature vector machine. Adv Neural Inf Process Syst, pp 779–786 13. Kong X, Hu C, Duan Z (2017) Generalized principal component analysis. In: Principal component analysis networks and algorithms. Springer, Singapore, pp 185–233 14. Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans Biomed Eng 56(4):1015– 1022. https://doi.org/10.1109/TBME.2008.2005954 15. Farrus M, Hernando J, Ejarque P (2007) Jitter and Shimmer measurements for speaker diarization. In: 8th annual conference of the international speech communication association, INTERSPEECH 2007, Antwerp, Belgium, August 27–31, pp 21–30 16. Galaz Z et al (2016) Degree of Parkinson’s disease severity estimation based on speech signal processing. In: 2016 39th international conference on telecommunications and signal processing, TSP 2016, pp 503–506.https://doi.org/10.1109/TSP.2016.7760930 17. Ranjan GSK, Kumar Verma A, Radhika S (2019) K-nearest neighbors and grid search CV based real time fault monitoring system for industries. In: 2019 IEEE 5th international conference for convergence in technology, I2CT 2019, pp 9–13. https://doi.org/10.1109/I2CT45611.2019. 9033691 18. Jin Z, Shang J, Zhu Q, Ling C, Xie W, Qiang B (2020) FRSF: employee turnover prediction based on random forests and survival analysis. Lectures notes in computer science (including subseries lecture notes in artificial intelligence, lectures notes in bioinformatics), vol 12343 LNCS, pp 503–515. https://doi.org/10.1007/978-3-030-62008-0_35 19. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: ACM SIGKDD conference on knowledge discovery and data mining 20. Zouhal LM, Denoeux T (1998) An evidence-theoretic k-NN rule with parameter optimization. IEEE Trans Syst Man Cybern Part C Appl Rev 28(2):263–271. https://doi.org/10.1109/5326. 669565 21. Zhang H, Li D (2007) Naïve Bayes text classifier. In: 2007 IEEE international conference on granular computing (GRC 2007). IEEE, New York 22. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106. https://doi.org/10. 1023/A:1022643204877

Unveiling the Success Behind Tesla’s Digital Marketing Strategy Pankaj Pathak, Vikash Yadav, Samaya Pillai, Subhadeep Das, and Gaurav Kansal

Abstract Tesla is an American Automobile company founded in 2003 and has been an ever-growing brand in terms of its revenues and sales since its early days of inception in the market. The entire marketing strategy of Tesla is done digitally, and very less or almost negligible amount is spent on traditional marketing methods. The entire promotion of the brand and its various products is done through the various online social media platforms like Facebook, Twitter, Instagram and YouTube. In fact, 70% of the company’s sales are done online. There are significant differences in the designing and the availability of the retail outlets as compared to the other big brands in the industry. This paper presents a detailed study of unique and innovative marketing strategies of Tesla for making its presence in the market. Keywords Tesla · Digital · Marketing · Social media · Twitter · Strategy

P. Pathak · S. Pillai · S. Das Symbiosis Institute of Digital and Telecom Management Symbiosis International (Deemed University), Pune, India e-mail: [email protected] S. Pillai e-mail: [email protected] S. Das e-mail: [email protected] V. Yadav (B) Department of Technical Education, Lucknow, Uttar Pradesh, India e-mail: [email protected] G. Kansal ABES Engineering College, Ghaziabad, Uttar Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_24

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1 Introduction Tesla Inc. formerly known as Tesla Motors is a Silicon Valley-based automobile company founded in 2003 and is headquartered in Palo Alto, California. It designs and manufactures plug-in-electric vehicles (PEV), lithium-ion battery packs and other electric vehicle-related components. Currently, Tesla has strength of 48,000 employees with 212 stores worldwide and many more to come down the line. Tesla caters to three major industries which are the automotive, energy storage and energy production. Tesla emerged as the most popular brand of the electric vehicles in the industry. The total revenue of Tesla Inc. for 2019 was 24.5 billion USD with automotive sales touching nearly 20 billion USD. Tesla’s first model, the Tesla Roadster, was launched in 2008, and 2100 units were sold in 32 countries. It had a base price of $109,900 USD and a 250-mile range. It was a luxurious sports car designed to entice customers in the luxury sports vehicle industry, with a range twice that of older electric/hybrid vehicles’ batteries [1]. Tesla introduced the Model S, a high-performance luxury electric sedan, in 2012. It is the ideal mix of high performance, special functionality and appealing features [1]. The Model S also has an all-glass panoramic roof that opens at the touch of a button. Customers could choose from three battery pack options: 40, 50 or 85 kWH, with ranges of 160 miles, 230 miles or 300 miles on a single charge [2]. The Tesla Model X, launched in 2015, was a mid-size lightweight SUV available with 5, 6 and 7 passenger configurations [3]. The passenger doors of this model are like the falcon-wing designed that opened vertically. In August 2015, around 30,000 pre-orders of Model X were estimated from the customers. In 2016, a total of 25,312 units and in 2017 a total of 46,535 units of the model were sold in the global automobile market. The Model 3 was a four-door sedan PEV that was launched in March 2016 [3]. As per Bloomberg News by the end July 2017, Model 3 received around 500,000 reservations globally which was a record in the 100-year history of the automobile market. The Tesla Model 3 also owned the title of bestselling electric car in the global automobile market for the second time in 2019 with annual deliveries crossing 300,000 units. In the present automobile market, there are four varieties of Model 3 currently available with EPA ranges of 250 miles, 322 miles and 220 miles [4–6].

2 Competitor Analysis Tesla Inc’s direct rival is Fisker Automotive. It has a model called the ‘Karma’ that can switch between all-electric stealth mode and fuel-assisted sport mode with the stroke of a paddle. It is priced at $95,900 and offers luxury performance and features. Target Market Segment: The market segment that was identified consisted of males between the age group of 25–60 years who had a good job, well-settled life and had an annual income of $100,000 or more [7, 8]. Many of them live in the urban and urban

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fringe areas and cover up to a distance of 2 km or even more to reach their offices. They wanted to have a high-performance luxury vehicle in order to have an enjoyable and comfortable ride. Many of them were parents who wanted a comfortable, spacious and a safe ride to take their families out for various purposes [2]. It was discovered that Tesla’s customers had a west coast mindset when it came to staying on top of environmental developments. Model S was found to target innovators, who made up about 2.5% of their clients, according to the product diffusion curve. These were the well-informed customers who were willing to take a chance on every new product on the market. The company’s marketing campaigns have trained the early adopters, who made up the next 13.5% of customers. Advertising: Tesla never really adopted any traditional modes of advertising. At the time of the launch of Model S, Tesla was still a new brand in the industry. Model S was planned to be the first major release of Tesla worldwide on a commercial basis [2]. In fact, at that time, Tesla was one of the brands which entirely targeted the Electric Vehicle (EV) market where there were very less competitors and also the segment was quite new for the customers of the automotive industry. Tesla planned to document a road trip across the United States. The one-month road trip commenced in San Francisco and ended in New York covering all the major locations in the shape of a ‘S’. It was one of the most unique strategies adopted by any company to advertise its product and also to increase the brand visibility among the audiences [2]. There were three main benefits of this campaign which are as follows. The campaign’s third benefit was that the road trip generated a lot of publicity [2]. This campaign changed the mindset of a lot of people for whom the electric vehicles were not suited for long and extensive rides. The public relations team of Tesla actively reached out to the media on their way to generate the publicity for the upcoming model in the market [9–12]. Model ‘S’ in front of them and also spread the awareness about the electric vehicles among them.

3 Data, Discussion and Analysis Tesla Ad Spend and the Market Share From the above marketing and promotional strategy adopted by Tesla, it can be seen that they never really went for any traditional methods of advertising. According to sources, in 2015 Tesla spend almost 58.3 million USD on its marketing which was almost negligible as compared to the hefty amounts spent by its competitors in the industry. A conducted 30-day survey in the month of March and April 2019 indicated the veteran automakers still spending a huge portion of their advertising budget in various social media platforms [13]. Compared to Tesla, the other big brands spent almost 50% of their marketing and advertising expenditure as the paid media expenses in various social media platforms such as Facebook, YouTube, Instagram and others. Tesla on the other hand from very beginning only never spent any amount on paid media expenses. It always believed in

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organic content but spent very less on paid advertising. According to a survey, Tesla produced only 88,000 vehicles in 2016 (Fig. 1) which is the lowest in the automobile industry as compared to its other competitors. In the same year, Toyota topped the chart (Table 1) followed by Volkswagen which can be seen from the chart below. In spite of the figures mentioned in the above chart, according to a two-year period survey between Q1 2015 and Q1 2017, it was found that Tesla was the only brand in the automobile industry to see a positive change in market capitalization.

Fig. 1 Vehicles produced by the automotive brands in 2016

Table 1 Auto brands paid media expenses

Automobile brand

Social media platform

Expenditure

Toyota

Facebook

62% of Paid media expenses

BMW

Facebook

46% of Paid media expenses

Honda

Facebook

38% of Paid media expenses

Audi

YouTube

54% of Paid media expenses

Infiniti

Facebook

52% of Paid media expenses

Ford

Facebook

55% of Paid media expenses

Cadillac

YouTube

40% of Paid media expenses

Porsche

YouTube

47% of Paid media expenses

Tesla

No Paid Ads

No Paid media expenses

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Fig. 2 Global sales of PEV models in 2019

As per the recent survey report, in 2019, Tesla Model 3 topped the list with a huge difference among the plug-in-electric vehicle models sold globally [5]. In the fourth quarter of 2019, Tesla Model S and Model X accounted for only 20% of total sales worldwide, whereas the rest 80% was occupied majorly by Model 3. Since 2012, Tesla Inc. has always seen an increase in its annual revenue, and the leap is more prominent in 2017 and 2018. This can be seen from the Tesla’s annual revenue chart for over a period of 10 years. The current annual revenue of Tesla Inc. in 2019 is 24,578 million USD. Starting from 2012, it can be said that Tesla has come a long way and has made a very strong hold in the electric vehicle segment all over the world (Fig. 2). Marketing through Customer Experience Tesla focused to achieve a high-quality customer experience which in turn helped in their marketing and promotional process. Here is an analysis of some the methods adopted by Tesla Inc. during their entire journey and how it helped them to increase their popularity. Tesla’s Referral Program—This program encouraged the Tesla customers to speak the word out about their cars. Before October 2015, the program offered the customers services and accessories of Tesla or a $1000 credit for every new Tesla car purchased on behalf of their referrals [14]. Tesla had to give away 80 new Roadsters for free by 2019 due to the massive promotion. Tesla recently updated the referral program’s offerings in order to keep it affordable. To continue to generate word of mouth and press coverage, the referral program must remain appealing [15]. Tesla has a higher number of transactions, and consumers [14] earn a reward that they may not be able to get from any other business or for which they must pay a fee.

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Sales Centers Tesla does not have a plan for its dealerships. Instead, they have distribution centers [2], which are also connected to service stations and online sales. Their sales centers are clean, comfortable, approachable, exiting and designed in such a way so as to promote the Tesla brand as a whole. The sales person at those centers is not bounded by any sort of commission structures as a result of which they do not upsell or force sell a product to any customer rather they provide information about the product, clear the customer queries and market softly about their various promotional offerings [3]. The aim of Tesla is to make all its sales online on their digital platform. All the models of Tesla products can be purchased online from their sales center or from anywhere, and the entire process is carried out very smoothly within 5–10 min. Tesla after sales servicing and maintenance Tesla realized that by becoming the first company to introduce a fully electric vehicle to the market, it would push consumers to spend more time at service stations. One of the major problems that Tesla mentioned was the problem of customers taking their cars to mechanics or service centers and waiting for long periods of time. Tesla developed a mobile team of repair called ‘Tesla Rangers’ that can meet anywhere as and when required [6]. All these sorts of approaches removed a lot of frustrations of the common customer who really praised Tesla for delivering such a superior quality customer experience. Marketing through media or social media Offering free test drives of Tesla vehicles to the press and requiring them to write and share their impressions with audiences was a creative and clever way for Tesla to get the word out about their goods [14]. Tesla began this with the Model S and got a lot of positive feedback. The New York Times analysis also demonstrated the disadvantages of such a technique. The image at the top of the page showed a Tesla stalled on a flatbed truck. In a blog post, Tesla CEO Elon Musk questioned The New York Times review, sparked a debate and eventually called the reviewer an anti-electric vehicle zealot. Tesla also followed the same strategy for Model 3 and also received several positive reviews including reviews from MSN and Fox News. Tesla Inc. is unusual in that its CEO, Elon Musk, also controls companies in other fields such as SpaceX, Solar City and The Boring Tunnel. Tesla takes advantage of this ability to cross-promote its goods [16]. Until now, the most appealing one was the launch of the Tesla Roadster into space. Sound waves from David Bowie’s ‘Space Odyssey’ pulsated outward into the void, with images of Roadster, Starman and Earth filling the backdrop. This stunt captivated the world and dominated the news for days. This approach is excellent because it improves both companies’ popularity and recognition at a lower cost and with less effort. Elon Musk, the CEO of Tesla, himself runs and manages his own Twitter account and actively engages with his followers. He tweets many important events and happenings (may be related to Tesla or his other ownerships), shares business and other technology-related ideas on the platform, seeks customer feedbacks, respond

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to his followers’ suggestions and often rolls out a solution instantly there. This is a unique approach which makes the customers feel connected with the brand and its owner by letting the customers know that the CEO wants to hear from them also shows the responsibility of Tesla in prioritizing the customer-centric approach.

4 Conclusions from Tesla’s Social Media Marketing Tesla has the greatest number of Twitter followers, i.e., 5.7 million as compared to its other competitors. Instagram official account has 7.3 million followers. According to analysts, Tesla is the most discussed brand in the social media in the electric vehicles [16]. There are some important observations that need to be noted which tell us the milestone behind Tesla’s gigantic success on the social media platforms. They are as mentioned below. Tesla’s Instagram account is more authentic than staged—Tesla posts photos and uploads videos of their cars either in action or at events. This portrays the authenticity of the claims made in favor of their products and also creates an image in customer’s mind regarding the exemplary performance, comfortability, safety and the various other factors required for a high-quality ride [17]. These candid Instagram posts help Tesla to connect more authentically and open mindedly with their audiences. Here is the post of a Tesla Model 3 pulling wagons out of tunnel. The Tesla Twitter handle has a coordinated interaction with the Elon Musk Twitter handle—Tesla does social listening which is a unique feature [16]. There is always a social media team of Tesla in standby who is ready to interact and pop up in the discussions regarding the Tesla products and Elon Musk wherever applicable even if they are not tagged. This sends a unique message to the customers regarding the company’s open mindedness to communicate and interact with them and listen to their suggestions and feedbacks. Elon Musk acts as the influencer—Elon Musk has his own personal brand which adds to the power of Tesla’s brand [15]. Musk has his footprints and a strong hold in many different sectors in the market. He keeps interests in many different fields. He is the CEO and Product Architect of Tesla Inc., Founder, CEO and Lead Designer of SpaceX as well as the Founder of X.com and The Boring Company and also a co-founder of many other organizations. Elon Musk has a Twitter handle of over 36.9 million followers. Instead of focusing on advertisements through celebrity endorsements, the company leverages the personal brand of their CEO to increase their brand value and market their products. Musk is the real man behind his own Twitter Account—It is highly unlikely for the CEO of any company to manage his own Twitter account. The case with CEO of Tesla Inc. is not the same. Elon Musk is exactly the opposite from other CEOs. Musk manages his own Twitter account, tweets himself and engages with conversations with his followers [17]. He is just like another ordinary man very active on social

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media handle and always ready to interact with his followers. Musk did live tweets on important events and happening related to his companies and also share his business and other technological ideas behind the execution. Musk also encourages interaction with his followers, loves to hear suggestions from them and also comes to a fix or conclusion, if possible, in the comments only [14]. He is open to hear any sort of complaints and also quick enough to respond. As an example, musician Sheryl Crow had some issues with her vehicle which she tweeted, and Musk was quick enough to respond her and also asked for her feedback regarding the process. Once a Tesla owner was having some technical problem with his car at the parking lot which he tweeted and Musk immediately responded within 30 min and the problem was solved via a software update. The mysterious and open-minded nature of Musk’s tweets and posts—One thing to be noted is the randomness of Musk’s tweets [18]. He likes to come close and interact at a personal level in the social media platforms. While driving back home he will share his business ideas and his future plans. He also discusses about his failures and what he is caring for and interested in at any particular moment. His opinions can be controversial, and he is not worried about what others feel or think about his behavior. The Twitter post shown below demonstrates the mysterious nature of his tweets. This makes the followers curious and often kept them more engaged in order to find some hint/solution in his upcoming posts.

5 Recommendations for Tesla’s Marketing Strategy and Future Scope Advertising through user-generated content (UGC): Like GoPro, Tesla can encourage its customers to post photos and videos of themselves having Tesla ride with various Tesla models at various geographical locations. This will give a sort of social recognition to their customers and will in turn increase the audience engagement with the brand. It will also send a message of authenticity of Tesla products to the audiences. Using virtual reality (VR) in sales center for promotions: Tesla can use AR and VR in their sales center to give the customer an actual feel of their Tesla ride as per the model selected by them. Many customers love to have a test ride before booking, and this attractive strategy will draw customer’s attention as most of the Tesla sales are done online. Using traditional and digital methods together for promotional purposes: Tesla can punch together the traditional and the digital methods of promotion. From the user-generated content on various social media platforms, they can take the attractive captions, tweets or photos and post them on billboards along the roadside. Though

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it is a paid ad mode, still it is less expensive that will grab the audiences’ attention and will help their customers gain some sort of social recognition. As we know that Tesla Inc. is the brand which sells the majority of the electric vehicles worldwide, and with the increase in the number of production and delivery units each year, we can surely conclude that the demand of electric vehicles is rising globally with every passing day. In 2019, Tesla Model 3 has a market share of 60% in the global Electric Vehicle market, and Tesla accounted for 80% of the electric vehicles sold in the US Electric Vehicle market. In order to have an approximation of the future sales of the electric vehicles, we need to understand the various opportunities and hurdles in the global market.

References 1. Moritz M, Redlich T, Krenz P, Boxbaum-Conradi S, Wulfsberg J (2015) Tesla motors Inc.: pioneer towards a new strategic approach in the automotive industry along open-source movement. IEEE Eng Manage Rev 43(4):103–112. https://doi.org/10.1109/emr.2015.7433690. Accessed 20 July 2021 2. Mangram M (2012) The globalization of Tesla Motors: a strategic marketing plan analysis. J Strategic Market 20(4):289–312. https://doi.org/10.1080/0965254x.2012.657224. Accessed 10 July 2021 3. Thomas V, Maine E (2019) Market entry strategies for electric vehicle start-ups in the automotive industry—lessons from Tesla Motors. J Clean Prod 235:653–663. https://doi.org/10.1016/ j.jclepro.2019.06 4. Global EV sales for 2019 now. In: Tesla Model 3 totally dominated (2020) InsideEVs. Available: https://insideevs.com/news/396177/global-ev-sales-december-2019/. Accessed 17 July 2020 5. Brown D (2020) Usatoday.com. Available: https://www.usatoday.com/story/tech/2019/04/26/ tesla-software-updates-feel-like-new-car-mode-list/2882449002/. Accessed 21 July 2020 6. Marks I (2020) Elon Musk and Tesla going all-in on online sales: let’s see what the data has to say about it. Available: https://www.similarweb.com/corp/blog/. Accessed 22 July 2020 7. Krebs S (2016) Silent by design? Tesla’s Model S and the discourse on electric vehicle sound. Sound Stud 2(1):93–95. https://doi.org/10.1080/20551940.2016.1154406. Accessed 2020 8. Wang J (2020) Marketing plan for Tesla Motors; Model S. Academia.edu. Available: https:// www.academia.edu/31944850. Accessed 06 July 2021 9. Todd J, Chen J, Clogston F (2020) Creating the clean energy economy. Analysis of the electric vehicle industry. Iedconline.org. Available: https://www.iedconline.org/clientuploads/. Accessed 15 July 2021 10. China’s Electric-vehicle market plugs in (2020) Available: https://www.mckinsey.com/~/ media/McKinsey/. Accessed 20 July 2020 11. Endsley M (2017) Autonomous driving systems: a preliminary naturalistic study of the Tesla Model S. J Cogn Eng Decis Making 11(3):225–238. https://doi.org/10.1177/155534341769 5197. Accessed 16 July 2020 12. Kissinger D (2020) Tesla Inc.’S marketing mix (4Ps) analysis - Panmore Institute. Available at: http://panmore.com/tesla-motors-inc-marketing-mix-4ps-analysis. Accessed 3 July 2020 13. Koetsier J (2020) Tesla spends zero on ads. Here’s where BMW, Toyota, Ford, and Porsche spend digital Ad dollars. Forbes. Available: https://www.forbes.com/sites/johnkoetsier/2019/ 05/06/tesla-spends-zero-on-ads-heres-where-bmw-toyota-ford-and-porsche-spend-digital-addollars/#5da9507e11d4. Accessed 15 July 2020 14. Luck I (2020)How Tesla used a $0 marketing strategy to dominate a market. Market Strategy. Available: https://www.marketingstrategy.com/marketing-strategy-studies/. Accessed 09 July 2020

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15. Khemchandani A (2020) 15 lessons to learn from Tesla’s marketing strategy. SEO and Content Marketing Blog – Growfusely. Available: https://growfusely.com/blog/teslas-marketing-str ategy-lessons/. Accessed 14 Jul 2020 16. Manecuta A (2020) Tesla: how to leverage social media to build a top brand. Medium. Available: https://medium.com/@13alexm/tesla-how-to-leverage-social-media-tobuild-a-top-brand-b7a043da68ff. Accessed 05 July 2020 17. Hogan M (2020) Social media marketing highlight: Tesla and Elon Musk. Stunning Strategy. Available: https://www.stunningstrategy.com/social-media-highlight-tesla-elonmusk/. Accessed 14 July 2020 18. Folschette C (2020) [UPDATED 2020] Tesla’s marketing strategy shows that it’s time for CEOs to get social. Talkwalker. Available: https://www.talkwalker.com/blog/tesla-marketingstrategy-social-ceo. Accessed 09 July 2020

A Comparative Survey of Consensus Algorithms Based on Proof of Work Poonam Rani and Rajul Bhambay

Abstract Blockchain is the foundation of cryptocurrencies and many other industries such as healthcare, supply logistics, and so on. It is a system of distributed ledger that is now attracting lot of research attention. Peer-to-peer and cryptography technologies are essential components of blockchain, as are consensus procedures that ensure blockchain systems’ transparency, decentralization, and security. The Proof of Work (PoW) consensus protocol is now adopted by most blockchain systems, although other variations are available. We examine PoW and its six variants and analyze their pros, cons, scalability, maintenance cost, block generation time, transaction cost, energy consumption, validator selection criteria, mining profitability, and 51% attack in this study. Keywords Blockchain · Cryptocurrency · Proof of Work

1 Introduction Nakamoto first implemented blockchain in 2008 in an electronic monetary system that is peer-to-peer [1]. The invention of coin currencies has promoted trade in tremendous ways, but these currencies had flaws, too, like the exchange of fake currencies. With time, the global economy started shifting to digital systems in which transactions and money are transferred through banks. The involvement of banks created the third party imposed charges on customers in the form of transaction fees. To address this issue, researchers began working to entirely decentralize the value exchange system. Blockchain technology has developed as a decentralization solution that enables peer-to-peer cryptocurrency transfers (bitcoin). Consensus protocols are used to reach agreement on actions among blockchain elements in a network. When other P. Rani (B) · R. Bhambay Netaji Subhas University of Technology, Dwarka Sector-3, New Delhi, India e-mail: [email protected] R. Bhambay e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_25

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nodes are not completely trusted, data distributed storage and transaction information are utilized [2]. Blockchain technology covers consensus algorithms, peer-to-peer communication, distributed storage, encryption algorithms and more. PoW is the oldest and safest consensus algorithm used in blockchain. As we have seen with the popularity of PoW over the last decade, it is widely accepted consensus, but there are several issues associated with it. PoW has weak security, energy inefficient, high maintenance cost, incline toward centralization, non eco-friendly, and many more. If we can reduce the issues, this might be extremely beneficial to the blockchain community. To solving the above issue, we examined algorithms in which minor adjustments are made to enhance factors like as security, miner profitability, block creation time, scalability, maintenance cost, and energy consumption. In this paper, we present the current Proof of Work (PoW) consensus algorithm and its variants. This paper explains PoW and compares six variants algorithms of PoW viz. Hybrid proof of work (HPoW), Proof of meaningful work (PoMW), Proof of work time (PoWT), Equitable chance or energy saving PoW (ePoW), Delayed proof of work (dPoW), and Semi synchronous proof of work (SSPoW).

2 Proposed Methodology 2.1 Blockchain A blockchain is a series of blocks that are linked together using cryptographic techniques. Due to the capability of decentralization and cryptographic hashing, it is used to provide a transparent and immutable record of digital assets and frequently referred as distributed ledger technology. Each block in a blockchain contains data, current block hash and previous block hash [1]. The data recorded in the block is determined by the kind of blockchain. The block’s hash is like a fingerprint. The hash of a block is computed after it is formed. If a block’s fingerprint changes, it is no more the very same block. The preceding block’s hash simply creates a chain of blocks, maintaining blockchain security. The very first block is known as the genesis block since it has no precedent block address. If someone tampers with the block, the block’s hash changes automatically. Consequently, all the blocks following the tampered block become invalid because they no longer store valid hash. PoW consensus is widely used in blockchain for security purposes. It is a straightforward method that slows the production of new blocks in the blockchain. This approach makes tampering almost impossible with the blocks. The three primary characteristics of blockchain technology are Immutability, Transparency, Decentralization. Blockchain is being used to reduce fraud and add more security to the system [3]. Blockchain-based security enhancements are employed in a variety of fields, including spectrum management and identifying rogue users in cognitive radio networks, fraud detection in credit or debit cards, supply chain network and in deep learning techniques [4–7].

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2.2 Consensus Protocols Consensus algorithms potentially allow participants to agree on a blockchain’s contents in a distributed and trust-free manner. Blockchain systems, according to the trilemma, can only have two of the three qualities viz. scalability, decentralization, and security [8]. Every consensus algorithm has a unique set of applications. There is no ultimate good or bad. The type of network and data determines the sort of consensus to utilize for blockchain implementation. We study different variants of PoW consensus algorithms.

2.2.1

PoW

In a blockchain network, PoW is a method of achieving consensus that is used by many cryptocurrencies such as bitcoin and Ethereum [9]. To create a new block, PoW selects one node/validator in each round in which validators need to compete with computational power and is given a cryptographic puzzle to solve. The validator who cracks the puzzle first has the opportunity to create a fresh block. A PoW puzzle is extremely tough to solve. A nonce is a random whole number (4 bytes) field, which the validators adjust to get the correct solution for the puzzle, which requires much computational power [10]. A defaulter or attacker can insert a malicious block into a chain, but as the number of legitimate blocks increases, so does the burden, thus dumping a long chain necessitates a large amount of processing power.

2.2.2

PoMW

The main idea of PoMW is to defend the blockchain by demonstrating that a specific amount of computing was committed to constructing each block. The existing system, which uses completely artificial hashing with the sole objective of burning “enough” energy, is far too wasteful to be useful and scales extremely poorly [11]. The energy expended in proving the miners’ computational strength is used in calculations that benefit public scientific research areas like medical research, chemical research, and astrophysical simulations [11]. The PoMW consensus method underpins the Vrenelium coin. Vrenelium has a Double Helix, which, together with PoMW and PoS [11], makes the Vrenelium blockchain an innovative, safe, and long-lasting third-generation blockchain. An attacker would need to command greater than fifty percent of overall processing power available to target the PoMW protection, as well as the majority of the stake to launch an attack on the PoS system [11]. It is exceedingly unlikely that both conditions will be met at the same moment.

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HPoW

A low-energy variation of the PoW protocol is the HPoW consensus protocol. It removes the monetary benefit for miners, leaving mining farms unable to mine a network, and instead encourages lone miners with the limited computational capacity to participate in the consensus. Lynx developed the HPoW solution [12, 13]. The Lynx network’s electrical costs are a fraction of bitcoin, reducing environmental effects like carbon emissions [12, 13]. In Lynx, HPoW requires only a few conditions to be met, the miner must not have earned a mining incentive in the previous sixty blocks. Each incentive address must contain at least 1000 coins, and the final two characters of the miner’s incentive address hash must replicate the block hash value’s last two characters [13]. Furthermore, it does not enable a particular miner to earn a block every thirty minutes [12, 13].

2.2.4

PoWT

The level of difficulty of bitcoin’s PoW is changed on a regular basis in order to generate blocks at periodic intervals. PoWT is an innovative way to reach a consensus by providing a flexible block time that grows with mining power, causing the blockchain to pick up speed as power grows [14]. PoWT improves blockchain scaling, boosts transaction speed with power, and enables auto-adjusting making mining more costeffective. Block time is difficulty-based (maximum 6.2 min and minimum 15 s) [14]. The reward gradually drops as mining power grows. This mechanism causes the reserve commodity supply curve to collapse due to growing demand, leading to a value increase over time and higher usage.

2.2.5

dPoW

The dPoW consensus protocol is used by the Komodo platform [15]. The PoW network is used by dPoW as a storage site for backups of Komodo transactions [15]. In the case of an attempted attack on Komodo’s blockchain history, even a single surviving clone of the Komodo main chain will permit the whole ecosystem to rewrite and overturn any changes committed by the attacker [15, 16]. The Longest Chain Rule is not recognized by the dPoW consensus process for any transactions older than that of any recent “backup” of the Komodo blockchain [15, 16]. If a conflict arises involving transactions older than the last “backup,” our consensus process searches the backups in the chosen PoW blockchain (Bitcoin) to determine the correct record. As a result, to destroy even the smallest Smart Chain using Komodo’s dPoW security, the fraudster would have to kill all existing copies of the Smart Chain, all clones of the Komodo main chain, and the associated PoW security network into which the dPoW recoveries are placed (Bitcoin) [15, 16]. This provides the Komodo ecosystem with greater security than Bitcoin while avoiding the enormous financial and environmental costs.

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ePoW

Hyundai digital access currency (HDAC) employs ePoW as a consensus mechanism for its blockchain [17, 18]. ePoW stands for “equitable chance and energy saving PoW”. The ePoW consensus technique can reduce the number of nodes involved in PoW and encourages several mining nodes to participate [17, 18]. As a result, energy waste caused by extra hashing power is reduced for mining competition and distribute equal mining opportunities. ePoW is a consensus method that reduces mining dominance by utilizing the block window idea. It reduces energy consumption in hash calculations by prohibiting spontaneous mining operations during the block window application time after mining is finished [17, 18]. If a node successfully mines a block, no further blocks can be mined during the block window’s application time, removing the need to search for an invalid block [17]. 2.2.7

SSPoW

Purple’s consensus algorithm is known as Semi Synchronous Proof of Work or SSPoW [19]. It is a variant of Satoshi’s original PoW consensus model [1]. Its purpose is to eliminate the PoW algorithm’s bottleneck on the network’s transaction throughput. It is accomplished by detaching the selection of validator nodes, which is accomplished using Proof of Work, from the actual transaction validation process [19]. When a node discovers a legitimate PoW, it is promoted to the validator pool, given a time limit to validate transactions. It is done asynchronously; however, validator node selection is completed synchronously. As a result, the consensus method becomes semi-synchronous, significantly enhancing network throughput while also offering a safety control mechanism that can be changed based on existing network conditions [19].

3 Comparison Results and Analysis The consensus protocols are examined in this section in terms of scalability, maintenance cost, block generation time, transaction cost, energy consumption, validator selection criteria, mining profitability, and 51% attack. Tables 1, 2 and 3 summarize compare and analyze these algorithms on the basis of above parameters.

3.1 Scalability Scalability refers to a network’s ability to sustain larger transaction throughput and is an important criterion in blockchain networks. Furthermore, the blockchain trilemma suggests that obtaining improved scalability would come at the expense of decreased security and decentralization [8].

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Table 1 Comparison on the basis of platform, scalability, maintenance cost Consensus Platform Scalability Maintenance cost algorithm 1 2 3 4 5 6 7

PoW PoMW HPoW PoWT dPoW ePoW SSPoW

Bitcoin Vrenelium Lynx Verium Komodo HDAC Purple

High – Moderate High High – –

Very high Very high Very low Low Low Low –

Table 2 Comparison on the basis of transaction cost, block generation time, energy consumption Consensus Transaction Block generation Energy consumption algorithm time 1 2 3 4 5 6 7

PoW PoMW HPoW PoWT dPoW ePoW SSPoW

0.00000012 Btc/byte – 0.0001 Lynx/kilobyte 0.0001 VRC 0.001 KMD – –

10 min – 30 s 15 s–6.2 min 1 min 3 min 15 s

Very high Very high Very low Low Low Low –

Table 3 Comparison on the basis of 51% attack chances, validator selection criteria, mining profitability Consensus 51% attack chances Validator selection Mining profitable algorithm criteria 1 2 3 4 5 6 7

PoW PoMW HPoW PoWT dPoW ePoW SSPoW

High High High High Low – –

Computation based Computation based Vote based Vote based Vote based Computation based Computation based

Yes Yes No Yes Yes Yes –

3.2 Cost of Maintenance Maintenance cost means what cost is paid by miners/validators monthly after setting the mining equipment to continue the mining. It is high when a particular blockchain has high energy consumption. It can make mining unprofitable too.

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3.3 Transaction Cost Miners are compensated monetarily for the huge amounts of processing power and energy they expend to support the network. With each block added to the blockchain comes a reward known as a block reward, as well as all fees provided with the confirmed and included transactions.

3.4 Block Generation Time In the context of cryptocurrencies, block generation time measures how long it takes to create a new block, or data file, in a blockchain network. It is the amount of time required to verify the presence of a new supply of tokens.

3.5 51% Attack 51% attack, often referred to as the majority attack, occurs when a single individual or group gets control of more than 50% of the hashing power on a blockchain. Typically, this is accomplished by leasing mining hash power from a third party. Skilled attackers can prevent new transactions from being verified and reorganize fresh transactions.

4 Conclusion The consensus mechanism ensures that blockchain systems operate consistently. The consensus protocol is used to reach an agreement among nodes on a specific value or transaction. Through investigation and comparison, we introduced some blockchain consensus algorithms similar to POW. We discovered their scalability, maintenance cost, block generation time, transaction cost, energy consumption, validator selection criteria, mining profitability, and 51% attack. We found out that “dPoW” and “HPoW” algorithms performs best by comparing all the parameters and can be used as an alternative to PoW for future use.

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References 1. Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Decentralized business review 21260 2. Zheng Z, Xie S, Dai H, Chen X, Wang H (2017) An overview of blockchain technology: architecture, consensus, and future trends. In: 2017 IEEE international congress on big data (BigData congress). IEEE, pp 557–564 3. Rani P, Balyan A, Jain V, Sangwan D, Singh PP, Shokeen J (2020) A probabilistic routingbased secure approach for opportunistic IoT network using blockchain. In: 2020 IEEE 17th India council international conference (INDICON). IEEE, pp 1–7 4. Khanna A, Rani P, Sheikh TH, Gupta D, Kansal V, Rodrigues JJPC (2021) Blockchain-based security enhancement and spectrum sensing in cognitive radio network. Wireless Pers Commun 1–23 5. Rani P, Jain V, Joshi M, Khandelwal M, Rao S (2021) A secured supply chain network for route optimization and product traceability using blockchain in internet of things. In: Data analytics and management. Springer, pp 637–647 6. Rani P, Jain V, Saif M, Mugloo SH, Hirna M, Jain S (2022) Improving accuracy of deep learning-based compression techniques by introducing perceptual loss in industrial IoT. In: International conference on innovative computing and communications. Springer, pp 61–69 7. Rani P, Shokeen J, Agarwal A, Bhatghare A, Majithia A, Malhotra J (2022) Credit card fraud detection using blockchain and simulated annealing k-means algorithm. In: International conference on innovative computing and communications. Springer, pp 51–59 8. Qin K, Gervais A (2018) An overview of blockchain scalability, interoperability and sustainability. Hochschule Luzern Imperial College London Liquidity Network 9. Antonopoulos AM (2014) Mastering bitcoin: unlocking digital cryptocurrencies. O’Reilly Media, Inc 10. Zhang R, Xue R, Liu L (2019) Security and privacy on blockchain. ACM Comput Surv (CSUR) 52(3):1–34 11. Tidei M, Affolter DM, Lorenz SP (2018) Vrenelium whitepaper v1.1 01.08.2018. Available at https://www.vrenelium.com/whitepaper.pdf(2021/11/12) 12. Lashkari B, Musilek P (2021) A comprehensive review of blockchain consensus mechanisms. IEEE Access 9:43620–43652 13. The Lynx Team (2019) Technical white paper 1.1. White paper. Available at http://cdn.getlynx. io/2019-03-17_Lynx_Whitepaper_v1.1.pdf(2021/11/12) 14. The Verium Team. Verium the reserve: the world’s first CPU mineable digital commodity. Available at https://vericoin.info/verium-digital-reserve/(2021/11/12) 15. Komodo Team. Komodo: an advanced blockchain technology, focused on freedom. Available at https://cryptorating.eu/whitepapers/Komodo/2018-02-14-Komodo-White-Paper-Full. pdf(2021/11/12) 16. Komodo Team. The Komodo: documentation orientation. Available at https://developers. komodoplatform.com/basic-docs/start-here/core-technology-discussions/delayed-proof-ofwork.html#ad-foundational-discussion-of-blockchain-security(2021/11/12) 17. The HDAC Team. HDAC: transaction innovation—IoT contract M2M transaction platform based on blockchain. Available at https://github.com/Hdactech/doc/wiki/ Whitepaper(2021/11/12) 18. Voulgaris S, Fotiou N, Siris VA, Polyzos GC, Jaatinen M, Oikonomidis Y (2019) Blockchain technology for intelligent environments. Future Internet 11(10):213 19. Oncescu O. Purple protocol—a scalable platform for decentralized applications and tokenized assets. Available at https://purpleprotocol.org/whitepaper(2021/11/12)

Advance Computing

Smart Spy in the Online Video Calls Without Notifying the Concerned and Alerting the Nearest Receiver Contacts N. Ravinder, S. Hrushikesava Raju, B. Venkateswarlu, Durga Bhavani Dasari, B. Revathi, and Harika Lakshmi Sikhakolli Abstract Now-a-days, video calls are used by everywhere and it became more essential service. In the video calling, the receivers suppose not able to lift the call due to many constraints. The sender may get vex with this kind of unresponsive possessed by the receiver. There are dangerous scenarios when the receiver is in danger or critical stage not able to communicate. The sender might want to know what the receiver is doing although the video call is ringing the receiver will not attempt it. This curiosity leads to develop a novel feature called hidden activation of the camera without notifying the receiver during the video call ringing but the receiver has to enable the spy feature. This feature may also help when the important person is kidnapped and trapped by some violent gang. The feature might show the things that are in front of the camera as well as the backside of it although the receiver mobile is switched off but having reserved battery power. This feature will recognize the scenes that are related to the danger zone clip or the normal clip. If the clip is a suspected zone, it automatically alerts the police near the location of the receiver. If not, the sender will be notified the receiver is busy. The proposed feature aims to know the environment of the receiver. This will benefit society a lot in the issues of the contemporary environment. The results will guide the necessity of this feature and its performance. There are apps to track the victim by the police control room by dialing before entering into the dangerous location but the user cannot know odd things to have happened in advance that time alerts the family contacts as well as close friends contacts. Keywords Automatic · Video calls · Smart phone · Online connection · Novel feature · And notifying N. Ravinder (B) · S. Hrushikesava Raju · B. Venkateswarlu · D. B. Dasari · B. Revathi · H. L. Sikhakolli Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur 522502, India e-mail: [email protected] D. B. Dasari e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_26

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1 Introduction There are many mobile vendors are available in the market. Each vendor supports online messaging services like whatsapp, telegram, and many more, etc. Not only by this mobile vendors but also any camera support device having online messaging service installed, will miss one feature. That is very much necessary for all users. Generally, when the sender will make a video call to someone’s about the tacking of the receiver and their absolute status. The video call while going will have few options but those options will not support what receiver is doing without receiver intervention. There are situations that demands to know receiver environment status. If the receiver is switched also, the available online messaging services apps will make call ringing unnecessarily. In that running video call, only sender who made video call, would see the environment of the send only but not know receiver environment although the receiver unable to pick the call. This might actually transcends the scenario to another level where suppose the receiver is in trapped by some gang and the receiver mobile is switched off and is laid on the floor or some place. That situation, the information of the receiver’s environment is known to the sender when sender makes a video call. The feature is now required that is added to mobile vendor or any device as built-in service that could be added when online messaging service app is installed. The proposed feature entitles as hidden activation of the camera without notifying the receiver side. This feature demanded now-a-days by every end user. The time might curse lives of few people that might suppose utilize this kind of novel feature will alert the police if the captured environment is danger zone. The danger zone identified by hidden activation of camera might send pictures also the police to understand the severity of the environment. The severity is identified by recognizing wild ornaments, and abuse words. Hence, the proposed method is much essential now-a-days for human kind. This introduction follows the proposed method working prototype and architecture and its pseudo codes scenarios that follows results where performance and scalability of the proposed method is discussed and finally overall concept is pin points its strategic nature in the conclusion. The proposed method originates its shape by referring many manuscripts and studies, cases published in many real time scenarios.

2 Related Work There were few studies that describe only for processing video calls in the mobile gadgets or other smart gadgets. There is no method dealing with on the camera although mobile or gadget is off, also send the snaps of the situation to the sender who would like know the receiver status, also like to know the location of the gadget through camera feature and GPS feature. The existing methods and their shortfalls are discussed in this chapter. According to [1–4], these sources describe about various techniques to support online messaging. Also, they tell about benefits as well as

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drawbacks of the techniques. Also, discusses about each technique is now outdated as well as overwritten by which new technique. With respect to works [5–9], conveys the usage of video calling and chatting where different people will talk with each other in video mode apart from different locations. According to [10–15] demonstrates that video call mechanisms supported by various apps and techniques that provide this kind of service. According to [16–18] the both advantages as well as disadvantages are specified using video calls. In this works also, the ways to provide video calling, and the ways without internet also. With these approaches, only objects will interact and will have live experience. Among these methods are discussed in two categories, nowhere the necessity of activation of camera as well as GPS service is not discussed. In [19], the discussion is done on video communication in a home through mediating factor. In [20], the video communication how it will be done in the future and the required facilities are discussed. In [21], the design of power factor is discussed and its controlled is done through a novel approach. In [22], the advanced level of correction and controlling the power voltages through electric spring. In [23], the factors such as throughput and energy consumption are considered for cognitive radio networks. In [24], the discussion is on reactive power control by single stage kind of motor using Solar type PV inverter. In [25], the cloud-based attacks, detection and prevention through a framework is discussed. In [26], the approach for securing an IoT device is discussed in a hybrid manner. In [27], the generation of hybrid distributed systems through optimization techniques. In [28, 29], the specific protocol is discussed for IEEE 802.11 type mobile adhoc wireless network. In [30], predicting the prices of the gold daily using specific models such as ARIMA and FFNN. In [31], the demonstration is on usage of Internet of Technology and sensors, their interaction in order to communicate and generate automatic reports as per the application. In the regard of [32], the present pandemic is to be restricted using the digital mask that reports the virus in the environment that the user is currently staying. The mask designed will provide statistics about the objects in the present environment. With respect to [33], the IoT is used in detecting the location and automatically takes its currency and converts that into the user’s currency. This user flexibility is provided in this context. As per [34], the IOT is used in the power banks and portable devices in order to exchange charging power in the user-friendly atmosphere. The customized way of charging is done through the designed app and IOT technology. With respect to the description given in [35], the IOT is used in communicating the weighted objects falling to the other devices in order to catch it and send it gently to the ground using automated net. As the information of [36], the IOT is used in the industries where level of gas is monitored and detects the leakage if any such is identified during the passage of gas over the pipes that are placed from the source to destination. This detection avoids harmful incidents over the people. From the aspect of [37], the IOT is useful over the users in such a way that users health bulletin to be monitored and provides a guide to maintain the fitness based on food diet. In the view of [38], the IOT and GSM are used in determining the popular places when a user wants to make a trip in the world. The guidance is to be provided about the top places and ranked places in those cities along with route map. As per the source demonstrated in [39], the GSM

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and IOT are used to monitor the garbage bins and alert the nearest the municipal office in order to clear it which avoids wastage of visiting many times of that bin. In the regard of [40], any intrusion is detected in the IOT based internet environment in the homes, which should be alerted and avoids future inconveniences. In the aspect description given in [41], the detection of premature bosom irregularity in the images related to especially personal healthcare systems is discussed and the role modality is explored in processing the system. With regard of demonstration of info in [42], the human effort is maximum minimized and increases automation through the app based on IOT devices and checks the eye sight remotely. The significance of IOT is clearly depicted and would be useful in making the proposed system. Among these methods discussion was taken in two categories, nowhere the necessity of activation of camera as well as GPS service is not discussed. Only interaction on video mode is discussed. The discussion about receiver’s status is not revealed when receiver is in hopeless stage. The hopeless stage may be judged by the circumstances that dragged into that panic situation. The receiver may be in that hopeless situation because of many factors such as kidnapping the receiver with the expectation of money or personal abuse, the receiver is trapped by a gang of violence, and sometimes, the receiver itself likes to undergo a panic world. Any of these circumstances would cause the receiver to enter into this kind of situation. To know the receiver status, one of the solution is made is online video call through the smart device. Even though, the receiver smart phone is switched off or the phone is locked mode or not in a position to pick up the video call, this proposed theme “novel feature for hidden activation of camera as well as activation of GPS service” will be demanded.

3 Proposed Approach In this, the proposed method named hidden activation of camera necessity in the gadgets that consist of online messaging service apps, working of the model through architecture, execution of it through pseudo code and its benefits are discussed. The online video call from a sender’s mobile gets receiver gadget id through online messenger installed in their mobile. Through online messenger like whatsapp or any other they has in their mobile, will activate the few components of switched off mobile also. This invention is demanded especially spy of one country was struck at their opponent country authorities. It is high demanded situation for the victim’s country to pass information of that spy to their family members. Compared to this, it will also be serve as identifying the victim at their place or in specific area through GPS also to be activated along with camera of that mobile. The impact of a novel attribute in the online video calls leads to do more works such as receivers online messenger software will activate the camera as well as location using GPS utility when some amount of battery is preserved. The screenshots of results will be discussed in results chapter.

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Now, in this proposed approach, the architecture, the pseudo procedure, and flow of interaction among the modules are discussed. In the following architecture, the modules identified are (a)

(b)

(c)

(d)

Sender video call software: The sender may use any of online messaging services like whatsapp, skype, face messenger, and, etc. When the sender installs the novel feature, it asks the sender to choose from contacts for lovable care takers security. Once the receiver accepts with mutual oral opinion, that feature will be added in the online video call services. Receiver video call software: When the receiver accepts the sender request for spying camera, the feature would be added. During receiver is not traced out, sender will use this feature and get status of the receiver. Novel feature on a video call (Danger alert app through video call): This feature supplies some cache amount of battery to the gadget, activates the camera and will give control to the sender, the sender will now monitor the receiver environment and will get details like location, and pictures of the environment in a report. Communication gadget: The report whatever is generated is stored in the cloud and will be sent to the sender.

The pseudo procedure for this proposed work is as follows: Pseudo_Procedure Impact_Smart_Spy_feature(sender,receiver,Nfeature): Input: sender, receiver Output: report after novel feature activated Step 1: Call the sender video call(Nfeature,receiver): 1.1 Choose the receiver and make a videocall 1.2 On the Nfeature, Calls the NovelFeature module 1.3 Status of receiver to be known Step2: Call the Novelfeature(battery,camera,GPS): 2.1 Supplies cache amount of battery 2.2 Activates the camera silently 2.3 Activates the GPS with remote sender data connection 2.4 Generates report would consists of location as well as sequence of pictures, and video Step3: Calls the receiver online video call module receiver video call(environment): 3.1 Through Data Connection GPS service is activated and location is extracted. 3.2 Camera activated and silently captures the environment in both front side as well as rear end. 3.3 Report is generated about these two

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Fig. 1 Flow of modules in Impact of novel attribute in online video calls

Step 4: Calls Communication Gadget (report): 4.1 Stores the report in the cloud The flow of these modules is specified in visual diagram so that end user will understand in a better way depicted in Fig. 1. The activities, the interaction of modules and flow is specified in ER diagram that was depicted in Fig. 2.

4 Results will be added at the sender video In our theme, the novel feature smart spy call service. The icon is added after installing novel feature called spy on the video call (Fig. 3). When clicks this spy, without receiver acceptance or permission, the receiver environment could be recorded and sent that report to sender who made a video call. The sequence of pictures are captured along with recorded video is created and is sent to the send who made a call. This is more secure because it monitors only as a spy when receiver and sender accepts the requests. There is an abuse to be happen without the receiver acceptance. The aim of this work is to minimize the crime rate

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Fig. 2 Demonstration of Smart Spy feature in the online video calls

Fig. 3 Novel feature smart spy is included in the supported online video call bot

as well as to protect the victim during the unavoidable situation also. By the sender’s and the receiver’s mutual understanding only, the novel feature app named virtual spy on a video call works. The crime rate reduced by 1% also greatly influences the population and their morality (Fig. 4).

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Fig. 4 Snaps from the 360° rotation

Along with sequence of images, video of that environment, location is also saved in the same directory (folder). The directory consists of sequence of images, recorded video, and the location. This is the report to be sent to the sender or agency of the originator. The following graph Fig. 5 depicts the crime rate using impact of spy vs traditional approach:

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Fig. 5 Traditional monitoring versus automatic spy on an online video call

The privacy of the receiver should be responsible by the receiver side only by enabling particular features. The scenarios like doing bathing by especially women, of bed room, are confidential time. To turn off this feature, an option in settings that will be off like similar to Wi-Fi on or off. The below is the visual graph Fig. 6 that demonstrating the accuracy of existing approaches against the spy. From Fig. 6, the traditional approach may not guaranty because it may take more time for saving the victim and is manual dependent, second approach is alert_emergency_approach may not be possible to communicate by the receiver and is purely depend on the victim responses and third approach smart spy would communicate immediately to the nearest contacts in the receiver book and alert the police control station to take action against that activity.

Fig. 6 Accuracies of smart spy against the listed approaches

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5 Conclusion The final output of this proposed approach is getting information of the victim is reported in terms of pictures of that location as well as location of that victim. With this information, the sender who is genuine means they are authenticated, will perform further step in order to save the victim or will trace the victim. The novel feature invented is activating the hidden camera in the switched off mobile or gadget, extracting the details of the victim through the IoT devices, and generated report will be sent to sender who made a video call to the receiver. Whenever the novel feature is added in their gadget, then only receive gadget will become control by the sender who makes a video call. The more benefits of this proposed theme are receiver’s status could be known, Activating the GPS and Camera of the gadget and silent capturing of the location in both the directions, and generating the report that consist of information of the victim. This helps to the sender who is having a trust from the receiver, will take action based on the information present in the report. This is used in many real time applications where a spy’s status or location is not known as well as the loved ones location or status is not known, there it will be useful and extract the details.

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An Exploratory Study on Internet of Things for Covid-19 Pandemic Rydhm Beri

and Raju Kumar

Abstract Internet of Things (IoT) has gained popularity since last recent years and applied in wide variety of applications including education, homes, offices and healthcare industry. IoT field changing the healthcare system by considering factors like economy, technology and social by which patients can be diagnose, treat or monitored in effective way. The world is facing current global challenge which can severe the respiratory syndrome Covid-19 which leads to serious health issues and even huge mortality. There are more than 31 cr. of people around the world suffering from Covid-19 with more than 5 cr. of mortality, when this paper was written. Since the pandemic started, several researchers put their efforts to use technologies to save world from effects of this virus. IoT plays a major role during this pandemic. IoT-enabled devices or applications helps to reduce the chances of virus spread of Covid-19 by early diagnose, monitoring of patients during active or recovery phase. This paper discusses the role of IoT during Covid-19 and how IoT implementation in healthcare helps in the phases of diagnosis, isolation and recovery. Keywords Internet of things · Covid-19 IoT · IoT applications · IoT in pandemics

1 Introduction Internet of things (IoT) is the term coined by Ashton during the implementation of RFID (Radio Frequency Distribution) for supply chain management organization Procter and Gamble [3]. IoT is the interconnection of several objects in a network with lesser or zero human interaction [1]. Specifically, IoT network is made up any object that can be connected within the network for monitoring or transferring information from one end to the other [8]. Recently IoT, has gained huge popularity and introduced as a new research area to the world which can be applied in several fields including, education, medical, R. Beri (B) · R. Kumar University Institute of Computing, Chandigarh University, Mohali, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_27

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industry, etc. Implementation of IoT in healthcare industry offers several benefits related to economical, technological and social perspectives. It is creating healthcare industry smart to monitor and diagnose health condition of the patient at his door step. IoT is increasing day by day in such a manner that it is now becoming the necessity of the individuals. IoT is increasingly becoming a vital technology in healthcare systems by offering health related services at low cost with better service quality and advanced user and health professional’s experience [6, 9, 11, 13]. As per the increasing capabilities of IoT network including health tracking, disease diagnosis, real-time healthcare data collection, it is estimated that revenue of IoT in healthcare is going to rise from 188 billion USD in 2025 [10]. Coronavirus 2 represents as a biggest syndrome for global public health crisis since pandemic influenza outbreak of 1918 [18]. As per the latest stats presented by World Health Organization (WHO) there are around 318,648,834 confirmed cases including, 5,518,343 deaths around the world till Jan 16 2022 [25]. The common symptoms associated with this disease includes fever, fatigue, cough, sneezing which taken by several individuals as the normal illness. But soon it took the strength of the body which may cause the death of the individuals. This illness needs to the diagnose at early stage so that the lives can be saved from this deadly virus [20]. The isolation period for patients suffering from Covid-19 ranges from 1 to 14 days depends upon the health condition of the person. The period of isolation is very much necessary because the patient without the symptoms can spread Covid-19 to other person in the society [15]. Although this disease is very dangerous and recovery period depends upon several factors including, patient’s age, immune system, environmental conditions surrounding the patients and so forth ranges from 3 days to 1.5 months [23]. Covid-19 have high potential to spread between the individuals even with many ongoing efforts used for mitigation of the spread. The patients suffering from Covid-19 needs to be deal in an effective way while considering the safety of the other person [19, 21]. The major contribution of the study is to identify the contribution of IoT technologies in to track or control the impact of Covid-19 along with the contribution of IoT solutions in the stage of Covid-19, i.e., early detection, isolation period and recovery period. Figure 1 shows the implementation of IoT-enabled devices and applications in pandemic. The detection of Covid-19 at early stages can stop the virus spread which leads to the breakage of Covid-19 chain [22]. Isolation period of suspected or confirmed cases increasing lockdowns which decrease the number of people get affected by Covid-19. Tracking the health conditions of the patients recovered from Covid-19 leads to the benefits for monitoring symptoms and potential infectivity [26].

2 Review of Related Studies The world is scarping by the health issues caused by pandemic Covid-19 by trying to control virus spread or reduce the mortality ratio occurring due to the virus [12]. As there is an increase in patients of Covid-19 even after two vaccination shots and there are prediction alerts regarding the third wave of Covid-19, some global

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Fig. 1 IoT-based applications utilized in Covid-19

monitoring system is required on an urgent basis to find the appropriate treatment or control the health issues related to Covid-19 or spread of Covid-19 virus. IoT has gained the attention of individuals in the healthcare field by offering health related services to the patients and doctors since past several years [5]. During the pandemic like Covid-19, there is a requirement of contact-less treatments of the patients so as to reduce the virus spread. The present study investigates the utilization of the IoT technology in several phases of Covid-19. Moreover, the study also discussed that what devices or applications will be used in Covid-19. Early diagnosis [14] of Covid-19 helps to reduce the virus spread where the patients does not have specific symptoms related to Covid-19. The early diagnose of Covid-19 also helps to reduce the impact of health related issues and the patient can get the required treatment ontime. The IoT devices can capture the information from patients in real-time includes, sample collection, body temperature checking or heartbeat variations and so forth using sensing technology, which speed up the process of diagnose. Moreover, the quarantine period [12], considered as the major period in which the patient needs to be isolated from the outside world to reduce the virus spread. The IoT devices can help in this phase by monitoring the patients remotely and provide the appropriate treatment to them [16]. The IoT devices like disinfecting devices, can also sanitize the areas without human intervention or wearable bands can track the help conditions of the patients. Centers for Disease Control and Prevention (CDC) [24], states that the mild symptoms patients can be recovered by isolation at home without any treatment. These patients needs to take care of themselves as they can reinfect even after recovery. The person can be reinfected with different symptoms of coronavirus-2 [4]. After recovery phase, if person will be reinfected, then the chances of potential infections and returning symptoms can be high. So it is necessary to maintain social distancing to reduce the effect of virus. The implementation of IoT devices like wearing bands, crowd monitors, can track the individuals and can be used to ensure appropriate distance maintaining. Actually IoT devices when implemented to handle the situation

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of Covid-19, proved as a useful resource for healthcare professionals, patients and government authorities. This section discusses about various IoT devices or applications like robots, drones, wearables, smartphones that can be prove as a useful resource for handling different situations in Covid-19. The specification of some of the IoT devices that can be used during Covid-19 is elaborated in Table 1. IoT devices can be proved as useful resource for isolated patients in their daily lives. Once the offices and business organizations will open gradually, then IoT devices can help to maintain safety efficiently. Several researches are going to for all these concerns so that we will start living in an open environment freely. Besides the several benefits offered by IoT device implementation in Covid-19, there is one major concern that arise is security and privacy. This concern occur due to the fact to increase contact-less services, IoT devices captures huge information regarding the patients which is a big concern. So, secured and encrypted communication channels implementation is the major area of the research while utilization of IoT device in pandemic. Smart cities enabled with IoT devices proves to be helpful in combining present or any future pandemic by collaborating healthcare centers and cities [17]. Besides the implementation of IoT applications discussed so far, Allam and Jones [2] emphasize the implementation of network of smart cities to handle the Covid-19 pandemic across the world. The implementation of the smart cities infrastructures helps to maintain social distancing by implementing smart transportation system includes smart parking, crowd detection and traffic re-routing [7]. Smart homes in smart cities can also helps to reduce the impact of virus by implementing smart home security systems and home doorbells to prevent touching surface for reducing the virus spread by touching the surface [17, 27].

3 Proposed Model The proposed model is related to identifying several health parameters of the patients with Covid-19 in real-time environment without getting contact with doctors or other individuals. The proposed system is divided into three different but related layers. The layers are related as every layer is responsible to provide some services to the layer above it. The proposed system is shown in Fig. 2.

3.1 Sensing Layer Sensing Layer (SL) is responsible to capture health related information about the Covid-19 patient in real-time environment. This data can be useful in different ways including analysis of the health condition of the patients, recovery rate checking by the doctors, the impact of Covid-19 onto the different organs of the patients and also prediction of different health related issues that can occur at the later stages. The sensing layer allows the contact-less checkups of the patient which helps to reduce virus spread.

An Exploratory Study on Internet of Things for Covid-19 Pandemic Table 1 IoT devices can be used during pandemic Description Technology Wearables

These are the devices that individual can wear to monitor the health conditions in real-time environment. These devices may build-up from several sensors to capture data and also the network capabilities so that the data can be transferred to the doctors in real-time

Smartphone applications

The applications that installed on mobile phones which also allow us some of the health parameters including, heart rate, walk tracking, etc. Some of the applications also provided to get information regarding the containment zones or the number of Covid-19 patients in nearby locations or allow us to track the availability of vaccination centers Drone is an aircraft system in which several sensors are attached to collect data. Moreover, drones includes the GPS system to share the location to the server and finding the path to the receiver’s address. Drones can be used in Covid-19 in variety of ways ranges from medicine delivery to real-time checkups Robots are the machines that behaves in a similar way as that of the human. These are having processing as well as decision making capabilities

Drones

Robots

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Benefits – Monitoring of health parameters in real-time environment – Improves the quality of healthcare services provided to the patient at his doorstep – Safer to use – Improves the contact-less treatment – Helps to maintain social distancing – Track the health activities performed by the individuals – High utilization with less or no cost

– Capable to perform several tasks including searching of locations, real-time monitoring and products delivery – Reach to the location which are hard to access – Less human interaction is required

– Contact-less real-time monitoring – Used to maintain social distance by disinfecting environment without human intervention

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Fig. 2 IoT based applications utilized in Covid-19

3.2 Data Processing Layer Data Processing Layer (DPL) is responsible to process the data in different ways. Firstly, DPL removes the abnormalities from the data captured in SL. Then the intelligent algorithms will be applied on the processed data on the basis of which further risk assessment will be performed.

3.3 Analysis and End-User Layer In Analysis and End-User Layer (AEL), the analysis of the data is performed. On the basis of the analysis performed in this layer, the prediction of different health related issues is performed. This analysis helps doctors to better understand the health condition of the Covid-19 patients. This analysis made available to the doctor independence of their location and device.

4 Conclusion and Future Aspects Several technologies has been implemented to fight against the Covid-19 pandemic. One of these technologies is IoT which proves as a life saver in healthcare industry. IoT has shown several interesting results to deal with the issues related to the virus. The current study is based on the survey of different IoT-based smart devices or applications that assists health professionals and professions during Covid-19 pandemic. We discussed about the different IoT technologies used in phases of Covid-19 including early detection, isolation and recovery phase. We evaluated several IoT-enabled technologies or applications including, wearables, smartphone, drones and robots along with the benefits or issues related to these technologies. IoT devices found to

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be very useful in pandemic but the major concern in all of the IT-enabled technologies and applications includes security and privacy of information. The improvement in security related aspects, increase the utilization of IoT devices by more patients during their treatment. Which results in better health recovery of the patient and health professionals can handle the pandemic in a better way. Consequently, the effects of the disease, mortality ratio and hospitalization can also be reduced. The future aspect of the present study is the implementation of IoT-based healthcare system proposed in the current study. The proposed model found to be useful in contact-less checkups and health consequences to the patient due to Covid-19.

References 1. Ali ZH, Ali HA, Badawy MM (2015) Internet of things (IoT): definitions, challenges and recent research directions. Int J Comput Appl 128(1):37–47 2. Allam Z, Jones DS (2020) On the coronavirus (COVID-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. In: Healthcare, vol 8. Multidisciplinary Digital Publishing Institute, p 46 3. Ashton K (2009) That ‘internet of things’ thing. RFID J 4986. http://www.rfidjournal.com/ articles/view 4. Can you get Covid-19 coronavirus twice? Here is an update on immunity. https://www.forbes. com/sites/brucelee/2020/07/19/can-you-get-covid-19-coronavirus-twice-here-is-an-updateon-reinfection/?sh=a2282587cbf4 5. Christaki E (2015) New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence 6(6):558–565 6. Da Costa CA, Pasluosta CF, Eskofier B, Da Silva DB, da Rosa Righi R (2018) Internet of health things: toward intelligent vital signs monitoring in hospital wards. Artif Intell Med 89:61–69 7. Gupta M, Abdelsalam M, Mittal S (2020) Enabling and enforcing social distancing measures using smart city and its infrastructures: a COVID-19 use case. arXiv preprint http://arxiv.org/ abs/2004.09246 8. HaddadPajouh H, Dehghantanha A, Parizi RM, Aledhari M, Karimipour H (2019) A survey on internet of things security: requirements, challenges, and solutions. Internet Things 100129 9. Hu F, Xie D, Shen S (2013) On the application of the internet of things in the field of medical and health care. In: 2013 IEEE international conference on green computing and communications and IEEE internet of things and IEEE cyber, physical and social computing. IEEE, pp 2053– 2058 10. Internet of things market size, share and global market forecast to 2022 | COVID-19 impact analysis | MarketsandMarkets. https://www.marketsandmarkets.com/Market-Reports/internet-ofthings-market-573.html 11. Islam SR, Kwak D, Kabir MH, Hossain M, Kwak KS (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708 12. Nussbaumer-Streit B, Mayr V, Dobrescu AI, Chapman A, Persad E, Klerings I, Wagner G, Siebert U, Ledinger D, Zachariah C et al (2020) Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review. Cochrane Database Syst Rev 9 13. Peeri NC, Shrestha N, Rahman MS, Zaki R, Tan Z, Bibi S, Baghbanzadeh M, Aghamohammadi N, Zhang W, Haque U (2020) The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? Int J Epidemiol 49(3):717–726 14. Phelan AL, Katz R, Gostin LO (2020) The novel coronavirus originating in Wuhan, China: challenges for global health governance. JAMA 323(8):709–710

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15. Quarantine if you might be sick 16. Rahman MS, Peeri NC, Shrestha N, Zaki R, Haque U, Ab Hamid SH (2020) Defending against the novel coronavirus (COVID-19) outbreak: how can the internet of things (IoT) help to save the world? Health Policy Technol 9(2):136 17. Responding to the COVID-19 pandemic—a framework for cities. https://www.iiot-world.com/ smart-cities-buildings-infrastructure/smart-cities/responding-to-the-covid-19-pandemic-aframework-for-cities/ 18. Scientists say the coronavirus is at least as deadly as the 1918 flu pandemic. https://www. cnbc.com/2020/08/13/scientists-say-the-coronavirus-is-at-least-as-deadly-as-the-1918-flupandemic.html 19. Singh RP, Javaid M, Haleem A, Suman R (2020) Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes Metab Syndr Clin Res Rev 14(4):521–524 20. Symptoms of COVID-19 | CDC. https://www.cdc.gov/coronavirus/2019-ncov/symptomstesting/symptoms.html 21. Ting DSW, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 26(4):459–461 22. To KKW, Tsang OTY, Yip CCY, Chan KH, Wu TC, Chan JMC, Leung WS, Chik TSH, Choi CYC, Kandamby DH et al (2020) Consistent detection of 2019 novel coronavirus in saliva. Clin Infect Dis 71(15):841–843 23. Wang W, Tang J, Wei F (2020) Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. J Med Virol 92(4):441–447 24. What to do if you are sick | CDC. https://www.cdc.gov/coronavirus/2019-ncov/if-you-aresick/steps-when-sick.html 25. WHO coronavirus (COVID-19) dashboard | WHO coronavirus (COVID-19) dashboard with vaccination data. https://covid19.who.int/ 26. Xing Y, Mo P, Xiao Y, Zhao O, Zhang Y, Wang F (2020) Post-discharge surveillance and positive virus detection in two medical staff recovered from coronavirus disease 2019 (COVID-19), China, January to February 2020. Eurosurveillance 25(10):2000191 27. Zhang K, Ni J, Yang K, Liang X, Ren J, Shen XS (2017) Security and privacy in smart city applications: challenges and solutions. IEEE Commun Mag 55(1):122–129

A Study of E-commerce Platform Issues Shared by Developers on Stack Overflow Yusuf Sulistyo Nugroho, Syful Islam, Dedi Gunawan, Yogiek Indra Kurniawan, Md. Javed Hossain, and Mohammed Humayun Kabir

Abstract Selecting an e-commerce platform is a very crucial task when developing an online store. Diversity of technology stacks has led to dozens of ecommerce platforms with a variety of features. Previous studies have shown the rapid growth of the e-commerce applications development. However, the problems faced by e-commerce application developers have not been analyzed. In this study, we explore 19 e-commerce platforms to understand whether their features correlate with the experience of developers. By analyzing the questions that developers ask on a question-and-answer platform, Stack Overflow (SO), we find that developers discussed 17 e-commerce website development-related issues that can be grouped into 5 major themes (i.e., database management, payment options, sales features, website design features, and website errors). The results also indicate that the technology stack of e-commerce platforms correlates to the development and maintenance experience of an online store by developers. Keywords E-commerce platform · Developers issues · Stack overflow

Y. S. Nugroho (B) · D. Gunawan Universitas Muhammadiyah Surakarta, Surakarta, Indonesia e-mail: [email protected] D. Gunawan e-mail: [email protected] S. Islam · Md. J. Hossain · M. H. Kabir Noakhali Science and Technology University, Noakhali, Bangladesh e-mail: [email protected] Md. J. Hossain e-mail: [email protected] M. H. Kabir e-mail: [email protected] Y. I. Kurniawan Universitas Jenderal Soedirman, Purwokerto, Indonesia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_28

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1 Introduction The rapid growth of Internet technology and the desire to reach customers from worldwide lead business institutions to adopt e-commerce technology [5]. E-commerce changes the way a business runs specifically in trading. Traditionally, a trading can be conducted when a seller meets a customer physically in a market. However, rather than meeting physically, one can use e-commerce applications to start a transaction virtually and easily. A survey [12] describes that during the last five years, retail e-commerce generates more than 4.2 trillion US dollars. Due to this promising potential revenue, companies start to compete to build their e-commerce platform. To realize their goal, the companies need to set up or hire a software developer team. However, developing an e-commerce platform is not a trivial task specifically when the team should build it from the scratch. Therefore, the developer team needs to discuss and find solutions of various issues during the development process. Several studies have reported the significance of e-commerce and platforms. For example, a prior work states that to make e-commerce become something more than just a marginal business activity, marketing capabilities need to be paired with digital business capabilities [10]. Digital business capabilities are the competencies by which companies align digital technology features with customer needs and wants [3]. For example, as the Auction House continually improves its website’s content, user experience and service features are based upon analyzing customer behavior and responses. The other study that analyzed 144 e-commerce platforms reports that there is a wide range of SME e-commerce platforms and this is better understood by viewing them in their strategic groups and by taking into account their size as an important measure of online success [6]. During the pandemic, the e-commerce topics have also been used in many papers to analyze the trends [2], the roles [8], and the effectiveness management [11]. However, the problems faced by e-commerce developers have not been analyzed in previous studies. In this paper, we investigate the e-commerce platform-related issues shared by developers in Stack Overflow (SO). We found that the e-commerce developers discussed 17 e-commerce website development-related issues which can be grouped into 5 major themes (database management, payment options, sales features, website design features, and website errors). In addition, developers face different type of issues depending on the e-commerce development platform choices. Technology stack of e-commerce development platform correlates with development and maintenance experience of developers.

2 Data Collection To understand the issues faced by developers that relate to different e-commerce platforms, we analyzed the developers discussion shared on Stack Overflow. We

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Table 1 List of the 47 manually validated tags discovered from step 2 of the data preparation Discovered relevant tags Initial tag E-commerce Virtocommerce, shopping-cart, enhanced-ecommerce, satchmo, commerce, django-oscar, oscommerce, broadleaf-commerce, spree, zen-cart, google-checkout, payment-processing, ubercart, activemerchant, authorize.net, credit-card, payment-gateway, marketplace, pci-compliance, hybris, magento-1.5, opencart, catalog, bigcommerce, cart, recurring billing, shipping, prestashop-1.5, virtuemart, shopify, prestashop, nopcommerce, payment, magento-1.7, magento-1.6, sylius, opencart2.x, magento-1.4, checkout, prestashop-1.7, magento, prestashop-1.6, magento-1.8, paypal, magento-1.9, woocommerce These tags were used to identify e-commerce-related posts

initially downloaded the SO data dump that is publicly available on the SOTorrent [1]. To collect the e-commerce platform relevant posts, we followed three distinct steps. In the first step, we used e-commerce tag to extract 4380 questions. Second, we extracted the co-occurring tags with e-commerce from 4380 posts to discover relevant tags. We manually checked the tags that co-occurred with e-commerce tag. The output of step 2 is manually validated 47 tags (i.e., Table 1) that associates with 19 e-commerce platform-related discussions. Table 2 shows the curated list of e-commerce platforms and their features used in our study. In the final step, we used the 47 tags to identify and extract posts to create final e-commerce post dataset. The output of this step is 114,949 SO question posts that relate to 19 e-commerce platforms used as the final dataset for the subsequent sections. We made the full e-commerce replication package publicly available online to ensure the reproducibility and reliability of our dataset and results. The replication package is available at: https://github.com/syful-is/E_commerce_replication_ package.

3 Methods In this study, we performed two analyses of developers’ issues from two different perspectives, that is, (i) question topics and (ii) e-commerce platform features.

3.1 Question Topics Exploration To explore the e-commerce topics from the dataset, we applied the latent Dirichlet allocation (LDA) topic modeling technique, which is also used in related work [4, 7]. First, we extracted the title of the posts and performed a preprocessing. This includes removal of emails, newline characters, stop words using regular expression1 and 1

Regular expression: https://docs.python.org/3/library/re.html (access date: January 2021).

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Table 2 List of e-commerce development platforms investigated in our study Business type Technology stack E-commence platform Magento Opencart Virtocommerce PrestaShop SpreeCommerce SimpleCart Sylius Broadleaf-commerce nopCommerce Shopify Virtuemart BigCommerce Hybris UberCart Zencart OsCommerce Django-oscar Satchmo Woocommerce

Midsize to enterprise Small to midsize stores Enterprise Small business Growing business Small to midsize stores Midsize to enterprise Enterprise Small to midsize stores Small stores to enterprise Small stores Enterprise Enterprise Small stores Midsize to enterprise Small stores Small stores to enterprise Midsize to enterprise Small stores

Php, mysql Php, mysql ASP.NET, GraphQL Php, Symfony, MySQL Ruby on rails, REST, GraphQL JavaScript, no database Php, Symfony Java, Spring and Hibernate ASP.NET, MS SQL Ruby on rails, REST, GraphQL Php, mysql Node.js Java, SAP hybris Drupal6 and 7 Php, MySql Php, MySql Django Django Php, MySql

python NLTK.2 We subsequently built a bi-gram model using Gensim3 and lemmatized the words to map the original words. Next, we used the Mallet implementation of the LDA technique [9] to create group of posts in our e-commerce dataset based on the keywords that exist in the dataset of title. To obtain the optimal number of topics k, we performed the modeling process in several iterations. Initially, we run the LDA for range (0–50) with 3 step size increment. We chose the sub-optimal range (14–44) based on the coherence score and run the model again for sub-optimal range with 1 step size increment and thus optimally come up with 27 topics with coherence score = 0.4021. Then, we run the model with topic number k = 27 and obtain 27 e-commerce topics with their associated keywords (10 keywords per topic). Finally, we manually labeled each topic merging a few in the process based on associated keywords, and we obtained 17 topics that can be grouped into 5 major e-commerce themes.

2 3

Python NLTK: https://www.nltk.org/ website (access date: January 2021). Gensim model: https://radimrehurek.com/gensim/ (access date: January 2021).

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3.2 Choice of E-commerce Platform and Developer’s Experience Co-relationship To investigate whether the choice of e-commerce platform co-relate developers experience, we initially performed mapping tasks of each e-commerce post based on the platform name and its technology stack as shown in Table 2. For instance, an e-commerce post tagged with Magento is identified as a post related to Magento e-commerce platform, and its technology stack is PhP, MySql. Thus, we characterized each SO post by e-commerce development platform and its technology stack. To analyze the differences between the features of a e-commerce platform and the kinds of topics that users ask on SO, we visualized them in a heatmap. The heatmap uses colored cells to show a two-dimensional matrix between the topics and the features of the e-commerce platforms. We show the frequency of each dimension that is reflected in the colored cells. The results of our analyses are discussed in two folds: (i) contrasting e-commerce development platforms with theme and topics and (ii) contrasting technology stacks with theme and topics.

4 Results 4.1 Question Topics Exploration LDA topic modeling on SO posts suggests that e-commerce website developers mainly discuss 17 topics which can be grouped into 5 major themes. The online appendix4 shows the theme, topics, and their associated topic keywords obtained from e-commerce-related post using LDA modeling. In addition, Fig. 1 shows the distribution of e-commerce website development-related topics and their higher level theme. The five main themes are database management, payment options, sales features, website design features, and website errors. Among the e-commerce platformrelated themes, sales features (38.45%) is the most discussed issues in SO discussion platform by developers. The second most discussed theme by developers is website design features (28.75%) followed by website errors, payment options, and database management, respectively. ● Contrasting e-commerce development platforms with theme and topics. In contrast of e-commerce themes and the development platforms as shown in Fig. 2a, we observed that developers face different types of issues depending on the e-commerce development platforms choice. For instance, e-commerce development platforms like Sylius, nopCommerce, Satchmo, and broadleaf commerce have more issues with website design features. E-commerce platforms like Woocommerce, UberCart, Magento, and Virtuemart have higher sales featuresrelated issues. In detail, as shown in Fig. 3, e-commerce websites developed using 4

LDA suggested e-commerce topics and keywords: https://cutt.ly/dQ5sZtA.

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Theme

Website errors

Database management

Website design features

Payment options

# Stack Overflow posts (%)

38.45 Sales features

Fig. 1 Distribution of e-commerce website development-related topics and their higher level themes

(a) E-commerce platforms vs. theme.

(b) Technology stack vs. theme.

Fig. 2 Analysis of issues faced by e-commerce developers based on a e-commerce platforms and b the use of technology stacks

Magento, Virtuemart, Woocommerce, and Bigcommerce platforms have more issues with stock item configurations. E-commerce development platforms like Virtuemart, Broadleaf Commerce, and Hybris users have more concerns with installation errors. While Sylius, nopCommerce, and Satchmo users are commonly struggling with basic website design functionalities-related issues. ● Contrasting technology stacks with theme and topics. In contrast to technology stacks with e-commerce topics and theme as shown in Fig. 2b, we observe that technology stack of e-commerce development platform correlates with devel-

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Fig. 3 Topic versus e-commerce platforms shows that developers face different type of issues depending on the e-commerce development platform choice

opment and maintenance experience of developers. For example, the e-commerce platforms that are built in PhP, MySQL and ASP.NET, GraphQL technology stacks have relatively easy experience in managing the design features of websites. One possible reason may be their availability of tutorials and strong developers community. In database management activity, our analysis shows that Java, SAP hybris users are the highest affected group followed by ASP.NET, MS SQL and Node.js. In terms of payment options, we find that Spring, Hibernate users are struggling most followed by Django and PHP, Symphony. As illustrated in Fig. 4, we find that ASP.NET, GraphQL has the highest installation-related issues followed by Spring, Hibernate. We also observed that, although PhP, MySQL and Node.js have less issues on managing basic website functionalities, users commonly struggle with stock item configuration-related issues.

5 Threats to Validity The first threat to internal validity relates to our collected data. We acknowledge that some posts may be mislabelled (i.e., missing tags or incorrect tags) on Stack Overflow. The second threat relates to the correctness of techniques used in this study, such as choosing the appropriate number of topics (k = 27) for the LDA model. A different number might have led to different results. Manually labeling the topics based on keywords introduced another threat to the validity. We mitigated this threat by involving multiple authors in the labeling process.

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Fig. 4 Topic versus technology stack used in e-commerce development platforms show that technology stack of e-commerce development platform correlates with development and maintenance experience of developers

6 Conclusion To understand the e-commerce platform issues faced by developers, we performed a study of 114,949 question posts shared in Stack Overflow (SO). In the study, we analyze (i) the topics of questions shared by developers and (ii) the co-relationship between the choice of e-commerce platforms and the experiences of developers. The findings of our study have shown that there are five themes of issues that relate to e-commerce website development, that is, database management, payment options, sales features, website design features, and website errors. Furthermore, the results show that the choice of e-commerce development platforms correlates with the developers’ experiences. Based on this study, there are many open avenues for future work: understanding the main cause of issues faced by developers, and further studies of e-commerce-related SO threads.

References 1. Baltes S, Dumani L, Treude C, Diehl S (2018) SOTorrent: reconstructing and analyzing the evolution of stack overflow posts. In: Proceedings of the 15th international conference on mining software repositories, pp 319–330 2. Bhatti A, Akram H, Basit HM, Khan AU, Raza SM, Naqvi MB (2020) E-commerce trends during COVID-19 pandemic. Int J Future Gener Commun Netw 13(2):1449–1452 3. Chaffey D, Edmundson-Bird D, Hemphill T (2019) Digital business and e-commerce management. Pearson UK 4. Choi S, Seo J (2020) An exploratory study of the research on caregiver depression: using bibliometrics and LDA topic modeling. Issues Ment Health Nurs 41(7):592–601

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5. Crespo AH, Del Bosque IR (2010) The influence of the commercial features of the internet on the adoption of e-commerce by consumers. Electron Commer Res Appl 9(6):562–575 6. Holland CP, Gutiérrez-Leefmans M (2018) A taxonomy of SME E-commerce platforms derived from a market-level analysis. Int J Electron Commer 22(2):161–201 7. Islam S, Nugroho Y, Hossain MJ (2021) What network simulator questions do users ask? A large-scale study of stack overflow posts. Indones J Electr Eng Comput Sci 21(3):1622–1633 8. Lin X, Wang X, Hajli N (2019) Building E-commerce satisfaction and boosting sales: the role of social commerce trust and its antecedents. Int J Electron Commer 23(3):328–363 9. McCallum AK (2002) Mallet: a machine learning for language toolkit. http://mallet.cs.umass. edu 10. Tolstoy D, Nordman ER, Hånell SM, Özbek N (2021) The development of international ecommerce in retail SMEs: an effectuation perspective. J World Bus 56(3):101165 11. Tran LTT (2021) Managing the effectiveness of e-commerce platforms in a pandemic. J Retail Consum Serv 8:102287 12. Verdon J (2021) Global e-commerce sales to hit $4.2 trillion as online surge continues, adobe reports. Forbes

Emerging Trends in Multimedia Shreyas Vijay, Prince Mann, Renu Chaudhary, and Aniket Rana

Abstract The rapid development of the network and devices has made multimedia technology to spread across all the aspects of the people’s life. Multimedia can be defined as the media which interactively spread the information, combining two or more media. Multimedia technology has impacted on our daily lives in various aspects like e-learning, virtual reality, journalism, Content Streaming and many more. The evolution of multimedia has been carried out in various phases over the last decade which has been further discussed under the topic Diversity in Multimedia. In this paper, we intend to explain about the various trends emerging in multimedia and the concerns related to the transmission of the user’s personal data and their privacy. The already existing technologies which are not yet fully explored such as Virtual reality, Hologram, Advanced Journalism and feedback-based content streaming and the security issues that arise with such advancement in technology has been talked about in depth in this paper. Keywords Multimedia · Communication · Authentication · Privacy · Deepfake · Hologram · Virtual reality

1 Introduction Multimedia refers to the combination of various types of media into a single package. The major elements of multimedia are text, video, graphic image and audio frequency S. Vijay (B) · P. Mann · R. Chaudhary · A. Rana Branch of Information Technology, HMRITM, New Delhi, Delhi, India e-mail: [email protected] P. Mann e-mail: [email protected] R. Chaudhary e-mail: [email protected] A. Rana e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_29

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Fig. 1 Multimedia elements

as shown in Fig. 1. Due to the improvement in processing of complex processes such as the transmission of data, be it in form of images or text has shown the potential of union of media forms. With ever growing need of providing content through online platforms, the innovation of blending of technological medium has surged and has given rise to several applications in the field of teaching, entertainment, education, art, scientific research, medicine and so on. The application of multimedia technology becomes more broad according to the need of people’s lives and with the development of communication technology. Conventional media technology was only concerned with providing information to the targeted subject but recent trends include choicebased propagation of a movie or a game which concludes in the manner as per user feedback [1]. The idea behind emerging trends in multimedia is to involve the right subset of channels to provide the relevant content as well as appropriate interface in which such implementations of applications can take place. Integration of such modern prototypes require a combination of the base components of media and hence devices such as holograms and automated teaching assistants can then be used as a part of some bigger developmental process in the future [2]. Multimedia has become an important part of our daily life. Multimedia is used in various fields like in business (sales/marketing presentation, staff training application, trade show production and so on), in education (online classes, e-learning, searching any information), entertainment (streaming videos and movies on OTT platforms, games), in public (smart card services and security) and in communication (video conferencing, SMS services) [3]. Multimedia has proven to be a boon is the field of education. It has made the field of education more effective than the older board

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teaching. Teachers can use the projectors to show the simulations of any topic to the students which has made the students to understand the working of any machine or method more effectively and easily rather than just looking at the figures in the textbooks. In the field of entertainment multimedia can be used to stream any movies on our devices or our smart televisions. The rapid development of multimedia in the last decade has enabled the movies and shows flexible according to the priorities and the timings rather than the older versions of televisions where the shows timings used to be fixed. We can watch any show at any time. Likewise multimedia has become a boon in various other fields too like medical, communication, marketing and so on [4]. These technologies have high capabilities and so they are used in military or hospital equipment and programs so the need of securing such applications increase many folds. Therefore, good encryption, authentication and authorization is necessary for the sustainability of amalgamation of various media components. Also for cloud applications, strong multilayered firewalls should be implemented to protect the data against any type of hacking attack [5].

2 Diversity in Media In the early 1990s the focus in multimedia was on images and by the mid-1990s, multimedia was similar to video. The main objective was to transfer the information through video which was considered as the only important multimedia communication. There have been discussions on the single media vs multiple media occasionally comparing the pros and cons among them. Nowadays with the improvement in communication through media it is necessary to identify the mode through which we wish to provide the information so the need for identification of correct mode or the combination of multiple modes is now of great significance. There has been an increase in the amount of research in the non-speech audio [6]. Due to large scale production and the decrement in the cost factors of sensors, there has been a radical use of sensors in different media such as optical sensor data, infrared, telemetric data of various sorts, financial representation through media, motion sensor information, location data captured by GPS devices, text in assorted formats, haptic sensor data, spatial data and animation data. All these media types are being researched and represented in the latest conferences. Consider the elements of multimedia as a large single set then whenever a request is received to make an application of specific criteria then single or multiple mode of media may be selected as per the requirement and others can be left [7]. Different applications require different sets of media communication and so each one of them is equally important. Nowadays the progress in multimedia can be seen as our field is getting shifted from asking questions about “what is multimedia” to “which is the most suitable and appropriate multimedia”. The problems arising due to the use of the diverse media are the appropriate and significant use of the content and absorbing the effective information in multimedia. The field of audio and visual media focuses on collecting

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the most appropriate and effective data from the huge pile of data available [8]. The work on the sampling of data is done by the engineering approach by generalizing the attention phenomenon to all the data types in the dynamic systems. From being just images and videos in the 1990s to currently being used in various fields (such as in schools for teaching, in business, at home, virtual reality and so on the list goes on) multimedia has progressed a lot and can be seen as the latest trend going on currently [9].

3 Emerging Trends of Multimedia The integration and combination of various technologies has given rise to multiple potential trends of multimedia. The application of various media elements provide multidirectional advancement on the features of existing technologies. The various enhanced trends of multimedia is shown in Fig. 2 [10].

3.1 Virtual Reality Virtual reality is one of the fastest growing areas under multimedia communication. Virtual reality works on the principle of creating an altogether different scenario with the use of headset, special eyeglasses and other devices. Virtual reality creates different scenes as per the requirement of a movie or a game. The use of this technology can be further branched to practical teaching and skill learning activities [11]. With the enhancement of graphics in games and a need to provide a surreal feel to the user, the demand of additional tools and modifications in virtual reality technology has surged exponentially. The gaming industry requires an immersive gameplay in Fig. 2 Emerging trends of multimedia

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which the various environmental conditions such as summer, spring or even dawn and dusk must be represented. These conditions can only feel real when mixed with certain sound effects and graphic representation of the behavior of the weather. Also, to really feel the movement of the character, enhanced modifications which track eye movements and hand motions are done [12].

3.2 Feedback-Based Content Streaming A newcomer in the category of trends in multimedia is user interactive movies. In recent times, the release of movies on online platforms calls for action on providing a lively experience to the user. The movie proceeds on the choices made by the user. This trend is seen in a Netflix movie in which at every crucial point, the user is asked to make a choice from the given two options and then the movie further branches. This movie can only be viewed on television and the feedback is taken through phone. This has been a major leap in multimedia communication as it takes user consideration which leads to the propagation of a series of events in the movie. Options are provided in the form of text and selection is done as per the route we want the movie to take [13].

3.3 Advanced Journalism Communication of information through text media has gone beyond newspapers and articles. Automated journalism has been growing rapidly because of the unbiased reporting and fact checking. Artificially intelligent processes generate journalism that develops news without pointing out a conclusion. This enables the reader to draw conclusions on their own. Furthermore, the users are provided with the option of giving their own viewpoints on the specific topic. This viewpoint, be it in favor or against, is processed with the help of algorithms to determine the percentage of people who support or condemn the policies or acts. A large amount of data such as exit polls can be predicted by feeding the algorithm a small portion of already counted votes to predict a trend and possibly the outcome of an election [14]. Elimination of fake and misleading news is an important task because such types of news can create misunderstandings. Social media websites like Facebook and Instagram are working on techniques to find and eliminate fake news automatically. When providing news to small screen devices such as watches or phones it is important to maintain the clarity and preciseness of the news. The relevant use of pictorial representations and pie charts help the viewers to visualize and understand the data which is of importance. This reduces the complexity of data visualization and gives a better grasp over the details of numeric data [15].

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3.4 Hologram The development of a three-dimensional picture into the physical environment of the user is done through holograms. Holographic technology does not require special headsets or eyeglasses; the image is directly mapped in thin air. Although the majority of such displays provide static images, they can also be developed to show and play video media [16]. These holographic devices will be extremely helpful in teaching and research purposes. Suppose a solid object is to be studied, then the three-dimensional images will provide a better visualization than two-dimensional images which in turn will give us a better idea about the internal structure and deformities. Holographic devices can be used in medicine to view patient’s scans from different angles and project organs, bones and muscles in a three-dimensional image which is a great help to surgeons [17]. When doctors are already aware of internal arrangement of the patient’s body and their organs, the accuracy increases and the complexity of the procedure decreases. Holographic displays can provide practical lessons of particular topics which are required in almost every field of study. Some companies like Kaledia, HYPERVSN, VNTANA are currently working on hologram technology [18].

4 Multimedia Security Security in multimedia is mainly concerned with the data of the user (in simple words it is content-based protection). With the growing use of multimedia as a latest trend, the safety of the user is the utmost priority [19]. Hence, while upgrading the technology and integrating various modes of media, its protection should be kept in mind and therefore the encryption should be strong. The potential risks include as shown in Fig. 3.

4.1 Deepfakes Deepfakes is a major threat to the privacy of the user. Deepfake technology creates and morphs a person’s face, expression and the motion tracking of features of a person to basically create a fake model of face through artificial intelligence, encoders and general adversarial networks which belong to deep learning which is then photoshopped on another person’s body to make it seem like the person whose identity has been stolen is saying or doing all the things [20]. This technology poses risk to politics, news related area and financial matters of people. Using this technology, politicians’ images are photoshopped to spread hoaxes and create hysteria. Audio deepfakes are used to produce the exact replica of celebrities or anchors so that the false information seems true to the people [21]. Deepfakes harm the reputation on

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Fig. 3 Potential threats

leaders and movie stars. The misuse of this technology is not just limited to living people but also to nonexistent people. Deepfake can create an individual’s face by markings and editing done on a realistic mannequin [22]. So, a face of a person who does not even exist can be created and circulated to widen personal propagandas. Therefore, the use of facial recognition technology should be allowed and controlled properly [23].

4.2 Eye Tracking Invading the Users Privacy Eye tracking technology is used for increasing the security for the users, however the eye tracking can lead to the leaking of the user’s data, while using VR or streaming the videos if a hacker may hack the device he/she can record the eye tracking of the user and may use it later for invading the user’s privacy [24]. The eye tracking uses the fixation points and the saccades to track down the users visual attention and the interests. The tracker can use the gaze points and the fixations to track the area (by creating heat maps) the user showed more interest. The tracker can use the fixation sequences (the fixation sequences locate the various points where the eye has more focused on, the time spent on one point and the time taken to locate on another point) to know where the user was looking at invading the privacy of the user [25]. If the tracker gets to collect the data about the pupil dilation, he/she can also know about the mental and emotional state of the user. Using eye tracking our gaze data can also be collected (gaze data is unique for every person, similar to the fingerprints) and it can be used as passwords or identifying the users identity [26].

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4.3 Authentication and Alteration of Data Whenever permission to access any file or a system is to be given to a person, the process of authentication takes place. This process takes into account the identity of the person which is usually checked through id [27]. But whenever changes are to be made in the files or system then the person trying to modify the documents is required to be authenticated and verified to check whether or not the person is eligible to perform changes. This may or may not involve any identification of the user since authentication checks the id of the user involved. The database of the military contains crucial and sensitive data [28]. This data is stored in the form of text, images, maps and videos. The data can be hacked and altered which may render the gathered data completely useless. So, some type of security measure is required to keep the information safe. This can include electronic signature each time an official accesses the files. Also, the security then should be at least of two tiers. The second tier may include strong key encryption of the documents which should nearly be impossible to decode [29]. Similarly, hospitals contain medical records of thousands of people and hence strong intrusion detection systems should be established in the system. Therefore, large companies have shifted the storage of data from local to cloud technology. Cloud manages the data and its protection through advanced security systems [30].

4.4 Privacy Concerns in Secure Communication Secure communication is multimedia is generally when two persons are trying to communicate with each other via text/audio/video and they do not want any third person/party to listen to it/see it. In multimedia the privacy of the communicators can be compromised if someone is eavesdropping or trying to get the encrypted codes to steal the encrypted messages [31]. The ways through which the privacy of communicators can be harmed are—eavesdropping, phishing and hacking. Eavesdropping is secretly listening to the conversation of two persons without their consent. Eavesdropping can be done through telephonic lines, emails, cellular networks and other messaging applications [32]. Phishing is done in digital communication through emails or messaging applications. In phishing the attacker tries to steal the sensitive information such as passwords, usernames and credit card information. Hacking is done by exploiting the loopholes in a particular system or website. If the system/device of the user is hacked his/her personal data and business data becomes vulnerable, it can be easily stolen. The hacker can also hack the device to get the encrypted codes or the encryption key to exploit the privacy of the communicators. There are several ways through which secure communication can become unstable and the privacy of users is compromised. All of our information is stored in some kind of media (like text or video), hence it becomes necessary to stabilize the security aspects of multimedia [33].

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5 Conclusion and Future Scope The branch of multimedia is one of the fastest growing in the field of information technology. The diversification of media has flourished in the past few decades. The integration of various forms of media to provide teaching modes, social platforms, virtual reality and holograms are great examples of multimedia growth. With so many options to choose from, it becomes highly necessary to detect and apply the right subset of media forms for the creation of a specific application. The technologies require complex partitioning and solving of smaller subproblems to generate the unique output which is to be easily understood and used by the users, be it in the form of images, text, video or a particular combination of those. Multimedia’s ability to simplify logics such as presenting the statistics and to help in complex tasks such as mapping of unseen places encourages new systems to be developed. Multimedia now is not only concerned with development of new user interactive applications but also with the security of such advanced implementations. Sensitive information of users possesses a certain risk of theft and misuse. As these technologies are based on the preferences made by the user, their data is also at risk and hence, security of applications that give such features should be multilayered. The data should be stored on cloud facilities and an effective software should be produced to detect image morphing. Therefore, systems and softwares which are helpful and reduce the difficulty of tasks should be developed with the security aspects carefully tested. Multimedia holds vast potential to be developed and used for the advancement in the technological fields like entertainment, business, news and so on. Digital media is continuing to evolve furthermore, which enabled to evolve the job market too. In the near future all the people will start preferring online video streaming rather than the cable television services. Already most of the people prefer to watch the movies and shows on online platforms like Netflix, amazon prime, hotstar, youtube and many others like them which has increased development of entertainment factors like movies, sports and shows. Due to this more multimedia professionals, security experts and people from all domains are required which increases the job availability. The revenue generation is directly proportional to the number of users. In the future when the usage of streaming videos will increase it will generate more revenue. Virtual reality and augmented reality are also growing at an exponential rate. In the future the technologies like AR, VR and holograms can use the real time data which will enable them to deliver a powerful and personalized experience to the users. The companies can use them to visualize their products and allow the customers to have an delightful experience of the product before they are going to buy it. The rise of video streaming, virtual reality, augmented reality. Advanced journalism, holograms will all influence the future of multimedia.

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IoT in Medical Science Aniket Rana, Anushka Kalra, Siddharth Gautam, and Shreyas Vijay

Abstract In a world dominated by medical science, the Internet of Things (MIOT) plays a cardinal role in various aspects of the field of medical science where information security is a major area of concern for the industry. This paper summarizes the basic principle of Medical IoT (MIOT) and gives information in detail about the involvement and application of internet of things in the medical industry. The Internet of Things in medical terms has a lot of specifications such as the memory size application, effective energy utilized and the capacity to process information to be taken care of which is discussed below. At last the paper mentions in detail about the various security protocols such as the radio frequency identification RFID, lightweight RFID mutual authentication (LRMI) protocol and a brief about the SecLap protocol which are used to establish an effective security system for transferring the information and maintaining an authenticated network between the tag and the reader. The paper also throws light on the architecture which plays a very important role in the enhancement of the existing IoT technology. Herein various layers of the system have been discussed which in totality helps in optimization of many medical related procedure and stabilize the growth of medical IoT. Keywords Internet of Things · RFID · Network · Medical Internet of Things · Patient · Architecture · LRMI

A. Rana (B) · S. Vijay Branch of Information Technology, HMRITM, New Delhi, Delhi, India e-mail: [email protected] A. Kalra Branch of Computer Science Engineering, Mahavir Swami Institute of Technology, Sonipat, India S. Gautam Branch of Information Technology, NSUT, New Delhi, Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_30

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1 Introduction The Internet of Things (IoT) alludes to a network of interconnected objects that are linked to each other for the purpose of displacing data over a structured network. It can be termed as a network that links all objects in the world. IoT has objects that possess the components like the sensors to detect, gather, and transfer the information collected from the adjoining environment and the radio frequency identification (RFID) devices. The devices have been allotted individual recognition id with the help of which they are given access to the Internet for dispersing the data from one destination to another [1]. Architectonically the IOT consist of five layers named— The Perpetual Layer: Used for gathering information and is a cluster of sensor and its web, Network layer: Used for data dispersion and is cluster for mobile communication, Middle layer: Processing of data takes place in this part, Application Layer: Used for smart applications and is considered for performing intelligent tasks the business layer: It is used for system management [2]. With the increasing importance of technology in the medical industry for medical diagnosis, analysis, processing and various other things IoT has taken an upper hand in the field. Recording an increase of IoT in the medical industry many reputed firms are investing in this sector. The use of technology in the medical domain helps to enhance the monitoring system of the patients, helps keeping a track of the medications to be given to patients and can help in increasing the reliability factor of the hospitals and the data of the patient [3]. The sensor devices also help in decreasing the overall expense of care that is to be given to a patient and helps in escalating the life span of geriatric sufferers. Medical IoT’s involvement in the medical sector would help us improve the quality standards of the healthcare industry [4]. Medical IoT devices have grown in for the usage of toddlers and infants. Renowned technologies such as Litnr, Temptraq, invasive surgery all work upon with the help of medical IOT to increase the efficiency of the technology [5]. Medical IoT is involved in blood coagulation testing used for treating diseases like heart strokes, diabetes, etc. It is also used to detect the breast cancer which can be considered as the second most dangerous among the group of cancers. The Medical Internet of Things (MIoT) uses the intelligent system, where objects can derive patient information and disperse it via a Gateway to the secure cloud-based destination where the data gets Managed and processed [6].

2 Applications of the Internet of Things in the Medical System Internet of things has vast applications, some of which are used in field of medicine. Internet of things has become crucial nowadays. The hospitals as well as Medicare units now depend on the advanced technology for the providence of utmost excellence and care. Hence, examples of areas of such applications are given below.

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2.1 Medical Emergency Management Sometimes uncommon situations are occurred, like when there are huge amount of accidental cases are comes, if family member not reach there with a time, or a serious ill. In this type of scheme, radio frequency identification (RFID) technologies are authentic and good and storage and depot and testing procedure will help instant identifications of related details to patient such as age of the patient name of the patient and some more details like blood group, contact number, and medical history of the patient [7]. This will decrease the admission time, i.e., boost up the admission process for emergency patient and give more time for treatment of the patient [8]. This has been demonstrated though the use of Fig. 1. Most important thing is 3G videos tools installed in ambulances, emergency department is already getting introduced with patient conditions when patient are on the path of hospital and emergency department easily ready for emergency rescue [9]. If patient is far away from then the hospital then there is chances of using remote medical imaging system as a factor of the emergency rescue process. Some important

Fig. 1 RFID hospital tracker system architecture

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requirement in hospital for information management are as follows: sample identification, identification, medical record identification [10]. Out of them, identification involve patient’ identification, identification of doctors and some sample identification involve identification of medicines, testing product identification, equipment of medical identification, and so on. The Internet of Things can link of the requirements of the hospital [11]. By using RFID technology bedside sample is taking by doctors easily and also identify the identification of patients if any error will occur then the attentive will call the doctor instantly.

2.2 Medical Equipment and Drug Monitoring and Management By using RFID’s, internet of things has start to search deep and largely applications in the line of medical material image. Internet of Things with RFIDs can facilitate ignore public health difficulties by promoting in the productions, distribution and medical devices tracking and medical tracking [12]. This raises the condition of medical treatment while management cost will decreases. By World Health Organization (WHO) point of view the counter medicines quantity in the worlds amount is extra than ten percent of worldwide drug sales. Information collect from the Chinese Pharmaceutical Association display that only in china, minimum 200,000 deaths arises each year due to false and inadequately used medications. Around 11 to 26% of patients take medications inaccurately. It includes nearly 10% of incorrectly prescribed medications [13]. Therefore, RFID’s play a crucial role in the monitoring of drugs and tools and the settlement of the market for medical products. In the last ten years developed countries hospitals have been supported by electronic drug management machines. Then a system should be made for health care and drug supply circulation and replenishment [14]. The system work transfer of drug, drug inventory management, and packaging of drug. Also machines are located at each point of dispensing and electronically attach with central department of pharmacy for handle drug administered to patients at that special health care unit [15]. This is illustrated through Fig. 2.

2.3 Rehabilitation System Physical medication with rehabilitation is sufficient for recovering the functional capability of a disability patient. Rehabilitation includes analyzing the difficulties and supporting the patients to attain their normal life. IoT applications in rehabilitation are distinct and seen in the cancer treatment, Strokes, injury in sports, and some other physical defect [16]. To monitor the walking structure of the patients and check out the movement metrics multimodal sensors can be used by recommendation of

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Fig. 2 System architecture of RFID-enabled healthcare management

smart walker rehabilitation arrangement. Smart walker is used by the patients when they used it, they can measured different types of movement matrices like force, orientation angle, elevation, and so on. Doctors use mobile apps to get this data and to handover the reports of diagnostics [17]. Furthermore, by organizing a machine learning algorithm, smart wearable band and hand of robotics a stroke rehabilitation arrangement was made. By using a less power IoT-based textile electrode a armband was made which can transmit the biopotential signal, measure, and preprocess [18]. After that, the 3-D printed robotics armband evaluates the activity of muscle and aids the patient to improve their motion pattern during the recovery of after-stroke. In another research, to monitor the motion posture, temperature, electrocardiography (ECG), electromyography (EMG) and so on and give feedback to the athlete, this is reported by a sports rehabilitation arrangement. Professionals of the Healthcare use recorded data to predict the recovery of patients and prepare rehabilitation programs [19]. The procedure through which the system would work is shown in the form of Fig. 3.

2.4 ECG Monitoring Electrocardiogram (ECG) represents the electrical heart activities because of the repolarization and depolarization of atria and ventricles. An electrocardiogram gives

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Fig. 3 Prototype of IoT-based smart rehabilitation system

data regarding necessary flow of the muscles of the heart and behaves as an indicator for different types of cardiac abnormalities. These types of abnormalities involve prolonged QT interval, arrhythmia, myocardial ischemia, etc. potential application is found in previous detection of heart abnormalities by monitoring of ECG by using Internet of Things technology [20]. Large no. of studies in the past have occupied Internet of Things monitoring. The study reported that an IoT-based ECG recording and observing structure has been proposed which contains a receiving processor and wireless data acquisition system [21]. To detect real time cardiac abnormality, it implies a search automation form. A little wearable low power ECG monitoring was developed that was combined with a t-shirt by using a biopotential chip to collect ECG data of good quality. Then by Bluetooth they transmit the recorded information to the end-users. The captured ECG information could be visualized by using mobile apps. The following system could be managed with a least power of 5.20 mW. In IoT systems the real Time monitoring can be possible when combined with large data analytics to handle higher data storage [22]. Bansal and Gandhi have developed an Electrocardiogram monitoring system that can operate long-term and continuous monitoring by combining the nanoelectronics concept, IoT and Big data. The authors developed a different style method, i.e., compressive sensing that raises consumption of power and gives an optimal performance in electrocardiogram monitoring [23]. This system is made to real time monitoring of patients which are elders by regularly checking their accelerometer data and ECG. Therefore, the architecture of the setup is provided below through Fig. 4.

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Fig. 4 Architecture of the IoT-based ECG monitoring systems

2.5 Blood Pressure Monitoring Measurement of the blood pressure (BP) is one of the most vital parts in any detection procedure. At least one person is required to do the recording of the measurement of the blood pressure (BP). Anyhow, previously BP monitoring was convert by the combination of the IoT and other sensing technology for example a gadget has been developed which calculate both diastolic and systolic pressure, i.e., wearable cuff less gadget [24]. The recorded data is saved in the cloud. After that the ability of this device applied to 60 persons and the accuracy was certified. Fog computing and cloud computing in measurement of BP on IoT-based programming has been carried out by Guntha. This developed a system for long-term real time monitoring [25]. The recorded information is also stored in this device for future references. In the same type of study, for the interpretation of the diastolic and systolic blood a deep learningbased CNN model with time-domain characteristics was used. Photoplethysmogram (PPG) and ECG signal recorded from the fingerstrip is used for the calculation of the blood pressure, in this, the BP was calculate using the attached microcontroller module and then the saved data were transferred to the cloud storage [26].

3 Security in Medical IoT It is essential to take care of the protection of the medical data and apparatus in the field of Medical IoT. The objects of Internet of Things have certain properties such as the capacity of the memory, ability to process data and the overall energy consumption. All these constraints make it difficult to have an effective security system. The safety and privacy of data associated with the patient are two inseparable logics [27]. By privacy of data, we ought to say that information is stacked safely and transmitted safely, which would assure its rectitude, time period and attestation. Data

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privacy assures that data is accessible only to certain people who are authorized to use it. To overcome the possibility of breach of security of various layers present in medical IoT and to make the supply chain more safe government organizations and other agencies are focusing to maintain a secure network. Use of Radio Frequency Identification (RFID) based security solutions are proposed for the maintenance of supply chain MIoT. Radio Frequency Identification (RFID) allows preprogrammed effortless detection and recognition of items, credits and people [28]. Radio frequency identification (RFID) is a type of unwired form of communication which allows detection from a huge range without the need of a line of sight. RFID is divided into two subclasses: active and passive. Active label required power supply for the usage which is acquired from the battery. Whereas on the other hand passive labels do not require any external power supply as they are constructed with three components which are a semiconductor, an antenna and encapsulation form [29]. RFID systems has four distinct parts which are reader, tag or label, antenna, and software system. The function of tag is to transfer and achieve radio waves and the reader reads all processing information that is forwarded to the software for further usage. RFID is used for authenticating and is considered as an alternate option for barcode in identification and reading of objects that are labeled. Nowadays hospitals use smart wrist bands with RFID technology to track the patient information effectively. The information can be patient name, address, and patient medical history. This leads to an effective management and healthcare system in a hospital [30]. Figure 5 shows architecture of RFID-based medical IoT which has structure of a RFID-based medical system.

Fig. 5 RFID-based healthcare system

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Recently lightweight system of radio frequency identification (LRMI) was initiated which has rotation assignment function which enables a fluctuating deviation which was supposed to tackle the safety issues like clock synchronization frequency matching and would assure regular consistency. But the system of LRMI had certain drawbacks due to which further enhanced systems were formed. Initially protocols such as the HB-family system which used matrix multiplication as basic logic was introduced but it faced issues such as vulnerability and loss of data due to which mutual verification was included in the RFID system but this system failed in the major aspect of authenticating the data to overcome which many solutions like desynchronized attack was tried out but it could not overcome the weakness [23]. To subdue this ultra-lightweight RFID mutual verification protocol for IoT came into picture to make the system more secure also it helped to create certain pointer which were to kept in checkboxes before the introduction of any other system which are mentioned below: 1.

2. 3.

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Bilateral authentication: Before the data is transferred in MIOT it should validate its permissibility before agreeing to the terms of system and then process the request of transferring information Message integrity: The tagged structure must maintain the essence of message being transmitted Entity security: There should not be breakage of bond and leakage of information or any sort of breach to the information being transmitted by the entity Immunity against external attack: There should be a protocol to provide security check to the entity so that the information is not revealed to external unauthorized user and is secure enough.

LRMI system introduced a function named cross function which enables system to follow the checkboxes and to ensure even more integrity of the system [31]. But LRMI failed to maintain the anonymity of the reader and the label tag. In LRMI the submissive nemesis intrudes the message and tries to refurbish the confidential data shared in protocol. So to maintain the anonymity of tag and the confidentiality of the message there are further more protocols that are being discussed to provide even more secure network. Further if the communication network between the server, entities and the tags is disturbed or has some data corruption then the complete logic of smart security-based model of MIOT disturbed and becomes baseless. To overcome this there is a need of giving object attestation before sending the information. To give a practical solution to the problem SecLAP authentication protocol was introduced which is also known as a lightweight authenticated system [32]. In SecLAP protocol, the movement between label and accepting reading object is decreased effectively, and along with this the hosting server set does not reserve the recognition label of the tag and the readerset (RID and TID). It uses a separate rotation function known as MRot (·) which is used as a building block in all transferred messages. The number of flows in SecLAP is much less than any other protocol which makes it even more effective than other protocols and has a huge scope and can make security in MIOT more effective.

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4 Radio Frequency Identification (RFID) Technology in IoT RFID refers to a type of technology that uses electromagnetic fields which is wireless technology it contain two element that is Tag (an object) and Reader (an interrogating device) Reader contain single or more antennas to release radio wave and accept signal back from tag. It means by using radio wave it allows communication and data transference between these two element (tag and reader). There are many kind of Tags some are cheaper and disposable and some are permanent. This technology is used to recognizing object location or object on demand. Now some more advanced version are also present like Real time location System (RTLS) it nonstop monitoring the location of object by using reader networks. Basically RFID tags are used for daily communication between objects and the main hub and gave their all status. In medical, for example the medical equipment which is movable if that have object tags then that can be easily founded on call. RLTS permit real time tracing of objects which are tagged and design a system through which connected devices transference data continuously about their position, situation, amount, etc. If we look at new generation of RFID and IoT. Sensor-enabled battery powered RFID tags are broadly used in many areas. According to research RFID-enable and placed in emergency rooms, clinics, elderly care centers, and other organizations of healthcare [33]. RFID technology in healthcare are very beneficial like by using RFID technology from inclusion of automation in hospitals to increase the speed of inventory to achieve good visibility to flow of people. Here some new application of RFID technology: 1.

2.

3.

4.

Asset tracking and management means on virtual map of hospitals required things like ventilators, Beds, and other emergency equipments are simply and quickly located. Which is easily founded when required. Increased safety for Staff and patients like it means tracking of all things like patients, new-born baby of hospitals, hospital patients, etc. or we say it provide all the activities of unavailable visibility through which security of hospitals should be increased. Automated inventory it means during RFID based process products which are easily disposable like scrubs, mask, gloves are counted and automatically located. Staff and patient tracking and management it is very important RFID application it provides tags and wearables for staff and patients for continuously monitoring the movements of peoples present in hospitals.

How RFID Technology with IoT Improve Hospitals described in points given below: 1. 2. 3. 4. 5.

Time to time maintenance of costly equipment of hospitals. Tracking and management should be effective. Increase productivity and decrease labor cost also reduced work efforts. Data of hospitals should be updated from time to time. Better experience for visitors, employee of the hospital, patients, doctors, etc.

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6. 7. 8. 9. 10.

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The fear of losing valuable data is reduced. It also improves the accuracy. Remove human error. Improve routines and working schedule of healthcare experts. Improve the security system of hospitals.

5 Architecture Architecture plays a very important role in the enhancement of the existing IoT technology. The architectural parts comprising IoT devices are a network part, Hardware and software implementation. The combination of these three aspects enable IoT devices to communicate and gather useful information. Generally, the information is collected through sensors such as oximeter and Thermometer this acts as the base layer through which the data or the situation of the patient is collected. The sensors along with the technologies such as Arduino raspberry pi act as the hardware layer. This data collected with the help of sensors then required to be connected to nodes to read and flow data. This connectivity can be achieved by Bluetooth, Wi-Fi or specialized networking equipment. Then comes the layer through which the data can be analyzed. In this layer the data from the sensors is analyzed by comparing it to the base value of the healthy person the output of this layer determines the need of medicine or equipment required in medical assistance of the patient. This data the transfer to a cloud-based storage after that the necessary allocation of equipment and resources are done. The procedure of storage and analyzing has been completed up to now. When the details of the particular patients is needed to be store on an application hence the software part comes into play. The patient is constantly monitor from the real time information provided by the sample and the network [34]. These layer is provide the necessary function through which the fast communication between the host and the server takes place therefore The important of patients’ needs to be well protected and compartmentalized.

6 Conclusion In today’s world Medical facilities are at utmost priorities for people, hence the advancement of the medical field has gone beyond the scope of just only treating patients. Now Internet of Things (IoT) explores vast field of technologies to be implemented in patient healthcare such as radio frequency medical science (RFID), Rehabilitation system, blood pressure monitoring, architecture. RFID technology used for object recognition and checking the status regularly. BP is most an important part of diagnosis is IoT and other sensing technologies. Rehabilitation system used to figure out walking structure and measure different types of movement matrices of patient.

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Architecture is to be used for gathering important information though sensors. Then with drastic development in technologies arises a need in the improvement of security of such devices and hence LRMI is necessary for the protection. These technologies help us in tracking monitoring to increasing the current healthcare system in many folds. In this paper it has been concluded that with the proper utilization of technical resources available to us. The optimization of many medical related procedure can be done easily. In this paper the various aspects related to the development and maintenance as well as the architecture and security thoroughly discussed. Therefore, the application of IoT in the field of medical science can stabilize and improve the feature of healthcare system exponentially.

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Gesture Recognition System for Real-Time Interaction in Dynamic Environment Meghna Nandy, Swagata Sinha, Basudev Halder, Kallol Bera, and Deep Suman Dev

Abstract Hand gesture recognition provides a significant impact in the field of human–computer interaction. It introduces the information, tools, and systematic design techniques, by which accuracy and easy implementation of daily tasks can be achieved. Gesture recognition is the approach by which computers can detect hand gestures. Human–computer interaction provides appropriateness in feedback, effortless implementation, and timely completion of the goal. Computer vision plays an important role in extracting high levels of comprehension from electronic images and videos. It is applied to a hand gesture recognition system to provide input to the computer to manipulate virtual objects by simply moving hand parts which act as a command. Providing a low-cost infrastructure device that alters the need for keyboards and mouse in laptops and computers. Keywords Hand gesture · Tracking · Virtual environment · Recognition · Virtual objects

1 Introduction Over the last decade, the keyboard and mouse played an important role in developing applications worldwide. We changed from keyboard and mouse to touchpad with the advancement in technology, which uses sensor control technology. Owing to the recent technological achievement, human–computer interaction [1, 2] has gain ground using machine learning and artificial intelligence. Changing from sensor technology to hand gesture technology can provide all the functionality initially provided M. Nandy (B) · S. Sinha · B. Halder · K. Bera · D. S. Dev Department of Computer Science and Engineering, Neotia Institute of Technology, Management and Science, Kolkata, West Bengal, India e-mail: [email protected] B. Halder e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_31

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by any mouse, keyboard, or touchpad. The same idea is used in human–computer interaction [3]. Gesture commands are given to the computer [4]. Computer interprets the meaning behind the gesture and performs the desired action [5]. Here gesture recognition is used for interaction in a dynamic environment. Ubiquitous computing provides dynamic environments where humans want to interface for interaction with media and information without any physical limitations [6]. However, still some challenges persist. ● Detection can become a complex problem due to varied hand shapes. Different shades of hand color can cause a problem in the detection process. ● Another restriction is that an angle will be detected if only it is less than equal to 90°. ● Due to specific measurements of height and width, the reason for the interest area is limited. However, the area can be increased by modifying its height and width measurement. ● Better camera quality is needed thus increasing the cost of production. ● In low light surrounding while capturing the image noise and distortion may occur due to the poor quality of the image captured. This may lead to an error while detection of the hand gesture. The primary objective is to use a natural device-free interface that recognizes the hand gestures as commands. To improve the interaction in the dynamic environment, the means of interaction should be feasible. This effort focuses on implementing an application that employs computer vision algorithms and gesture recognition techniques to make daily tasks time-bound, easy and efficient [7]. The following are the parameters and conditions based on which a vision-based technology should be developed: ● In the real world the pictorial information (hand gesture) can contain noise, disturbances due to dynamic background, low lighting, etc. This can lead to an error while detecting the hand gesture which might lead to no or incorrect action. A vision-based application must be user-independent and be free from all the factors that lead to robustness. ● The image processing algorithm works on the principle of a vision-based learning technique. This algorithm should be effective and also cost-efficient. ● This technology must be adapted easily by different applications to improve its productivity and use. ● The system should be able to perform real-time hand detection. ● The gestures which needed to control the actions in the program must be simple so that it is easier to remember.

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The methodology presented in this paper refers to real-time hand gestures for handling objects in a virtual environment. Proposed application is more effective and user-friendly method of human–computer interaction intelligently with the usage of hand gestures. The application has been designed to be cost-effective and uses low-cost input tools like a webcam for capturing hand gestures as input.

2 Methodology The hand is captured using the primary camera of the laptop. Now the image captured by the webcam will be passed into the logic, frame by frame, and each frame will be converted to Hue Saturation Value (HSV). HSV provides us with the dominant wavelength, color, and intensity of the captured frames. All the colors that are captured by the webcam will be filtered. Bitwise AND is used to perform the filter function. This process is used to detect hand color and recognize the hand. Other colors captured will be discarded. Next, the pixel values are inverted, and the result is enhanced to get better output. Then specific-colored objects are contoured that are making the background black and the object that needs to be identified as white. Here the hand is converted to white. The threshold function is making sure that any value above the specific threshold value is not detected. To remove noise, dilation and erosion are performed over the current picture. Erosion erodes the boundaries of the foreground object and is used to diminish the features of an image in other words it removes white noises which results the image to shrink. Hence dilation is performed to increase the object area in this case increase the area of the image. Masking and filtering values are used to detect contours for the specific-colored object. These are saved in list format. Among all the contours that are generated, the maximum contoured is used to fix the figure. That is, all the points of connections will be visible, and quality is enhanced. The convex hull is drawn around the desired object, and after finding the maximum area contour, the hull is drawn on the live feed. The next step is to find the Convexity defect that is any defect or distortion while capturing the hand. Then cosine method is applied on the sides of the triangle. With the help of trigonometry, law of cosines is used to relate the lengths of the sides of a triangle to the cosine of one of its angles. After finding the sides of the triangle, gamma is used to find the required angle. Flow diagram of gesture recognition system is shown in Fig. 1. The angle between the fingers must be less than equal to 90° Fig. 2. Cosine theorem is used to identify the angle between finger/s and if the angle/s exceeds 90° then the gesture will not be recognized by the program. γ = cos−1



a 2 + b2 − c2 2ab



After applying the above formula, it is followed by binding the hand gesture with the keyboard keys. Applying this, the desired outcome of the code is generated.

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Fig. 1 Flow diagram of gesture recognition system

Fig. 2 Angle calculation

3 Result This application uses the computer vision technique for hand gesture recognition. Using OpenCV library in Python this system provides a dynamic user interface using image processing technique. The libraries imported into the programs are NumPy, OpenCV, PyAutoGUI. In this application, the NumPy package is used for

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faster and easier operation on array implementation. OpenCV is the most important open-source library that has many computer vision algorithms. By using OpenCV image processing, video analysis, including motion detection, is possible. PyAutoGUI library is used, which provides the feature of controlling mouse and keyboard with the hand gesture. In other words, the hand gesture gives the command such as video mute, volume up, volume down, change the aspect ratio of the video. These actions get triggered by using PyAutoGUI library functions. The actions performed by the proposed system are shown in Table 1. Table 1 List of actions performed by the proposed system No action: As per the code if no angle is detected then no action takes place

Increase Volume: When any two consecutive fingers are placed in the region of interest area one angle is detected. As per the code when one angle is detected the volume will increase

(continued)

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Table 1 (continued) Decrease Volume: Next detecting two angles when three consecutive fingers are placed in the reason of interest area. Two angles are detected, the volume starts decreasing as long as the angles are detected

Volume Mute: Another action included in this project is by detecting three angles when four consecutive fingers are brought inside the reason of interest area. By this gesture the mute option is enabled

Aspect Ratio Change: The final action that can be performed in by placing all five fingers inside the reason of interest area. As soon as four angles are detected the aspect ratio of the window starts changing

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4 Practical Use of the Methodology Technology has immensely improved in the past few years—from black and white to LED television, from stock cars to electric cars. In recent years software companies have developed technologies that are user-friendly and effortless to use. The proposed application works on the principle of image processing where the user gives specific commands using hand gestures and the computer interprets the gestures into their respective commands and gives the desired output. For instance, if the user shows two consecutive figures, then the application is going to increase the volume of the video that is being played. Since this application is based on videos there are many places where this can be implemented to upgrade the technology that is already present. 1.

2.

3.

4. 5.

Desktop PC/Laptop: The first possibility that comes to our mind is our desktop PC/laptop where we can implement so that whenever we are watching YouTube, Netflix, or any kind of video then we do not need to use the keyboard to control the video. This will be very useful for those people who do not know how the keyboard works. Television: Another gadget where we can implement this technology is on our television. We can increase and decrease the volume or mute the video according to our wish. The biggest advantage of implementing this technology will be that the users do not need to worry about the remote control. One does not have to keep track of where the remote control is and also the space for the remote is not needed in this case. Hence the user can change the volume from a distance without using a remote control. Video/Audio Playback Devices: Devices that play videos or music can be watched according to the user’s convenience. If we implement our technology and on adding a few new features such as forward/backward then the user can interact with the television from a distance without any physical interaction with the remote control. Virtual Games: Games can be developed where to control the game instead of using a gaming controller or joystick hand detection technology is used. Medical Field: Gesture recognition can also be useful in the medical field. This technology can be used to interact with medical equipment.

5 Conclusion The dynamic user interface is designed using the image processing techniques which are implemented in python with the use of OpenCV Library. In this application, the PyAutoGUI library is used which provides the feature of controlling the mouse and keyboard with the hand gesture. In other words, the hand gesture gives the command such as video start, pause, forward. After the user makes any assigned hand gesture it will be captured by the webcam. Then the algorithm is going to process the number

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of angles that are formed between any two fingers. If the angles detected are less than or equal to 90° then the desired action assigned to the respective hand gesture will be performed. Otherwise, no action is performed. This methodology aims to design a system that will be used for gestural interaction between a user and a computer. The system is capable of accurately detect hand gestures even under bright or medium light. The model can predict 4 different hand gestures made by the user. The most important advantage of the usage of hand gesture-based input modes is that using this method the user can interact with the application from a distance without any physical interaction with the keyboard or mouse.

References 1. Rautaraya SS, Agrawala A (2012) Real time gesture recognition system for interaction in dynamic environment. Proc Technol 4:595–599 2. Choi Y-J, Lee J-S, Cho W-D (2008) A robust hand recognition in varying illumination. In: Pinder S (ed) Advances in human computer interaction. IntechOpen, pp 53–70. https://doi.org/10.5772/ 5938 3. Bai X, Li C, Tian L, Song H (2018) Dynamic hand gesture recognition based on depth information. In: 2018 international conference on control, automation and information sciences (ICCAIS), pp 216–221. https://doi.org/10.1109/ICCAIS.2018.8570336 4. Wu Y, Huang TS (1999) Vision-based gesture recognition: a review. In: Braffort A, Gherbi R, Gibet S, Teil D, Richardson J (eds) Gesture-based communication in human–computer interaction, vol 1739. Springer, Berlin, pp 103–115 5. Sun J-H, Ji T-T, Zhang S-B, Yang J-K, Ji G-R (2018) Research on the hand gesture recognition based on deep learning. In: 12th international symposium on antennas, propagation and EM theory (ISAPE), pp 1–4. https://doi.org/10.1109/ISAPE.2018.8634348 6. Yoon HS, Soh J, Bae YJ, Seung Yang H (2001) Hand gesture recognition using combined features of location, angle and velocity. Pattern Recogn 34(7):1491–1501. https://doi.org/10. 1016/S0031-3203(00)00096-0 7. Badi H (2016) Recent methods in vision-based hand gesture recognition. Int J Data Sci Analytics 1:77–87

Chaos-Based Image Encryption with Salp Swarm Key Optimization Supriya Khaitana, Shrddha Sagar, and Rashi Agarwal

Abstract Substantial data is being transferred across the unsecured channel; with this considerable data transfer comes the need to protect this data. Thus, to achieve security during transmission, several encryption algorithms have been proposed. Chaos-based maps are widely employed for multimedia encryption due to their characteristics, like pseudo-randomness sensitivity to initial conditions. Inspired by researchers, we proposed an image security algorithm based on a chaotic tent map integrated with the Salp Swarm Algorithm (SSA) for key generation and optimization, for grayscale images. A diffusion and permutation are carried out in each round to make it secure. A simple XOR function is applied to encrypt and decrypt the data. Different statistical analysis has been applied to images, and results has been discussed to justify proposed techniques effectiveness. Keywords Tent map · Salp swarm optimization · Encryption · Chaotic map · Image encryption

1 Introduction The global spread of coronavirus pandemic has given the world push to relook digital communication technology’s rapid growth. The world under lockdown has led to the popularization of digital resources and information storage; with this comes the need for secure and decisive digital media monitoring. A massive amount of information is shared online that includes image, text, audio, and video. Image protection is increasingly considered these days because images may contain classified data, computerized image data has become mainstream. Generally, image encryption techniques S. Khaitana (B) · S. Sagar School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India e-mail: [email protected] R. Agarwal Department of MCA, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_32

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involve two stages: extraction and highlight encryption [1, 2]. Feature determination is one of the key parts of a multi-instance consent framework; for example, image sequencing [3]. Multimedia data protection has gradually become important for applications, like video conferencing, therapeutic imaging, mechanical and military imaging structures [4]. Data security issues have gradually become obscure by preparing and organizing correspondence with the computerized image’s rapid advancement. Image encoding has become a significant research subject. For secure transmission of this data, many algorithms were proposed. Traditional encrypting mechanisms AES, DES, IDEA, and RSA exhibit some drawbacks and weaknesses in encrypting digital images and high computational power for large images. These algorithms have low entropy values and have highly correlated data; thus, they are unsuitable for image encryption [5–7]. Therefore, considering the properties of images consequently, better techniques for several image encryption were presented employing various types of technologies such as DNA encoding [8–10], scalable encoding [11], and quantum theory [12–14]. Over the last decade, there is enthusiasm among researchers to study the chaosbased system. Matthews first proposed a chaotic encryption algorithm in 1989 [15]. Chaos-based systems are nonlinear dynamic systems that are sensitive to dependencies on their initial condition. They have properties like unpredictable behavior, ergodicity, and pseudo-randomness; this makes them popular with researchers for encryption algorithms. Chaotic systems have properties that meet the requirement of diffusion and confusion required for a good cryptography algorithm. A chaos-based system’s attractive property is its high affectability to its underlying situation, control criteria, and straightforward usage, resulting in high encryption rates by providing properties like avalanche effect, confusion, and diffusion. Many researchers proposed different kinds of chaos-based systems like Arnold map [16], Hennon Map [17], Logistic Map [18], Lorentz Map [19], tent map [20, 21], hyperchaotic map [22, 23], and combination of one or more maps.

2 Related Work The chaos-based system has an intense property of being ergodic. Some of these methods are non-resistant to attacks [24, 25]; some have limited keyspace [26, 27]. In 2019 Zhou and Xin [28] proposed a Joseph ring that dynamically destroys the pixels’ correlation and scrambles the pixel through complex nonlinear operations and bit reorganization. Liu and Miao [29] applied a parametric varied one-dimensional chaotic map for shuffling the pixels. Zero mean logistic maps control the chaotic map. Cavusaglu and Kacar [30] introduced a parallel algorithm and a random number generator to decrease the time taken by encryption and encryption. The technique is nine times faster than unparallel chaos-based techniques. In 2019 Abbasi et al. [31] proposed an encryption technique that converts an image into another meaningful image by adopting a wavelet transform method. An intervening logistic map was

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employed to apply the properties of confusion and diffusion. Different components of modern cryptography like Gray Substitution box and XOR operation were used to get an intermediate image. The technique was resistant to many attacks; however, data is lost during transformation. Rozouvan [32] used a symmetric algorithm generated from a fractal image; then modulo operation was performed on an image to get the encrypted image. The scheme has been proved to be robust to a variety of assaults. Broumandnia [33] presented a 3-dimensional space-based encryption technique on color pictures, replacement, and permutations on an image utilizing a 3-dimensional modular chaotic map. The first stage is to transform 2-dimension photos into 3-dimension images. The RGB spectrum was then split into k equivalent pieces in the second stage. The XOR technique was then used to alter the content of each pixel or group of pixels. Wu et al. [34] applied a two-dimensional logistic map of high complexity for encryption; two-dimensional logistic permutation, diffusion, and transposition was performed. Each intermediate cipher was itself a cipher image. The proposed technique was resistant to statistical and differential cryptanalytic attacks. Most of the researchers has worked on symmetric key-based schemes, Dong [35] proposed a discrete mapping-based asymmetric encryption technique. The initial number of iterations and the hash function is used as the key, and a piecewise linear chaotic map was used to decrypt the data. Wu et al. [36] proposed a cylindrical diffraction asymmetric encryption technique with compressed sensing to prevent a phase retrieval attack and information leakage. Liu et al. [37] proposed a methodology based on the four-wing chaotic system that used a hash function of 512-bits to generate a one-time-key to encrypt RGB components of odd and even components. In this paper, we have combined bio-inspired Swarm optimization techniques and chaos theory. Combining these two has been used in many applications; however, their usage is limited in cryptography. A two-step process is applied for confusion and diffusion of image pixels. Chaos-based tent map is used to apply encryption, and the Salp swarm optimization algorithm is used to optimize the key to apply decryption. The paper is organized as follows: Sects. 1 and 2 demonstrate the proposed technique’s literature survey and related work. Section 3 describes a proposed method, and Sect. 4 presents the result of the proposed techniques, followed by the paper’s conclusion.

3 Proposed Methodology A modern cryptosystem includes the following components, Key generation algorithm, encryption, and decryption algorithm.

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3.1 Chaos-Based Key Using Tent Map A two-step process for the generation of key is proposed in this paper. The key used is 16 characters long. This key is used to create a sequence of the key matrix, the same as the total pixels of an encrypted image. In the proposed asymmetric cryptography approach, two keys are generated, the secret key by using chaotic tent map, and Public Key using Salp swarm optimization; Fig. 1 explains the proposed methodology in brief. The simple chaotic tent map is an iterated function between the interval of [0, 1]. The name comes from the tent-like shape of the map, a discrete dynamic system given by the following equation: v(i+1) = f (vi , λ)  f (vi , λ) =

f L (vi , λ) = λvi , if xi < 0.5 f R (vi , λ) = λ(1 − vi ) otherwise;

(1)

(2)

Here, vi ∈ [0, 1] i ≥ 0 v0 is the system’s starting value, {v0 , v1 , …, vn } is the system’s orbit. The map contains the control parameter λ, where λ ∈ [0, 2], depending on the control parameter λ. The map ranges from predictable to chaotic when λ = 1.99999, x 0 = 0.000001, and i = 1 to 200,000. The key generation makes highly random key sequences using the chaotic method. The benefit of using a Tent map compared to another chaotic map is that it significantly simplifies the calculation; the higher-order iterates of the tent map involved in analyzing asymptomatic dynamics are simple to measure.

Fig. 1 An overall diagram of the proposed method

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3.2 Salp Swarm Optimization Algorithm Salp swarm optimization was proposed by Mirjalili et al. [38], a bio-inspired algorithm that mimics Salp swarms’ behavior and their populations’ social interaction. It is a kind of Salpiadae that have a transparent barrel-shaped body and tissues like jellyfishes’ structure. They live in the deep ocean and move by water forces to find food that makes them organize as Salp chains. The Salp chains are categorized into two parts the leader and follower Salp. For d different variable, the position of Salp is defined by a d-dimensional search space. The target of the swarm is food source f . The following algorithm is used in the proposed technique. Input: Salps, Number of iterations (T ) Output: Optimum Salp Position, Bets Fitness Salp Algorithm: Generate Initial population of Salps x i , where i = 1, 2, 3…n using chaotic tent map while (key! = optimized or t < Maximum number of iterations) calculate the fitness of each Salp using the following equation f = min[MSE, MAE] (3) Set best Salp as bx update position of e1 4t

e1 = 2e− T (4) where t = current iteration and T = total number of iterations for each x i if(i < N\2) update the position of leading salp  f x + e1 ((ux − lx) ∗ e2 + lx)e3 ≤ 0 bx = f x − e1 ((ux − lx) ∗ e2 + lx)e3 ≤ 0

(5)

Where e2 & e3 are random numbers between [0, 1], u is upper bound, l is lower bound

g = v final v0

else update the position of follower salp  bx = 1 2gt 2 + v0 t (6) (7)

0 (8) v = b−b t end if end loop update the position of Salp based on u and l end while return bx (optimal key)

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3.3 Encryption Phase This portion simply explains the simple image encryption algorithm based the chaotic key generated in the above step. Input: Chaotic Key Sequence (P), Plain Image Output: Encrypted Image Algorithm: Convert the key sequence into 8-bit blocks Convert Plain Image into 8-bit blocks for all blocks of size 8-bit Key sequence ⊕ 8-bit Image block end loop

3.4 Decryption Phase This section explains decryption by applying a simple XOR operation on the encrypted image and optimized key. Input: Optimal Key Sequence, Encrypted Image Output: Decrypted Image Algorithm: Convert the optimal key sequence into 8-bit blocks Convert Cipher Image into 8-bit blocks for all blocks of size 8-bit Optimal Key sequence ⊕ 8-bit Encrypted Image block end loop

4 Experimental Results For implementing the proposed technique, we have used MTLAB 7.12. The experimental investigation was carried out on the Windows-10 operating system using an Intel Core i5 CPU with 4 GB RAM and a clock speed of 1.6 GHz. All the images used are “512 × 512” sized that are freely available online. Some of the images used are shown in Fig. 2. To evaluate the performance of system some commonly used performance parameters like Encryption time, Entropy, Cross-correlation, Unified Averaged Changed Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Decryption time are used. Table 1 shows the image generated after applying encryption algorithm.

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Fig. 2 Sample input database images

Table 1 Image sequence encryption and decryption

Table 2 Time analysis

Encryption time

Decryption time

Lena

0.22802

0.21811

Baboon

0.22119

0.22375

Camera man

0.23433

0.26505

Barbara

0.24765

0.23452

Puppy

0.23118

0.24241

4.1 Encryption Time and Decryption Time Encryption time is defined as amount of time it takes to convert plain image to cipher image. The amount of time machine takes to convert it back to plain image is known as decryption time. Table 2 shows the encryption and decryption time on some of the images.

4.2 Entropy Entropy is used to calculate the average unpredictability of data source and is given by

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Entropy = −

L L G −1 G −1

x=0

p(x, y) × log( p(x, y))

(9)

y=0

where p is the probability of occurrence of a symbol

4.3 Peak Signal to Noise Ratio (PSNR) The PSNR is a ratio of max power of a signal and the de-noised signal.  PSNR = 10 log10

2552 MSE

 (10)

4.4 Mean Square Error (MSE) The cumulative squared error between the signal’s maximum power and its corrupting noise is known, and the mean square error. MSE =

M N 2 1   O(i, j) − O  (i, j ) M ∗ N x=1 y=1

(11)

where; O(x, y) à Input image; O  (x, y) à De-noised image. The Entropy, PSNR MSE analysis of is shown in Table 3. All Entropy values are above 7 that is very close to the ideal entropy value 8 of 256 × 256 Image. Closer is the entropy to its ideal value greater is the complexity of a cipher image. Table 3 Experimental results Entropy

PSNR

MSE

NPCR

UACI

Lena

7.96

33.29

0.000468

99.5

33.46

Baboon

7.89

32.31

0.000587

99.52

33.35

Camera man

7.89

32.01

0.000628

99.6

33.34

Barbara

7.93

32.47

0.000566

99.61

33.46

Puppy

7.96

31.957

0.000637

99.6

33.43

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4.5 Differential Cryptoanalysis Attack (UACI & NPCR) The difference in the average intensity of pixels between the two images is measured by Unified Averaged Changed Intensity (UACI) ⎡ ⎤ 1 ⎣ |c1 (i, j ) − c2 (i, j )| ⎦ × 100% UACI = L × M i, j N −1

(12)

NPCR gives the change rate of the pixels values in an image. If the value is nearing 100 presents, the system is highly sturdy against differential cryptoanalysis attack. NPCR =

M L 1  x(i, j ) × 100 L × M i=1 j=1

(13)

where L and M are the width and height of an image, e1 and e2 represent cipher images after a 1-pixel value change. For any algorithm to be secured from a differential cryptoanalysis attack, the value of NPCR needs to be above 99, and UACI needs to be above 33. Both NPCR and UACI in Table 3 shows the proposed scheme is resistant to differential cryptoanalysis attack.

4.6 Cross-Correlation The cross-connection is a proportion of closeness of two arrangements as an element of the relocation of one comparative with the other. For any two vectors, X and Y cross-correlation is given by eq. 14. E(x) =

N 1  xi N i=1

D(x) =

N 1  (xi − E(x))2 N i=1

cov(x, y) =

N 1  (xi − E(x))(yi − E(x))2 N i=1

cov(x, y) rx y = √ D(x)D(y)

(14)

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Table 4 Correlation analysis Correlation original image

Correlation analysis encrypted image Horizontal

Vertical

Diagonal

Lena

0.9917

0.00611

−0.0282

0.00583

Baboon

0.9859

0.00617

0.00868

−0.01856

Camera man

0.9946

0.00829

−0.02446

0.00779

Barbara

0.9921

0.00766

0.01077

−0.00623

Puppy

0.9936

0.00711

0.01105

0.00856

Fig. 3 Distribution of adjacent horizontal pixels of input and encrypted Lenna image a input image, b encrypted image

where E is expected value and x and y are pixel coordinates. The pixels of plain image are highly correlated to each other. A good encryption algorithm breaks the linearity of this relation it can be seen from Table 4 original image has correlation of more than 0.99 which has been decreased by the proposed technique. Distribution is plotted to show the correlation between two adjacent pixels horizontally in an Input and Encrypted Leena Image; refer to Fig. 3. With the plot, it can be seen the randomness of pixel relation in Encrypted image.

4.7 Histogram Analysis Histogram characterizes the diffusion of pixels where each color intensity lies between 0 and 255. Any histogram that is uniformly distributed can prevent the statistical attack. Figure 4, shows the Encrypted Lenna image histogram is evenly distributed, so it is resistant to statistical attack.

Fig. 4 Histogram analysis a input image, b encrypted image, c decrypted image

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Fig. 5 Key sensitivity analysis, a input image, b decrypted image with wrong key, c decrypted image with correct key

4.8 Key Sensitivity Analysis In a cryptosystem, high key sensitivity is necessary; a slight change in the key results in a considerable output change. Figure 5 depicts a picture encrypted using two keys that differ by one-bit, resulting in a cipher image that differs by more than 90%.

5 Conclusion A bio-inspired public key cryptosystem has been proposed in this paper that uses a chaotic tent map function to encrypt and Salp swarm optimization to optimize the key used in decryption. The image and key are segregated into block size of 8-bit each, and a simple XOR function was carried out. Different performance indicators, including as encryption time, entropy, cross-correlation, UACI, PSNR, MSE, and MAE, are utilized to evaluate the suggested methodology’s performance. The average entropy calculated 7.30, avalanche effect could be shown by key sensitivity analysis as one bit change results in more than 90% change in an image. The result attains a better chaotic public key system than the existing traditional methods is proposed. In the future, an evolutionary algorithm can be used to bring potentiality to the cryptographic process.

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Role of IOT in Automated Hydroponic System: A Review Pooja Thakur and Manisha Malhotra

Abstract Many components are assumed for the growth of country economy. Agriculture is the major component for the growth of economy. The increasing demand for food, regarding quantity and quality, has raised the requirement for intensification of agriculture sector. Traditional methods use the soil for the growth of crop. It is an expensive process to prone the diseases. With the act of modern farming techniques, plants can be developed without the need of soil by utilizing nutrient solution. For this Hydroponics and Aeroponic can be used. Using IOT, smart hydroponic farming is implemented. IOT allows interaction of machines and controlling the hydroponic system without human interaction. Such systems are not harmful for environment as well as for the crops quality. IOT provide the innovative way for the modernization of agriculture. This paper provides an overview about the various types of hydroponic system, role of IOT in it and its application. Keywords Hydroponic · IOT · NFT · Vegetables · Water · Sensor · DFT

1 Introduction To live humans, need air, food, water and living space. Such phenomena are not unlimited in nature and thus land area optimization is the essentials on which human beings are dependent. Lands for agricultural around the world are now shrinking. This is because of the development of fertile land into industry and intent of settlement. This is attributed to the environmental and social anomalies, land assets depletion, demographic development and industrial growth [1–3]. Agricultural technology is now growing rapidly in metropolitan areas and is also referred to as the urban farming or urban agricultural. Urban agriculture or urban farming is one important approach P. Thakur (B) · M. Malhotra Chandigarh University, Chandigarh, India e-mail: [email protected] M. Malhotra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_33

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to cope with the reducing agricultural property. Using vacant or barren property in industrial areas such as rooftops, balconies, terraces and on house foundations, industrial agriculture. Hydroponics is one of the agricultural methods used on industrial farms. This year, other hydroponic farming techniques became common in urban farming practices. The term “Hydroponic” describes any means by which plants can be grown through a process that does not require soil use but includes the important nutrients or solution for nutrient. Gerick defined a method for the cultivation of plant in the media of liquid (nutrient solution) came up with the terminology Hydroponics. In addition to Gerick, during the thirties several attempts were made to follow the soilless growing plant methods. However, due to the insufficient information regarding the nutrients and the higher prices involved in the processes, technical developments were too inadequate [4–6].

2 Literature Survey As per the World population prospectus, India’s rank number two in the country list by population. The population is growing 1.2% annually. Food is the primary need of every human beings. To meet the requirement of food for the population that is growing rapidity, it is important to introduce the concept of gardening that is to be done in vertical, which in a limited space can deliver optimum produce. In India there are many states that suffer from extreme condition of drought. In the sector of agriculture, there is a harmful impact of the same condition [7]. According to the survey of economy, in Maharashtra there was a decline of 2.5% in the sector of agriculture. However, if the above said technologies are applied, the farmers would not remain relying on the climate changes, lack of water, and challenges faced by fluctuating moisture levels. As stated earlier, average farmer’s income in our country is about 6426 INR that will prevent the farmer from implementing these beneficial technologies [8]. Smart hydroponic is the alternate to overcome such kind of issues. IoT is used for the smooth working of smart hydroponic system. Internet of Things is a recent form of network of computers in which small, sensor-equipped electronic devices are used to monitor the system’s operating environment and, along with data from other sources, recognize activities that should be performed on behalf of customers to improve values or develop new system functions [9]. Automation is usually one of the aims of implementing the Internet of Things at home or at work. The Internet of Things (IoT) refers to a network of items, devices, machines, buildings and other electronic sensors and network communication tools for the sharing of knowledge. The IoT will allow the subject perceiving the world through established network infrastructure and remotely monitoring it. In IoT the real environment is combined with computer programs and simulated services on the Internet to provide end-users with value-added knowledge and features [10]. The components of the IoT include Radio Frequency Identification (RFID), various sensors and a processing node. The system has to provide an internet link such that such systems can send and retrieve data and communicate with other IoT apps as well as the Internet network. Sensor nodes

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generate a series of various Wireless Sensor Networks (WSNs) attached to devices to identify various phenomenon (physical phenomenon) on the network such as light, temperature and pressure [11–13]. The Internet of Things has recently begun to affect the wide range of businesses or sectors, spanning production, education, communications and resources to agricultural sector, in order to eliminate inefficiencies and boost efficiency in all markets [14–17]. Thinks the most recent executions are simply starting to expose what’s underneath and the full impact of IoT and its uses is not yet being observed. All things considered regarding development, particularly before, it can expect that IoT technologies would play the major role in various agricultural sector applications [18–21]. This is due to IoT’s technologies, including simple connectivity network and variety of services, like collection of data either remotely or locally, cloud-based smart knowledge processing and decision-making, app interfacing, or farm activity automation. These abilities will revolutionize the cultivating business that today is without a doubt one of the most insufficient parts of our economic worth chain [22, 23].

3 Types of Hydroponic System Working of all the hydroponic systems are based on growing plants without soil and grow in a sterile that allows delivery of nutrients to the roots directly from nutrient contain solution [24]. All these systems are differing from each other with respect to their structure. The following are hydroponic system:

3.1 Wick System Passive system is the name of wick system. It is good for the smallest plants that require less water and nutrients. In such system nutrients and water are provided to the plants with the help of wick. It does not require any air pump or waterpump (Fig. 1).

3.2 Deep Water Culture In such system, a container holds the nutrient solution and the plants root are suspended to the nutrient solution so that he roots can get constant supply of water, nutrients and air. Air pump is used to provide the oxygen to the roots.

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Fig. 1 Wick system

Fig. 2 NFT

3.3 Nutrient Film Techniques In this system, recirculation of nutrient rich water is done constantly. Roots of plants are bare to the proper supply of air, water and nutrients (Fig. 2).

3.4 EBB and Flow The working of such system is performed by forming tray with nutrient solution and then drumming the solution back in to the container. To perform it successfully a submerged pump is used that is connected with the timer. Flow of nutrient solution

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is done when the time is on, and when the timer is off, solution of nutrient flow back to the container.

3.5 Aeroponic The Aeroponic method is the most technically sophisticated form of hydroponic gardening. As above the rising medium the N.F.T. device is mainly air. The stems remain in the air and become misted with a solution of nutrients. The misting’s normally take place every few minutes. Because the roots are exposed to air in the N.F.T. method, if the misting phases are interrupted the roots may dry out easily. A timer operates the nutrient pump in almost the same manner as of other hydroponic systems, but the Aeroponic system required a quick period timer for operating the pump every minutes [25].

3.6 Drip System The most commonly used form of hydroponic system is the drip systems. In this system timer is used to regulate the pump and make the operation more convent. When the timer gets on, drip line sends the nutrient solution to the base of plants. The extra nutrient solution that flows off in a Recovery Drip System is recycled back into the tank for the purpose of reuse. The non-recovery system absorbs no run off. A recovery system utilizes the nutrient solution a bit more effectively because the extra solvent is recycled, and often makes the usage of an efficient timer as a recovery system and does not require to monitor different watering cycles. The non-recovery method needs a more accurate timer to adjust the irrigation cycles and insure that plants absorb a proper solution of nutrients and that the runoff is held to a minimum (Fig. 3).

3.7 Fogponics Fogponics is an accurate description of the Aeroponic. Here too, air is the medium of production. Although, fogger emitters (also referred to fogger) are used to generate smaller droplets (ranging from 5 to 30 µm) as compare to Aeroponic. The artificial fog delivers water and nutrients to the root of the plants. Fogponics is better than Aeroponic because smaller (fog) droplets encourage more nutrient absorption [26], fog can also nourish more areas of the plant’s root compared to spray droplets and the lack of flowing nutrient solution enables faster monitoring of crops [27].

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Fig. 3 Drip system

4 Nutrient Solution to Hydroponic System Nutrients are required for the growths of any plant. In soil system plants observe nutrient from the soil with the help of roots. In hydroponic system, nutrients are dissolved in the water and then roots of the plants are put inside the solution that is responsible for soaking required nutrients for the growth of the plant. Nutrient solutions are used here rather than the media in which the growth of plants are possible. In hydroponics system there are various methods by using which the nutrients are supplied to the root of the plant [28]. Table 1 depicts the analysis on the requirements of growing media and water pumps and which system saves water by recirculation (Table 1). Table 1 Method for providing nutrient solution to crop

Type of system

Grow media

Water recirculation

Pumps

NFT

May or may not

Yes

Air + water pump

Wick

Yes

No

Air pump

Drip

May or may not

Yes

Air pump

DWC

No

No

Air + water pump

Ebb

May or may not

Yes

Water pump

Aeroponic

No

Yes

Water pump

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5 Challenges Numerous quantitative approaches on the basis plants parameter have been made for smart hydroponic system. Still, there are several challenges for the development of smart hydroponic system. Some of these challenges are: 1. Security of system is highly required. The primary concern of any company and industry is to maintain the security of smart hydroponic system from intruders. 2. Harvesting is the peak point of any crop. Automating harvesting with the use of IoT has not been explored [29]. 3. Sensor impacts on plants growth has not been explored [30]. 4. Impact of fluctuating value of EC, pH and light intensity on plants has not been investigated [31]. 5. Impact of controller in Aeroponic and hydroponic system should also be investigated. Cheaper controllers are prone to oscillation, therefore it is necessary to investigate how far a fluctuation hydroponic systems and aquaponics systems are willing to tolerate. 6. Proper Datasets of crop parameter are not available that may leads to incompetent AI-based smart hydroponic systems [26].

6 Conclusion Nowadays population is increasing at a rapid rate. There is the requirement of advanced technologies for the agriculture in order to meet the food demand of humans. Various new methods are used for improving the agriculture sector. Younger people are now choosing the agriculture as their profession. Agriculture means planting of seeds, keeping the track for the growth of crop, harvesting of crop. In many areas agriculture totally depends upon the weather condition. Many flaws are there for cultivation. This review paper focus on the hydroponic system used for agriculture. It also explains the methods and role of IoT used to provide nutrient solution for plant. It also describes the various challenges faces by the industries in hydroponic system development.

References 1. AliPio MI, Cruz AEMD, Doria JDA, Fruto RMS (2019) On the design of nutrient film technique hydroponics farm for smart agriculture. Eng Agric Environ Food 12(3):315–324 2. Srivani P, Manjula SH (2019) A controlled environment agriculture with hydroponics: variants, parameters, methodologies and challenges for smart farming. In: 2019 fifteenth international conference on information processing (ICINPRO). IEEE, pp 1–8 3. Wongpatikaseree K, Hnoohom N, Yuenyong S (2018) Machine learning methods for assessing freshness in hydroponic produce. In: 2018 international joint symposium on artificial intelligence and natural language processing (iSAI-NLP). IEEE, pp 1–4

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4. Gertphol S, Chulaka P, Changmai T (2018) Predictive models for Lettuce quality from internet of things-based hydroponic farm. In: 2018 22nd international computer science and engineering conference (ICSEC). IEEE, pp 1–5 5. Jaiswal H, Singuluri R, Sampson SA (2019) IoT and machine learning based approach for fully automated greenhouse. In: 2019 IEEE Bombay section signature conference (IBSSC). IEEE, p1 6. Marques G, Aleixo D, Pitarma R (2019) Enhanced hydroponic agriculture environmental monitoring: an internet of things approach. In: International conference on computational science. Springer, Cham, pp 658–669 7. Changmai T, Gertphol S, Chulak P (2018) Smart hydroponic lettuce farm using internet of things. In: 2018 10th international conference on knowledge and smart technology (KST) 8. Son JE, Kim HJ, Ahn TI (2020) Hydroponic systems. In: Plant factory. Academic Press, pp 273–283 9. You X, Xu X, Wan J, Jiang C (2009) Analysis and evaluation of the scheduling algorithms in virtual environment. In: International conference on embedded software and systems, 2009. ICESS’09. IEEE, pp 291–296 10. Nurhasan U, Prasetyo A, Lazuardi G, Rohadi E, Pradibta H (2018) Implementation IoT in system monitoring hydroponic plant water circulation and control. Int J Eng Technol 7(4.44):122–126 11. Sisyanto REN, Kurniawan NB (2017) Hydroponic smart farming using cyber physical social system with telegram messenger. In: 2017 international conference on information technology systems and innovation (ICITSI). IEEE, pp 239–245 12. Pitakphongmetha J, Boonnam N, Wongkoon S, Horanont T, Somkiadcharoen D, Prapakornpilai J (2016) Internet of things for planting in smart farm hydroponics style. In: 2016 international computer science and engineering conference (ICSEC). https://doi.org/10.1109/icsec.2016. 7859872 13. Sambo P, Nicoletto C, Giro A, Pii Y, Valentinuzzi F, Mimmo T, Lugli P, Orzes G, Mazzetto F, Astolfi S, Terzano R (2019) Hydroponic solutions for soilless production systems: issues and opportunities in a smart agriculture perspective. Front Plant Sci 10 14. Mandal R et al (2022) City traffic speed characterization based on city road surface quality. In: Tavares JMRS, Dutta P, Dutta S, Samanta D (eds) Cyber intelligence and information retrieval. Lecture notes in networks and systems, vol 291. Springer, Singapore. https://doi.org/10.1007/ 978-981-16-4284-5_45 15. Modu F, Adam A, Aliyu F, Mabu A, Musa M (2020) A survey of smart hydroponic systems 16. Komninos A, Georgiadis G, Koskeris A, Garofalakis J (2019) Improving hydroponic agriculture through IoT-enabled collaborative machine learning 17. Saraswathi D, Manibharathy P, Gokulnath R, Sureshkumar E, Karthikeyan K (2018) Automation of hydroponics green house farming using IoT. In: 2018 IEEE international conference on system, computation, automation and networking (ICSCA). IEEE, pp 1–4 18. Pitarma R, Marques G, Ferreira BR (2017) Monitoring indoor air quality for enhanced occupational health. J Med Syst 41(2):23 19. Ojha T, Misra S, Raghuwanshi NS (2015) Wireless sensor networks for agriculture: the state of-the-art in practice and future challenges. Comput Electron Agric 118:66–84 20. Chowdhury MEH, Khandakar A, Ahmed S, Al-Khuzaei F, Hamdalla J, Haque F, Reaz MBI, Shafei AA, Al-Emadi N (2020) Design, construction and testing of IoT based automated indoor vertical hydroponics farming test-bed in Qatar 21. Malhotra M (2017) A study and analysis on simulators of cloud computing paradigm. IJATCA (2017) 22. Crisnapati PN, Wardana INK, Aryanto IKAA, Hermawan A (2017) Hommons: hydroponic management and monitoring system for an IOT based NFT farm using web technology. In: IEEE 2017 5th international conference on cyber and IT service management (CITSM). Denpasar, Bali, Indonesia (2017.8.8–2017.8.10), pp 1–6 23. Ruengittinun S, Phongsamsuan S, Sureeratanakorn P (2017) Applied internet of thing for smart hydroponic farming ecosystem (HFE). In: 2017 10th international conference on Ubi-media computing and workshops (Ubi-Media).

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24. Goraya MS (2015) Cloud computing in agriculture. Int J Technol Innov Res (IJTIR) 25. Balducci F, Impedovo D, Pirlo G (2018) Machine learning applications on agricultural datasets for smart farm enhancement. Machines 6(3):38. https://doi.org/10.3390/machines6030038 26. Mell P, Grance T (2011) The NIST definition of cloud computing 27. Mehra M, Saxena S, Sankaranarayanan S, Tom RJ, Veeramanikandan M (2018) IoT based hydroponics system using deep neural networks. Comput Electron Agric 155:473–486 28. Zanella A et al (2018) Humusica 2, article 17: techno humus systems and global change—three crucial questions. Appl Soil Ecol 122:237–253 29. Dudwadkar A, Das T (2020) Automated hydroponics with remote monitoring and control using IoT. Int J Eng Res Technol (IJERT) 9(06) 30. Adidrana D, Surantha N (2019) Hydroponic nutrient control system based on internet of things and K-nearest neighbors. In: 2019 international conference on computer, control, informatics and its applications (IC3INA). IEEE, pp 166–171 31. Yuefa D, Bo W, Yaqiang G, Quan Z, Chaojing T (2009) Data security model for cloud computing. In: Proceedings of the 2009 international workshop on information security and application (IWISA 2009), pp 141–144

AI-Based Real-Time Surveillance Himani Mittal, Himanshu Tripathi, and Shivansh Shrish Tripathi

Abstract Automatically describing the content material of an image can be an essential hassle in artificial intelligence that connects PC imagination and prescient and tongue processing, the problem will increase whilst we attempt to apprehend faces, gadgets and try and have a look at facial feelings with it or indifferent phrases whilst we attempt to generate the records we observe whilst we see something or a person. The idea of this venture after the desired amendment can permit one to maintain tune of suspicious or undesirable activities if passed off without continuously tracking them or without looking at the entire collection to be expecting the wrongdoer because it works in real-time. Also, it could be cross-checked if required because it stores the records that are secure from any leakage as its miles are encrypted and password protected. Keywords Artificial intelligence · Undesirable activities · Real-time · Encrypted

1 Introduction In the world of the increasing need for the labour force and their cost as well, the requirement of the intelligent machine is in demand. The paper completes one of those demands only. The aim is to generate the information a human notice when looks at something/someone. By doing this and successfully achieving the aim we can reduce the cost of labour required for some visual support, for example, CCTV surveillance staff, an instructor for visually impaired persons. The aim can also include the storage and security of the data.

H. Mittal (B) · S. S. Tripathi Department of E&C, R.K.G.I.T. Ghaziabad, Ghaziabad, India e-mail: [email protected] H. Tripathi Department of CS, R.K.G.I.T. Ghaziabad, Ghaziabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_34

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2 Literature Survey 2.1 Literature Review Abundant testing has been performed on how the human brain analyses objects. An increasing number of applications are being created for performing tasks like face recognition and object detection. The purpose of this paper is to deal with the cutoff of the price of CCTV security staff, examination invigilation, enhance the audio description for the visually impaired, etc. Automated facial recognition [1] has been with us for a long time and significant development has been seen. On the other hand, Object, can Recognition has emerged as an inspiring field in computer vision and AI [2, 3]. With the development of Convolutional Neural Network architectures [4], supported by big training data and advanced computing techniques, a machine is capable of object recognition function under some specific settings, for instance, face recognition [5]. Emotion generators work the same as facial recognition. The more challenging task is with the Caption Generator [6].

2.2 Drawing Inference The inference drawn out of the literature survey is as follows, 1. Creation of caption generator is a multiplex task for resulting in maximum accuracy along with performance and compatibility to work with other systems. 2. It has introduced a well-defined method to save encrypted data into a database within the server along with password protection.

3 Problem Statement 3.1 Challenge Faced The application uses four modules at the same time each for, 1. 2. 3. 4.

Caption generation. Face recognition. Object recognition. Emotion recognition.

As the name suggests, Caption generation provides caption to the occurring activity, face recognition recognizes faces, object recognition, recognizes object, emotion recognition recognizes the emotion of the person-focused. The challenge is to make every module work at its best all together in real-time. Non-functional activities such as security should also be provided.

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4 Analytical Solution The basic application consists of 10 python files, Table 1 shows each python file with their functionalities participating in making the project work successfully. Figure 1 shows a block diagram of how everything works, how every file is connected and performs its functionalities.

4.1 Caption Generation Caption recognition in this project uses Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) networks, an exceptional RNN, efficient enough for learning long term-dependencies. The main purpose of LSTM was to Table 1 File functionalities S. No. File name

Functionality

1

Securiy.py

Security.py is responsible for password protection and match the key.txt file. Without passing through it no one can enter the application

2

Mail_Sent.py

Mail_Sent.py is responsible for sending mails to the user if he or she forgets the password and provide them the required.txt file to change the password. It is also responsible for the generation of the.txt file

3

Caption.py

Caption.py is responsible for the generation of the caption

4

Face_Rec.py

Face_Rec.py is capable of matching a human face from a digital image. It uses biometrics to map facial features from an image

5

Obj_Rec.py

Obj_Rec.py is used for the detection of objects

6

Expression_Setup.py Expression_Setup.py recognizes the expression

7

Main_Body.py

Main_Body.py is nothing but a structure that takes the values returned from Caption.py, Face_Rec.py, Obj_Rec.py, Expression_Setup.py and shows them to the frames allotted to them in the interface

8

Server.py

Server.py is responsible for storing the data received from Main_Body.py to the server’s database but before that it encrypts the data with ROT-13 encryption also it is the one that retrieves the data. In other words, without Server.py one cannot store and retrieve the data from the server’s database

9

Rot-13.py

ROT-13.py is responsible for the encryption and decryption of the data that has to be stored using Rot-13 encryption

10

Ext_Config.py

This python file is responsible is quite different from the others. Using this one can access the login history, password history, history of the results

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Fig. 1 Block diagram

circumvent DOIM’S complications. On the other hand, CNN is efficient in deep neural networks which can operate on data comprising 2-D shaped matrix. The main purpose of CNN was to identify objects in the image. With the help of two, we can give captions on the live feed provided to the two networks. We have used Flickr8K dataset for training.

4.2 Face Recognition To recognize faces we have used ‘face_recognition’ package. In which we first have used ‘face_encodings’ which return 128 dimensions face encoding of a given image. After encoding the image, we use ‘face_locations’ which returns an array of bounding a box on human faces. Then we encode all the known faces in the dataset and use ‘compare_faces’ for matching. To use this the prerequisite is that we must have sufficient face data of a person for recognition to take place successfully.

4.3 Object Recognition You Only Look Once (YOLO) is a collection of models related to end-to-end deep learning models built for fast object detection. YOLO takes a single deep convolutional network and distributes the load into a grid of cells and every individual cell in return anticipates bounding box and object classification. For object recognition, we have used the COCO dataset.

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4.4 Emotion Recognition For emotion recognition, we have used a pre-trained deep learning module that is created by the research group at Facebook, ‘DeepFace’. ‘DeepFace’ is a facial analysis framework that works in python. It is a hybrid of human-like accuracy models such as ‘VGG-Face’, ‘Google FaceNet’, ‘OpenFace’, ‘DeepID’, ‘ArcFace’, ‘Dlib’.

4.5 Screenshots The below are some screenshots of the project in running state, Figure 2 shows that the project asks for the password to enter. Figure 3 shows how the mainframe and the interface of the project look. Figure 4 shows external configuration Using this one can access the login history, password history, history of the results. Figure 5 shows the history stored and can also view the image as they are stored in binary format. Figure 6 shows how all the data is encrypted and safe. Figure 7 shows the text file which is sent to the user via mail and consists of random characters which have to match with the file inside which changes continuously to change the password.

Fig. 2 Login security

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Fig. 3 The mainframe and working

Fig. 4 External configuration

5 Conclusion and Future Work 5.1 Conclusion Finally, we can conclude that we had successfully created a sample application that takes input from the webcam and studies it to provide a caption, recognized faces, objects as well as the emotion of the face as the output and store in the database of

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Fig. 5 History

Fig. 6 Data stored in encrypted

the server in an encrypted format. We have also run some test cases of which we received the following data, Table 2 shows the accuracy of each system when put to test. Therefore, we can conclude that the project is 71.25–78.75% accurate.

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Fig. 7 File mailed to change password

Table 2 Accuracy

System

Accuracy (%)

Caption generation

25–40

Face recognition system

90–94

Object recognition system

80–85

Facial expression recognition

90–95

5.2 Future Work This research mainly deals with the security and ease the work burden for the human, there are abundant possibilities and scopes for this model to be used in daily life with some changes and also with the help of growing technology and their implementation in the same this model can be made more efficient and embodiment and also it may additionally assist the visually impaired to get a concept of the front eye situation without relying on a person else.

References 1. Bruce V, Young A (1986) Understanding face recognition. Br J Psychol 77(Pt 3):305–327. https://doi.org/10.1111/j.2044-8295.1986.tb02199.x PMID: 3756376 2. Mathew D, Shukla VK, Chaubey A, Dutta S (2021) Artificial intelligence: hope for future or hype by intellectuals? In: 2021 9th international conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO), pp 1–6. https://doi.org/10.1109/ICR ITO51393.2021.9596410

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3. Mandal A, Dutta S, Pramanik S (2021) Machine intelligence of pi from geometrical figures with variable parameters using SCILab. In: Samanta D, Rao Althar R, Pramanik S, Dutta S (eds) Methodologies and applications of computational statistics for machine intelligence. IGI Global, pp 38–63. https://doi.org/10.4018/978-1-7998-7701-1.ch003 4. Du J (2018) Understanding of object detection based on CNN family and YOLO. J Phys Conf Ser 1004(1). https://doi.org/10.1088/1742-6596/1004/1/012029 5. Taigman Y, Yang M, Ranzato M, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. In: 2014 IEEE conference on computer vision and pattern recognition, pp 1701–1708. https://doi.org/10.1109/CVPR.2014.220 6. Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3156–3164. https://doi.org/10.1109/CVPR.2015.7298935

Smart Car with Safety Features and Accident Detection Alert System Subhadip Ghosh, Soumen Maity, Sourav Chowdhury, and Sanjay Chakraborty

Abstract Technologically the automobile sector is rising fast and the road vehicles on road is stepping up every day. On other hand, the safety of vehicles and riders also matters. We must ensure adequate safety in each vehicle. Our paper aims to provide safety features to a vehicle with real-time location access of the vehicle. Our prototype will consist of features like an accident detection and alert system, drunk driver alert, passenger safety button, and automatic airbag triggering. Various types of modules and sensors has intermixed with Arduino Hardware for making this prototype, such as, accelerometer (ADXL335) sensor to identify accidents, SW420 impact sensor to detect vibration in cars and activate the accident prevention system of the car, MQ3 alcohol sensor to detect whether the driver is drunk or not, GPS and GSM modules to locate car’s location and to send an alert notification/message to its nearest authority. Keywords Arduino · GSM · GPS · LCD · Vibration Sensor · Accelerometer · MQ3 sensor

1 Introduction Nowadays amateur rash drivers, careless driving, and delayed access to first aid to victims have been a major cause of deaths. In India (2016) from the analytical report on Road Vehicle bad news, released by Control Dept. of Roads and Highways Transport. Our Country has Crossed over 4,60,852 accidents and 1,45,685 deaths [1–3]. Almost 423 people died by 1227 road vehicle accident per day, Just cause of Unable to use of suitable treatment in needed time are minimum 16 deaths of Passengers happening per hour. The use of modern-day cab services has skyrocketed S. Ghosh · S. Maity · S. Chowdhury Computer Science and Engineering Department, JIS University, Kolkata, India S. Chakraborty (B) Computer Science and Engineering, Techno International Newtown, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_35

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in which cases of harassment, robbery in cabs are rising. Only a few high-end luxurylevel cars support adequate safety features. Our prototype is cost-efficient and can be implemented in low-end cars also using Internet of Things technology. It tells us the connection say network between the physical objects, those are encapsulated with sensing softwares and many other technologies for collecting or sending data between other systems or devices through the Internet [4–6]. We are going to use accelerometer (ADXL335) sensor to detect accidents and the speed of the car at the time of the accident. After that, we shall use SW420 Impact Sensor to detect vibration in cars and to activate airbag circuitry instantly for safety purposes. Apart from that, we shall interface an MQ3 alcohol sensor with Arduino to detect whether the driver is drunk or not and an alert notification/message will received by the car authority. Now for the passenger’s safety purpose, some panic buttons will be placed inside the car, whenever a passenger feels unsafe he/she may press anyone of the panic buttons. After pressing a panic button, an alert notification/message will be going by sending an SMS to the car owner or authority. Now to detect the car’s location and to send alert SMS on pressing a panic button or to send an alert after accident detection or drunk driver alert we shall use the GPS module and GSM module, respectively. Regardless of alert type, a Google map link will be sent with an alert message to detect the car’s exact location and to reach it using Google map navigation. Rest of the paper is organized as follows. Section 1 describes the parallel works and their impact to boost up this proposed idea, Sect. 2 explains the proposed work and the necessary hardware components used to build the model, Sect. 3 discusses the benefits and applications of the proposed approach and at last Sect. 4 deals with the conclusion of this paper.

2 Background and Proposed System We implement a device that will make a car smart in terms of safety and alert purpose. We use the Arduino Uno microcontroller as the backbone of this device. We shall use various sensors to implement this device.

2.1 Components Used 2.1.1

Arduino

The Arduino is a microcontroller board. We are using Uno version of it, which consists of an Atmega328 chip. Input/output pins are there for interfacing [7, 8].

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Robot Chassis Kit

It is an assemble and user-friendly robot chassis platform. This chassis kit can be used as a robot car and various sensors and controllers can be added to it.

2.1.3

GSM SMS Alert System

SIM900A is a GSM module. We are using it to send and receive messages and voice calls. One valid mobile SIM to be attached to it. It can works on two frequencies, i.e. 900 and 1800 MHz usually it gets controlled via AT commands.

2.1.4

GPS Tracking

The NEO-6 m module is a GPS module. This works in DC input. It has a UART interface for serial communication which can be configured, but by default it works on 9600 baud rate. It consists of a patch antenna. It consists of an EEPROM for storing configuration setting. It gives us the best possible positioning system [9, 10].

2.1.5

Alcohol Sensor

This type of sensor is used to detect the existence of alcohol. Here we are using MQ3 sensor. It is a cost-effective semiconductor sensor. Its conductivity proportionally changes with the changes of alcohol concentration [11, 12].

2.1.6

SW420 Impact Sensor

This sensor is used to detect whether a physical shock or impact has occurred or not. This sensor is usually installed in front of the car. This sensor is highly sensitive and non-directional. It consists of a LM393 comparator IC.

2.1.7

Accelerometer

We are using ADXL335 module. This measures acceleration force. Its range is ± 3 g in the x, y, z axis. Its output is analog voltage, which is proportional to the analog voltage.

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Push Button

The push button is an electrical component, which is usually used in circuits. Using this buttons, we can turn on and off the control circuit.

2.2 Proposed Algorithm BEGIN Step 1: start the car Step 2: Arduino wait for input Step 3: if drunk driver detected alert message sent else if accident detected alert message sent with live location link else if panic button pressed alert message sent and led start blinking else if high vibration detected airbag triggered else no input received Step 4: if travel not finished go back to step 2 else do nothing; END The above algorithm and Fig. 1 represent the overall workflow of our proposed working system. This system is nothing but a collection of various automatic alert systems to warn the co-passengers. When a driver is going to start the smart car engine, the proposed Arduino system is activated and waiting for input. Now, when the different alert systems are activated, then they send different messages through GSM and GPS modules to the nearest police station or rescue center. If the system detects any kind of high vibration in the car due to rash driving or collision, then the safety measures (airbag etc.) are automatically opened or else do nothing.

2.3 Proposed Prototype Model Figure. 2 shows the various necessary components and different embedded systems or modules in our proposed model. These are managed through a powerful Arduino

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Fig. 1 Flow chart of our proposed working system

system. This proposed model is cheaper, flexible to implement, and can be easily attached with the existing system of a car. Therefore, it also provides great reliability and flexibility as well [13–15].

3 Applications We can use it in modern personal cars or in cars used for cab services. Nowadays most of the accidents are happening due to drinking and driving reasons. This system has a feature to alert drunk drivers beforehand. For cab services passenger safety is an important issue, our system provides a panic button to inform the cab authority directly about unsafeness when a passenger feels unsafe. The automatic airbag can be enabled through our system, which can reduce the accident’s severity. Our system has accident detection features that send an alert message with location link and car’s speed during an accident to the authority or owner. So quick action can be taken after an accident and victims can be taken to the hospital immediately. Our system implementation will be cost-effective.

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Fig. 2 Block diagram of our proposed model

4 Conclusion The main purpose of our paper is to put forward our prototype with the hope of being further developed by the community and finally being scaled and deployed by the autonomous vehicle industry. Our proposed system is a compact prototype of a car model system made of Arduino and other sensors like, ADXL335, SW420, MQ3, etc. to detect the accident, activate the prevention system and identify the drunker on the driver seat, respectively. The sensors and types of equipment used in our work are efficient and cost-effective. However, in order to implement these features with an existing vehicle’s embedded system, a much more compact unit need to be built that can be added on-chip. With this system, we can add more smart features like automatic car door control, fingerprint car lock, etc. in the future.

References 1. Krishnan M (2017) India has the highest number of road accidents in the world. Deutsche Welle 2. Das A, Dhuri V, Desai A, Ail S, Kadam A (2021). Smart car features using embedded systems and IoT. arXiv:2101.00496 3. Angrula MK, Kaur MN (2019) Car accident detection and reporting system. e-ISSN: 2395-0056 4. Topinkatti A, Yadav D, Kushwaha VS, Kumari A (2015) Car accident detection system using GPS and GSM. Int J Eng Res Gen Sci 3(3):1025–1033 5. Prabha C, Sunitha R, Anitha R (2014) Automatic vehicle accident detection and messaging system using GSM and GPS modem. Int J Adv Res Electri Electron Instrum Eng 3(7):10723– 10727

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6. Mounika J, Charanjit N, Saitharun B, Vashista B (2021) Accident alert and vehicle tracking system using GPS and GSM. As J Appl Sci Technol (AJAST) 5:81–89 7. Sivanantham N, Dinakaran A, Sujith M (2021) Arduino based footboard accident prevention system for electric locomotives. In: Advances in science and technology, vol 105, Trans Tech Publications Ltd, pp. 172–178 8. Sharma S, Sebastian S (2019) IoT based car accident detection and notification algorithm for general road accidents. Int J Electri Comput Eng 9(5):2088–8708 9. Selvaraj S, Umakanth N (2021) Providing safety and detecting accidents in bike transport with smart helmets using IoT. In: Handbook of research on decision sciences and applications in the transportation sector. IGI Global, pp 280–303 10. Kumar AS, Amith N, Jagadeesan A, Bhat V, Kumar S (2021) Smart vehicle accident detection system. In: 2021 international conference on design innovations for 3Cs compute communicate control (ICDI3C). IEEE, pp 51–54 11. Almohsen MK, Alanazi TH, BinSaif SN (2021) Smart car seat belt: accident detection and emergency services in smart city environment. In: 2021 1st International conference on artificial intelligence and data analytics (CAIDA). IEEE, pp 109–114 12. Mandal R. et al. (2022) City traffic speed characterization based on city road surface quality. In: Tavares JMRS, Dutta P, Dutta S, Samanta D. (eds) Cyber intelligence and information retrieval. Lecture notes in networks and systems, vol 291. Springer, Singapore. https://doi.org/10.1007/ 978-981-16-4284-5_45 13. Goyal SB, Bedi P, Kumar J (2022) Realtime accident detection and alarm generation system over IoT. In: Multimedia technologies in the internet of things environment, vol 2 Springer, Singapore, pp 105–126 14. Agarwal N, Jangid A, Sharma A, Kumar N, Kumar M, Kumar P, Chakraborty P. (2021) Camerabased smart traffic state detection in india using deep learning models. In: 2021 International conference on communication systems & networks (COMSNETS). IEEE pp 690–696 15. Dutta M, Mondal S, Chakraborty S, Chakraborty A (2020) A human intention detector—an application of sentiment analysis. In: Emerging technology in modelling and graphics. Springer, Singapore, pp 659–666

CARGIoT: Concept Application Review in Green Internet of Things Raveena Yadav and Vinod Kumar

Abstract The Internet of Things (IoT) is an emerging technology around the world that helps in connecting different sectors and human beings to the Internet. This connection has different network technologies and application protocols that contain physical objects with embedded sensors that can transfer information to the Internet with the help of network technologies. Several factors challenge the development of the Internet of things. Security and privacy are the measure concern, data released in IoT is a huge amount. Managing this big data is one of the challenges. Sensors used in IoT devices are low-powered devices, its efficient use is also a challenge in IoT. This is the motivation for doing research on energy-efficient and moving toward Green IoT. In this paper, we have given a light to review Green IoT and its architecture, its application, and protocols used in Green IoT. This research paper helps in giving a comprehensive review to other researchers. Keywords IoT · Green IoT · Energy-efficient

1 Introduction 1.1 A Subsection Sample The number of digital and electronic devices is increasing day by day. According to the Accenture strategy, 100 billion devices will be connected among themselves by 2030. Concerning this, the dependency of human life on digital objects is increasing. These objects are getting smarter by connecting to the Internet and this network is known as IoT. IoT architecture can be divided into three layers, four layers, and five layers. But the basic components required in this architecture are sensors for sensing the surrounding, RFID for identification of objects in this massive network, communication technologies, and services that users can access and use the devices. R. Yadav (B) · V. Kumar Department of Computer Science and Engineering, Delhi Technological University, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_36

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These devices have some challenges also such as security and privacy which is the apex challenge, collection, managing, and decision making of a large amount of data that are producing by these devices. The devices used in this network works in a resource constraint environment. So, making them energy-efficient for working efficiently in this environment is also one of the major challenges. The power consumption, emission of CO2, and waste of these devices are also increasing. This is one of the serious problems for our planet. From this, the Green Internet of things has come into the light. Green IoT is defined as the IoT network which is eco-friendly and helps in working inefficiently way in the respect of energy. There are various protocols are also given by researchers in this paper will discuss them also. The main aim of the green Internet of things, make devices that are less harmful to the environment and consume less energy. These energies efficient concepts help to increase the lifetime of things on the Internet. This paper is divided into different sections (Fig. 1). Section 1 has described the basic concepts of IoT, in which we have discussed the different definitions given by researchers and different layered architecture. Section 2 has mentioned the applications of IoT and also mentioned the future applications and how they would work. Section 3 gives a comprehensive review of Green IoT and different protocols used in making Green IoT network which helps the researcher in doing further research in the field of green Internet of things and last Sect. 6, a conclusion is present.

2 Applications of IoT Internet is a vital part of our life and it has given a lot of application. From its application, one is the Internet of things. Internet of things makes a network of devices and it has also given a lot of applications that help in saving the energy and

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power consumption, also helps in giving a fast result. Some of the applications are mentioned in this section. Smart Vehicle One of the main applications of IoT is connecting vehicles and making them smart. Smart vehicles IoT make a dynamic environment, it also gives a challenge in making smart vehicles. These help in giving a prior indication of an accident and send a notification to his/her connection. This also helps in making a limit of the speed of the vehicles in the rainy season that helps to prevent the accident. Smart vehicles also include intelligent traffic lights, it will sense them in nearby areas. Based on the crowd, it will change the lights of the traffic signal. Smart Farming Smart farming is a very new concept to implement in India. In most of the states, farming is still doing by conventional methods. Smart farming helps in many ways such as bits of help in getting the nature of soil from that it gives information which crop is suitable for this soil and how much water it requires. It also gives the weather information. So that farmers can take appropriate steps for their crops. Smart farming technology also helps in animals like sensors can be attached to the ears of pets. From which its owner can get information about the location of his pet. This farming gives a lot of benefits and one of the major challenges in this field is it is dynamic and sometimes it has to update its data automatically. Farming depends on the weather so, giving accurate predictions is also one of the major challenges. Smart Health The health of humans is an important aspect of human life. Making a smart health system is one of the applications of IoT. Smart health IoT network helps in getting information about the patient remotely. It also helps in giving the information of pulse rate and oxygen level in blood. If these levels are not in the range, then it will give a notification to the user for consulting with the doctor. The doctor can consult remotely his patient. Smart health network also helps in tracking the equipment that is used in health service. There is one issue also present that if intruders change any information, then it will give a direct impact on the life of the person. Smart Home Smart home plays a very important role in saving the power consumption. Solar panels can be used on the terrace of a house. When home appliances are using power generated by solar then the meter will stop. This will help in conserving energy. This network also has smart lights, which have proximity sensors. These lights will glow only when anyone enters the room and switch off automatically when no one presents in the room. This network also gives a facility, like it notifies when anyone enters into the house without crossing security gate then it will give notification to user. But in this intruder can get the information when no one is present at home and take advantage of this situation.

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3 Architecture of IoT For building any structure, there is a need for architecture. While IoT does not have any standard architecture. Many researchers have given three-layered, four-layered, and five-layered architectures (Fig. 2). Based on these, different IoT devices have been made. In this section, we will discuss IoT layered architecture. Three-layered architecture, a perception layer where sensors gather the information and send it to the network layer. The next layer is the network layer, which carries different communication protocols for transferring the data from the perception layer to the user layer which is the application layer. The main task of the user layer helps in viewing the information to the end-user. The three-layer architecture was modified by researchers to a four-layered architecture and five-layered architecture.

3.1 Perception Layer The perception layer is the bottom layer of IoT architecture [11, 12]. It is also known as the physical layer and sensor layer of the architecture. It consists of a massive number of physical objects such as sensors and actuators which are used for

Fig. 2 IoT layers with components

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collecting the data from the surroundings and transferring it to the communication layer. Sensors play a crucial role in the perception layer. They are very small in size, cost-effective, and consume less power. A smartphone is one of the basic devices which has different types of sensors such as location sensor, movement sensor, light sensor. There are numerous sensors available in this technical world, like neural sensors, medical sensors (fitness bands and healthcare bands which are used for heart patients and for keeping an eye on daily activities), environmental sensors check the temperatures, measure humidity, soil texture, air pollution, and many others sensors available. Radio Frequency Identification (RFID) is another important component of the perception layer. It is used to identify objects and record metadata through radio waves. Actuators also play a very essential role in this layer. It is a device that helps in giving an action by converting one sort of energy into another.

3.2 Network Layer Network layer which helps in making a bridge between the bottom layer and topmost layer via wired network or wireless network. This layer uses many communication protocols such as Bluetooth, Zigbee, Wi-Fi, Sigfox, Z-wave, and many others in a row. The conventional Bluetooth protocol consumes a massive amount of energy because of this new type introduced, Bluetooth low energy protocol. It has gained more success than conventional protocol as it consumes less amount of energy. A wireless sensor network consists of ten to millions of sensor nodes and all are connected using wireless technology. They integrate the data from its surroundings and communicated to the Gateway device and relay the information over the internet. The IoT is an immense network, which connects billions of things and also contains various networks.

3.3 Application Layer The application layer is the topmost layer and it is also known as the forefront of the entire architecture. It has two main tasks, to manage applications of the IoT and provide services to end-users. It is the only layer through which users can access IoT devices. It can be used in various domains such as education, health, industry. This layer carries different protocols for providing services such as CoAP, MQTT, XMPP, and DDSI.

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3.4 Four-Layered Architecture Four-layered architecture is quite similar to three-layered architecture. In this, there is a support layer that lies between the perception layer and the network layer. Cloud computing is a technology used in the support layer of the four-layered architecture of IoT.

3.5 Five-Layered Architecture Five-layered architecture has two additional layers, the processing layer, and the business layer. The perception layer function is the same as the three and four-layer architecture. The transport layer, transmit the data from the sensor to the processing layer, via different communication protocols. When the information has been transmitted, the processing layer comes into the picture that does the decision-making process and identifies redundant data and stores the relevant information, and processes it, as per the user’s requirement. It employs various databases, cloud computing services as well as big data processing modules to store as well as process the information. Then data moves to the application layer, which is responsible for delivering various services to the end-user. The topmost layer is the business layer that helps in analyzing the data. This helps in providing a better decision.

4 Literature Review In [1] authors have discussed that IoT network is made up of a large number of sensor nodes and produce a huge amount of data. For transferring data from one node to another, it has to follow a routing metric. For efficient use, nodes have to maintain their routing table according to the number of links and total distance from the source node to the destination. In this, they can also follow the multihop path. As not choosing an efficient path for transmitting then there is the requirement of changing the battery or sensor node again and again. This will disturb the network and take more time. Green components are mentioned in [2], green RFID is smaller in size as compare to conventional RFID. It helps in reducing the e-waste related to RFID. By using an energy-efficient algorithm, also helps in moving toward you. Green Wireless Sensor Network, this network contains a lot of nodes, making it a green network. There is the requirement to put the nodes in sleeping mode when they are not in use, using efficient routing protocols and radio optimization techniques. In [3] the issue of dynamic routing has been mentioned by researchers, as the dynamic routing table requires updating the table from time to time which can increase the load of the network link and consumes more power of devices. This makes an obstacle in making

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green IoT. In [4] makes a cluster of network and measure the radius of cluster and cluster head, this helps in sending data in an energy-efficient manner. In [5] authors have used multihop communication in wireless sensor networks, this communication helps in achieving green internet of things. In [6] two concepts that have been used for making green IoT are; using green computation for design the network and using eco-friendly IoT devices. Data produced by them, cannot be maintained efficiently by using conventional SQL, there is a need for Hadoop for moving toward a green IoT concept. In [7] green smart agriculture has proposed with some term green design in which devices and network made that is not harmful to the environment. Green Communication helps in the communication of devices in long-range with less power usage. In [8] uses a security framework using hybrid techniques and is light-weighted. This security framework, works into three steps first is registration of device, second step is authentication of the device, and the last step is the security of data. The author [9] discussed the potential applications domains of IoT which cover some broad areas like smart homes, smart farming, smart vehicles, smart cities, smart environment, and plenty of others. Smart cities would require RFID and sensors which could use in parking meters, street lights, and connected to the internet. Internet of things is often employed in smart farming to monitor the weather conditions such as rain, snow, and drought which help to manage the temperature and humidity level to forestall fungus. In the domain of Smart homes, IoT is applied to the devices which are controlled by remote to avoid wasting energy. Refrigerators can have an LCD screen that could tell the items available inside, what’s almost expiring and what have to be restocked. The smart environment with IoT technology is employed for sensing, tracking the things of the environment to realize sustainability, and a green world. IoT technology is used to calculate Air Quality Index which tells about the pollution within the air. It is applied in the measurement of pollution levels within the water. In [1] Green IoT consists of some steps for a reliable green IoT structure; first, there is a need of making a green design. In the second step, a green computation is required for green design. The third step is about the utilization and maintenance of green IoT devices. The last step is about disposing of devices and this disposing of should be eco-friendly. Ref. [10] supervised learning algorithm used for finding the route for transmitting information from one node to another. By using this algorithm, nodes will learn about the best route from their experience.

5 Challenges and Issues in Green IoT In this section, we have put effort into bringing challenges and issues in green IoT. Maintainability IoT network contains nodes with different features, the connection among nodes of different networks and also carries a huge amount of data. Maintaining this large amount of data, the connection among nodes, and the heterogeneity of nodes is one of the challenges. Nodes of IoT can move from one place to another; they are mobile.

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For these types of nodes maintaining a database is a challenge because that database should be dynamic. Scalability Scalability means handling an increase or decrease in performance in the presence of a large number of nodes. A scalable system should work efficiently even when the number of nodes is increasing significantly. Throughput in IoT decrease at a rate of (1/N)^1/2, where N is the number of nodes. Failure Handling It is expected from IoT to auto repair the failures immediately and the failure of a node should not affect the whole setup. Users trust the systems this helps in increasing the productivity of IoT devices and the growth of IoT networks. When these devices are not able to handle some fault then it can degrade the performance and growth of the IoT network. It is a challenge for developers to make devices that are robust and work efficiently. Environmental Challenges Environmental challenges, create certain levels of shock and vibration that occur in almost every application in the industrial field. The presence of humidity can damage electronic devices. Heatwaves and heating of devices also damage IoT devices. Environmental pollution like sulfur dioxide, dust, and acid might cause damage to the memory in IoT systems. So, maintaining a system regarding these challenges is also a great task. Security Technology comes with several challenges, in those challenges’ security is one of the topmost challenges. As we know, an IoT network contains a huge amount of data and it moves from one node to another in the wireless network. In an IoT network, sometimes there is the requirement of communication with an unauthenticated node. Maintaining security for the unauthentic node is a very difficult task. This network also contains our personal information, maintaining data confidentially and saving from eavesdroppers and intruders is also important.

6 Conclusion In this paper, we have discussed the comprehensive review of green IoT. It helps in identifying the need of moving toward green IoT. The main motivation for writing this paper is the growth of IoT networks and their devices. The integration of power consumption of these devices is very high and this is not safe for the environment. For making a network that is reliable not only for humans but also for the environment. We investigated different components that helped in building a green IoT network and the issues and obstacles are present in moving toward it.

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References 1. Wehlitz R et al. (2017) A smart energy platform for the internet of things–motivation, challenges, and solution proposal. In: International conference on business information systems. Springer, Cham 2. Nazish M, Banday MT (2018) Green internet of things: a study of technologies, challenges and applications. In: 2018 International conference on automation and computational engineering (ICACE). IEEE 3. Varjovi, AE, Babaie S (2020) Green internet of things (GIoT): vision, applications and research challenges. Sustain Comput Inform Syst 28. 100448 4. Albreem MAM et al. (2017) Green internet of things (IoT): an overview. In: 2017 IEEE 4th international conference on smart instrumentation, measurement and application (ICSIMA). IEEE 5. Shaikh FK, Zeadally S, Exposito E (2015) Enabling technologies for green internet of things. IEEE Syst J 11.2. 983–994 6. Bashar DA (2020) Review on sustainable green Internet of Things and its application. J Sustain Wireless Syst 1(4):256–264 7. Zhu C et al. (2013) Green internet of things for smart world. IEEE Access 3 (2015):2151–2162 8. Huang, Jun, et al. (2014) A novel deployment scheme for green internet of things. IEEE Int Things J1.2, pp 196–205 9. Li, Z et al (2018) Minimizing convergecast time and energy consumption in green Internet of Things. IEEE Trans Emerg Topics Comput 8(3):797–813 10. Batra, Isha, et al (2020) Hybrid logical security framework for privacy preservation in the green Internet of Things. Sustainability 12(14):5542 11. Mandal R. et al. (2022). City traffic speed characterization based on city road surface quality. In: Tavares JMRS, Dutta P, Dutta S, Samanta D. (eds) Cyber intelligence and information retrieval. Lecture notes in networks and systems, vol 291. Springer, Singapore. https://doi.org/ 10.1007/978-981-16-4284-5_45 12. Yadav R (2021) ADAS authentic data allowed security in Internet Of Things. Turkish J Comput Math Edu (TURCOMAT) 12(13):1761–1765

Heterogeneous BigData Analysis in IoT Cloud Environment P. V. Manjusha Nambiar and E. Anupriya

Abstract People may now gain important insight into huge heterogeneous data created by IoT devices, thanks to a variety of bigdata, IoT, and analytics technologies. This paper examines the most recent academic initiatives focused on massive IoT data analytics. The connection between bigdata analytics and the IoT is explained. Furthermore, by introducing a novel paradigm for massive IoT data analytics, this research review introduces different types, methodologies, and technology for massive IoT data analytics. The advantages of data analytics under the IoT paradigm are then highlighted. Finally, future study directions include open research challenges, visualization, and integration. The Internet of Things is considered as a catalyst for the creation of intelligent, context-aware services, and applications. These services could respond to changes in the environment in real time. Keywords Bigdata · IoT (Internet of Things) · Data analytics

1 Introduction IoT is a network of Internet-connected things. These objects can collect data, receive information, and send it through the Internet. Emergence of IoT has resulted in a data deluge. Data with huge variabilities, ambiguities, and complexities generally named as heterogeneous data needs careful management when it comes to its storage, manipulation, and integration. Most values of IoT sensors are influenced by time and space, as well as a variety of other variables, on an irregular basis. Additionally, there is no fixed format or architecture to describe various IoT data sets. As a heterogeneous IoT data set depends on time, space, and other related factors [1], to analyze the data, it is necessary to account for the relationships between many factors. The sensors, communication medium, cloud infrastructure, and the application constitute to make an IoT solution. All solutions in IoT mainly involve the sensors that collect data and send it through the Internet. Data from sensors on automobiles which are associated P. V. Manjusha Nambiar (B) · E. Anupriya KL University, Hyderabad, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_37

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with geospatial and location-based intelligence, GPS, heating and cooling systems on buildings, tire pressure systems, biometric systems, airplane sensors and engines are few of them to mention. The connectivity which is wired or wireless to connect the objects and devices is an important factor. The processing of data which is the most challenging and crucial phase as the data collected by the IoT devices are enormous. Lastly the user interface contributes to the efficient coordination between the system and the user. Amount of data generated and the space needed to store this data is huge. Even though there is not a universally accepted IoT architecture, a three and five layered architecture has been proposed by different researchers [2]. Researched and developed a set of agricultural Internet system considering the 3 layered architecture, basically the perception layer, network layer and application layer. Perception layer deal with sensing and actuation, network layer is responsible for transmitting the information or connectivity and application layer provide the specific application to the consumer [3]. Suggests a five layered architecture with a vision of 5G IoT systems as a future generation game changer. It has an added communication layer which uses radio access technology and architecture layer where cloud storage, intelligence, and analysis is performed. The widely distributed sensors in the perception layer produce massive data. There are a large number of different types of application units in the application layer. In the meanwhile, because storage formats and data specifications vary, it is a good idea to create a data layer that combines multiple heterogeneous data sources. Data manipulation and data exchange are more complex with these data sources [4]. The data could be multisource data with significant heterogeneity, large-scale dynamic data, low-level data with weak semantics, or erroneous data [5]. Furthermore, the huge data sets collected from diverse locations are in a variety of forms and are typically semi-structured and unstructured. The data sets are usually in a raw state, with inconsistencies, excessive redundancy, and a lot of worthless information. Without preprocessing activities on the data sets, not only would a large amount of storage be necessary, but the data might not fit into the pre-defined database structure. As a result, data preprocessing techniques such as data integration, redundancy reduction, and data purification become extremely valuable in order to prevent wasting storage space and improve computational efficiency [6]. Data acquisition and integration is one of the crucial phases which involve devices such as RFID, zigbee sensors, GPS devices, temperature sensors. Fig. 1 gives a summary of the data taken as raw input and processed and analyzed to reach into a meaningful insight. Heterogeneous data brings a big challenge to IoT applications when the developers need to integrate massive structured, semi-structured, and unstructured data. To highlight contributions and further motivate new studies leveraging the capabilities of IoT and heterogeneous bigdata and analytic methods, this article provides a review of the state-of-the-art potentials and challenges. This paper is focused on IoT and bigdata in the context of the analytics of a huge amount of heterogeneous data. This paper proposes a frame work for analysis of the data with data acquisition and integration as the first step followed by data processing and analytic methods. This aids domain experts and professionals in identifying the many strategies for dealing

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with heterogeneity in IoT data, hence increasing the number of interoperable IoT solutions.

2 Related Works This section provides background information on the basic aspects of bigdata generated by IoT devices, as well as uncertainty and the analytics techniques that address it. The importance of merging bigdata with IoT for real time data storage and analysis is highlighted in [7]. It focuses on how to successfully integrate the capabilities of IoT, bigdata, and analytics to handle the huge volume and velocity difficulty of real time data in the smart building area. Cai et al. [8] propose a functional paradigm for identifying IoT and bigdata collecting, processing, and administration. It indicates that semantic relationships between heterogeneous IoT data will lead to better intelligent and inter operational capabilities by examining parallel processing approaches for semi structural data [9] illustrates the interconnection between IoT and bigdata, as well as how bigdata applications in IoT speed up research and commercial models in IoT. They have also covered classification, clustering, association rule mining, and prediction as well as bigdata analytics methodologies. Under predictive analysis, SVM and fuzzy logic algorithms are addressed. Nait Malek et al. in their paper [10], suggest combining IoT and bigdata technologies into a unified platform for real time data monitoring and processing. Environmental data is collected using Apache Kaa applications, and it is processed using Apache Storm apps based on customized algorithms. Machine learning is a significant contributor to the rapid processing of vast amounts of data and the generation of patterns of interest for data analysis. The emphasis of [11] is on applying machine learning to data in order to study real world situations. Many systems leverage remote cloud computing, either for data storage or for quicker processing, to efficiently analyze the massive amounts of data generated in modern AI applications [12]. A distributed processing strategy for IoT using OpenCV-Python and picture data as the primary sensing environment is proposed

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in [13]. It also contains a vast number of organized, semi-structured, and unstructured data. Traditional data storage and processing systems and tools are unable to store and process the data sets because they are too massive and complicated. As a result, large-scale and disordered datasets are stored using Not Only Structured Query Language (NoSQL). The Storm real time computing framework is used to process energy data that requires rapid processing [14]. Meanwhile, the non-real time energy data is processed using the Hadoop computer platform.

3 Data Acquisition Data Acquisition focuses on how to acquire heterogeneous data from mobile and distributed devices. The process of data acquisition can be considered as an input layer which gathers data. It consists of the Things connected to Internet and by means of the embedded sensors and actuators it sense and gather information and pass onto IoT gateways for further processing To sort the data based on datatype, naming files, etc. can be done at this stage. More than performance focus is on memory capacity and unlimited storage. Figure 2 shows the proposed framework with data acquisition layer as the input layer. Sensors and actuators include all devices that acquire real world signals. This layer should associate appropriate hardware for the computer to convert sensor signal into digital information. This includes signal conditioning and data interpretation. A gateway device is a hardware that bridges the gap between sensors and IoT platform. MQTT, CoAP, and REST are some of the IoT protocols that enable communication. For example, An Arduino microcontroller can embed sensors and actuators. Kaa platform can be used for getting data from various sensors. MQTT can be used for communication between Arduino and Kaa. With the heterogeneous nature of variety of devices, it is difficult to build a data acquisition system which would provide a uniform data structure to be consumed by the analytic tools.

3.1 Data Integration Data integration creates a single view of data from several sources and combines the views of the data. When it comes to data integration, there is no one-size-fits-all solution. A large amount of time can be saved on data preparation and analysis if appropriate precautions are taken to correctly integrate the data. Different types of data are constantly generated through social media, IoT, and other communication and telecommunication technologies at any given time. All activities involved in collecting data from many sources, as well as storing and distributing data with a single picture, are referred to as data integration. Pentaho is tool for bigdata aggregation and integration. Tools like Apache Impala and Pentaho can be integrated with

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other business intelligent tools to create inexpensive analysis platforms. Talend automates bigdata integration by providing various services to handle data from multiple sources.

3.2 Data Representation Model DR models are fundamentally used for Data Acquisition and Integration. Design of the Data representation model must be flexible with a common format and should be based on the application required. Moreover data and metadata should be represented in a machine understandable way. XML encodings can be used to represent the sensors. For describing the real world data use of Resource Description Framework (RDF) is becoming more popular. It provides an encoding and interpretation mechanism and allows linking and correlating with other RDF data. The massive

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event data which rely on diverse interrelated data sources can be enriched and transformed into RDF format for further processing [15]. The concept of Linked Stream Data applies the Linked Data principles to streaming data, so that data streams can be published as part of the Web of Linked Data. RDF database supports streaming data querying with the help of SPARQL [16]. By means of RDF documents the devices can describe themselves and auto configure. There is no need of any manual configuration.

3.3 Data Middleware The main technology for integrating and managing remote and heterogeneous data is data middleware. As a result, the research on data middleware is crucial for data integration [17]. Middleware is software glue that allows the client and server to connect by binding services. A middleware allows many processes running on different machines to communicate with one another. These services are not included in the underlying operating system. Wrappers are used by the mediator to access and transform data from the data sources into the global data model [18].

4 Data Processing and Analysis The initial step in data processing is to coordinate the interactions of devices that generate vast amounts of data in various forms that arrive to the server at various speeds. This is the data that will be used as input. These data must then be stored in cloud-based distributed fault-tolerant databases in the second stage. The final step involves the use of analytics software. Historical data is the first level of analytics, followed by analytics tools, queries, and reports. One of the most prominent parallel processing technologies on cloud platforms is MapReduce and its open source implementation Hadoop [19]. MapReduce, on the other hand, does not support more complex operations like joins. IoT data analysis is a term used to describe the process of analyzing vast amounts of data generated by devices. According to McKinsey [20], 90% of the world’s data has been created in the last five years. Businesses should be able to analyze and visualize sensor data from Internet-connected devices using IoT data analytics tools.

5 Recent and Popular Data Analytics Technologies IoT solution relies on automated analysis to generate descriptive reports and visualizations and to trigger actions and alerts. A number of platforms and tools are

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developed recently for the analysis of data. Table 1 provides the different data analysis techniques available along with the IoT platforms and the protocols they use to function smoothly. It also gives the information about the different input formats that they support from the input devices. Analysis techniques of the huge data can be classified into the following. Prescriptive Analysis is a blend of descriptive and predictive analysis that allows for future forecasts in commercial applications and business intelligence. Platforms like as AWS and SAP HANA cloud can be used as an ideal IoT platform for prescriptive analysis, machine learning, and natural language processing. In a healthcare situation, for example, the prescriptive analytic tool can foresee the spread of an infection and increase the amount of workers on hand to effectively manage the inflow of patients. Spatial Analysis examines the spatial link between physical things using geographical patterns. Lumify is an open source platform for large data research and visualization. Lumify, in an AWS environment, aids in the discovery of complicated connections via 2-D and 3-D graph visualizations, interactive geospatial views such as Google Maps or ESRI, and real time collaborative workspaces. This topic includes crop planning, parking, and smart vehicles. Streaming Analysis deals with real time and sensitive data. Financial transactions and air traffic data needs real time attention frameworks that are designed for real time stream analytics include Apache Storm and Apache Samza which is used with Apache Kafka and Hadoop Yarn. Among many, Yahoo, Alibaba, Groupon, Twitter, Spotify uses these technologies extensively [21]. Platforms like MS Azure IoT Suite deliver powerful insights from the streaming data. Time Series Analysis is used to discover patterns or trends in time-based data, such as in health and weather monitoring applications. On the IBM cloud platform, Ref. [22] demonstrated a scalable analytic solution that can effectively search enormous time series data. Apache Mahout may be used to implement popular machine learning algorithms such as various MapReduce clustering applications and time series analysis.

6 Applications Applications of IoT can be broadly classified into industrial-based (IIoT), infrastructure-based (buildings and smart cities) and business intelligence. Some of the following leading companies exploits IoT and bigdata technologies to drive value. With a wearable, RFID-enabled MagicBand that monitors customer traffic patterns, grants access to hotel rooms, and allows guests to bill purchases back to their room, Disney is using advanced analytics and Machine Learning techniques to offer individualized in-park experiences [23]. Because of their imaging capabilities, portability, and ease of disinfection, handheld and portable ultrasound technologies have become essential tools for clinicians treating COVID-19 patients [24].

IaaS

PaaS

PaaS

PaaS

MQTT, HTTP

MQTT, AMQP, HTTP

MQTT, HTTP

MQTT, HTTP

MQTT, HTTP, CoAP

MQTT, CoAP, DDS, AMQP

Amazon web services

Microsoft azure IoT suite

IBM Watson IoT

Google cloud

SAP HANA analytics cloud

ThingWorx

PaaS

Server (Centralized Architecture)

Solution type

Protocols

Platforms Apache Spark, Hive Presto, Knime Pig HBase Flink MapReduce

Data analysis technologies

–

JSON CSV

NoSQL data storage format

JSON, CSV, TXT, all OPL data structures (data access using NoSQL)

Python JavaScript PHP Ruby

C# Python Java Node.js

Java Python R Scala

C Node.js Python Java

Integrated languages

–

Python Java

Apache Kafka R connector Postgre SQL Java Machine learning and Python SAP predictive analysis

Apache Hadoop, Pig, Hive, Spark (machine intelligence with tensorflow)

Apache Kafka (extensive analysis using cognitive computing, machine learning, and natural language processing)

JSON or CSV, Apache Spark, Storm, Avro, UTF-8 Kaf ka, Drill encoded document DB

CSV, delimited text files, JSON flat files, Video stream (data access using NoSQL)

Input data format

Table 1 Different data analysis techniques available along with the IoT platforms

IIoT/real time (continued)

Predictive/descriptive analysis

Real time and non-real time

Real time/time series/streaming analysis

Stream Analytics/real time

Real Time and Batch Processing/spatial/prescriptive analysis

Analytic type/level

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Protocols

MQTT, CoAP, WebSocket

Platforms

Everything

Table 1 (continued)

SaaS

Solution type –

Input data format –

Data analysis technologies –

Integrated languages IIoT

Analytic type/level

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7 Conclusion The Internet of things (IoT) is an industrial revolution similar to the introduction of electricity in the early 1900s and forward, where electricity is now the internet in every gadget. The world will shift dramatically as cloud computing power improves and 5G technology becomes available. Bigdata analytics platforms hold the key to unlocking this information by evaluating unstructured IoT data—for example, foot traffic at a theme park, weather trends, or patient health—along with other data sources to create a comprehensive picture of the issue. Platforms then arrange the data into easily digestible insights that businesses can utilize to improve their processes. This implies that environmental data from sensors, surveillance footage, log files, and geo-location data may be combined with social media and consumer behavior insights to give a greater knowledge.

References 1. Moon J Kum S, Lee S (2019) A heterogeneous IoT data analysis framework with collaboration of edge-cloud computing: focusing on indoor PM10 and PM2.5 status prediction. Sensors 19:3038. https://doi.org/10.3390/s19143038 2. Fu B (2016) The research of lOT of agriculture based on three layers architecture. In: 2016 2nd International conference on cloud computing and Internet of Things 3. Chettri L, Bera (2020) A comprehensive survey on internet of things (IoT) toward 5G wireless systems. IEEE Internet Things J 7(1) 4. Ahmed E, Yaqoob I, Hashem IAT, Khan I, Ahmed AIA, Imran M, Vasilakos AV (2017) The role of big data analytics in internet of things. Comput Netw. https://doi.org/10.1016/j.comnet. 2017.06.013 5. Sharma SK, Wang X (2017) Live data analytics with collaborative edge and cloud processing in wireless IoT networks. Special section on future networks: architectures, protocols, and application 6. Krishnamurthi R, Qureshi B (2020) An overview of IoT sensor data processing, fusion, and analysis techniques. Sensors. 20(21):6076 7. Bashir MR, Gill AQ (2016) Towards an IoT big data analytics framework: smart buildings systems. In: 2016 IEEE 18th international conference on high performance computing and communications; IEEE 14th international conference on smart city; IEEE 2nd international conference on data science and systems 8. Cai H, Xu B, Jiang L, Vasilakos AV (2016) IoT-based big data storage systems in cloud computing: perspectives and challenges. IEEE Internet Things J. https://doi.org/10.1109/JIOT. 2016.2619369 9. Marjani M, Nasaruddin F, Gani A (2017) Big IoT data analytics: architecture, opportunities, and open research challenges. https://doi.org/10.1109/ACCESS.2017.2689040 10. Nait Malek Y, Kharbouch A (2017) On the use of IoT and big data technologies for real-time monitoring and data processing. In: The 7th international conference on current and future trends of information and communication technologies in healthcare 11. Adi E, Anwar A, Baig Z, Zeadally S (2020) Machine learning and data analytics for the IoT. Neural Comput Appl 12. Cai Y, Genovese A, Piuri V, Scotti F, Siegel M (2019) IoT-based architectures for sensing and local data processing in ambient intelligence: research and industrial trends. In: 2019 IEEE international instrumentation and measurement technology conference (I2MTC). IEEE pp 1–6

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13. Furuichi T, Mineno H (2016) Distributed data processing method for next generation IoT System. In: 2016 IEEE 5th global conference on consumer electronics 14. Zhang Y, Ma S, Yang H (2018) A big data driven analytical framework for energy-intensive manufacturing industries. J Clean Prod. https://doi.org/10.1016/j.jclepro.2018.06.170 15. Hasemann H, Kremer A, Pagel M (2012) RDF provisioning for the internet of things. In: 2012 3rd IEEE international conference on the internet of things 16. http://linkeddata.org 17. Liu H, Liu Y, Wu Q, Ma S (2013) A heterogeneous data integration model. https://link.spr inger.com/conference/bdas. 18. Bansal S, Kumar D (2020) IoT ecosystem: a survey on devices, gateways, operating systems, middleware and communication. Int J Wireless Inf Netw, LLC 19. Marjani M, Nazaruddin F (2017) Big IoT data analytics: architecture, opportunities, and open research challenges. https://doi.org/10.1109/ACCESS.2017.2689040 20. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-thenext-frontier-for-innovation 21. Nasiri H, Nasehi S, Goudarzi M (2019) Evaluation of distributed stream processing frameworks for IoT applications in smart cities. In: Nasiri et al. J (eds) Big Data 6:52. https://doi.org/10. 1186/s40537-019-0215-2 22. Xu X, Huang S (2014) TSaaaS: time series analytics as a service on IoT. In: 2014 IEEE international conference on web services 23. Islam MS, Verma H, Khan L Secure real-time heterogeneous IoT data management system. In: 2019 first IEEE international conference on trust, privacy and security in intelligent systems and applications (TPS-ISA) 24. Umair M, Cheema MA, Cheema O, Li H, Lu H (2021) Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Sensors 21(11):383

IRIS: A Pragmatic Approach to Build an Integrated and Robust IOT System to Counter Malware Gaytri Bakshi, Romil Verma, and Rohil Chaudhry

Abstract In the current scenario, the necessity of IoT security and the prevention of system misconfiguration has become unequivocal. Recent inclinations have presented a staggering upsurge in the rate of malware targeting IoT and other endpoint devices. Cyber-attacks on IoT devices are booming, as people and organizations are purchasing ‘smart’ devices such as routers, security cameras, on surplus mode and do not considers them worth protecting and often these devices are misconfigured making them vulnerable to attackers. Majority of attacks on IoT devices are not sophisticated but more stealth-like so that the user does not notice that their devices are being exploited. This paper depicts implementation of IRIS. IRIS, an acronym for integrated and robust IoT system, focuses on the security of IoT devices as well as caters to the need of proper access control, two-factor authentication and also offers real-time scanning and malware protection. Keywords Internet of Things (IoT) · Cyber-attacks · Privacy · Access control · Two-factor authentication · End-point security · Configuration management · Malware · Malicious activity

1 Introduction The Internet of Things (IoT) incorporates many devices that are connected to the Internet all over the world. IoT devices, like virtual assistants, routers and smart

G. Bakshi (B) School of Computer Science, University of Petroleum and Energy Studies, Bidholi, Dehradun, Uttrakhand, India e-mail: [email protected] R. Verma Ernst and Young GDS, Noida, Uttarpradesh, India R. Chaudhry Deloitte USI, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_38

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cameras make our lives more convenient but are either misconfigured or their software is plagued with weaknesses and vulnerabilities [1]. Authentication [2–4] has always been one of the guards to protect the system from anonymous user who could be an attacker. Nowadays, life is dependent on Internet where most of the tasks are run in digital mode, and the tasks such as online payments, digital communications, remotely accessing devices or documents have attracted another threat such as IoT malware [5, 6]. Moreover, the attacks on various end-point devices have increased with a staggering rate, and new malware are appearing regularly. There are estimated to be nearly 31 billion IoT devices [7] currently installed. This makes them both more attractive and easier to exploit at this particular moment. To connect IoT devices within a network, routers play an important role in terms of connectivity but at the same time, they are vulnerable too. Once a router breaches, attackers can infiltrate all the connected devices. The mobile applications used to run a smart home accessory through family’s smartphones and all the information stored within are at high risk. The major cause of this weakness happens to be the default passwords [8–10], which often left unchanged. Cyber-attacks on IoT and home devices [11] are thriving, people and organizations are acquiring ‘smart’ devices such as routers and security cameras in large number. This makes them both more attractive and easier to exploit at this particular moment. The devices usually come pre-configured and contain very weak and easy to crack default passwords which must be changed immediately, and the devices are also provided with time to time updates which should be installed but these steps are neither realized nor adopted by majority of users and continue to use those devices with vulnerabilities. Access control and security configuration plays a vital role in the hardening of systems and if done correctly can make an organization more secure because malware implantation in IoT system is a new upcoming threat when compared to the older one. This paper presents an approach to detect malware in real-time scanning and protect and IoT system within an organization or remotely installed.

2 Literature Review While enjoying the ease and proficiency that IoT brings with it, new threats have emerged too [12]. Even though enormous endeavors have been worked out to resolve the threats but still the issues exist. In a IoT framework with heterogeneous devices, routers and connected cameras are the most susceptible devices and accounted for 75 and 15% of the attacks, respectively [13]. Unsurprisingly, in most of the scenarios, routers have been the prominent target within an IoT network given their accessibility from the Internet. Routers are easy target as they deliver an operative jumping-off point for attackers. With routers performing as the one of the gateways within a network, firms and establishments apply controls to access any network so as to regulate the edge devices their communication in a network [14]. Within an IOT network, the end terminals comprise various end points such as policy judgment

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points, policy implementation points and aspiring devices targeting to connect the network. The policy judgment points estimate and calculate the data received from the aspiring devices to decide the granting of permission to connect a network. In this era of digitization, the devices are intelligent to make decision within a network based on the access policy of authentication [15]. This consists of different arenas such as online banking systems, online payment systems, rights to access any system, digital communication including voice and text and video. The methodology adopted for above-mentioned process was made possible with the development of processes of authentication systems toward multi-factor authentication (MFA) starting from single-factor authentication (SFA) and through two-factor authentication (2FA). Still if an IoT network is considered, the passwords implemented could be presumed easily because of simple algorithm implementation with no complexity [16]. In another aspect to consider within an IoT network is the updating of packages which is done without encryption. Various scenarios which could be listed in t are as follows (a) Within an IOT network, updating of the software is done at different levels which does not employ TLS or encryption. (b) The process of standardizing remote devices needs updation, when done in remote area with no security or authentication control. (c) The access rights are given to anonymous user at the storage locations, where updated files are stored, which leads to modification of the firmware and makes the system vulnerable to attacks. Embedded processors are abundant in the market thus influencing our personal lives [17] especially when considering IOT devices, where detection of malware [18] is constrained because of the limited available resources. Out of that, various other constraints are limited memory, low processing, fast power depletion, ruining of network performance, systems’ inability to detect viruses worms and Trojans [19].

3 Methodology With the review of the related work done in this area the methodology, named IRIS follows a holistic approach to secure different IoT devices within a home network. The major objectives include providing a solution for the common misconfigurations and vulnerabilities present in IoT devices within a smart home network and even aims to provide real-time as well as on-demand malware analysis scans and help secure these devices from a variety of malware. It provides an authentication mechanism to secure the sensitive controls from being tampered by malicious users.

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3.1 System Requirement IRIS is implemented in Python and would aim to provide security from these threats in a user-friendly and interactive way. The following is the requirements that the system would need to run/execute the framework: Software Requirements 1. Operating system Linux, Windows 2. Python 3 Hardware Requirements 1. Intel Core i3 64-bit processor 2. Hard disk—15 GB 3. RAM—4 GB [minimum] The framework of IRIS deals with the real-time scanning of the devices with in a home network and even checks the strength of the password applied on any specific device. Figure 1 shows the data flow within the entire frame work and Fig. 2 depicts the various levels of the working of the frame work.

Fig. 1 Data flow diagram

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Fig. 2 State chart diagram

4 Implementation and Results In a smart home, various smart devices are connected to one another and all are accessed with some voice-controlled assistant and mobile phone. All these devices are then connected with IRIS application. To access application, new users need to register themselves username, password and email as shown in Fig. 3. Fig. 3 Registration page

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After registration, the user will be directed toward the login page where the user will enter the username, password and with the help of Google authenticator they will scan the or code to get the MFA code. The user can use forgot password option, in case the user forgot the password as shown in Fig. 4. The main screen appears after login which have three options. Discover which shows you the devices available in the network, device option which shows you the router login and active scanning which shows you any malware present, and if it found any malware, it directly gets stored in your recycle bin as shown in Fig. 5. When the user access the DISCOVER button, the entire network is traversed. The application shows the devices available in the network. Advanced view shows the Fig. 4 Sign in page

Fig. 5 Discovering and scanning an IoT device

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Fig. 6 Discovered device

IP, mac address of the network as shown in Fig. 6. The application gives access to the user to block and unblock any device. In device option, the application allows the user to login into the router and checks the password strength. By clicking check password strength, the user can check strength of router password directly. A pop up box will appear as shown in Figs. 7 and 8. Despite router, other devices can even be checked by clicking the option of other device. This can be done with the help of IP address and port number as shown in Fig. 9. To scan the entire smart home network, the option of ACTIVE SCANNING is present in the IRIS application. In this, the user gets the information regarding the malware presence. If the malware exists, it directly get moved to recycle bin so that it cannot harm the system or the network as shown in Fig. 10.

Fig. 7 Route login page

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Fig. 9 Other device password strength scanning

Fig. 10 Active Scanning

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5 Conclusion As IOT systems are vulnerable at many instances within the connective network, especially at the end points. Taking this as the major intent, this paper provides an elucidation for the common misconfigurations and susceptibilities existing and even targets to deliver real-time malware investigation scans and aids to protect these devices. It also aims to provide an authentication mechanism to secure the sensitive controls from being tampered by malicious users and thus protect the entire system.

References 1. Bakshi G (2021) IoT architecture vulnerabilities and security measures. Security incidents & response against cyber attacks. Springer, Cham, pp 199–215 2. Shah T, Venkatesan S (2018) Authentication of IoT device and IoT server using secure vaults. In: 17th IEEE international conference on trust, security and privacy in computing and communications/12th IEEE international conference on big data science and engineering (TrustCom/BigDataSE). IEEE, pp 819–824 3. Gope P, Sikdar B (2018) Lightweight and privacy-preserving two-factor authentication scheme for IoT devices. IEEE Int Things J 6(1):580–589 4. Ferrag MA, Maglaras L, Derhab A (2019) Authentication and authorization for mobile IoT devices using biofeatures: recent advances and future trends. Security and communication networks 5. Su J, Vasconcellos DV, Prasad S, Sgandurra D, Feng Y, Sakurai K (2018) Lightweight classification of IoT malware based on image recognition. In: IEEE 42nd annual computer software and applications conference (COMPSAC). IEEE, vol. 2, pp 664–669 6. Costin A, Zaddach J (2018) IoT malware: comprehensive survey, analysis framework and case studies. BlackHat USA 7. Wang A, Liang R, Liu X, Zhang Y, Chen K, Li J (2017) An inside look at IoT malware. In: International conference on industrial iot technologies and applications. Springer, Cham, pp 176–186 8. Zeller M (2011) Myth or reality—does the aurora vulnerability pose a risk to my generator? In: 2011 64th annual conference for protective relay engineers, IEEE, pp 130–136 9. Cerrudo C, Apa L (2017) Hacking robots before skynet. IOActive Website 1–17 10. Kobara K (2016) Cyber physical security for industrial control systems and IoT. IEICE Trans Inf Syst 99(4):787–795 11. Shah Y, Sengupta S (2020) A survey on classification of cyber-attacks on IoT and IIoT devices. In: 11th IEEE annual ubiquitous computing, electronics & mobile communication conference (UEMCON). IEEE, pp 0406–0413 12. Zhou W, Jia Y, Peng A, Zhang Y, Liu P (2018) The effect of IoT new features on security and privacy: new threats, existing solutions, and challenges yet to be solved. INSPEC accession number: 18653871. IEEE 13. Internet security threat report 24 (2019) 14. Rathore H (2012) Google patents-infected end-point containment using aggregated security status information. Application number: US13/730/793, Dec 2012 15. Ometov A, Bezzateev S, Mäkitalo N, Andreev S, Mikkonen T, Koucheryavy Y (2018) Multifactor authentication: a survey. Cryptography 2(1). https://doi.org/10.3390/cryptography201 0001 16. Diaz Lopez D, Blanco Uribe M, Santiago Cely C, Vega Torres A, Moreno Guataquira N, Morón Castro S, Nespoli P, Gómez Mármol F (2018) Shielding IoT against cyber-attacks: an event-based approach using SIEM. Hindawi Wireless Commun Mob Comput 2018, Article ID 3029638

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17. van Oorschot PC, Smith SW (2019) The Internet of Things: security challenges. In: INSPEC, accession number: 18953928. IEEE 18. Xiao L, Li Y, Huang X, Du XJ (2017) Cloud-based malware detection game for mobile devices with offloading. IEEE Trans Mob Comput 16(10):2742–2750 19. Lu CJ, Zeng H, Liu JY, Zhang R, Chen YK, Yao YG (2017) Network security log analysis system based on ELK. ISBN: 78-1-60595-476

Quantum Implementation of Reversible Logic Gates Using RCViewer+ Tool Shaveta Thakral, Pratima Manhas, and Jyoti Verma

Abstract To investigate the quantum implementation method for estimation of quantum cost of reversible logic gates, proposed quantum implementation method is used for estimation of quantum cost of reversible logic gates and further verification using RCViewer+tool to justify quantum cost. Proposed quantum implementation method is applied on R gate and tested using RCViewer+ tool to justify quantum cost. The quantum implementation and related quantum cost of many invented gates are still unknown in literature. This paper deals with the approach to determine quantum implementation of reversible logic gates so that it can be beneficial to utilize processes in investigation of new proposed reversible logic gates and their associated architectures. Keywords Reversible · Ancillary · Garbage · Quantum cost · R gate

1 Introduction Conventional computer hardware will soon reach their limits and power consumption becomes very high. Reversible computing is the evolving and promising technologies to reduce such issues. Designing reversible logic-based circuits faces some optimization metric challenges like reduction of ancillary inputs, garbage outputs and quantum cost. Quantum cost is one of the important parameters to compare new inventions with existing circuits, and therefore, quantum implementation of

P. Manhas · J. Verma Manav Rachna International Institute of Research and Studies, Faridabad, India e-mail: [email protected] J. Verma e-mail: [email protected] S. Thakral (B) Zeal College of Engineering and Research, Pune, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_39

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reversible logic gates is the foremost requirement to analyze any complex reversible logic-based circuit. Irreversible logic leads to information loss as only one bit is received at output and specification of any logic gate can be n*1, where n is the number of input lines and output will always be a single line. In this way, n–1 bits get erased during irreversible operation, and consequently, lost bits are responsible for heat loss as per relation defined between thermodynamics and information theory by Landauer [1]. Many solutions have been proposed by researchers to avoid heat loss and one of such solutions is switching from irreversible logic to reversible logic. Bennett [2] claimed almost no power dissipation if reversibility is maintained by mapping ‘n’ number of output lines corresponding to ‘n’ number of input lines. Various reversible gates have been proposed over past decades. Among them, major contributions were given by Toffoli, Feynman, Fredkin. Toffoli gate [3] is a reversible gate that plays a role of a universal gate for reversible circuits. Fredkin gate [4] is controlled swap gate which is also found to be conservative as well as universal logic gate described three reversible primitives, i.e., NOT, controlled NOT (CNOT) and controlled controlled NOT (CCNOT) that are used to make a universal circuit. Peres in 1985 proposed Peres gate [5] which was found to be 3*3 reversible logic gate with lowest quantum cost. Extensive literature survey on gates was done by Garipelly and many other gates are found popular in literature like TR gate, NG gate, R gate, URG gate, BJN gate, MCL gate, NFT gate, TKS gate, TSG gate, MTSG gate, SCL gate, MKG gate, BVF gate, BME gate, DPG gate, DKG gate, PTR gate, NCG gate, SBV gate and HNG Gate. Fault tolerance property can be seen in many conservative reversible logic gates [6]. For designing any reversible logic circuit, it needs attention on various optimization metrics such as quantum cost, ancillary input, garbage output and delay. There are many combinational and sequential circuits in literature based on reversible logic [7]. Some of the researchers proposed novel reversible multiplier circuit [8], reversible binary subtractor [9], reversible ALU [10, 11], low-power comparator [12], a treebased comparator and memory unit [13]. The quantum cost of a reversible gate is determined by the number of elementary reversible gates of unity quantum cost required in the design. The review of truth table based reversible logic synthesis methods are presented [14]. To avoid bit loss and maintain reversibility, sometimes constant input lines need to be introduced called as ancillary inputs and sometimes unnecessary output lines are produced which do not fulfill any purpose hence called garbage outputs. Delay is defined as the maximum number of gates in a path from any input line to any output line. The values of quantum cost, ancillary inputs, garbage outputs and delay for any reversible logic circuit should be as low as possible. The categorization of optimization metrics is indicated in Fig. 1. Quantum cost in any reversible logic-based circuit can be calculated by unity cost gates like NOT, controlled NOT, controlled V and controlled V+ gates based on NCV library, therefore, the quantum cost calculation methodology needs to be understood and optimized properly so as to get quantitative analysis with existing designs.. The

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Fig. 1 Classification of optimization metrics of reversible logic circuit

primitive components of reversible logic synthesis are discussed in research work [15]. The quantum cost calculation method is proposed in Sect. 2.

2 Quantum Cost Calculation Method In order to convert a reversible function into an optimized network of Toffoli gates, various synthesis methods are used. Truth table-based synthesis method is one such approach. This method takes input in the form of a truth table of a given reversible function and applies transformations on it. Transformation is done by applying Toffoli gates and synthesis flow goes from output to input. Transformation process gets complete when a given reversible function achieves an identity function that means output combinations of given reversible functions run backwards to obtain input. In this paper, the basic algorithm of the Toffoli network is designed. Quantum cost calculation flowchart is given in Fig. 2. Methodology to build Toffoli network by basic algorithm and its optimization process to reduce quantum cost is described in Sect. 2.1.

2.1 Methodology to Build Toffoli Network First step is to convert all reversible functions into truth table representation with ABC as inputs and A0B0C0 as output without transformation. Truth table of R gate is considered to explain methodology as given in Table 1. Truth table-based approach synthesizes apply transformation on outputs of given reversible function. Hence, transformation targets step by step on putting all elements of A0B0C0 starting from first element to last to their intended position to achieve identity function that matches with input. Here, the first target is to convert 001–000 by inverting bit at position C using Toffoli gate T1(C). After applying T1(C), 001 automatically reaches its intended position. The next second target is to convert 100– 010 by using two Toffoli gates T2(A: B) and T2(B: A). After applying a combination of T2(A: B) and T2(B: A), 010, 011, 100, 101 take their respective intended positions Then, the third target is to convert 111–110 by using one Toffoli gate T3(A, B: C). No further target needs to be achieved as 111 already takes its intended position along with 110.Transformation steps from output in the truth table to identity function are

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Fig. 2 Quantum cost calculation flowchart

Table 1 Representation of truth table for reversible function A

B

C

A0

B0

C0

0

0

0

0

0

1

0

0

1

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0

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1

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1

1

1

1

1

0

1

0

given in Table 2. The Toffoli network built with the above transformation steps is given in Fig. 3.

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Table 2 Basic algorithm transformation steps ABC

A0B0C0

A1B1C1

A2B2C2

A3B3C3

A4B4C4

000

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000

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010

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T1(C)

T2(A : B)

T2(B : A)

T3(A , B : C)

Fig. 3 Toffoli network built by basic algorithm

Fig. 4 Toffoli network to NCV library

2.1.1

Conversion of Toffoli Network into Primitive Gates Using NCV Library

Toffoli network built in Fig. 3 is estimated for quantum cost and it is determined by adding the quantum cost of all Toffoli gates present in the network. Toffoli gates T1(C), T2(A: B) and T2(B: A) are taken with quantum cost unity. Toffoli gate T3(A, B: C) is replaced with primitive gates as per NCV library as shown in Fig. 4. After replacing T 3 gate with its primitive gates, it gives quantum cost 5. Therefore, the total quantum cost of the Toffoli network built with the NCV library is 8.

2.1.2

Proposed Template

Now, to optimize the Toffoli network, a template is chosen as shown in Fig. 5. And its proposed matched template is given in Fig. 6. This proposed arrangement can be applied as per matching rule and the selected template t in Toffoli network as shown in Fig. 7 can be replaced with proposed matched template as shown in Fig. 8.

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Fig. 5 Proposed template

Fig. 6 Proposed matching template

Fig. 7 Selected template for matching

Fig. 8 Quantum implementation after template matching

2.1.3

Deletion Rule

After applying template matching, a template is chosen for deletion as shown in Fig. 9 and quantum implementation after applying deletion rule is shown in Fig. 10. Fig. 9 Selected template for deletion rule

Fig. 10 Quantum implementation after deletion rule

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Fig. 11 Apply moving rule

Fig. 12 Optimized quantum implementation for QC calculation

2.1.4

Moving Rule

If the target bit of one gate is not control bit of another gate and control of another is not target of first one, then moving rule can be applied to implement logic as per requirement, and Fig. 11 illustrates direction of movement as per moving rule and optimized quantum implementation after applying moving rule is shown in Fig. 12. Now, further optimization is not possible and this optimized quantum implementation can be referred to calculate quantum cost. As optimized quantum implementation is showing all elementary gates with unity quantum cost. Therefore, the quantum cost of optimized quantum implementation is found to be 6. This calculated quantum cost can be verified using RCViewer+ tool.

2.2 A Quick Guide to RCViewer/RCViewer+ Tool RCViewer is simple application software written in C++ .TFC format consists of prescribed sequence of strings given in Table 3. After # sign, if any text information is written, then it is known as comment and should be ignored. Gate representation in. TFC format is given in Table 4. For any reversible circuit, cost metrics analysis can be determined using three options. One way is by clicking on the icon named Q in the toolbar. Second way is by using the shortcut key, i.e., ctrl + Q and third way is go to view option and select quantum cost as shown in Fig. 13.

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Table 3 Sequence of strings for TFC format Ist string

IInd string

IIIrd string

Shows list of all Shows list of all Shows list of all variables in network input variables in output variables in network network

IV string

Subsequent strings

Shows list of all input constant in network

Gates are listed as per prescribed format in order of appearance in actual format between BEGIN and END

Starts with Starts with Starts with Starts with BEGIN keyword.v/.variables keyword.i/.inputs keyword.o/.outputs keyword.c/.constants shows beginning of gate/network and END shows termination of gate/network

Table 4 Notation of gates for TFC format

TFC format

Gate NOT gate

TOF(Φ ;a)

t1 a

CNot gate

TOF(a; b)

t2 a, b

CCNOT gate

TOF(a, b; c)

t3 a, b, c

SWAP gate

FRE(Φ ;a, b)

F2 a, b

FREDKIN gate

FRE(a; b, c)

F3 a, b, c

V Gate

V

V+ Gate

V+

3 Conclusion It is not only important to draw quantum implementation but it is also important to optimize quantum implementation so that quantum cost should be reduced as minimum as possible. Quantum cost of any complex reversible logic-based synthesis is ultimately dependent upon the quantum cost of individual gates in circuit. RCViewer+ tool enables to verify proposed implementation with extent to which optimization is possible. A new researcher may begin with proposed methodology and go ahead in designing optimized reversible logic-based circuits.

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Fig. 13 Quantum cost estimation of optimized implementation using RCViewer+

References 1. Landauer R (1961) Irreversibility and heat generation in the computing process. IBM J Res Dev 5(3):183–191. https://doi.org/10.1147/rd.53.0183 2. Bennett CH (1973) Logical reversibility of computation. IBM J Res Dev 17(6):525–532. https:// doi.org/10.1147/rd.176.0525 3. Toffoli T (1980) Reversible computing. In: International colloquium on automata, languages, and programming, pp. 632–644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3642-38986-3 4. Fredkin E, Toffoli T (1982) Conservative logic. Int J Theor Phys 21(3):219–53. https://doi.org/ 10.1007/BF01857727 5. Peres A (1985) Reversible logic and quantum computers. Phys Rev 32(6):3266. https://doi. org/10.1103/PhysRevA.32.3266 6. Parhami B (2006) Fault-tolerant reversible circuits. In: 2006 fortieth asilomar conference on signals, systems and computers. IEEE, pp 1726–1729. https://doi.org/10.1109/ACSSC.2006. 355056 7. Thapliyal H, Vinod AP (2007) Design of reversible sequential elements with feasibility of transistor implementation. In: 2007 IEEE international symposium on circuits and systems. IEEE, pp 625–628. https://doi.org/10.1109/ISCAS.2007.378815 8. Haghparast M, Jassbi SJ, Navi K, Hashemipour O (2008) Design of a novel reversible multiplier circuit using HNG gate in nanotechnology. World Appl Sci J. https://scialert.net/abstract/?doi= itj.2009.208.213 9. Thapliyal H, Ranganathan N (2009) Design of efficient reversible binary subtractors based on a new reversible gate. In: 2009 IEEE Computer Society Annual Symposium on VLSI. IEEE, pp 229–234. https://doi.org/10.1109/ISVLSI.2009.49 10. Morrison M, Ranganathan N (2011) Design of a reversible ALU based on novel programmable reversible logic gate structures. In 2011 IEEE computer society annual symposium on VLSI Jul 4,pp. 126–131, IEEE. https://doi.org/10.1109/ISVLSI.2011.30. 11. Moallem P, Ehsanpour M, Bolhasani A, Montazeri M (2014) Optimized reversible arithmetic logic units. J Electron (China) 31(5):394–405. https://doi.org/10.1007/s11767-014-4081-y

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12. Nagamani AN, Jayashree HV, Bhagyalakshmi HR (2011) Novel low power comparator design using reversible logic gates. Indian J Comput Sci Eng (IJCSE) 2(4):566–74. 10.1.1.300.8996&rep=rep1&type=pdf 13. Morrison M, Lewandowski M, Ranganathan N (2019) Design of a tree-based comparator and memory unit based on a novel reversible logic structure. In: 2012 IEEE computer society annual symposium on VLSI. IEEE, pp 231–236. https://doi.org/10.1109/ISVLSI.2012.61 14. Thakral S, Bansal D, Chakarvarti SK (2015) Review of truth table based reversible logic synthesis methods. In: 2015 International conference on soft computing techniques and implementations (ICSCTI). IEEE, pp 164–169. https://doi.org/10.1109/ICSCTI.2015.7489587 15. Thakral S, Bansal D, Chakarvarti SK (2016) Primitive components of reversible logic synthesis. In: Second international conference on computational intelligence & communication technology (CICT). IEEE, pp 649–654. https://doi.org/10.1109/CICT.2016.134

Intelligent Process Automation for Detecting Unauthorized Entry by Actors in IoT Imbedded Enterprise Setting Amit K. Nerurkar and G. T. Thampi

Abstract IoT shall be facilitating visualizing and identifying people who consciously/or by accident enters in to restricted parts of enterprises, which spreads over large land mass. The paper executes detailed study of the concept and construct of robotic process automation in the overall system design. The central theme of the problem-solving strategy is revolving around deploying IoT-enabled devices in a structured layout to offer a solution which is 100% reliable, cost effective, and higher longevity. One of the initial solution buildings was based on the use of audio-visual technique to see the unauthorized entry and subsequently document the complete event in a quasi-judicial framework. Another procedure involves providing communication between the authorized user and the person attempted the violation of existing protocols of the enterprise. The paper envisages authenticating the newly enrolled personnel’s remotely and instantaneously by capturing person’s biometric markers and modifying the database which controls the entry. Further, the paper also delves into providing safeguard from fire and gas leakage using the IoT-enabled smoke/gas sensors. Keywords IoT · Intelligent process automation (IPA) · Authentication · Smart door lock

1 Introduction The arrival of COVID-19 in various parts of the world was contained by various Governments by imposing lockdowns that caused a bad impact on productivity of manufacturing. To continue with fail free operation, the remote sensing process automation is needed [1]. A. K. Nerurkar (B) · G. T. Thampi Thadomal Shahani Engineering College, Mumbai, India e-mail: [email protected] G. T. Thampi e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_40

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Key is vital, common, and well-tested substance of numerous people. But the greatest con for any organization requiring get to as well numerous distinctive private buildings with its claim bolt and key. The dispersion of keys to the correct employee at the specific time could be a complex and expensive issue. Moreover, carrying numerous keys may be a burden for each employee and increments the chance of keys getting stolen or misplaced. Other than this issue of physical security, there is too a require of good confirmation method to permit as it were trusted individual to enter the manufacturing plant by utilizing the innovation. To create it more secured, the correct to permit anybody can be kept with the authorized representative who can nowadays oversee this remotely utilizing IoT. Over the past 20 a long time, the Internet of things has ended up a cover term for such a gigantic number of arrangements, ventures, and innovations that it is troublesome to enroll all of them and talk about each in sensible detail. This unending waterway of mechanical arrangements has gotten to be the drive behind the IoT ventures in numerous different circles of life, counting but not constrained to wellbeing administrations, mechanical applications, shopper applications, and amusement. Keeping in intellect the relentless pace of improvement of the Internet of things innovation, it is regularly accepted that the list of conceivable outcomes for common sense IoT applications is nearly boundless. One among the pushed regions of commerce to benefit immensely from the arrangements given by the Web of things is the division of the so-called resource-constrained gadgets. This category incorporates any small-sized, low-power, and (regularly) battery-operated gadgets like sensors or actuators utilized in endeavors where progressed versatility, vigor of communication, and diminished vitality utilization are key to victory.

2 Existing System With the widespread and lockdown numerous of the workers are doing work from domestic, but shockingly few workers who are included in ground level work must proceed getting to plant for fabricating of the cars and the save parts. To guarantee these representatives do not enter the private room of the office either they utilize physical keys. The issue with physical keys has indeed greater suggestion for companies within the commerce of products and stock stockroom. These companies require get to numerous diverse private parts of their properties. Entryways spread over a wide topographical range and administer by numerous distinctive proprietors. People have to be carried keys for each single entryway to get to the stock. Carrying all these keys could be a bother within the day by day utilize and the helpless to robbery. Numerous of the manufacturing plants have gone for biometric sensors for verification, but nowadays with the utilize of IoT, the authorized representatives can too have appropriate control that will make them get it who all are attempting to enter the room and can indeed deny the consent in the event that required remotely [2].

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3 Literature Survey In this section, cited are the relevant past literature that uses various IoT techniques for process automation. Pinjala, Gupta [11] have proposed a shrewd entryway bolt which can be totally observed and controlled from a farther area utilizing an android application on a smartphone. The verification of concept given in this paper emphasizes the real-time farther openness within the owner’s hand (smartphone) on an intelligently graphical client interface (GUI) over a cloud server. Park and Cheong [3] proposed a security framework that combines the capacities of shrewd phone and domestic arrange framework. It empowers the clients to screen guests in real-time, remotely through the IoT-based doorbell introduced close the entrance entryway to a house. On the off chance that an untouchable breaks into the house, the framework can offer assistance distinguish the trespasser by obtaining CCTV prove. Arshey [4] propose framework that points at recognizing the guest from the existing database of guests utilizing picture handling (IP). This could be done utilizing OpenCV libraries with Python on a Raspberry Pi board. Bahrudin et al. [5] proposed a fire caution framework which may be a real-time checking system that recognizes the nearness of smoke within the discuss due to fire and captures pictures by means of a camera introduced interior a room when a fire happens. The advantage of utilizing this framework is it will decrease the plausibility of untrue caution detailed to the Firefighter. Dorothy et al. [6] present a thought for picture investigation to identify, recognize, and coordinate the picture with the put away dataset of the confirmed individuals or pets. Sowjanya and Nagaraju [7] proposed framework which is executed by utilizing biometric scanner, watchword, and security address with IoT. Deshmukh and Nakrani [2] extreme to supply the data to the client utilizing open-source innovation which comprises of OpenCV2, LBPH calculation, SMTP, Raspberry Pi3, Pi camera. The usage range is categorized more on neighborhood level like domestic, workplaces, and campus. The framework gives genuine time confront location and acknowledgment once the chime is activated. The captured picture is analyzed with the accessible database and if it could be a coordinate, the get to is allowed, and entryway will open. On the opposite in the event that the confront did not coordinate the captured picture is at that point sent to the client mail utilizing SMTP. Martinez et al. [8] executed a essential proof-of-concept Bluetooth work organize in their office building for a well-known office mechanization application: a savvy doorbell. The shrewd doorbell employments Bluetooth work innovation to send and get occasion messages interior a building and extend its reach with hand-off hubs. This paper investigates the plan in equipment and program of a Bluetooth work arrange in a genuine environment considering a few assessment measurements, such as bundle misfortune over separate and control utilization. Nag et al. [9] propose framework basically comprises of picture capture, confront discovery and acknowledgment, email notice, and programmed entryway get to administration. The captured picture from Pi camera will be sent to the authorized individual through mail for security purposes. Jamadagni et al. [10] show the development within the mechanical observing system’s plan utilizing Web of things (IoT). The sensor utilized is MQ-2

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which recognizes the spillage of gas at any barometrical condition and fire sensor as a basic and compact gadget for assurance against fire. When gas and smoke are recognized at that point, framework will send brief message benefit (SMS) to the client at that point client will take activity.

4 Proposed System The proposed framework (Fig. 1) employments a Wi-Fi empowered camera which is interfaces with backend equipment like Intel Galelio, Raspberry Pi, etc. This Wi-Fi empowered camera is set exterior the private room of plant. The camera features a sensor button which when squeezed, triggers camera to begin and the individual standing before the entryway is captured [3]. Utilizing an picture preparing procedure, on the off chance that the picture matches with the one which is in database, it sends notice to the proprietor of that room approximately the individual willing to enter the room, and in the event that the picture does not coordinate with any of the put away pictures; at that point, the proprietor will get a notification with the approximately a unused individual is attempting to enter the room [4]. In first case, proprietor will get a notice that an confirmed individual is attempting to enter the room, and in moment case, the proprietor will be take a choice whether to open the entryway and permit the individual or not. Proprietor can open the entryway remotely without the physically having to open the entryway. But if owner does not want to open the door for the guest, then owner can simply select the ‘reject’ option on notification. On doing so, the door will remain closed. The system also has the feature, where the owner can interact with the person standing outside the room. Owner will be using the mobile phone to do all above-mentioned operations [8]. For more practical scenarios, if the owner is not available at given point of the time and anyone wishes to enter the room, the person will first be authenticated by the camera, and then that person will be asked to enter a password for verification. Followed by the fingerprint scanning. Thus, while opening the door, the user gets verified by three level of authentication, i.e., face detection, password, and fingerprint, thereby providing maximum security [2, 6]. Additionally, in case if any smoke detected, then the system also has smoke detector. The cameras automatically start sending the footage to the owner, security guard, and to the nearest fire station. The smart image processing identifies whether it is not the false detection, and only then, it will start the water sprinkler for safety [5, 10]. The most point of the framework is to supply security highlights to keep the plant secure. The taking after are the major highlights given by the framework to realize the security objectives are: 1. Message Notification When the camera sensor starts and captures the image of the person, it will send notification to the owner. The owner can check the mobile app to verify whether it is authorized person or someone else [11–14].

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Fig. 1 Proposed system

2. Video Video call feature is proposed where the owner will also be able to talk and see the person waiting outside [9]. 3. Face Recognition and Database Database containing information of authenticated and authorized people are compared with the image of the visitor by the face recognition algorithm [15–17]. 4. Automatic Door Lock This feature is extremely helpful when the owner wants to open the door for people visiting the factory in his/her absence [1, 3]. 5. Smoke Detection for Fire Smoke detector will detect the smoke, and using intelligent processing, it will prevent the false detection of fire [5, 10]. 6. Mobile Application This will help the owner to track all the activities remotely through the mobile application [13]. 7. Buzzer This will start ringing when anyone tries to break the system [18].

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5 Conclusion and Future Scope Within the paper proficient, secure, client available, auto-configurable, remotely controlled arrangement for interruption discovery has been presented. The approach talked about inside the paper is novel and has accomplished the target to direct entryway guarding framework remotely. It bargains with the framework that works on predication on video technology which may be an awfully elegant for giving security and security in urban zones. It employments confront acknowledgment, secret word, and unique finger impression sensor as this approach offers one awesome advantage which is user invitingness. Thus, it is frequently concluded that the desired objectives and goals of our thought are accomplished. The framework is extensible, and more levels are regularly encourage created utilizing programmed motion/glass breaking finders; subsequently, the arrangement is regularly coordinates with these and other location frameworks. Further, if someone tries to interrupt the circuit, then notification is going to be sent to owner also on the safety guard and therefore, the camera will start to capture the recording.

References 1. Agape A, Postolache M (2018) Internet-enabled access control system using a mobile application. In: 2018 22nd International conference on system theory, control and computing (ICSTCC) 2. Deshmukh A, Nakrani M, Bhuyar D, Shinde U (2019) Face recognition using OpenCv based on IoT for smart door 3. Park W, Cheong Y (2017) IoT smart bell notification system: design and implementation. In: 2017 19th International conference on advanced communication technology (ICACT) 4. Dhangekar A (2018) Smart doorbell: the product of IoT Era. Int J Res Appl Sci Eng Technol 6:2034–2037 5. Bahrudin MSB, Kassim RA, Buniyamin N. Development of fire alarm system using raspberry Pi and Arduino Uno. In: 2013 International conference on electrical, electronics and system engineering (ICEESE) 6. Dorothy AB, Kumar SBR, Sharmila JJ (2017) IoT based home security through digital image processing algorithms. In: 2017 World congress on computing and communication technologies (WCCCT) 7. Sowjanya G, Nagaraju S (2016) Design and implementation of door access control and security system based on IOT. In 2016 international conference on inventive computation technologies (ICICT) 8. Martinez C, Leonardo E, Dominguez F (2018) The smart doorbell: a proof-of-concept implementation of a bluetooth mesh network. In: 2018 IEEE third ecuador technical chapters meeting 9. Nag A, Nikhilendra JN, Kalmath M (2018) IOT based door access control using face recognition. In: 2018 3rd International conference for convergence in technology (I2CT) 10. Jamadagni S, Sankpal P, Patil S, Chougule N, Gurav S (2019) Gas leakage and fire detection using raspberry Pi. In: 2019 3rd International conference on computing methodologies and communication (ICCMC) 11. Pinjala SR, Gupta S (2019) Remotely accessible smart lock security system with essential features. In: 2019 International conference on wireless communications signal processing and networking (WiSPNET)

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Raj Kamal (2011) Mobile computing, Oxford Meier R (2012) Professional Android 2 application Rogers R, Lombardo J (2009) Android application Nerurkar A (2019) Comparative analysis of biometric systems. In: INDIACom-2019, IEEE conference, ID: 46181 16. Thakre S, Gupta AK, Sharma S (2017) Secure reliable multimodel biometric fingerprint and face recognition. In: International conference on computer communication and informatics (ICCCI) 17. Li SZ, Juwei Lu (1998) Generalizing capacity of face database for face recognition. In: IEEE international conference on automic face and gesture recognition 18. Nerurkar A (2019) IoT based approach for automatic irrigation system and securing the farm. In: INDIACom-2019; IEEE Conference ID: 46181

Path Segmentation for Visually Impaired People Using U-Net Architecture Amit Chaudhary and Prabhat Verma

Abstract According to WHO, at least 2.2 billion people are suffering from some type of visual impairment, and the number is rising continuously. So, the research to assist the visually impaired person is gaining much importance nowadays. So far, there are many assisting methods like white cane, guide dog and several electronic travel assist (ETA), but they all come with various limitations. To overcome these limitations, we have proposed an assistance system to help the visually impaired person in low structured environment. The system will capture the images from the low structured environment with a camera. The image will be processed using a GPU at backend which in turn segment the path from the image with the help of artificial neural network and will provide the appropriate feedback for the visually impaired person. This paper will present the segmentation of the traversing path in the captured low structured environment images using the artificial neural network. A dataset is formed, and UNET architecture is evaluated. The optimized architecture is managed to segment the image with the IOU score of 0.9012 and can also perform real-time segmentation with a frame rate. Keywords Visually impaired · Navigation · Segmentation · Neural network

1 Introduction According to WHO, around 2.2 billion people have some sort of vision impairment [1]. In at least 1 billion cases, vision impairment could have been prevented. Vision impairment possesses an enormous global financial burden with the annual productivity losses. So, the need of the assistant device for visually impaired people is increasing continuously. There is wide range of navigational device which can assist A. Chaudhary (B) · P. Verma Harcourt Butler Technical University, Kanpur, India e-mail: [email protected] P. Verma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_41

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a visually impaired person ranging from white cane to many other advance tools. The white cane and the guide dogs are the most used methods. In daily commute visually impaired person uses various mobilty aids like guide dogs and white can. But these traditional methods of navigation has lot of limitations and disadvantages. Several researches have helped in developing various assistive navigation systems to overcome these limitations. According to Dakopoulos and Bourbakis [2], system can be classified into electronic travel aid (ETA), electronic orientation aid (EOA) and position locator device (POA). ETA transforms the information from the environment into a form that can be easily understood by visually impaired people. EOA provides orientation information prior to or during the travel. POA includes the technologies like GPS and GNSS to assist the visually impaired people [2]. A range of many assistive tools has been published till date. These systems are confined to indoor and outdoor environment and are unable to perform up to the mark in the low structured environment like parks, off-road and narrow streets, etc. This low structured environment lacks clear structure as in urban cases, but still there are some color and structural differences to distinguish between walkable path and environment. Using this structural differences, Ramer et al. use image processing for lane detection [3]. The proposed approach could detect the lane, but it was complex, the accuracy varies with the lightening conditions, and it is also not able to detect the edges of the lanes in the real time. This paper mainly focuses on the real-time path segmentation using convolutional neural network. It has become state-of-the-art procedure aiming at image processing and computer vision. In this paper, we will present a systematic plan of action for selecting and then optimizing the state-of-the-art CNN architecture with a view to path segmentation with respect to high accuracy. The paper structure is as follows: Sect. 2 contains the related work of ETA in low structured environment with CNN. Section 3 contains CNN description for image segmentation and its architecture UNET. Section 4 contains the dataset generated, how many images are used in training and validation images, etc. Section 5 will be dealing with selecting and optimizing the performance of the CNN architecture and fine tuning of the hyperparameters to get the best accuracy and frame result. Section 6 contains conclusion and future work. Last section will underline the references used during the paper.

2 Literature Review The development of the various electronic travel assists has been possible because of low-cost cameras and smartphones. The image and data processing aims either at avoiding obstacle or in assisting in path navigation. A lot of complex systems fuse both the approach to give more holistic result to the visually impaired person. But we are mainly focusing on path detection in an image by using semantic segmentation as this paper will focus mainly on path detection in color image for the navigation of visually impaired persons.

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It is very true that as the demand for the visually impaired people increases, the topic becomes more challenging which requires handling various tasks simultaneously and also to coordinate various aspects of perception needs efficiently. The publications related to the taken topic can be divided according to different facets like the environment in which they are operating (for instance, indoor and outdoor environment), sensor used (RGBD camera, ultrasound or radar sensor) or the feedback which is given to the visually impaired person. Elmannai and Elleithy [4] review the 21 most used ETA systems that show many limitations like they are able to detect obstacle detection in short range, but they are vulnerable to light and color variations. Singh and Kapoor [5] review the aids for the visually impaired person. He divides the electronic travel assist into three categories—computer vision-based electronic travel assist, ultrasound-based device and hybrid devices which employ both the computer vision and ultrasound to assist the visually impaired person. He stated that none of the systems fully satisfy all the requirements of the visually impaired person. Yang et al. [6] propose the combination of semantic segmentation and depth estimation using CNN architecture for terrain awareness. ERPnet pyramid scenebased architecture has been proposed which provides great speed and favorable accuracy for real-world application by a comprehensive set of experiments on a navigation system. Bologna et al. [7] propose color interface which transforms a small position of a colored video image into the sound sources. See color interface allows the user to receive a feedback auditory signal from the environment and its colors. Aladrén et al. [8] propose navigation assistance for the visually impaired people using RGBD camera with range expansion. The prototype is called as NAVI, and the author has used only one sensor, i.e., RGB-D camera. They have used the combination of depth information with image intensities resulting in robust expansion of rangebased floor segmentation. Author enhanced the depth with the long-range visual information. In recent times, mainly two approaches are used—one is traditional way of image processing, and the other is based on convolutional neural network. The traditional method to solve the segmentation for visually impaired people such as color clustering analysis and texture clustering analysis [6] does not make them correct choice for navigation tool for the visually impaired people in the current scenario of complexities. The main drawback of the traditional approach is that they are sensitive to color and illumination which leads to the poor performance, and they are also not very much suitable to the real-time performance. Convolutional neural network differs from the traditional approach as it learns and discriminates across features straight from the image while using deeper abstraction of layers. Recent development in the field of deep learning has accomplished a quantum leap in vision-based tasks containing semantic segmentation. Since traditional approach detects different targets independently, whereas the semantic segmentation focuses to provide the awareness in a unified way because it permits to solve many tasks at once and exploit their inter-relationship and context.

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In semantic segmentation, most of the research is focused on the efficiency by proposing different architecture that could reach near real-time semantic segmentation. We are making an effort to provide a near real-time semantic segmentation for blind people. The main contribution of this paper will be as follows: • Path segmentation from an RGB image. • Real-time path segmentation.

3 Image Segmentation with Convolutional Network CNN has been applied on various variety of computer vision problems. Recent advances in semantic segmentation have enabled CNN to be used in many fields like image classification, image segmentation and super resolution [9]. CNN takes an input image and assigns weights and biases to various aspects/object in an image due to which CNN is able to differentiate one object to another. CNN has the capability to successfully capture the spatial and temporal dependencies in an image through the application of relevant filter [10]. CNN architecture is composed of repeating layers like convolutional layer for feature extraction, pooling layer for reducing size which results in information aggregation and dense layer or fully connected layer for classification. Diversification of dataset can lead to better result because the CNN will be able to train on diverse dataset due to which it will perform better in test set. Basic architecture of CNN is made up of encoder and decoder part. Initially, the image is processed using encoder part which is usually a pretrained CNN architecture on famous dataset like ImageNet dataset and COCO dataset. The encoder part is called as backbone. It extracts the characteristic feature and reduces the size, whereas the decoder part merges the features map and increases the image size further which results in a segmented image which is having equal size as the input image. We are using a special architecture of CNN called as UNET architecture which was proposed by Ronneberger et al. [11] for biomedical image segmentation. We are employing the same architecture in path detection for visually impaired person. It is encoder–decoder type network architecture for image segmentation in the form of U shape model where features are learned at different layers with combination of convolution layer and max pool layer. It is considered as standard CNN architecture for image segmentation task. Encoder path captures the context of image which produces features map. Encoder path consists of stacks of convolution layer and pooling layer, whereas decoder path is used for precise localization using transposed convolutions. U-Net only consists of convolution layer due to which it can accept image of any size (Fig. 1).

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Fig. 1 U-NET architecture

4 Generated Dataset To build a decent and optimal convolutional neural network architecture for our application, proper and suitable dataset needs be selected. Dataset like cityscapes dataset [12] is meant for urban, road traffic and structured environments, but our navigation system is meant to cater to the needs of visually impaired person in low structured environment like parks, off roads, etc. We have used the RGB camera to collect the images in low structured environment. Due to the difference between the circumstances of our application and lesser availability of public dataset in low structured environment, we have created our own dataset. We have collected 700 images and labeled them using LabelMe tool and generated their ground truth. Sample image and generated ground truth are shown in Fig. 2. Dataset is divided in to training and validation data. 20% of data is kept for the validation task.

Fig. 2 Sample image and generated ground truth

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5 Hyperparameter Optimization and Results We have implemented the CNN-based UNET architecture and later optimized the hyperparameter configuration to bring out sufficient accuracy in addition to efficient implementation on an embedded GPU. We have used transfer learning approach by which we pretrained the encoder part using ImageNet dataset. We have frozen the encoder part and only fine-tuned the decoder part. We have used UNET architecture [11] and resnet34 as backbone of our architecture which is pretrained with ImageNet dataset. We have used segmentation model Python library based on keras (Tensorflow) framework. Segmentation model library is a high-level API in which it can create a segmentation model just by writing four lines of code and supports various binary segmentation and multiclass segmentation. The training is accomplished with 560 images with the following parameters. The result of optimized architecture is represented in Table 1. Figure 3a contains the original frame, Fig. 3b contains the ground truth, and Fig. 3c contains the predicted image. • • • • • • •

Size of input image—128 × 128 Optimizer used—Adam optimizer Batch size—16 Epochs—200 Activation function—Softmax (for multiclass segmentation) Loss function—categorical cross entropy Metric—intersection over union

Table 1 Optimized architecture

Segmentation architecture

Backbone

IoU

U-Net

Resnet34

0.9012

Fig. 3 a Figure contains the original frame, b contains the ground truth and c contains the predicted image

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Learn rate—0.002 Pooling—average pooling Batch normalization—false Encoder weight pretrained with ImageNet dataset We have used Google Colab in order to run the project.

6 Conclusion and Future Work Our approach guides the way to navigate the visually impaired people through low structured environment. Since the public dataset is not diverse enough to cater to the need of visually impaired person especially in low structured environment so we created our own dataset of 700 images and generated their mask. We implemented the dataset using UNET architecture, and then, we optimized it to get better IoU score which is satisfactory in low structured environment. The UNET architecture is able to give IoU score of 0.9012. A final assessment with test data verifies the result. Nevertheless, adding more training data can enhance the IoU score. The applicability to path planning algorithms is making sure by transforming the segmented image into a 2D map. The same approach can be used for other application where accuracy is main focus point. In future, we can fuse both obstacle detection and path planning in order to give proper terrain awareness for visually impaired persons.

References 1. World Health Organization (2020) World report on vision 2. Dakopoulos D, Bourbakis NG (2010) Wearable obstacle avoidance electronic travel aids for blind: a survey. IEEE Trans Syst Man Cybern C 40(1):25–35 3. Ramer C, Lichtenegger T, Sessner J, Landgraf M, Franke J (2016) An adaptive, color-based lane detection of a wearable jogging navigation system for visually impaired on less structured paths. In: 6th IEEE RAS/EMBS international conference on biomedical robotics and biomechatronics (BioRob), UTown, Singapore, 26–29 June 2016, pp 741–746 4. Elmannai W, Elleithy K (2017) Sensor-based assistive devices for visually-impaired people: current status, challenges, and future directions. Sensors 17(3) 5. Singh B, Kapoor M (2021) A framework for the generation of obstacle data for the study of obstacle detection by ultrasonic sensors. IEEE Sens J 21(7):9475–9483. https://doi.org/10. 1109/JSEN.2021.3055515 6. Yang K et al (2018) Unifying terrain awareness for the visually impaired through real-time semantic segmentation. Sensors 18(5) 7. Bologna G, Deville B, Pun T (2009) Blind navigation along a sinuous path by means of the see color interface. In: Bioinspired applications in artificial and natural computation, pp 235–243 8. Aladrén A, López-Nicolás G, Puig L, Guerrero JJ (2016) Navigation assistance for the visually impaired using RGB-D sensor with range expansion. IEEE Syst J 10(3):922–932. https://doi. org/10.1109/JSYST.2014.2320639 9. Kayalibay B, Jensen G, van der Smagt P (2017) CNN-based segmentation of medical imaging data. In: CoRR, vol abs/1701.03056

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10. Towards Data Science. A comprehensive guide to neural network 11. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Proceedings of medical image computing and computer-assisted intervention, pp 234–241 12. Cordts M et al (2016) The cityscapes dataset for semantic urban scene understanding. In: The IEEE conference on computer vision and pattern recognition (CVPR)

Segmentation of Sidewalk for Visually Impaired Using Convolutional Network U-Net Amit Chaudhary and Prabhat Verma

Abstract Image segmentation can play the significant role in helping the visually impaired people to walk freely. We are proposing image segmentation on our custom dataset of tactile paving surface or blind sidewalk. The underlying model for the image segmentation is U-Net. We have used intersection over union (IoU) as a metric to know how our model is performing. We have achieved IoU score of 0.9391. Keywords Tactile paving · U-Net · Image segmentation · Blind sidewalk

1 Introduction According to the WHO, around 2.2 billion people suffer from vision impairment; i.e., they have to rely on some external source to walk or to cross the road on their own. In order to assist the visually impaired people, the research community has forwarded and came up with state-of-the-art technologies [1]. Most of the tools that have been made basically deal in obstacle detection for the visually impaired people, but there is very less research that has been focused on the navigation of the visually impaired people. The blind sidewalk is the area or the path on which visually impaired people walk with the function of marching, arriving and turning. Safety and independent travel of visually impaired people is a crucial part for guiding visually impaired persons. The fundamental aim of visually impaired sidewalk image localization is to locate and segment the tactile surface or sidewalk for visually impaired person and extracting its boundaries. There are various techniques for blind sidewalk identification which are studied home and abroad. There are various traditional techniques to solve the blind sidewalk segmentation for visually impaired people for instance A. Chaudhary (B) · P. Verma Harcourt Butler Technical University, Kanpur, India e-mail: [email protected] P. Verma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_42

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color clustering analysis and texture clustering analysis [2]. But the main drawback of these traditional method is that they are susceptible to color and illumination, which result in bad performance. In this way, these conventional strategies cannot tackle our goal to segment the blind sidewalk from the image. Image scene recognition is nothing but the recognition or identification of the blind sidewalk. In the recent years, the image classification and image segmentation have seen great advancement because of the methods which are based on convolutional neural network [3]. These methods have gained immense success and have brought promising results in various computer vision problems. The deep learning network like U-Net [4], SegNet [5], and RefineNet [6] have brought pretty good result in the area of image segmentation. There are various architectures in convolutional neural network, and out of all those, U-Net has given best result in many computers vision problems. The other architecture like SegNet fails to capture multi-scale information as good as U-Net. Identification of blind sidewalk is a challenging task because of the number of reasons. (a) (b)

There are a very few available datasets for the blind sidewalk image and labeled data. Visually impaired people need a fast model which can work in real time.

On that account, the aim behind this research is to develop a model with high accuracy and with better performance by using U-Net model that can work on small number of training data. In this paper and image segmentation method based on U-Net, CNN has been presented which is meant for blind sidewalk recognition, and we have used intersection over union metrics to evaluate the performance of the model.

2 U-Net Architecture U-Net is a type of architecture that falls under the category of contracting-expansive path decoder type network for image segmentation in the form of U-shaped model where features are learned at different layers with combination of convolution layer and max pool layer (Fig. 1). It is considered as standard CNN architecture for image segmentation task. As it consists of encoder–decoder model also known as contracting-expansive path. Encoder path is meant to capture the context of image which produces features map and is made up of stacks of convolution layer and pooling layer, whereas decoder path is used for precise localization using transposed convolutions and consists of up-sampling and followed by transposed convolution. U-Net can accept image of any size because of convolutional layers. U-Net architecture is shown in Fig. 1. The encoding path is made up of four blocks, and every block consists of 2, 3 × 3 convolution layer followed by Relu activation function with batch normalization and one 2 × 2 max pooling layer (Table 1).

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Fig. 1 U-Net architecture

The number of features map doubles at each pooling, starting with 64 features map for the first block, 128 for the second and so on. The main focus of encoder or contracting path is to capture the context of the input image in order to be able to do segmentation. The decoding path consist of four blocks, and each block consists of deconvolutional layer with stride 2. Decoder path also uses skip connection by concatenating the output of the transposed convolution with the feature map from the encoder at the same level. U-Net combines the location information from the down-sampling path with the contextual information in the up-sampling path to obtain the general information which combine localization and context is must to obtain a good segment map. U-Net can accept input from any different size because it does not have any dense layers. At last step, the sigmoid function is used to give the result or output. It converts the value of the pixel into 0 and 1 which mean the pixel belongs to sidewalk or not. 1 1 + e−(x) Sigmoid Function f (x) =

3 Experiments (a)

Prepared Dataset: As there is no standard dataset for our work, so we have collected 200 images and labeled them using Vgg annotator tool. We have also applied data augmentation using Albumentation library to increase the number of annotated samples [7]. Albumentation library is applied during the training process due to which the number of the images are not increased in the disk. Augumentation of images prevents the model from overfitting.

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Table 1 Parameters about the U-Net model

(b)

Layer name

Block type

Output width

Conv block 1

3×3

32

Conv block 1

3×3

32

Max pooling 1

2×2

32

Conv block 2

3×3

64

Conv block 2

3×3

64

Max pooling 2

2×2

64

Conv block 3

3×3

64

Conv block 3

3×3

64

Max pooling 3

2×2

64

Conv block 4

3×3

128

Conv block 4

3×3

128

Max pooling 4

2×2

128

Conv block 5

3×3

256

Up-sampling 6

2×2

128

Concatenate 1

–

384

Conv block 6

3×3

128

Conv block 6

3×3

128

Up-sampling 7

2×2

Concatenate 2

–

Conv block 7

3×3

64

Conv block 7

3×3

64

Up-sampling 8

2×2

Concatenate 3

–

Conv block 8

3×3

64

Conv block 8

3×3

64

Up-sampling 9

2×2

32

Concatenate 4

–

96

Conv block 9

3×3

32

Conv block 9

3×3

32

Conv block 10

1×1

2

Sigmoid

–

1

64 192

64 128

Training: All the images are resized to 256 × 256 by using resize function in TensorFlow 2.0 and converted into gray scale. We have used batch size as 4 due to limited availability of GPU in Google Colab and number of iterations used 20. We have used ADAM optimizer which very often used in semantic segmentation problem. It is a binary classification problem as we are only interested in blind sidewalk. The loss function is selected as the binary cross entropy as our loss function which is only meant for binary classification and

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Fig. 2 Sample output

Table 2 Comparison result

(c)

(d)

Architecture

IoU

U-Net

0.9391

SegNet

0.8785

segmentation problems. The 200 samples are randomly divided into 4:1 ratio of training and testing samples (Fig. 2). Experiment Setup: These experiments are done on Windows 10 operating system. We have used Google Colab in order to run our deep learning model. We have also used Python 3.6, TensorFlow 2.0, OpenCV and NumPy. Metrics for Evaluation: Intersection over union (IoU) is employed as the evaluation metric for evaluating the performance of the model. IoU = TP/(FN + TP + FP)

4 Result To demonstrate the efficacy of the U-Net, we have compared it with the SegNet model. Higher the IoU higher the segmentation result (Table 2). U-Net model has performed better than SegNet model in terms of IoU.

5 Conclusion and Future Work Navigation for visually impaired person is a less research area, and most of the research is focussed on object detection for the visually impaired people. We tried to assist the visually impaired people by segmenting the image which will extract the tactile paving surface or sidewalk from the images. For this, we have presented an image segmentation method for tactile paving surface or sidewalk identification by employing the CNN U-Net which have achieved promising result in edge detection

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and subdue the noise. We can increase the IoU by increasing the number of samples. We can also increase the resolution of the image and the labels which will further increase the feature extraction. We can try different optimizer function and different loss function. For future work, we can improve the prediction of our model by incorporating more data along with their annotated labels. We can also work on images with higher resolution that can able to give better IoU score. In future, we will also try to incorporate obstacle detection which will give more holistic feedback to the visually impaired people.

References 1. Gonnot T, Saniie J (2016) Integrated machine vision and communication system for blind navigation and guidance. In: IEEE international conference on electro information technology, pp 0187–0191 2. Bologna G, Deville B, Pun T (2009) Blind navigation along a sinuous path by means of the see color interface. In: Bioinspired applications in artificial and natural computation, pp 235–243 3. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Commun ACM 60(2) 4. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234–241 5. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495 6. Lin G, Milan A, Shen C, Reid I (2016) RefineNet: multi-path refinement networks for highresolution semantic segmentation. In: Computer vision and pattern recognition (CVPR), pp 5168–5177 7. Xie H, Lin C, Zheng H, Lin P (2018) An UNet-based head shoulder segmentation network. In: IEEE international conference on consumer electronics, Taiwan, pp 1–2

Hybrid Security for Data in Cloud Computing: A Review R. Mary Sheeba and R. Parameswari

Abstract Cloud computing is the most current technology to emerge in the last several decades. It is a great platform for the users to share data or applications on remote server that can be processed and accessed through Internet. Users are always concerned with security issues which are really challenging in cloud computing because many customers were sharing the same cloud. The cloud service provider must ensure that the sensitive information in the cloud is secured using the latest security techniques to protect the data, applications, and infrastructure associated with the cloud. This study aims in discussing, various cloud security hazards and different hybrid cryptosystem used for security. A new method is proposed for high security which includes Blowfish symmetric algorithm and RSA asymmetric algorithm for data privacy along with Password-Based Key Derivation Function 2 (PBKDF2) algorithm for strong password security. Keywords Blowfish · RSA · PBKDF2 · Cryptosystem

1 Introduction Cloud refers to server that are accessed over the Internet. The information and applications are managed and stored in the remote server that are hosted on Internet instead of computer’s hard drive. In current world, many small industries face difficulties to adopt advance technology due to over cost and time. Cloud computing provides better solution for such difficulties and so that it is much familiar among Information Technology sectors, Program developers, Educational Institute, Banking, etc. It is very cost-effective and profitable technology, because here the user pays only for the resources utilized. The term Cryptography implies a technique to change the original set of data which are easily readable by human into an encrypted type which could not be R. Mary Sheeba (B) · R. Parameswari Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_43

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accessible by anyone easily. When the private information or data like passwords, credit card number, bank information, etc., were shared through the cloud, the data are kept secret by using the process known as Encryption. Encryption performs descrambling or modifying a communication in order to conceal the original text. To get back the original data unscramble the encrypted data that makes it readable form. The classification can be done in two ways such as Symmetric (Private key) cryptography algorithm and Asymmetric (Public key) cryptography algorithm. • Symmetric or Private Key Cryptography: In this type, the sender and receiver communicate and exchange the same key to encode and decode the message is called Symmetric Cryptography. The sender encodes the plain information into unreadable format using the private key and directs the secret message to the receiver. Now the receiver can decode the information by using the same private key. • Public or Common Key Cryptography: This concept mainly concerned with two different keys called public key for data encoding and secret Key to decode the data. The public key which is used to scramble data can be shared or exchanged with anyone to generate a private or encoded message, but this message can be unscrambled by only using the secret key. Public Key cryptography is the basics of all protected content on the web.

1.1 Different Forms of Cloud Services and Models The cloud service model is the reference model on which cloud computing is based on Infrastructure as a Service (IaaS), Platform as a Service (IaaS), and Software as a Service (SaaS) [1]. The SaaS act as an end user, where it is connected to the end user. It consists of software packages, system software and application related server storage networks. The PaaS is mainly used by the application developers to develop an application which is provided by cloud. The IaaS completely deals with the hardware service, virtual machine, and network architecture infrastructure. Cloud environment has Public, Private, Community, and Hybrid cloud as its deployment models [2]. Public cloud can be accessed by everyone, whereas the private cloud is used organizations. In Community cloud multiple organizations distribute the same cloud and hybrid cloud is a union of both private and public cloud.

1.2 Security Hazards in Cloud Because of its low-cost storage service, cloud computing now plays a significant role in everyone’s life. However, users are still concerned about security issues because the cloud acts as a big black box, so the client cannot see what is inside the cloud and, also the user has no idea where all the data resides inside the cloud. The administrator can

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tamper with sensitive data, thereby violating confidentiality and integrity. However, the cloud environment faces issues with confidentiality, integrity, and privacy. SQL Injection Attack is a kind of virtual attack which destroys SaaS due to its weak application design. The execution is done by SQL command which benefits vulnerable interface. Session Hijacking is also a technique used by the intruder to gain the power via the Session ID, which leads to hacking user information. Another type of security threat is the Man-In-The-Middle Attack, which is carried out by establishing an independent connection with the user device and demonstrating that it is private and secure. Flooding is a form of denial-of-service attack that increases network connectivity by overflowing the network with a huge capacity of various types of traffic [3].

2 Review of Security Algorithms This section defines and investigates previous work in security to provide a better understanding of the effectiveness of encryption algorithms. Singh [6] analyzed about the performance of different security algorithms on a cloud network. A detailed study has been done for various encryption algorithm such as RSA, DES, 3DES, and AES for data security. They concluded that AES algorithm is most reliable in terms of performance, duration, efficiency, and avalanche effect when compared to other encryption algorithms. Saini and Sharma [7] proposed a hybrid encryption model that uses three algorithms : DSA, DES, and steganography, to ensure data security in cloud computing, but they determined that the time complexity is high due to its one-by-one process. Albugmi et al. [4] deal with data shield methods and approaches for maximum data protection by decreasing threats and risks. This paper discussed the risk and security threats to data in the cloud and a complete study done on data security aspects during data-in-conduct and data-at-rest. Timothy and Santra [11]suggested a hybrid model that uses RSA, Blowfish, and SHA-2 algorithms which comprises both symmetric and asymmetric algorithms. This concept ensures huge safety on data communication over the Internet and data integrity also achieved by using SHA-2 algorithm. Guesmi and Saïdane [10] introduced identity-based cryptography for cloud repository which permits access to authorized user to store and use the data safely. It focuses on the safety and confidentiality for data stored in public cloud. Barona and Anita [1] discussed various security threats and challenges occuring in cloud environment. Chinnasamy and Deepalakshmi [5] proposed a new method to secure Electronic Health Records (EHR) by implementing various combinations of cryptographic techniques. The data are encoded via symmetric algorithm, and keys are scrambled using asymmetric algorithm. They used Blowfish and extended version of RSA algorithm as a projected method, which perform encryption and decryption in less duration when compared to other methods. (Table 1).

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Table 1 Significant analysis of security algorithms Purpose

Methodology used

Data storage on cloud [8]

Implementation

Outcomes

Limitations

AES, Blowfish, Files with and MD5 different size were tested

The hybrid cryptography provides better efficiency and speed to encryption process

The maximum file size of 10 MB can be used for testing

Cloud security using hybrid cryptography [2]

DES, RSA, and cloudsim simulator tool

Performance test was done by using files in cloud

Encoding and decoding of files done in minimum time Provides the transparency to the cloud and helps the CSP to reduce the security threats

Only text file can be used for encryption and decryption

Security of cloud using various cryptography and steganography [9]

AES and RSA algorithm, SHA-256 hashing for validation

Using LSB encrypted data hidden in an image and compress data using LZW algorithm before hiding it

It has been proved that PSNR values of stego-image was good for compressed data. To ensure data security in cloud, a powerful and efficient method is used

The results showed 1 kB data hidden in stego-image

Data storage concealment in cloud computing [10]

Identity-based cryptography

Comparison was done with CloudaSec an RIBE schemes to evaluate duration and efficiency

The results of average time consumption show proposed method (CloudIBE) improves file validation time and utilization of user resources

Data can be shared from one-to-one user and one to many users

Cryptography algorithm for cloud computing security [11]

Blowfish and RSA algorithm, SHA-2 algorithm

Encrypt the file by applying Blowfish and use RSA to encrypt the secret key. Apply digital signature for data integrity

It gives high – security and increases the difficulty level for unauthorized person to decrypt the data (continued)

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Table 1 (continued) Purpose

Methodology used

Implementation

Outcomes

Data security and privacy in cloud environment [12]

RSA and AES algorithm

Algorithm Ensure security implemented in and privacy for cloudsim data in cloud framework. Uses OTP for user authentication and hashing and salting techniques used for data security

Limitations Recovery and archive of files should be done and some extended work to be included in multi cloud environment

3 Overview of an Algorithm Used 3.1 Blowfish Algorithm It is an effective encryption algorithm designed by Bruce Schneier. It is a very secure and fast block ciphers, which does not include any patent and freely available to use. The key length is flexible, ranging from 32 to 448 bits. The main specifications of this algorithm are 64-bit block, 32–448 bits variable size, 18 subkeys stored in P-Array, 4 substitution boxes, and the number of encryption and decryption takes place is 16 rounds. The process done by generating subkeys which is used for both encoding and decoding process, then initialize substitution boxes where each S-boxes has 256 entries each of 32 bits [13]. The decryption process contains Rounds (each round will take input of plaintext from previous round sand corresponding subkeys) and post-processing (various functions like XOR, Modulo, Addition was used). Finally, 64-bit cipher text gets generated.

3.2 RSA Algorithm It is an asymmetric cryptographic technique which supports the process of encoding and decoding of messages. It works on two keys where public key is known to all and private key was kept secret. The steps followed in RSA algorithm are as follows [14]:

1. 2.

Assume two big prime numbers namely ‘a’ and ‘b’. Then calculate the value of ‘n’ by computing the product of prime numbers a and b.

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Perform Euler’s totient function φ(n) where phi calculate all possible integers up to n which are relatively prime to ‘n’. Therefore, φ(n) = (a − 1) * (b − 1). Choose an integer value ‘E’ for encryption where ‘E’ should be greater than 1 and less than φ(n) [1 < E < φ(n)], such that GCD (E, φ(n)) = 1. Then compute decryption ‘D’ where it should be greater than 1 and less than φ(n) [1 < D < φ(n)], such that D * E mod φ(n) = 1. Therefore, Encryption or public key is (n, E) and Decryption or Private key is (n, D). To perform Encryption process, compute C = M E mod n and for decryption process, use M = C D mod n. Here ‘M’ and ‘C’ denote the plain text and cipher text.

4 Proposed Methodology This section introduces a new hybrid model with the combination of both symmetric and asymmetric algorithm for excellent security in cloud environment. This proposed methodology completely depends on encryption and decryption of the process. In encryption process, the plain text will convert to unreadable format called cipher text, which cannot be easily read by anyone. The process of getting original data from the scrambled text is called decryption. In this study, the Blowfish symmetric algorithm and RSA algorithm were used along with PBKDF2 hashing technique.

4.1 Concept of PBKDF2 Password-Based Key Derivation Function2 is a secure password hashing algorithm that employs a key enhancing technique to randomly increase the complexity of a brute-force attack. PBKDF2 applies a pseudorandom function (PRF) to the input password, including a salt or randomly generated value, and replicates the process several times to yield a final key, which is used as a secret key in subsequent operations. Password hashes require a salt to avoid identical passwords from mapping to the same hash. They also include an iteration count, so an attacker must do more work to calculate the hash of each possible password (Fig. 1).

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Fig. 1 Process model of PBKDF2

4.2 Use of PBKDF2 in Proposed Model Login page requires users’ information like username and password credentials. Usually, the password details were stored in the database as a plain text or stores as a hash value. The hackers can guess the password using multiple combinations. If an unauthorized user gets access to the database, then they could use precalculated table for the hashing algorithm we use. This table is called as Rainbow table which perform backward process to figure out the passwords easily. To overcome these issues and to generate a strong password, password-based key derivation function is used. The login page information of the client should be maintained confidentially and safely. Every password entered by the client will undergo this hashing algorithm, then a unique hash key is generated along with the salt value. For each identical password this method will produce different salt value. This process will continue to a particular iteration count and finally a strong password gets generated.

4.3 Encoding and Decoding Process Blowfish algorithm is a symmetric or private key cryptography method, which utilize a secret key to scramble the original content. This symmetric process uses single key for both encryption as well as decryption. An issue can occur while the secret key is exchanged via Internet between both sender and receiver. This can be tackled by implementing RSA asymmetric or public key method. When the information passed over the cloud environment, the private or secret key also sent along with it. So, the secret key should undergo the encryption process using the RSA asymmetric algorithm. RSA algorithm uses two set of keys public and private, where the public key is shared common to all, and the private key is known only to the receiver. This ensures that the information is secured with double

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Fig. 2 Encryption mechanism

Fig. 3 Login mechanism

encryption process. The decoding process involves the conversion of cipher text into original text. As a first step in decoding, the RSA algorithm decrypts the cipher text, which helps to produce original information. Using the decrypted key, apply blowfish algorithm to decrypt the data (Figs. 2 and 3).

5 Conclusions To ensure user authentication, data privacy and security in cloud, a new hybrid cryptography technique was proposed using Blowfish and RSA algorithm. This model

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provides user authentication by using PBKDF2 hashing technique and generates a strong password key for individual user during login process. Many layers of rigid security were given in this new methodology, for the user in cloud environment.

References 1. Barona R, Anita EM (2017) A survey on data breach challenges in cloud computing security: issues and threats. In: 2017 international conference on circuit, power and computing technologies (ICCPCT), Apr 2017. IEEE, pp 1–8 2. Kumar S, Karnani G, Gaur MS, Mishra A (2021) Cloud security using hybrid cryptography algorithms. In: 2021 2nd international conference on intelligent engineering and management (ICIEM), Apr 2021. IEEE, pp 599–604 3. Sasubilli MK, Venkateswarlu R (2021) Cloud computing security challenges, threats and vulnerabilities. In: 2021 6th international conference on inventive computation technologies (ICICT), Jan 2021. IEEE, pp 476–480 4. Albugmi A, Alassafi MO, Walters R, Wills G (2016) Data security in cloud computing. In: 2016 fifth international conference on future generation communication technologies (FGCT), Aug 2016. IEEE, pp 55–59 5. Chinnasamy P, Deepalakshmi P (2018) Design of secure storage for health-care cloud using hybrid cryptography. In: 2018 second international conference on inventive communication and computational technologies (ICICCT), Apr 2018. IEEE, pp 1717–1720 6. Singh G (2013) A study of encryption algorithms (RSA, DES, 3DES and AES) for information security. Int J Comput Appl 67(19) 7. Saini G, Sharma N (2014) Triple security of data in cloud computing. Int J Comput Sci Inf Technol 5(4):5825–5827 8. Bermani AK, Murshedi TA, Abod ZA (2021) A hybrid cryptography technique for data storage on cloud computing. J Discrete Math Sci Cryptogr 1–12 9. Abbas MS, Mahdi SS, Hussien SA (2020) Security improvement of cloud data using hybrid cryptography and steganography. In: 2020 international conference on computer science and software engineering (CSASE), Apr 2020. IEEE, pp 123–127 10. Guesmi H, Saïdane LA (2017) Improved data storage confidentiality in cloud computing using identity-based cryptography. In: 2017 25th international conference on systems engineering (ICSEng), Aug 2017. IEEE, pp 324–330 11. Timothy DP, Santra AK (2017) A hybrid cryptography algorithm for cloud computing security. In: 2017 international conference on microelectronic devices, circuits and systems (ICMDCS), Aug 2017. IEEE, pp 1–5 12. Arora A, Khanna A, Rastogi A, Agarwal A (2017) Cloud security ecosystem for data security and privacy. In: 2017 7th international conference on cloud computing, data science & engineering-confluence, Jan 2017. IEEE, pp 288–292 13. Bansal VP, Singh S (2015) A hybrid data encryption technique using RSA and Blowfish for cloud computing on FPGAs. In: 2015 2nd international conference on recent advances in engineering & computational sciences (RAECS), Dec 2015. IEEE, pp 1–5 14. Saravanan N, Mahendiran A, Subramanian NV, Sairam N (2012) An implementation of RSA algorithm in google cloud using cloud SQL. Res J Appl Sci Eng Technol 4(19):3574–3579

Issues of Commodity Market and Trade Finance in India and Its Solutions Using Blockchain Technology Swapnil Sonawane and Dilip Motwani

Abstract A commodity market is a marketplace where different primary products and raw materials like precise metals, base metals, energy products, and agriculture products get traded. The entire trading process is managed by different governing agencies like MCX, NCDEX, NMCE, ICEX, etc. All these exchanges manage different types of commodities like future commodity trading as well as option commodity trading. For the economies of developing countries, the commodities sector is very important, and it is estimated that more than 100 developing countries rely on primary commodities for their export earnings. Currently, commodity and finance trading is facing different problems like getting new commodities, attracting small participants, fewer commodities of agriculture products, managing a huge set of documentations, inefficiency due to physical marketplace, the collaboration of all stakeholders, etc. These problems many times increase rates of commodities and surveys reported that commodity price rise can bump up inflation by 1%. This paper suggested solutions to these problems using a blockchain distributed ledger, which helps to create a network of trust and digitize every product connected to the trade and finance industry using Ethereum blockchain and smart contracts. Keywords MCX · NCDEX · Commodity · Smart market

1 Introduction A blockchain is digital ledger of transactions which are distributed across entire blockchain network. It is distributed ledger technology in which all transactions, which can be in any format like text, image, spreadsheets, PDF can be stored across entire network. Blockchain supports peer to peer distributed network and entire S. Sonawane (B) · D. Motwani Vidyalankar Institute of Technology, Mumbai, India e-mail: [email protected] D. Motwani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_44

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copy of blockchain is present at every participant or every node in blockchain network [1]. The entire working of blockchain is based on 3 pillars, i.e., Decentralization, Immutability, and Transparency. In this decentralization represents there is no centralize governing authority in blockchain as it follows distributed network. immutability represents once the transactions has been recorded on blockchain it cannot get altered or changed and transparency indicates that copy of all transactions in the form of ledger is present at every node of the blockchain. The commodity transaction lifecycle integrates value and the supply chain, and the commodity transaction lifecycle function is to describe what lies between products passing from production to consumers. It may sound easy and straightforward, but the commodity transaction lifecycle is long and complex, involving many different participants to get products from producer to consumer. In the value chain, value is added to the product by different participants who use resources such as money, labor, materials, equipment, administration, and management. The supply chain illustrates a series of market-based transactions that include each company involved in bringing a product or service to market by balancing efficiency and cost reduction strategies [2]. The commodities trading industry is a very diverse industry. Exchanges can range from small local operations to exchanges between large multinational companies. The products are as diverse as coffee, wheat, sugar, cotton, petroleum, natural gas, precious metals, etc. it is the glue that holds value chains together and that belongs, at least to some extent, to commodity trade. However, information is increasingly available on a large scale, in real time, and at lower cost, reducing the impending competitive advantage used to access higher quality information. The transaction lifecycle, value, and supply chain, also includes several intermediaries providing services. Such as trade finance, trade facilitation, insurance and risk management, onsite inspection, verification, certification, shipping, and logistics. The commodities trading industry is a very active business and the margins tend to be slim due to all players participating and each player taking a piece of the pie [3]. Commodity market trading which is normally performed by different exchanges is always said to be volatile and possesses many issues like high risk for small capital investment, global instability, unpredictable future demand and supply, domestic and international government policies, as well as technical advancements. Many of such problems arise due to long supply and value chain which gets used in commodity marketplace. Using blockchain we can create a distributed ledger where all transactions that takes place in commodity marketplace gets recorded and stored in the form of blockchain network. Also, all actors who are directly participated in commodity trading can be considered as single node and all real time transactions between those participants can be recorded in blockchain network [4]. This technical review paper is organized as follows: In Sect. 2 current scenario of commodity marketing is discussed, Sect. 3 describes different problems related to commodity trading whereas in Sect. 4 solutions of those problems using blockchain distributed ledger is getting proposed, in Sect. 5, different advantages of using blockchain in commodity trading is illustrated, Sect. 6 gives insights of different problems of trade finance in India as well as their solution after using blockchain distributed ledger, in Sect. 7, we highlighted few challenges of using blockchain in

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commodity trading and finally in Sect. 8 we mentioned conclusion about described problem and its proposed solution.

2 Commodity Trading in India Commodity trading basically used to trade assets as well as risk associated with that asset. It involves different types of products like: A.

B.

C. D.

Bullion Products It involves different precise or precious metals like gold, silver, platinum, etc. Base Metals It involves different types of metals like Aluminum, Copper, Lead, Nickel, and Zinc, etc. Energy Products It involves different types of energy products like crude oil and Natural gas. Agri Commodities It involves different agriculture products like Cardamom, Cotton, Palm oil, Soyabeans, Mentha oil, Rubber, etc.

Commodity trading always required a marketplace, where buying and selling of assets will take place. There are two main types of commodity trading: A.

B.

Physical Commodity Trading: It is called as physical commodity because it requires physical marketplace and people can come and sell or purchase different commodities at that place. Normally it is used for different agriculture products which possesses comparatively less value as that of metals and energy products. Financial Commodity Trading: It is called as financial because here, different commodities are traded electronically using papers and not using physical marketplace. It is also called as future market and it is mainly used for precious commodities like metals and energy products.

In India, commodity market is governed by Multi-Commodity Exchange (MCX) and the platform is used to perform different operations on commodity products illustrated in Table 1.

3 Inefficiencies in Commodity Trading Trading raw materials is inefficient, error-prone, and costly because it involves tedious processes and multiple intermediaries at various points in the life cycle [5]. Some applications are used in processes such as verification, coordination, trade

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Table 1 Operations in commodity marketplace Operation

Description

Trading and surveillance

It involves different operations like tracking holidays, calendar events, future prices, trade verification, reports, and policy generation as well as participants login management

Clearing and settlement

It involves different operations related to settlement of claims like collaterals, banks, etc. It also deals with data request for claims and different PFMI disclosures

Delivery

It involves different commodity delivery operations like reports, delivery settlement calendar, delivery details which represents information about highest and lowest quantity about different commodities

Warehouse and logistics

It represents details about Assayer details, validity matrix, stock position for metals and energy products, calculators, warehouse inspection, status reports, etc.

finance, payments, operations and shipping. In particular, it is used for physical and financial commodity transactions that slow the exchange of information between the parties. Transaction costs and security requirements are very high in the industry due to the associated counterparty risk. Below is a list of indicators of inefficiency in commodity trading (Table 2) [6].

4 Solutions Using Blockchain When all stakeholders in a commodity transaction are on the same interoperable network and have real time access to the same verified transaction, the need for intermediaries and processes to perform various deal lifecycle events is eliminated [7]. The use of blockchain in commodity trading will address and solve all problems that commodity trading is currently facing [8]. We need to build combinatorial auction or smart market that is used to create new marketplace using blockchain distributed ledger. We can collaborate with all stakeholders of commodity trading to create a new marketplace and to build a new exchange which is decentralized. It will be easier for new people having small liquidity to participate in commodity trading as well as we can create our own cryptocurrency for group of participants in commodity trading [9]. Blockchain technology uses decentralized platform and smart contract which provides functional and mathematical structure which creates and run combinatorial auctions which can run small markets and it is even used to solve NP-hard problem. Blockchain uses network of trust and using it we can digitize every product connected to trade and finance industry. This mechanism will eliminate brokers or third party and can be used for both physical as well as financial market. Following are various activities gets performed if we use blockchain in commodity trading (Fig. 1) [10]:

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Table 2 Inefficiencies in commodity trading Inefficiencies

Description

Ineffective administrative processes

Confirmation of both counterparties in the commodity trading is required to accept the conditions of the transaction Physical transactions require letters of credit and bank guarantees from external trading finance companies Intermediate system requires multiple adjustments

High cost of transaction

Brokerage and clearing fees increase transaction execution costs High collateral and capital requirements for margins have increased transaction costs Supply chain bottlenecks and lack of contract standardization between counterparties

Slow delivery and billing cycle

Stakeholders use a variety of information systems, which makes it difficult to effectively track shipments for delivery due to interoperability issues Transfer of ownership involves slow paperwork Payments and financial intermediaries involved in settlement slow down the process

Intermediaries or brokers

Intermediaries such as clearinghouses, clearing brokers, execution brokers, banks, operators involved in various phases of the life cycle Multiple interventions in the intermediary system required to close a transaction

(1) (2) (3) (4)

The trader enters the transaction against a Smart Contract based on terms agreed with the counterparty. The consensus algorithm is used to verify the transaction, and it is then joined with other transactions in a block. The blockchain is updated with a new block, which is recorded in the distributed ledger. Smart Contracts are set up to run automatically when certain conditions are met. Based on the terms, the contract is automatically sealed.

5 Advantages of Proposed System The proposed system which describes the used of blockchain distributed ledger for managing transactions of commodity trading can provide following different advantages [11]: Fast Information Exchange: Faster trade of records enabled with the aid of using the disbursed ledger era can lessen the agreement timescale considerably main to quicker motion of commodities decreasing counterparty risk.

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Fig. 1 Blockchain-based workflow

Reduce Settlement Time: Reduction in agreement cycle will result in low capital and margin necessities so as for the counterparties to go into right into a deal thereby growing the marketplace liquidity. Improvement in Trade Finance: Improved change finance availability as Banks are onboarded directly to the identical dispensed environment which allows banks to affirm the firm’s credit score worthiness. Digitisation of Assets: Digitisation of commodity belongings will allow the recipients to switch belongings as collateral in opposition to payment. Traceability: The traceability of commodities throughout the delivery chain may be improved.

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Elimination of Third Party Cost: Intermediary expenses which include Broker, Clearing, and Settlement charges will not be relevant bringing down the fee according to transaction. Apart from all above-mentioned advantages, following are few more benefits the commodity trading market would get if it used blockchain platform: • The application will be secure as blockchain used asymmetric key cryptography and secure hashing function • Blockchain used trusted digital ledger, so all transactions and documents can be audited at real time • Blockchain provides strong anti-fraud features, because of which no participant can change transaction record at given time • Use of blockchain also provides protection against different cyber attacks • By creating distributed ledger network, we can restrict a single entry for each data item in blockchain network • As blockchain is distributed ledger, all data is always available to all participants on a single platform • Blockchain and integration of smart contract also provide real time transaction monitoring from start to finish • Task duplication and verification of all transactions and documents can be easily dome using blockchain mining process • Blockchain technology is comparatively simpler, cheaper, and transparent as compared with current mechanism used in financial trading.

6 Blockchain in Trade Finance International trade finance is backbone of world economics which trades different products globally like fruits, vegetables, electronics, etc. It is having complicated process which involves several participants and required huge range of documents like shipping invoices, letter of credit, bills of lading, etc. It is also suffering from currency issues and restricted to only certain markets [12]. In finance trading, use of smart contract which is used by blockchain technology will eliminate manual verification and validation of data elements. Blockchain can provide restricted access based on authorization and all participants and regulators will get real time view. Blockchain provides secure shared ledger which can generate unambiguous shared view of contract terms and conditions and provide status of goods across importer, exporter, banks, shipping companies, port authorities and well as customs. Blockchain also provides automation which improve transparency and accuracy and incorporate flexibility to model variety of constraints.

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7 Challenges of Using Blockchain Blockchain era is yet to be rolled out at a synthetic scale that allows us to endure addressing several the specialized, criminal, and nonsupervisory demanding situations earlier than its relinquishment alternatives similarly pace [13]. It provides different challenges for adoption of blockchain in commodity and financial trading like Scalability or speed issue of blockchain, performance issue due to cumbersome of consensus mechanism, anonymity of the transactional facts is a challenge as all the marketplace contributors with inside the allotted community will preserve a localized reproduction of the facts and Regulatory and Compliance necessities round trading, obligatory clearing and reporting has to adapt primarily based totally at the era advancement.

8 Conclusion Current commodity market and finance trading is very complicated and facing enormous problems like collaboration of different stakeholders, problems of current marketplace, involvement of third party and brokers during transactions and huge range of documentation which makes the process ineffective and expensive. Blockchain-based distributed ledger network can create network of different stakeholders or participants of commodity trading and finance to digitize every product and asset connected to trade and finance industry. To validate the feasibility and possible savings, a blockchain-based application will be operated on certain functionalities such as confirmation, smart contracts, trade financing, and settlement. It will create network of trust among all participants to generate secure, trustworthy, effective, and transparent distributed application.

References 1. Zheng Z, Xie S, Dai H, Chen X, Wang H (2017) An overview of blockchain technology: architecture, consensus, and future trends. In: 2017 IEEE 6th international congress on big data 2. Wu J, Wu T, Wu Y, Chen J, Lin K, Si H (2020) Improved blockchain commodity traceability system using distributed hash table. In: 2020 Chinese automation congress (CAC). 978-1-72817687-1/20/$31.00 ©2020 IEEE. https://doi.org/10.1109/CAC51589.2020.9326639 3. Raveendarnaik A (2017) The major issues in development of commodity derivatives market in India. IOSR J Bus Manag (IOSR-JBM) 41–46. e-ISSN: 2278-487X, p-ISSN: 2319-7668 4. Wyman O (2016) Blockchain in capital markets. Oliver Wyman, London 5. Dayama PS, Jidugu B (2009) Multi-strategy supplier selection for commodity sourcing. In: 5th annual IEEE conference on automation science and engineering, Bangalore, 22–25 Aug 2009

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6. Ghosh I, Sanyal M, Jana RK (2020) An ensemble of ensembles framework for predictive analytics of commodity market. In: 2020 4th international conference on computational intelligence and networks (CINE). 978-1-7281-5688-0/20/$31.00 ©2020 IEEE. https://doi.org/10. 1109/CINE48825.2020.234398 7. Deloitte (2016) Blockchain applications in energy trading. Deloitte Publication, London 8. Stevens A (2017) Blockchain technology represents a new frontier of innovation in the commodity trading industry. California: CIOReview. https://blockchain.cioreview.com/cxo insight/blockchain-technology-represents-a-new-frontier-of-innovation-in-the-commodity-tra ding-industry-nid-25269-cid-176.html 9. Su L, Wang H (2020) Supply chain finance research in digital bulk commodities service platform based on blockchain. In: 2020 international conference on E-commerce and internet technology (ECIT) 10. Zhang L, Fan D (2020) Analysis of the application of blockchain technology in the financial industry. In: 2020 international conference on big data economy and information management (BDEIM). 978-1-6654-0331-3/20, 2020 IEEE. https://doi.org/10.1109/BDEIM52318. 2020.00033 11. Infosys white paper on “Can blockchain disrupt energy and commodity trading?” 12. Jain N, Sedamkar RR (2020) Blockchain technology approach for the security and trust in trade finance. In: 2020 14th international conference on innovations in information technology 13. Golosova J, Romanovs A (2018) The advantages and disadvantages of the blockchain technology, Nov 2018. 978-1-7281-1999-1/18

Optimal Selection of Cloud Service Provider Using MCDM Approach M. Krithika and A. Akila

Abstract Cloud computing services have become progressively much desired since it gives the user a variety of adapting features, systematic and genuine computational services. As Cloud Services are very popular among users nowadays, it grows very fast and almost many IT service providers engage to give services with good quality and well-structured which would adapt the features actually required by the users’. As there is a vast range of Cloud Provider, choosing the best provider had become a great challenge for Cloud users. Multiple criteria have to be analysed by the user before selecting the best Cloud Service Provider (CSP) which is a complex problem. Thus, the problem comes under multi-criteria decision-making (MCDM) problem where the MCDM handles the data in a systematic way, so that the user gets the best provider. This study incorporates different methods of MCDM techniques which would rank the Cloud Service Providers (CSPs) according to the user specifications. The techniques include Weighted Sum Model (WSM), Weighted Product Model (WPM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS). Thus, the ranking will help in giving out the efficient Cloud Service Provider for the customers’ specification. Keywords Multi-criteria decision-making (MCDM) · Service selection · Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) · Weighted Sum Model (WSM) · Weighted Product Model (WPM) · Evaluation Based on Distance from Average Solution (EDAS)

M. Krithika (B) · A. Akila School of Computing, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu 600117, India e-mail: [email protected] A. Akila e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_45

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1 Introduction Cloud computing is scattered model which offers user the computing infrastructure, software and platform for the user demands on the basis of pay-as-use. The user who requires service from Cloud Provider demands for the service and pay the cost for the service as they use them. The service offered by the Cloud Service Provider can be accessed by the user at any time they need, which is one of the best advantages of accessing the services from Cloud. All Cloud Service Providers offer best Quality of Service (QoS), which is the responsibility of the provider. Generally, Cloud offers many services to the users according to their requirement and demand which is broadly classified into three: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Infrastructure as a Service, which is also called as Hardware as a Service, which is one of the Cloud Services which offers users with hardware (physical resources) which may include network, servers and storage. These physical resources offered by the Cloud can be used by the customers’ according to their demand. IaaS services such as storage, servers and network are used by the customers’ in the form of virtualisation technology. Platform as a Service (PaaS) is a Cloud Service that offers services to the user in the form of operating systems, software development kit (SDK), integrated development environments (IDE) and programming languages. This form of services offers users to create the required applications they need onto the given Cloud infrastructure. As users create application, they can only monitor the applications created and not the background infrastructure offered by the Cloud for creating the application. For example, if developers for creating a specific application are working at different location, the infrastructure they use to create the application been the same, and thus, integration of the stack for creating and deploying the application is easy. Software as a Service (SaaS) is a Cloud Service that offers users with a ready to use application created by the Cloud. This application created can be thus replaced with the local machine application of the user. As the application created is on Cloud, different users can access the application simply through web browsers on different Cloud devices. Some of the advantage of using the SaaS is accelerated feature delivery, centralized configuration and hosting and centralized configuration and hosting. As this service offers only the applications, the underlying infrastructure and functionality cannot be accessed or managed by the user. But, the user can access some limited sort of specifications configuration settings. Thus, as stated above, there is a vast range of Cloud Service Providers available for providing Infrastructure, Platform and Software as a Service. From among the providers, it is complex for a user to choose their provider with respect to their requirements. Hence, multiple criteria have to be handled by the customer to make their decision on selecting the appropriate Cloud Providers. Therefore, multi-criteria decision-making (MCDM) approach can be used to help the company/user to get the best Cloud Provider depending upon the users’ stated requirement.

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The remaining of the paper is given as sections where in the next section, we discuss about the related and current works in the field of Cloud Service Providers and the MCDM techniques used in relevant with the different Cloud Providers. In Sect. 3, we discuss about the overview of the different MCDM technique; in Sect. 4, we discuss about the attributes (criteria) taken for each Cloud Provider, and the final section elaborates about the conclusion and future work.

2 Related Works As there is a vast option in Cloud Service Providers, the selection of optimal provider has become very difficult. Thus, MCDM approach is used to solve this difficulty. In this section, the literature review of the existing paper works and its contributions is elaborated. In [1], a structure/framework is proposed to rank and select the Cloud Service Provider. It uses the TOPSIS approach for ranking the CSPs and thus to help the user to select the best provider. In [2], a work is proposed to select the best Cloud Service Provider by incorporating the existing two MCDM methods which includes Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the best–worst method (BWM). Here, evaluation criteria are used so as to characterize the services provided by the Cloud Providers. In [3], they propose methodology for ranking the Cloud Service Provider with respect to multi-criteria decision-making technique. The multiple criteria here are the multiple applications provided by multicloud environments. Mukherjee et al. [4] gives a new HHO algorithm which is used to rank the Cloud Providers. This algorithm is compared with some existing algorithm TLBO and Jaya and gives better result. In [5], a model is proposed to rank the Cloud Service Providers based on the fuzzy logic techniques. The attributes of the CSPs for the selection of the providers include security, storage and financial attributes. In [6], a traditional brokerage-based architecture is proposed to select the best Cloud Service Provider. This architecture uses the Cloud broker in the process. The information/data of each Cloud Service Provider is been stored in the form of indexing structure (B Cloud-Tree) so as to manage them for further evaluation process. An efficient algorithm is then proposed to select the best CSP from the Cloud-Tree to the user. The backend of the work is to create a B Cloud-Tree and thus, they are been fetched using the proposed algorithm. Kumar et al. [7] incorporates the existing methods of MCDM for ranking the Cloud Providers. The method includes Best–Worst method and the TOPSIS method. The best–worst method is using for calculating the weight of each attribute of the CSP, and finally, the ranks are provided for the CSP using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. In [8], the proposed work uses the MCDM method which includes the fuzziness of data. The MCDM methods used are the TOPSIS and AHP with fuzzy concept. This paper works on the CSMIC parameter with analytic and fuzzy method. It thus proves that TOPSIS is better method then AHP. In [9], a study to help customer with the best Cloud Service Provider with Software as a Service was mentioned. They present the extraction and analysis of various parameters of multiple CSPs with

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SaaS facility. In [10], SelCSP framework is created to select the best Cloud Service Provider. The parameters included in this parameter to analyse CSP are the SLA of each provider in terms of their transparency and the feedbacks of the CSPs in terms of trust, reputation and context specific. In [11], new framework for decision-making is created for calculating the criteria weights, so as to select the best Cloud Provider. This framework which is particularly made for decision-making is based on hybrid DANP (combination of decision-making trial and evaluation laboratory) method and analytical network process (ANP) method. In [12], a technique was used for ranking the CSPs. The best CSP is ranked according to certain criterion of the CSP which includes its reliability, efficiency and maintenance. Thus, the calculated value with highest weightage is given as the software with best quality. This proposed study is on a novel MCDM approach that includes Weighted Sum Model (WSM), Weighted Product Model (WPM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS) to rank CSPs based on criteria selection.

3 Multi-criteria Decision-Making (MCDM) The purpose of MCDM is to support decision-maker (users) facing multiple criteria problem. For example, if a user requires a Cloud Service provider with Infrastructure as a Service and searches for the best provider, their comes the multiple criteria problem where decision had to be made by the user in selecting Cloud Provider which matches the requirements from among multiple providers. This makes user confused and complex to select the efficient provider. Thus, MCDM helps in ranking the Cloud Provider according to the user requirement with specific criteria. All the MCDM methods converts the data to a decision matrix or evaluation table which is shown in the below (1) form

(1)

where A1 , A2 and An are called the alternatives (Cloud Service Providers), while x 1 , x 2 , and x n are the criterion (Agility, Assurance, Security and Privacy ). The following are the MCDM methods which are included in this study: 1.

Weighted Sum Model (WSM):

Weighted Sum Model is a MCDM method which works on the multiple alternatives (CSPs) to determine the best alternative (Cloud Service Provider) based on multiple criteria. The steps followed in ranking the alternatives are elaborated below.

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Step 1: The alternatives with its multiple criteria dataset which is converted to decision matrix (1) are normalized. The linear normalization is used in carrying out this step. The normalization of attribute in each alternative (Cloud Service Provider) is calculated in this step. Equation (2) is necessary for normalizing the beneficial attributes, and Eq. (3) is for non-beneficial attributes: Xi j =

Xi j X MAX j

Xi j = 1 −

Xi j X MAX j

(2) (3)

Step 2: Multiply each value of X ij by the corresponding weightage value. The formula for calculating the Weighted Decision Matrix is (4): Xi j = W j Xi j

(4)

Step 3: From the value obtained in Step 2, sum all the values to get the Preference score. The rank for each alternative is given from the obtained Preference score from highest score to the lowest. 2.

Weighted Product Model (WPM):

The steps followed in WPM to acquire the ranks of alternatives are as follows: Step 1: A decision matrix obtained from the dataset is converted to a normalized decision matrix. The linear normalization is used in carrying out this step. Step 2: Multiply each value of X ij by the corresponding weightage value. The formula for calculating the Weighted Decision Matrix is given in Eq. (4). Step 3: From the value obtained in Step 2, multiply all the values to get the Preference score. The rank for each alternative is given from the obtained Preference score from highest score to the lowest. 3.

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS):

The main aim of TOPSIS is that the best alternative selected should be closer to the positive ideal solution and far from the negative solution. The following elaborates the steps of TOPSIS approach. Step 1: With the help of dataset, decision matrix is constructed in the form of (1) which includes the alternatives (CSPs) with its respective value for each criterion. Step 2: Now, from the decision matrix constructed from Step 1, the normalized decision matrix is calculated using Eq. (5).

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xi j r i j = /∑

m k=1

xk2j

, i = 1, 2, . . . , m,

j = 1, 2, . . . , n

(5)

Step 3: In this step, weighted normalized decision matrix is constructed using Eq. (4). Multiply each value of X ij by the corresponding weightage value. Step 4: Now, decide the ideal best value and ideal worst value. The ideal best is the value with positive impact from the whole criteria in the weighted normalized decision matrix and the ideal worst is the value with negative impact from the whole criteria in the weighted normalized decision matrix. Step 5: In this step, the distance between the ideal worst value and the target alternatives is calculated. Equation (6) is the following for calculating the worst condition ⎡ |∑ | n ( )2 ti j − tw j , i = 1, 2, . . . , m, diw = √ (6) j=1

and thus followed by the ideal best with the target alternative is calculate. The formula for calculating this is given in Eq. (7) ⎡ |∑ | n ( )2 ti j − tbj , i = 1, 2, . . . , m dib = √

(7)

j=1

where d iw and d ib are the calculated distance from the ideal worst and ideal best with respect to the target alternative i. Step 6: Finally, calculate the performance values, from the values obtained in Step 5. siw = diw /(diw + dib ), 0 ≤ siw ≤ 1 i = 1, 2, . . . , m. Step 7: Thus, according to siw , value obtained, and the rank is given for each alternative. 4.

Evaluation Based on Distance from Average Solution (EDAS):

The Evaluation Based on Distance from Average Solution (EDAS) calculated the distance of each alternative with respect to the average solution obtained. In TOPSIS, the distance is calculated from the ideal best and ideal worst solution, which is replaced by the average solution of each criterion. The steps followed in EDAS method are discussed below:

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Step 1: The main perspective of EDAS is to calculate the average solution. The average solution is calculated with respect to each criterion (criteria of all CSPs), with the help of arithmetic mean method. Step 2: Now, calculate the positive and negative values of positive distances (PDA) and negative distances (NDA) from the average solution. The following are the equations used for beneficial attributes (8): ) ( max 0, (ei j + 3σi j − E j ) Ej ) ( max 0, (E j − ei j − 3σi j ) o ndai j = Ej ) ( max 0, (ei j + 3σi j − E j ) p pdai j = Ej ) ( max 0, (E j − ei j + 3σi j ) p ndai j = Ej pdaioj =

(8)

and non-beneficial attributes (9): pdaioj

=

ndaioj = p pdai j p

=

ndai j =

) ( max 0, (E j − ei j + 3σi j ) Ej ) ( max 0, (ei j − 3σi j − E j ) Ej ) ( max 0, (E j − ei j − 3σi j ) Ej ) ( max 0, (ei j + 3σi j − E j ) Ej

(9)

Step 3: Now, find the weighted sum (sum of all alternatives) of the positive and negative distance calculated in Step 2, and the equation is (10): spio =

m ∑

w j pdaioj

j=1

snio =

m ∑

w j ndaioj

j=1 p spi

=

m ∑

(10) p w j pdai j

j=1 p

sni =

m ∑ j=1

p

w j ndai j

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Step 4: Convert the positive and negative values obtained in Step 3 to normalized values using Eq. (11), as shown below: spio maxi (spio ) snio nsnio = 1 − maxi (snio )

nspio =

p

p nspi

spi = p maxi (spi )

(11)

p

p

nsni = 1 −

sni p maxi (sni )

Step 5: Finally, the appraisal scores are calculated using Eq. (12). ) 1( o nspi + nsnio 2 1( p p p) asi = nspi + nsni 2

asio =

(12)

Step 6: Rank the CSPs from highest to lowest appraisal scores. The CSP alternative with the highest score is the best choice among all CSP alternatives, in concern with the criteria.

4 Criteria Selection The study of this paper is mainly focused on the existing MCDM methods to rank the CSPs. Thus, to calculate the rank of the CSPs using the MCDM method is done only with respect to the criteria from the CSPs compared with each on the Provider. Therefore, to rank the CSPs using the MCDM method, list of criteria has to be identified to implement the criterions onto the MCDM method. The criterion which is included in this study are been discussed below: • Agility: Agility is simply defined as the ability or power of moving into a work quickly without any thinking of the other concerns necessary for the work to be completed. For example, if an organization wants to develop a new application without having much concern about hardware, software and networking, as it will be taken care by the CSPs. • Assurance: CSPs will promise the user/organization for the resources and provide the same for them. CSPs assure best security to the user and thus provide them as assured. This attribute typically gives the assurance a CSPs would promote for the user/organization.

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• Security and Privacy: As the data stored in Cloud is purely an outside vendor, the Security and Privacy for the user to store and access data should be well arranged. • Usability: As user/organisation may use Cloud for various purpose, the use of the resources, software, devices, etc. should give overall satisfaction to the user. For example, if the user asks for the infrastructure service from CSP, then the infrastructure the provider promises for should give the user the whole satisfaction. • Accountability: Accountability is an important concern, where the data Security and Privacy of data should be accomplished with responsiveness. As user/organization stores personal data in Cloud, the responsibility is purely in the hand of the Cloud Provider and thus the provider has to safely handle data of the user with good Security and Privacy policies. • Performance: Cloud Providers always promise users/organization for the best service which includes the infrastructure, reliability and scalability. The CSPs should fulfil the claims and promise made to the user with good attention and effort. • Billing: Though cloud offers pay-as-use concept, the billing is also a concern to be included in the criteria. This is because, a company/user who indeed uses Cloud Service will have to set a cost limit. Thus, the cost of CSP is a major criterion.

5 Conclusions and Future Work The study is basically to rank the Cloud Service Providers. The ranking of the CSPs depends on the criterions selected which includes Agility, Assurance, Security and Privacy, Usability, Accountability, Performance and Billing of Cloud Services, and the priority of each criterion is determined. The MCDM methods are used to make decision from among the multiple criteria selected along with the multi-cloud provider option. Finally, set of ranks are to the cloud provider, from which the user may select the best Cloud Service. In future, appropriate analysis can be made on the existing MCDM method which includes WSM, WPM, TOPSIS and EDAS. The betterment is analysed and research is made with the existing methods and thus, to propose a better MCDM method for ranking the Cloud Service Provider.

References 1. Kalaiarasan TTC, Venkatesh KA (2020) Cloud service provider selection using fuzzy TOPSIS. In: 2020 IEEE international conference for innovation in technology (INOCON), pp 1–5. https://doi.org/10.1109/INOCON50539.2020.9298207 2. Youssef E (2020) An integrated MCDM approach for cloud service selection based on TOPSIS and BWM. IEEE Access 8:71851–71865. https://doi.org/10.1109/ACCESS.2020.2987111

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3. Ramamurthy, Saurabh S, Gharote M, Lodha S (2020) Selection of cloud service providers for hosting web applications in a multi-cloud environment. In: 2020 IEEE international conference on services computing (SCC), pp 202–209. https://doi.org/10.1109/SCC49832.2020.00034 4. Mukherjee P, Patra SS, Pradhan C, Barik RK (2020) HHO algorithm for cloud service provider selection. In: 2020 IEEE international women in engineering (WIE) conference on electrical and computer engineering (WIECON-ECE), pp 324–327. https://doi.org/10.1109/WIECONECE52138.2020.9397936 5. Mishra, Daniel AK (2020) An efficient cloud ranking protocol for user service selection using fuzzy logic. In: 2020 advanced computing and communication technologies for high performance applications (ACCTHPA), pp 64–69. https://doi.org/10.1109/ACCTHPA49271.2020. 9213222 6. Lin D, Squicciarini AC, Dondapati VN, Sundareswaran S (2019) A cloud brokerage architecture for efficient cloud service selection. IEEE Trans Serv Comput 12(1):144–157. https://doi.org/ 10.1109/TSC.2016.2592903 7. Kumar RR, Shameem M, Khanam R, Kumar C (2018) A hybrid evaluation framework for QoS based service selection and ranking in cloud environment. In: 2018 15th IEEE India council international conference (INDICON), pp 1–6 8. Supriya M, Sangeeta K, Patra GK (2016) Trustworthy cloud service provider selection using multi criteria decision making methods. Eng Lett 9. Boussoualim N, Aklouf Y (2015) Evaluation and selection of SaaS product based on user preferences. In: 2015 third international conference on technological advances in electrical, electronics and computer engineering (TAEECE), pp 299–308. https://doi.org/10.1109/TAE ECE.2015.7113644 10. Ghosh N, Ghosh SK, Das SK (2015) SelCSP: a framework to facilitate selection of cloud service providers. IEEE Trans Cloud Comput 3(1):66–79. https://doi.org/10.1109/TCC.2014. 2328578 11. Zoie RC, Alexandru B, Delia Mihaela R, Mihail D (2016) A decision making framework for weighting and ranking criteria for cloud provider selection. In: 2016 20th international conference on system theory, control and computing (ICSTCC), pp 590–595. https://doi.org/ 10.1109/ICSTCC.2016.7790730 12. Kaur S, Sehra SK, Sehra SS (2016) A framework for software quality model selection using TOPSIS. In: 2016 IEEE international conference on recent trends in electronics, information & communication technology (RTEICT), pp 736–739. https://doi.org/10.1109/RTEICT.2016.780 7922

Plant Species Recognition from Leaf-Vein Structures Using ResNets Abdul Hasib Uddin

and Abu Shamim Mohammad Arif

Abstract Leaf-vein structures are equivalent to fingerprints for a plant species. Each species of plant has a unique vein structure and this structure identifies species. In this paper, we have used a leaf-vein dataset that contains 64 × 64 pixel greenchannel center-focused images for four species, two from monocotyledon and the other two from dicotyledon categories. We have trained two state-of-the-art Residual Neural Network (ResNet) models with a recently introduced leaf-vein image dataset. We also introduced two extended versions of those models. Our study shows that ResNets are efficient in recognizing vein structures from those partial leaf-vein images with 78.98% accuracy for ResNet50 and 81.63% accuracy for ResNet101. Also, our proposed extended versions of the ResNets prove to be more efficient than the existing ones with around 82% accuracy for DenseResNet50 and approximately 83% accuracy for DenseResNet101 models. Keywords Species classification · ResNet · Leaf-vein identification · Extended ResNet

1 Introduction Pattern recognition is a very challenging task. Every natural creature has different sorts of unique patterns embedded into it, which often correspond to its characteristics. Efficient analysis and identification of those patterns are thus crucial for advanced experiments. Nonetheless, in numerous cases, manual recognition of the patterns is excessively troublesome, such as leaf-texture analysis. Machine Learning and Neural Network-based approaches have recently proven to be excellent alternatives to manual analysis. Tan et al. introduced a methodology to extract features and classify plants using their leaf-vein morphometric pattern [1]. They used a deep learning process for their work and it gave them a better result than other methods they compared. In their work, A. H. Uddin (B) · A. S. M. Arif Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_46

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they used CNN to extract the features from leaf images of chosen plant species. Their research methodology contains 4 main steps: sampling, image preprocessing, feature extraction, and classification. They collected 43 different species of plants with 1290 leaf images. Lee and Hong made use of some significant processing that are useful to plant classification based on some features [2]. They used 1907 plant leaf images from 32 different species which were collected by Wu et al. [3]. Each plant species contains at least 50 and at most 77 samples of leaves. They proposed a leaf identification procedure for plant species classification using features like leaf-vein and shape. Leaf shape feature was used by leaf contour extraction. In [4], Grinblat et al. proposed a method to identify plants by utilizing vein morphological patterns using deep CNN. The deep CNN helps to extract features in a standard way avoiding the handcrafting feature extraction method. Using a deep learning approach gave them some facilities to improve accuracy referring to training neural network architectures. The authors used deep learning to extract leafvein features. In the rest of the paper, we have described the dataset in Sect. 2, our approach in Sect. 3, results and performances in Sect. 4. Then we discussed our experimentation and concluded the article in Sect. 5 and mentioned some probable aspects of future contributions in Sect. 6. Price et al. deployed a software for leaf network extraction to a global leaf dataset [5]. He et al. introduced deep residual network and applied it on COCO object problem [6]. Again, they proposed newly developed residual unit for improved generalization and easier training [7]. On the other hand, Kadir et al. applied probabilistic neural network for leaf classification [8]. Also, Larese et al. introduced a procedure legume leaf image segmentation and classification [9]. Moreover, Bruno et al. applied leaf image fractal dimensions for plant identification [10]. Additionally, Uddin et al. utilized residual neural networks (ResNets) for small-scale image classification [11].

2 Data Description For our work, we have considered the dataset introduced by Uddin et al. [11] and the dataset is publicly available on Mendeley [12]. It contains images from four species of plants, namely Cocos nucifera, Eichhornia crassipes, Cucurbita moschata, and Neolamarckia cadamba. First two species are Monocotyledonous and last two are Dicotyledonous. Each class contains 64 × 64 pixel 10,914 leaf-vein images, summing up to a total number of 43,656 images. All the images are center-focused, thus holding only vein patterns. Figure 1 shows some examples of the images.

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Fig. 1 Leaf image samples a Cocos nucifera, b Eichhornia crassipes, c Cucurbita moschata, d Neolamarckia cadamba, and e green-channel image

3 Methodology At first, we trained the ResNet50 model on the dataset. Then, we constructed an extended form of ResNet50 (DenseResNet50). We replaced the top of the original ResNet50 with three consecutive Dense layers, containing 128, 256, and 512 neurons, respectively, and trained it on the dataset. Figure 2 demonstrates the learning trend of DenseResNet50 and Fig. 3 represents the corresponding validation accuracy versus training accuracy plots. Then, we trained ResNet101 and extended form of ResNet101 (DenseResNet101). Similar to the proposed DenseResNet50 architecture, we removed the top of the original ResNet101 and added three Dense layers at the end of it. Figure 4 shows Fig. 2 Training loss versus validation loss for proposed DenseResNet50 architecture

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Fig. 3 Training accuracy versus validation accuracy for proposed DenseResNet50 architecture

Fig. 4 Training loss versus validation loss for proposed DenseResNet101 architecture

the learning curve for DenseResNet101, while Fig. 5 gives an overview of training accuracy versus training loss. We trained each model until there were 15 consecutive epochs with no improvement on validation loss. In all cases, the optimizer was Adam, with a learning rate of 0.0001. For better generalization, while learning, we utilized a beta-1 regularizer with a value of 0.9 and a beta-2 regularizer holding the value of 0.999. We used categorical cross-entropy as the loss function and accuracy as the classification metrics. The batch size was set to 32 in every case. We implemented the Softmax function as the classifier.

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Fig. 5 Training accuracy versus validation accuracy for proposed DenseResNet101 architecture

4 Results Table 1 depicts test accuracy and test loss values for the applied ResNets and proposed DenseResNets. Test accuracy and test loss values for ResNet-50 are 78.98% and 0.72, respectively. On the other hand, the corresponding test accuracy and test loss values for the proposed DenseResNet-50 architecture are 82.02% and 0.69. Hence, the proposed DenseResNet-50 undoubtedly performs better for vein-pattern recognition. Again, for ResNet101, the respective test accuracy and test loss are 81.63% and 0.61; while for proposed DenseResNet101 architecture, these values are 83.10% and 0.62. Therefore, in all the cases, the proposed models outperform the corresponding existing ResNet structures. The confusion matrix, comparing DenseResNet50 and corresponding ResNet50 classification performances is provided in Table 2. The amount of correctly identified species for ResNet50 are 753 (Cocos nucifera), 868 (Eichhornia crassipes), 914 (Cucurbita moschata), and 896 (Neolamarckia cadamba). In contrast, these amounts Table 1 Performance for existing and proposed networks

Model

Test accuracy (%)

Test loss

ResNet50

78.98

0.72

Proposed DenseResNet50

82.02

0.69

ResNet101

81.63

0.61

Proposed DenseResNet101

83.10

0.62

Bold signifies better performance

476 Table 2 Confusion matrix (proposed DenseResNet50 vs. ResNet-50)

A. H. Uddin and A. S. M. Arif 851 (753)

8 (4)

28 (91)

199 (238)

24 (4)

906 (868)

75 (113)

81 (101)

31 (1)

31 (55)

889 (914)

135 (116)

51 (43)

61 (43)

57 (104)

917 (896)

Values in first braces correspond to ResNet-50 Bold signifies correct predictions

Table 3 Classification report (proposed DenseResNet50 vs. ResNet50)

Species

Precision

Cocos nucifera

0.89 (0.94) 0.78 (0.69) 0.83 (0.80)

Recall

f 1-score

Eichhornia crassipes

0.90 (0.89) 0.83 (0.80) 0.87 (0.84)

Cucurbita moschata

0.85 (0.75) 0.82 (0.84) 0.83 (0.79)

Neolamarckia cadamba 0.69 (0.66) 0.84 (0.83) 0.76 (0.74) Values in first braces correspond to ResNet50

in the case of the proposed DenseResNet50 network are 851, 906, 889, and 917, respectively. Table 3 contains precision, recall, and f 1-score metrics for ResNet101 and proposed DenseResNet101 models. For Cocos nucifera, the precision for ResNet50 is 0.94 and DenseResNet50 is 0.89, recall for ResNet50 is 0.69 and DenseResNet50 is 0.78, f 1-score for ResNet50 is 0.80 and DenseResNet50 is 0.83. Again, for Eichhornia crassipes, the precision for ResNet50 is 0.89 and DenseResNet50 is 0.90, recall for ResNet50 is 0.80 and DenseResNet50 is 0.83, f 1-score for ResNet50 is 0.84 and DenseResNet50 is 0.87. Furthermore, for Cucurbita moschata, the precision for ResNet50 is 0.75 and DenseResNet50 is 0.85, recall for ResNet50 is 0.84 and DenseResNet50 is 0.82, f 1-score for ResNet50 is 0.79 and DenseResNet50 is 0.83. Finally, for Neolamarckia cadamba, the precision for ResNet50 is 0.66 and DenseResNet50 is 0.69, recall for ResNet50 is 0.83 and DenseResNet50 is 0.84, f 1-score for ResNet50 is 0.74 and DenseResNet50 is 0.76. The confusion matrix, comparing DenseResNet101 and corresponding ResNet101 classification performances, is given in Table 4. The number of correctly identified species for ResNet101 is 1018 (Cocos nucifera), 900 (Eichhornia crassipes), 864 (Cucurbita moschata), and 764 (Neolamarckia cadamba). In contrast, Table 4 Confusion matrix (proposed DenseResNet101 vs. ResNet101)

865 (1018)

0 (4)

27 (14)

194 (50)

24 (64)

935 (900)

62 (76)

65 (46)

24 (49)

48 (57)

853 (864)

161 (116)

20 (150)

38 (44)

71 (128)

957 (764)

Values in first braces correspond to ResNet101 Bold signifies correct predictions

Plant Species Recognition from Leaf-Vein Structures Using ResNets Table 5 Classification report (proposed DenseResNet101 vs. ResNet101)

Species

Precision

477 Recall

f 1-score

Cocos nucifera

0.93 (0.79) 0.80 (0.94) 0.86 (0.86)

Eichhornia crassipes

0.92 (0.90) 0.86 (0.83) 0.89 (0.86)

Cucurbita moschata

0.84 (0.80) 0.79 (0.80) 0.81 (0.80)

Neolamarckia cadamba 0.69 (0.78) 0.88 (0.70) 0.78 (0.74) Values in first braces correspond to ResNet101

these amounts in the case of the proposed DenseResNet101 model are 865, 935, 853, and 957, respectively. Table 5 contains precision, recall, and f 1-score metrics for ResNet101 and proposed DenseResNet101 models. For Cocos nucifera, the precision for ResNet50 is 0.79 and DenseResNet50 is 0.93, recall for ResNet50 is 0.94 and DenseResNet50 is 0.80, f 1-score for ResNet50 is 0.86 and DenseResNet50 is 0.86. Again, for Eichhornia crassipes, the precision for ResNet50 is 0.90 and DenseResNet50 is 0.92, recall for ResNet50 is 0.83 and DenseResNet50 is 0.86, f 1-score for ResNet50 is 0.86 and DenseResNet50 is 0.89. Furthermore, for Cucurbita moschata, the precision for ResNet50 is 0.80 and DenseResNet50 is 0.84, recall for ResNet50 is 0.80 and DenseResNet50 is 079, f 1-score for ResNet50 is 0.80 and DenseResNet50 is 0.81. Finally, for Neolamarckia cadamba, the precision for ResNet50 is 0.78 and DenseResNet50 is 0.69, recall for ResNet50 is 0.70 and DenseResNet50 is 0.88, f 1-score for ResNet50 is 0.74 and DenseResNet50 is 0.78.

5 Discussion and Conclusion As the results show that plant species can be efficiently classified from only the vein patterns from a very small portion of leaves by using residual neural networks (ResNets). Also, we have applied two state-of-the-art ResNet architectures, extended their structures, and compared their performances. In both cases, our proposed models outperformed the existing models. Moreover, 64 × 64 pixel center-focused leaf images only contain vein patterns. Hence, we can conclude that these neural network models are capable of distinguishing plant species from only vein patterns.

6 Possible Future Contributions The dataset only contains four species of plant images. A comprehensive dataset with more plant species can be examined upon the same procedures. Also, along with using 64 × 64p images, even lower resolution images can be applied to determine

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the strength of these neural network models. Moreover, new models can be built following further investigation. Acknowledgement This work is funded by the division of Information and Communication Technology (ICT), Ministry of Posts, Telecommunications and Information Technology, Government of the People’s Republic of Bangladesh.

References 1. Tan JW, Chang S-W, Kareem SBA, Yap HJ, Yong K-T (2018) Deep learning for plant species classification using leaf vein morphometric. IEEE/ACM Trans Comput Biol Bioinform 2. Lee K-B, Hong K-S (2013) An implementation of leaf recognition system using leaf vein and shape. Int J Bio-Sci Bio-Technol 5(2):57–66 3. Wu SG, Bao FS, Xu EY, Wang Y-X, Chang Y-F, Xiang Q-L (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. In: 2007 IEEE international symposium on signal processing and information technology. IEEE, pp 11–16 4. Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424 5. Price CA, Wing S, Weitz JS (2012) Scaling and structure of dicotyledonous leaf venation networks. Ecol Lett 15(2):87–95 6. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778 7. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision. Springer, Cham, pp 630–645 8. Kadir A, Nugroho LE, Susanto A, Santosa PI (2013) Leaf classification using shape, color, and texture features. arXiv preprint arXiv:1401.4447 9. Larese MG, Namías R, Craviotto RM, Arango MR, Gallo C, Granitto PM (2014) Automatic classification of legumes using leaf vein image features. Pattern Recogn 47(1):158–168 10. Bruno OM, de Oliveira Plotze R, Falvo M, de Castro M (2008) Fractal dimension applied to plant identification. Inf Sci 178(12):2722–2733 11. Uddin AH, Mahamud SS, Arif ASM (2022) A novel leaf fragment dataset and ResNet for small-scale image analysis. In: Intelligent sustainable systems. Springer, Singapore, pp 25–40 12. Uddin AH, Sharder SM, Arif M, Shamim A (2020) Leaf vein fragment dataset. Mendeley Data, V1. https://doi.org/10.17632/ngds35s8vr.1

IoT-Enabled Automated Analysis and Classification of COVID-19 Disease in Lung CT Images Based on Edge Computing Environment Ayman Qahmash

Abstract A new coronavirus outbreak (COVID-19) has created a dire scenario around the world, making it one of the most acute and severe diseases to strike in the last century. Daily, the number of people infected with COVID-19 increases across the globe. Despite the fact that there are no vaccines for this pandemic, deep learning techniques have shown to be a helpful addition to the arsenal of diagnostic tools available to clinicians. IoT-enabled edge computing environments necessitate the use of the federated deep learning (FDL-COVID) COVID-19 detection model. First, the FDL-COVID method allows IoT devices to collect patient data, and secondly, using SqueezeNet architecture, the DL model is developed. The encrypted variables are uploaded to the cloud server by the IoT devices, and the SqueezeNet model is used to perform FL on the major variables in order to generate a global cloud model. As a result, a glowworm swarm optimization (GSO) algorithm-based hyperparameter optimizer is applied to the SqueezeNet model’s hyperparameter selection. The CXR dataset was used to run a large number of simulations on the SqueezeNet model, and the results were analyzed using a variety of metrics to create a global cloud model. Additionally, the SqueezeNet architecture’s hyperparameters are optimized using the glowworm swarm optimization (GSO) technique. The benchmark CXR dataset was used to conduct a wide range of experiments, the results of which were analyzed using several metrics. The experimental results showed that the FDL-COVID technique outperformed the other methods in terms of performance. Keywords COVID · Internet of Things · Edge computing · Classification · CT images · Deep learning

A. Qahmash (B) Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_47

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1 Introduction Globally, the COVID-19 epidemic has a devastating effect on the health and wellbeing of individuals. As a result, research on COVID-19 diagnosis and detection is critical [1, 2]. The most common symptoms of COVID-19-infected pneumonia include fever, chills, and a dry cough. Some people experience stomach discomfort. As a result, it may be necessary to conduct testing on a larger number of people quickly. This method relies heavily on machine learning and computer vision. Doctors can now use the deep learning as an effective tool for analyzing and evaluating situations in the healthcare industry because of its major role in the classification of images. The key to a strong and accurate depth module is substantial and varied training data. Getting all of the necessary training data was a major challenge. DL approaches for COVID-19 detection have generated insufficient data up to a particular point. This difficulty can be addressed in a number of ways, one of which is through the FL. Using the FL, various organizations can learn from each other without sharing any personal information. Data silos and data privacy issues can be solved with a new approach. It is critical to diagnose as many potential cases as possible using viral genetic testing and CT scans to keep the illness from spreading quickly and to adhere to quarantine procedures. When COVID-19 illness is detected early, it can help guide clinical treatment and keep patients well-monitored as they undergo treatment. Current medical technology relies on swabs taken from the nose and throat to diagnose COVID-19 illness [3]. As a result of these drawbacks, this technique is deemed ineffective. When it comes to early detection, the RT-PCR assays, also known as reverse transcription polymerase chain reactions, have been shown to be ineffective [4]. With computer-assisted automated detection and diagnosis systems, doctors’ diagnostic powers can be improved while diagnostic time is reduced. There are systems like these because they are designed to assist professionals in making rapid and precise choices. There are promising investigations on FL applications in the field of medicinal big data. A distributed module can be built using data from various devices without the necessity for local raw data sharing. This protects personal information in an effective manner. The FDL-COVID detection model on an IoT-enabled edge computing environment is presented in this study as a federated deep learning (FDL) model. First, the FDL-COVID method allows IoT devices to collect patient data, and secondly, using SqueezeNet architecture, the DL model is developed. The encrypted variables are uploaded to the cloud server by the IoT devices, and the SqueezeNet model is used to perform FL on the major variables in order to generate a global cloud model. As a result, a GSO algorithm-based hyperparameter optimizer is applied to the SqueezeNet model’s hyperparameter selection. The benchmark CXR dataset was used to run a large number of simulations, and the results were evaluated using a variety of metrics.

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2 Literature Review Artificial intelligence (AI) is the study of how to program machines or computers to do human functions [5]. Artificial intelligence (AI) is becoming more complex as time goes on, and it is being employed more and more because of its potential to perform human tasks and hence enhance productivity in the workplace. Artificial intelligence has several scientific applications. Artificial intelligence has found use in the field of computer vision. Artificial intelligence’s branch of computer vision analyzes and equips machines to perform similar tasks to the human eye [6]. The usage of computer vision has benefited a wide range of scientific fields. In the meantime, computer vision is widely used in the health industry. According to Vávra et al.’s [7] findings, doctors are becoming increasingly interested in using AR during surgery because it improves surgical safety and effectiveness. Liu et al. [8] proposed the use of FL for COVID-19 data training and conduct study to test the efficiency of their method. Additionally, the researchers tested four common modules (COVIDNet, ResNet18, and MobileNet) with and without the FL architecture. PSO, rather than FedAvg, was employed by Park et al. [9] to improve the global module by aggregating the weights of learned modules that are most frequently used in the FL. FedPSO is a strategy that uses score values instead of greater weights to boost its strength on unstable network platforms. COVID-19-related CT anomalies can be detected with external authentication using the FL approach, as demonstrated by Dou et al. [10]. Using TensorFlow and Keras together, by creating two ML modules, Abdul Salam et al. [11] investigated the effectiveness of FL against traditional learning (FL and conventional ML modules). As part of their effort to discover which factors influence the module’s accuracy and prediction, they plotted and recorded the module’s predictive loss for every training round to see which factors had the greatest impact on the module’s efficiency. They found that softmax activation function, SGD optimizer (SGD), and the number of rounds and rate of learning all had the greatest impact on module efficiency [2, 12]. A dataset, which includes both training and testing data, is critical in the development of a deep learning architecture. An effective deep learning model needs a huge number of training data to be effective [13]. However, substantial datasets, such as those for cases of COVID-19, a novel disease, are difficult to come by. Even if only a few datasets are used, we require a strategy for creating models that perform well. Transfer learning is the term used to describe this approach [14]. Using transfer learning, knowledge from one domain (the source domain) is applied to another domain (the target domain) with which it has a relationship. There are fewer datasets available for the target domain, yet transfer learning can still improve performance. In the above-mentioned COVID-19 CT scan research, a pre-trained model was utilized to construct and change the top layer of the model. Pre-trained models are those that have been trained using data that is comparable to that used by the users of the pretrained models. The capacity to extract features from pre-trained models means they can be used even with a small amount of training data. The starting point for all three

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studies is a pre-trained model. When it comes to M-Inception and COVID-19Net, the models use pre-trained Inception models, DenseNet 121 pre-trained models, and ResNet50 pre-trained models (both of which are used by the COVNet model). In [1], an ensemble of VGG16, inceptionV3, ResNet50, DenseNet121, and DenseNet201 was used to obtain 88.34% accuracy and an F1-score of 86.7%. ResNext+ is a weekly supervised approach developed by Delalleau and Bengio [13] that predicts slice level with an F1-score of 81.4% based on volume level input. Researchers also utilized CT scan images to try to differentiate COVID patients from those who had got community-acquired pneumonia (CAP). For this, Bengio et al. [2] presented the adaptive feature selection-guided deep forest method. CT scans of COVID patients and CAP cases totaled 1495 and 1027, respectively. An adaptive feature selection-driven deep forest classifier with an F1-score of 93.07 was used to achieve a 91.79% accuracy and an F1-score of 93.07, which included information such as volume (infected lesion number) and histogram distribution (surface area) (radiomics).

3 Methodology 3.1 Network Architecture The SqueezeNet architecture is employed in this study to detect COVID-19 using CXR images. CNN is one of the most commonly used deep learning (DL) algorithms for image classification [15]. Five layers are commonly seen in a fully connected network: the output, input, recursive convolution, and a pooling r. As a result, CNN’s real-world support contains fewer parameters, which significantly reduces learning time and training data requirements. A CNN may be trained from start to finish to choose and extract features from an image and then used to forecast or categorize the image. Accordingly, a new network method has been proposed to reduce the number of parameters while preserving accuracy, namely SqueezeNet [16]. In SqueezeNet, the Fire model serves as the foundational model, and their framework is shown in Fig. 1. Expand and Squeeze frameworks make up this model. Using 1 × 1 convolution kernel, Squeeze is deliberations on network structure are becoming more focused on the 1 × 1 convolution layer. The expressiveness of networks was improved by using an MLP instead of the typical linear convolution kernel. The convolution kernel parameters get bigger as the number of output and input channels grows. All inception models feature 1 × 1 Conv, which reduces the number of input channels, as well as the convolution kernel parameter and the operational complexity of the model. This is followed by a 1 × 1 Conv, which increases the number of channels and improves feature extraction [17]. Substituting 3 × 3 convolution with a single convolution reduces the number of parameters by one-ninth. An overdue sampling decrease procedure may necessitate the use of an extremely large activation graph for the convolution layer in order to provide the best classification accuracy.

IoT-Enabled Automated Analysis and Classification of COVID-19 Disease …

Input

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Fig. 1 Proposed work flow of CNN architecture for COVID-19 disease detection

3.2 Dataset Description This virus, COVID-19, is so new that it is hard to get any images or data about it yet. This study is significant for the field of radiology since radiologists use as many X-ray images as possible to detect disease. It has a set of 15,282 images where 13,703 images are used to train the model and the rest of the 1579 images are utilized to test the method. All datasets utilized in this research are freely available online, and the appropriate URLs are provided in the report.

3.3 Performance Metrics To determine how accurate your results are, divide the total number of observations by the number of correctly predicted ones. An important criterion for evaluation of the system is accuracy when features are spread and the mistakenly predicted COVID positive and wrongly predicted COVID negative are about equal. Apply Eq. (1) and ensure that the datasets utilized are symmetrical in order to determine the correctness. Accuracy =

TP + TN TP + FP + TN + FN

(1)

Precision measures the number of accurately predicted COVID-19-positive patients among all COVID-19-positive patients. Low false-positive rates are associated with high precision. Equation is used to figure out the precision (2). Fewer erroneous positive predictions are indicated by a higher precision metric value. This data answers the question of how many patients have really survived. Precision =

TP TP + FP

(2)

The recall sensitivity (also known as sensitivity) is a measure of how many patients in a group were properly predicted to be COVID-19 positive. Using the following Eq. (3), the recall can be calculated. Using this metric, we can see how accurate the algorithm was at predicting which patients will survive. Recall =

TP TP + FN

(3)

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Federated LearningRewsNet50 Sensitivity

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FDL-COVID (Proposed)

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Fig. 2 Comparison study of the performance evaluation of proposed model

The F1-score is calculated by averaging the recall and precision of COVID-19 test results for each to determine the F1-score Eq. (4). When the dataset has an unequal distribution of classes, this statistic becomes even more significant. When FP and FN are equivalent, this statistic is a good way to compare results. F1-score =

2 ∗ (Precision ∗ Recall) Precision + Recall

(4)

4 Results and Discussion The proposed FDL-COVID (federated deep learning) technique has been tested on a freely available COVIDx dataset. COVID-19 chest X-ray dataset, Actualmed COVID-19 chest X-ray dataset, and COVID-19 radiography dataset are all included in this dataset. To train the model, 13,703 of the 15,282 images are used, while the remaining 1579 are used to test the approach. Normal, pneumonia, and COVID-19 images are included in the dataset. First, FL is used to perform a quick result analysis of various model sensitivities in Fig. 2. A look at the table values shows that the MN-v2 model has shown the least outcome results on the training and testing sets, respectively, with sensitivity values of 0.95 and 0.87. As a result, the COVIDNet and Res-NXT approaches have shown a slightly improved performance on the applicable training and testing sets, respectively. Even more impressive is the RN-18 technique’s moderate performance on training and testing sets, which yielded sensitivities of 0.962–2.093. In contrast, the FDL-COVID

IoT-Enabled Automated Analysis and Classification of COVID-19 Disease … Table 1 Performance model evaluation Methods Accuracy Federated leaning-VGG16 Cen.-VGG16 Federated learning-ResNet50 Cen.-ResNet50 FDL-COVID (proposed model)

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Specificity

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approach has a sensitivity of 0.976% and 0.976%, respectively. Comparisons of the FDL-COVID methodology to various categorization methods are presented in Table 1. As a result, the MN-v2 FL method has a lowered sensitivity of 0.734, a specification of 0.925, and an accuracy of 0.912. The COVIDNet FL method has showed marginally improved outcomes with an average sensitivity of 0.696, specificity of 0.929, and accuracy of 0.928. An average sensitivity of 0.813 was obtained using the Res-NXT FL method. This was followed by an average spectrum (0.946) (accuracy) (0.936) (0.813). Because of its high sensitivity, specificity, and accuracy (all above 0.942), the technique known as the RN-18 FL has proven successful in the market. The proposed FDL-COVID approach has been evaluated and reached a maximum performance level of 0.869, 0.974, and 0.970.

5 Conclusion In this study, a new FDL-COVID approach for identifying and classifying COVID-19 in an IoT-enabled MEC environment was proposed. Data acquisition for the proposed FDL-COVID technique is done using an IoT-based data collecting procedure. The SqueezeNet technique is utilized for COVID-19 detection and classification. The cloud server uses the SqueezeNet model, which produces a global cloud model from encrypted data, to conduct FL on significant variables. This is followed by the use of GSO’s hyperparameter optimizer to find the optimal SqueezeNet model hyperparameters for COVID-19 identification. Numerous simulations were run using the benchmark CXR dataset, and the outcomes were evaluated using a wide range of metrics. The IoT-enabled MEC platform can be utilized as a platform for data offloading and resource management in the future.

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References 1. Piccinini G (2004) The first computational theory of mind and brain: a close look at McCulloch and Pitt’s “logical calculus of ideas immanent in nervous activity”. Synthese 141(2):175–215 2. Bengio Y, LeCun Y et al (2007) Scaling learning algorithms towards AI. In: Large-scale kernel machines, vol 34, no 5, pp 1–41 3. Dash S, Chakravarty S, Mohanty SN, Pattanaik CR, Jain S (2021) A deep learning method to forecast COVID-19 outbreak. New Gener Comput 1–25 4. Satpathy S, Mangla M, Sharma N, Deshmukh H, Mohanty S (2021) Predicting mortality rate and associated risks in COVID-19 patients. Spat Inf Res 1–10 5. Wong HYF, Lam HYS, Fong AH-T, Leung ST, Chin TW-Y, Lo CSY, Lui MM-S, Lee JCY, Chiu KW-H, Chung TW-H et al (2020) Frequency and distribution of chest radiographic findings in patients positive for covid-19. Radiology 296(2):E72–E78 6. Brown PD, Lerner SA (1998) Community-acquired pneumonia. The Lancet 352(9136):1295– 1302 7. Vávra P, Roman J, Zonˇca P, Ihnát P, Nˇemec M, Kumar J, Habib N, El-Gendi A (2017) Recent development of augmented reality in surgery: a review. J Healthc Eng 2017 8. Liu B, Yan B, Zhou Y, Yang Y, Zhang Y (2020) Experiments of federated learning for covid-19 chest x-ray images. arXiv preprint arXiv:2007.05592 9. Park J, Samarakoon S, Elgabli A, Kim J, Bennis M, Kim SL, Debbah M (2020) Communicationefficient and distributed learning over wireless networks: Principles and applications. arXiv preprint arXiv:2008.02608 10. Dou Q, So TY, Jiang M, Liu Q, Vardhanabhuti V, Kaissis G, Li Z et al (2021) Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ Digital Med 4(1):1–11 11. Abdul Salam M, Taha S, Ramadan M (2021) COVID-19 detection using federated machine learning. PLoS One 16(6):e0252573 12. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q et al (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 13. Delalleau O, Bengio Y (2011) Shallow vs. deep sum-product networks. In: Advances in neural information processing systems, vol 24, pp 666–674 14. Montúfar GF (2014) Universal approximation depth and errors of narrow belief networks with discrete units. Neural Comput 26(7):1386–1407 15. Laxmi Lydia E, Anupama C, Beno A, Elhoseny M, Alshehri MD, Selim MM (2021) Cognitive computing-based COVID-19 detection on internet of things-enabled edge computing environment. Soft Comput 1–12 16. Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131 17. Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635– 640

Secure 6G Communication in Smart City Using Blockchain Saikat Samanta , Achyuth Sarkar , and Yaka Bulo

Abstract The next-generation cellular network will aim to overcome the existing Fifth Generation (5G) networks’ shortcomings. At the moment, academics and business are concentrating their efforts on the Sixth Generation (6G) network. This 6G technology is expected to be the next great game-changer in the telecommunications sector. Due to the outbreak of COVID-19, the entire globe has turned to virtual meetings and live video interactions in various fields as healthcare, business, and education. We explore the most recent viewpoints and future technology trends that are most likely to drive 6G in this paper. The incorporation of blockchain in 6G, will allow the network to efficiently monitor and manage resource consumption and sharing. We explore the potential of blockchain for sharing in 6G utilizing a variety of application scenarios in the smart city. To strengthen security and privacy in 6G networks, we introduce potential difficulties and solutions with various 6G technologies. In addition, we examine the security and privacy issues that may arise as a result of the current 6G standards and prospective 6G uses. Overall, our study aims to give insightful direction for future 6G security and privacy research. Keywords Big data · Cyber security · Edge computing · Industrial IoT · Network security

S. Samanta (B) · A. Sarkar Department of Computer Science and Engineering, National Institute of Technology, Arunachal Pradesh, Jote, India e-mail: [email protected] A. Sarkar e-mail: [email protected] Y. Bulo Department of Electronics and Communication Engineering, National Institute of Technology, Arunachal Pradesh, Jote, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_48

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1 Introduction The 5G wireless communication standardization process is complete, and implementation has begun in several nations throughout the world. A variety of technologies can be developed to play important roles after the 6G research phase to achieve complete specifications for a 6G worldwide standard [1]. Major research efforts are now focused on quantum technologies, which will interact with 6G implementation. The architectural and performance components of the 6G mobile system are still mostly unknown. One of the goals of this research is to develop a new comprehensive, effective and coherent vision for a 6G-enabled Industrial Internet of Thing (IIoT) system. The 6G IIoT system can be formed by the convergence of Information Communication Technology (ICT), Operational Technology (OT), and future 6G communications. The fact is that the 5G communication system is not yet complete but experts have already anticipated 6G mobile communication [2]. Blockchain is considered to be a crucial technology for 6G applications. Blockchain is a distributed ledger system that uses cryptography and hash functions to construct a chain of data blocks that are created when an event happens and validated in a decentralized manner using consensus methods. Blockchain is currently being utilized in various application domains in smart cities, while it was previously exclusively used for cryptocurrency. In blockchain systems, the consensus is a critical feature that guarantees that all nodes agree on the network state. 6G can be utilized with communication-intensive techniques like Practical Byzantine Fault Tolerance (PBFT) if the system has to converge quickly. Our study focuses on the security of communication in smart city using integrated technology of 6G blockchain. The remaining paper is divided into parts. Section 2 explains the literature review, while Sect. 3 presents 6G applications in the smart city. Section 4 discusses the 6G challenges and Sect. 5 presents discussions and future studies. Section 6 concludes our work.

2 Literature Review There is so much considerable research that has been conducted on the security and privacy solution of 6G technology in recent years. We summarize some recent literature analyses and show how our approach differs from previous research. Alsharif et al. published a paper on the 6G wireless network in 2020. The authors of this paper focused on the most promising areas of research in common directions for the 6G project [3]. The authors of [4] highlight technologies that will help wireless networks advance to 6G, and which we see as enablers for a variety of 6G use cases. Another approach for emphasizing the major difficulties and potential in developing

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Holographic Multiple Input Multiple Output Surface (HMIMOS) enabled communications is discussed in [5]. In [6], the authors described the future 6G wireless communication vision and network design. In addition, the authors in [7] provided a system for delivering Distributed AI as a Service (DAIaaS) in the Internet of Everything (IoE) and 6G settings. The author of the paper [8] incorporates a comprehensive literature analysis of 6G, IoT, IoE, and IIoT to advance understanding and enable theory building. Similarly, the researchers of [9] proposed a new set of service classes, as well as corresponding 6G performance targets. The ideas mentioned above present a broad vision of the future for research and innovation in these fields. We propose a blockchain integrated 6G architecture with the best resource management and data sharing via smart contracts to manage data access in smart city networks.

3 Application Area of 6G in Smart City Most recent technologies and applications are being introduced in 6G with greater data speeds, high dependability, less latency, and secure transmission. The following sections will concentrate on a few of the most important 6G application fields recognized by the literature as shown in Fig. 1. At the same time, the security requirements for these application domains are strict. Here is a brief overview of the domain-specific security standards.

3.1 Automation Automation, robotics, and autonomous systems are some of the topics studied by researchers. These technologies will be supported by 6G, which will allow direct communication between them. 6G will enable complete automation, including automated control processes, automated systems, and automated devices. Smart city planners can use blockchain to help them build an efficient transit network that allows people to check for and pay for services directly. The efficiency of public transit can be improved with smart mobility systems built on the blockchain.

3.2 Industrial Applications In recent years, the integration of Information and Communication Technology (ICT), IoT, and intelligent devices has revolutionized manufacturing and production systems Industrial IoT has changed the design of production units, transforming them from automated to autonomous [10]. 6G is a technique that is at the top of the

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Fig. 1 6G applications and security requirements in smart cities

IIoT. The leaking of sensitive production information and the denial of access to a manufacturing device are two typical security attacks used in smart manufacturing systems. These security risks aim to interrupt either the manufacturing process or the production plan.

3.3 Wireless Power Transfer Wireless Information and Energy Transport (WIET) is a revolutionary technology that will enable the creation of battery-free smart gadgets [11]. Traditional energy structures are centralized, fuel or coal-based. The wireless power transfer is a network of electricity generators, producers, and customers that is created by communication systems [12]. The primary goal of this system should be to ensure low-cost, and efficient energy transfer. WIET protection standards derive from domain-specific issues, including ensuring the integrity of data exchanged between power operators and customers.

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3.4 Wireless Brain-Computer Interface Wearable gadgets have become more popular in recent years with brain-computer interface (BCI) applications. The brain will be able to connect using BCI technology, which processes and translate brain signals. Computing technologies will also be used in BCI, in which gadgets will work differently based on the user’s mood [13]. BCI application needs additional spectrum resources, a high data rate, extremely low latency, and excellent dependability. Five sense information transmissions will be supported by 6G, which will transport data created by the human’s five senses, allowing interaction with the environment [14].

3.5 Healthcare Health services have begun to adopt innovations to make healthcare more efficient and accurate. Such smart healthcare systems must deal with computational and security issues. Blockchain technology has several applications in smart healthcare. In this regard, blockchain will solve data protection, data confidentiality, accountability problems. 6G will enable the complete existence of remote surgeries through robotics, automation, and Artificial Intelligence (AI) [15]. Tera Hertz (THz) band’s short wavelength facilitates communication and the creation of nano sensors, enabling the development of novel nanosized devices that can function inside the human body [16].

3.6 Underwater Communication The underwater environment is unpredictable and difficult. Due to the significant attenuation of radio signals in saltwater, auditory communication is the only alternative for communication. The velocity and density variations in the water make node movement problematic [17]. Underwater sensors are costly, and they are intended to resist the harsh conditions of the ocean. It needs powerful transceivers and a huge memory. Because solar power cannot be used, the power source must be big. Optical fiber is the greatest option, but it is also the most expensive. 6G must overcome the obstacles of underwater conditions to provide effective underwater communication.

4 6G Communication Challenges in Smart City The deployment of 5G technology has just recently begun and there is no user experience on 5G. The 5G should experience many things in a real-world scenario rather

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Fig. 2 6G communication challenges in smart cities

than in a laboratory [18]. Some challenging criteria in 6G wireless communication must be met to global technological demands shown in Fig. 2. The main challenging problems are analyzed and discussed in this part.

4.1 Lack of Technology The 6G claims to deliver many promises, however, it is hampered by a lack of genuine technologies. The needs grow dramatically from 5 to 6G mobile communications. People have little experience with 5G technology, therefore a lack of 5G user experiences is a major concern. To accomplish 6G, significant changes to 5G technology are necessary. To progress 6G, AI must be integrated into 5G technology.

4.2 Over Expectation from 5G Before creating 6G technology, problems with 5G must be resolved. Furthermore, 5G is primarily a campus-based solution that does not enable high mobility. Satellite

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communication can help with high mobility, but it is highly expensive and beyond the reach of many people. Although there is no defined path for providing low-cost access to solve the problem.

4.3 THz Band The THz band is the major issue in the 6G technology. The high data speeds and high frequencies will overcome the high path loss. To solve the problem of frequency dispersion, new multipath channel models must be created. Existing modulation and coding techniques are insufficient for the THz band [19]. Thus, developing new modulation and coding techniques is a difficult task. Furthermore, the researchers face significant health and safety problems as a result of the high power and frequency.

4.4 Device Capabilities All wireless communication methods were not compatible with all devices. Industries have just begun developing 5G equipment, which should be able to handle 6G as well. Smartphones are consuming far more energy than in the past. Wireless energy transfer methods should be designed to enable multiple charging ways.

4.5 Network Security Smart devices, AI, smart cities, and satellites will be connected via the 6G wireless communications network [20]. The security approaches applied in 5G will not sufficient in 6G. Thus, new security strategies based on creative cryptographic methods should be explored.

5 Discussion and Future Direction This section gives an overview of issues, precautions, and potential research directions of blockchain and 6G integrated smart city applications as shown in Table 1. The path to 6G is undoubtedly long, and the existing 5G will continue to improve. 6G will be a revolution rather than an evolution. Blockchain may be used to implement network security, surveillance, accountability, and governance. 6G networks must address the security concerns raised by innovative 6G applications. Blockchain systems can provide the highest level of security.

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Table 1 Issue and precaution with research directions Issue

Precaution

Potential research directions

Accessibility

The elasticity of the network Non-terrestrial networks

Resource management that is both flexible and automated Placement in 3-D space is difficult because there is a limited amount of energy available

Affordability

Convergence of multiple radio access technologies Everything is provided as a public service

Over diverse radio frequency and optical bands, smooth resource allocation Concerns about interoperability and cost-effectiveness

Spectrum

Spectrum cognition on a large scale Operators in the microscale

Instead of complicated data, only local information is shared New local entrants are encouraged by innovative business and regulatory frameworks

Maintenance

Maintenance that is predictive or preventative Automatic fallback

Mechanisms for real-time computation offloading are required Keeping services up

Power

Sharing of networks Automated energy management

In multi-tenant networks, full competition, autonomous control, and security services are all important Capabilities for component-centric energy metering combined with pervasive intelligence

With various combinations of computer science and telecommunication research, it will be fascinating to look into the commercial implications of the many options available when adopting IoE. Furthermore, it is still unclear how edge, fog, and cloud technologies should be distributed and used in diverse areas. Intelligent Transportation Systems (ITS) will likely be one of the most important applications to emerge in the next decade.

6 Conclusions We explored the possibilities of blockchain and 6G for securing smart city communication. To make the relationship clearer, we have split 6G application needs into performance and security categories. We have demonstrated how blockchain’s fully decentralized nature makes it easier to manage 6G networks with complicated structures. We listed the active 6G research projects that are mostly related to security and privacy. Furthermore, security-related concerns of 6G applications might be easily handled by selecting the right blockchain type and consensus methods. The combination of blockchain with 6G may enable safe and pervasive communication.

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References 1. Giordani M, Polese M, Mezzavilla M, Rangan S, Zorzi M (2020) Toward 6G networks: use cases and technologies. IEEE Commun Mag 58:55–61. https://doi.org/10.1109/MCOM.001. 1900411 2. Shahraki A, Abbasi M, Jalil Piran M, Taherkordi A (2021) A comprehensive survey on 6G networks: applications, core services, enabling technologies, and future challenges. IEEE Trans Netw Serv Manag XX:1 3. Alsharif MH, Kelechi AH, Albreem MA, Chaudhry SA, Zia MS, Kim S (2020) Sixth generation (6G) wireless networks: vision, research activities, challenges and potential solutions. Symmetry 12:676. https://doi.org/10.3390/SYM12040676 4. Giordani M, Polese M, Mezzavilla M, Rangan S, Zorzi M (2019) Towards 6G networks: use cases and technologies. IEEE Commun Mag 58:55–61 5. Huang C, Hu S, Alexandropoulos GC, Zappone A, Yuen C, Zhang R, Di Renzo M, Debbah M (2019) Holographic MIMO surfaces for 6G wireless networks: opportunities, challenges, and trends. IEEE Wirel Commun 27:118–125 6. Chowdhury MZ, Shahjalal M, Ahmed S, Jang YM (2019) 6G wireless communication systems: applications, requirements, technologies, challenges, and research directions. IEEE Open J Commun Soc 1:957–975 7. Janbi N, Katib I, Albeshri A, Mehmood R (2020) Distributed artificial intelligence-as-a-service (DAIaaS) for smarter IoE and 6G environments. Sensors 20:5796. https://doi.org/10.3390/S20 205796 8. Padhi PK, Charrua-Santos F (2021) 6G enabled industrial internet of everything: towards a theoretical framework. Appl Syst Innov 4:11. https://doi.org/10.3390/ASI4010011 9. Saad W, Bennis M, Chen M (2019) A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Netw 34:134–142 10. Dwivedi SK, Amin R, Vollala S, Chaudhry R (2020) Blockchain-based secured eventinformation sharing protocol in internet of vehicles for smart cities. Comput Electr Eng 86:106719. https://doi.org/10.1016/J.COMPELECENG.2020.106719 11. Wongthongtham P, Marrable D, Abu-Salih B, Liu X, Morrison G (2021) Blockchain-enabled peer-to-peer energy trading. Comput Electr Eng 94:107299. https://doi.org/10.1016/J.COM PELECENG.2021.107299 12. Ferrag MA, Derdour M, Mukherjee M, Derhab A, Maglaras L, Janicke H (2019) Blockchain technologies for the internet of things: research issues and challenges. IEEE Internet Things J 6:2188–2204. https://doi.org/10.1109/JIOT.2018.2882794 13. Fernández-Caramés TM, Fraga-Lamas P (2018) Towards the internet-of-smart-clothing: a review on IoT wearables and garments for creating intelligent connected E-textiles. Electronics. https://doi.org/10.3390/electronics7120405 14. Xie J, Tang H, Huang T, Yu FR, Xie R, Liu J, Liu Y (2019) A survey of blockchain technology applied to smart cities: research issues and challenges. IEEE Commun Surv Tutor 21:2794– 2830. https://doi.org/10.1109/COMST.2019.2899617 15. Saad W, Bennis M, Chen M (2020) A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Netw 34:134–142. https://doi.org/10.1109/ MNET.001.1900287 16. Nawaz F, Ibrahim J, Junaid M, Kousar S, Parveen T, Ali MA (2020) A review of vision and challenges of 6G technology. Int J Adv Comput Sci Appl 643–649. https://doi.org/10.14569/ IJACSA.2020.0110281 17. Ogbebor JO, Imoize AL, Atayero AAA (2020) Energy efficient design techniques in nextgeneration wireless communication networks: emerging trends and future directions. Wirel Commun Mob Comput 2020. https://doi.org/10.1155/2020/7235362

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Social Engineering Attacks Yuvraj Singh Saini, Lucky Sharma, Pronika Chawla, and Sanidhya Parashar

Abstract We live in an Internet-based world, and our personal and professional lives are more intertwined. Systems of information technology (OS). These mechanisms are frequently subjected to public scrutiny, to avoid being hacked or infected with a virus, we are all on the lookout for the greatest antivirus and anti-spyware software. Install the programme, yet the malware still exists. Our system has been hacked into. The most influential people Social Engineering is a type of system attack. These attacks aim to tricking individuals or businesses to perform acts that benefit the attackers or to provide them with sensitive information such as social security numbers, medical records, and passwords. Because it takes advantage of the inherent human desire to trust, social engineering is one of the most difficult problems in network security. This paper provides a comprehensive overview of social engineering attacks, including classifications, detection and prevention procedures. Keywords Attacks · Security · Social engineering · Cyber security · Phishing · Privacy · Knowledge worker

1 Introduction We spend the majority of our time in our daily lives staring inward or on our phones (computers). As a result, we share information and data with persons we may or may not know. Because of their quick expansion, several social networks, Facebook and Twitter, for example, have grown to be the most essential and biggest sources of information, data exchange, and Internet services. In addition to data exchange, social networks provide comprehensive support for making new acquaintances [1]. As a result, we now have access to a new source of information. Because of the Y. S. Saini (B) · L. Sharma · P. Chawla · S. Parashar Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India e-mail: [email protected] P. Chawla e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_49

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massive amount of information, they contain and the high number of people that utilise them, most social networking sites are clearly crucial in terms of user security and privacy [2]. Companies need their workers to work utilising their possess gadgets as well as be greatly portable and adaptable in terms of their workspace and there’s a developing slant towards anticipating workers and information specialists to utilise their possess gadgets for work, both within the office and somewhere else [3]. Because of the increased flexibility and, on the other hand, as face-to-face contact and shared office space become less common, more data must be made available to others via online means. Because of this massive shift in communication and information sharing, systems have become more vulnerable to hacking, particularly via social engineering assaults. In order to be successful, social engineering does not always necessitate a high level of technical expertise. Instead, curiosity, civility, gullibility; avarice, thoughtlessness, shyness, and apathy are used through social engineering [4, 5]. Friendship requests and communications from other users are more likely to be believed by members of online social networks, according to research. As a result, the following is a list of the deadliest social engineering attacks: 1. 2. 3. 4.

The victim and the attacker have developed a high level of trust. And the victim may not be aware that he was breaking into their home and stealing. The ease with which social engineering assaults may be carried out because they do not necessitate a high level of technical competence. No technology or software exists that can prevent or defend yourself against social engineering assaults. Most significant firms and news organisations, such as Google, Facebook, and the New York Times have been victims of cyber-attacks on their information systems [6–8].

2 Literature Review 1.

2.

3.

Social Engineering Attacks—This paper provides a complete overview of social engineering assaults. Because social engineering attacks are based on psychology, no technology or software can prevent or fight against them. As a result, individuals must be educated to defend against them. Advanced Social Engineering Attacks—This study offers a classification of well-known social engineering attacks as well as a detailed examination of advanced social engineering attacks against information workers. In virtual communities, social engineering has arisen as a major danger that may be used to assault information systems. The Social Engineering Personality Framework—The paper provides a complete overview Suggestions for possible relationships between the FiveFactor Model personality characteristics and the principles of persuasion. We will assess the “Social Engineering Personality Framework” (SEPF) in future empirical study.

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Social Engineering Attacks: A Survey—This paper provides a comprehensive overview of social engineering assaults, including classifications, detection methodologies, and preventative methods. Malicious activities using human interaction may persuade a person to expose confidential information or break security rules. A Multi-Level Defence Against Social Engineering—This paper is expected to benefit the security community in three ways: by joining current social psychological inquire about into talks approximately understanding and standing up to social building, by utilising mental writing to create a multilevel cautious procedure for solidifying workers to social designing dangers, and by creating the concept of “social building arrive mines” as portion of the multi-level resistance against social building.

3 Social Engineering Taxonomy 3.1 Phases of a Social Engineering Attack Whereas all social building attacks are diverse and special, there are numerous classic attack strategies that can be used to achieve the intended results of the attack. These patterns are split into four steps (Fig. 1) (information gathering, relationship development, exploitation, and implementation) or in the words of some authors, they are organised into four stages (research, hook, play and out) [9, 10]. During the information gathering or research phase, the attacker uses a range of tools and tactics to discover more about the target, such as dumpster diving, target websites, public documents, and personal contact. When attempting to target a certain user, thorough research is required.

Fig. 1 An overview of the stages of a social engineering attack

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Hook or Relationship Development: During this stage, the attacker tries to establish or maintain a relationship with the person by starting a conversation or using another approach to discover a variety of methods to thrill the victim. Play or Exploitation: The major goals of this stage are to deepen the relationships by continuing the discussion in order to gather the info needed to finish the plan and design the programme or create new Spyware. Everything has now been completed. Execution is the final phase. Exit or Execution: That’s the last stage of a social engineering attack, where the attacker carries out an attack and terminates communication with the target without leaving any evidence that the victim is aware of what has occurred.

3.2 Social Engineering Categories In general, social engineering may be split into two types based on how it is carried out: computer-based and human-based. Human-Based Social Engineering: In this type of social engineering attack, a human conducts the social engineering attack directly. To put it another way, the attacker communicates directly with the victim in order to obtain information. Because of the inferior capacity of a human-based social engineering attack compared to a softwarebased attack, the number of targets is limited.

3.3 Types of Social Engineering Social engineering can be categorised into two types based on their objectives. The method that can be used to do it, which is human-based as well as being computerbased.

3.3.1

Social Engineering Based on Humans

The social engineering attack is carried out directly by a person in this type of social engineering attack. To put it another way, the attacker communicates directly with the victim in order to get information. Because of the inferior capability of a humanbased social engineering assault compared to a software-based attack, the number of targets is limited.

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Social Engineering Based on Software

Software-based social engineering refers to assaults that use system software (such as a computer or a mobile phone) to obtain the necessary information. The Social Engineering Toolkit (SET) is one example, which may be used to create spearphishing e-mails [11].

3.4 Approaches to Social Engineering Social engineering attacks are diverse, and attackers have utilised it in a variety of ways, including:

3.4.1

Physical Approaches

Physical approaches are actions taken by an attacker throughout order to obtain information well about victim. Searching through rubbish (dumpster diving) is a popular way. For attackers, a dumpster could be a useful source of various types of information [12].

3.4.2

Social Approaches

The most significant feature of successful attacks is use of social techniques. Assailants commonly utilise socio-psychological techniques [3] like persuasive ways to influence their victims in order to increase the probability of such attacks succeeding (e.g. the use of ostensible authority). Or, as in spear-phishing and luring attacks, employ the most popular social vector, curiosity. Phone-based social attacks are the most common.

3.4.3

Technical Approaches

Technical approaches have been primarily carried out across the Internet, in which social networking sites have evolved into valuable information sources. To collect personal info about victims, hackers frequently use search engines. There are additional tools that can collect and aggregate data from various Web resources, such as Maltego1, which has become one of the most prominent in this field. Users frequently use the same (basic) passwords for different accounts, making the Internet a particularly attractive target for social engineers looking to harvest credentials [11].

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Socio-Technical Approaches

Successful social engineering attacks frequently incorporate some or all of the above-mentioned tactics. Social engineers’ most powerful tools, however, are sociotechnical approaches. One example of a technological and social strategy is when attackers take advantage of people’s curiosity by leaving malware-infected storage media, such as a Trojan horse-infected USB device in a public place where subsequent victims are likely to find it [13, 14].

4 Social Engineering Skills This section delves into some of the most widely utilised social engineering techniques are (Fig. 2): Dumpster diving entails digging through trash to find information. Sensitive information (such as a future victim’s name) passwords, filenames, and other private information which can be used to bring down the system or a user account with a specified name. This is a skill that can be useful in a variety of circumstances. Humans and software both participate in the process. Phishing is just the act of impersonating a trustable entity through an electronic communication channel in order to get sensitive information or cause someone to act in a specific way. They are mainly aimed for large crowds. Those who claim to be from the lottery department and tell you that you have won a million dollars, for example. They want you to provide information such as your initial name, address, age, and city by clicking on a link in the e-mail. A social engineer can collect social security numbers and network information using this strategy. Phishing attacks can be carried out through nearly any channel (see the next section for more information on channels). Spear-phishing refers to attacks that are directed at specific

Fig. 2 Social engineering skills

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Fig. 3 Reverse social engineering attack types

people or businesses. Spear-phishing necessitates the attacker gathering information on the intended victims prior, although it has a higher success rate than traditional phishing. The term “whaling” refers to phishing attacks that target high-profile targets in businesses. Reverse Social Engineering: This sort of attack is a stand-alone technique that is extremely efficient; the attackers does not directly contact the victim, but instead convinces the victim who is calling for her that the attacker is a trustworthy entity. For example, some users may be suspicious if the attacker calls them on the phone and asks for their credentials. By impersonating a system administrator’s e-mail, in a reverse social engineering form of the same assault, a phone number can be e-mailed to the targets a few days ahead of time. In the event of a problem. This phone number may be included in the e-mail. Reverse social engineering assaults are particularly appealing for online social networks because they have the ability to reach a large number of registered members whilst also bypassing current behavioural and filter-based detection mechanisms aimed at preventing widespread uninvited contact. If a victim makes contact the attacker, suspicion is reduced, and a social engineering attack is more likely to succeed [15, 16]. Social engineering in reverse RSE is divided into four kinds based on the sort of attack (Fig. 3).

4.1 Direct Attack The attacker’s action is apparent to the targeted users in a direct attack. An attacker could, for example, send a message or upload a photo to a website.

4.2 Mediated Attack The baiting is collected by an intermediate agent, who is then responsible for distributing it (typically in a different form) to the intended consumers.

504 Table 1 Reverse social engineering assaults are classified based on the setting of online social networks

Y. S. Saini et al. RSE Direct Mediated Targeted Untargeted

RB-RSE

VTB-RSE √

√ √

DB-RSE √

√ √

4.3 Targeted Attack The attacker refers to a single consumer in a targeted attack. However, in order to carry out this type of assault, the attacker must have prior knowledge of the target (such as the victim’s login or e-mail address).

4.4 Untargeted Attack An untargeted attack is one in which the attacker is only concerned with contacting as many people as possible (Table 1). RSE assaults can be classified into three types based on the environment of online social networks. RB-RSE Recommendation [Targeted, Mediated]—Based User associations are suggested by recommendation algorithms in social networks based on secondary information about members derived from registered users’ interactions with their friend relations, as well as backgrounds and other artefacts obtained from the users’ social network activities. For example, in order to generate friendship suggestions, a social networking website may attempt to automatically figure out where the users know each other or track when a user visits a specific profile. A recommendation system is an intriguing goal. The attacker has a decent probability of getting victims to contact him if he can control the recommendation system and persuade the social network to make targeted suggestions. Figure 4a shows an RSE assault scenario based on a recommendation system. Demographic-Based DB-RSE [Untargeted, Mediated]—Friendships can be formed depending on demographics in social networks. A person’s profile information a social situation. This approach is used by networks as a standard. Users in the same geographical place are linked together. Persons of the same generation, or those who have expressed an interest preferences that are comparable A RSE is shown in Fig. 4b, a cyber-attack based on demographic data during the attacker simply constructs a profile (or a set of profiles) and uses it to launch an attack. A large number of profiles with a high chance of success. It makes an effort to appeal to specific people, and then it waits for them to respond to make contact with their victims.

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Fig. 4 Reverse social engineering can take a variety of forms

VTB-RSE [Targeted, Direct] Visitor Tracking—Based some social networks allow members to track who has visited their online accounts using a function called visitor tracking. By accessing the user’s profile page, the attacker is able to take advantage of the user’s interest. The fact that the page has been visited may pique the user’s attention, causing them to look into the attacker’s background and maybe taking action [1]. This attack is illustrated in Fig. 4c. Shoulder surfing is when you gather information by peeking over your shoulder [3]. A person’s shoulder at a computer or keyboard. Baiting is a large-scale attack that is carried out by using a variety of techniques. Advertisements and webpages on the Internet included in this are some websites that allow users to download files, or a message claiming to have found a problem with when a victim clicks on the pop-up, the victim’s machine will be infected solve. A user clicks on the bait’s links to learn more. Malware could be downloaded by the system on its own. Because they require some technical understanding, Watering holes attacks are frequently more sophisticated than most other forms of social engineering. To infect, use reputable websites, similar to baiting. Where the hackers gain access to the victim’s computer waiting for a victim on the internet. Advanced Persistent Threat refers to long-term, primarily Internetbased espionage assaults carried out by an attacker with the capacity and motivation to permanently breach a system [3].

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Fig. 5 Social engineering and attack channels are classified

5 Social Engineering Channels The following channels can be used to carry out attacks: The most popular Instant Messaging Apps (IMA) are phishing and reverse social engineering are both common channels for phishing. It is also a good tool for social engineering attacks. To take advantage of a trusted relationship, identity theft is used. Amongst social engineers, e-mail is gaining traction, as a phishing and social engineering reverse tool attacks (Fig. 5). Because of their capacity to create fictitious identities and make it simple for attackers to conceal their identity and capture critical information, social networks provide a variety of options for social engineering assaults. The attacker uses physical means to obtain information about the target (dumpster diving). Cloud services may be used by attackers to place a file or piece of software in a shared directory and force the victim to reveal information. Attack vectors such as telephones and Voice over IP are used to dupe victims into providing attackers critical information. Watering hole and baiting assaults are frequently carried out through websites. It can also be used to launch phishing attacks using e-mails. Many types of social engineering assaults, Dumpster diving, phishing, reverse social engineering, shoulder surfing, and baiting are just a few examples, rely exclusively on a physical attack channel. Physical security must be enhanced in order to defend against this type of attack.

6 Social Engineering Attacks at Mobile Applications The increased use of mobile applications in both a corporate and personal environment has resulted in the Mobile applications are becoming an increasingly popular

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avenue for social engineering assaults, with social engineers particularly interested in mobile messaging and e-mail services. Business employees typically use personal devices, such as PCs, smartphones, and tablets, or a combination of these. And it is been a long-standing business policy. As a result, more employees are using their cell phones to accomplish their tasks, check company e-mails, and exchange documents in the cloud. Many smartphone apps, on the other hand, can be used to undertake social engineering attack. Because many smartphone apps are highly susceptible and might leak critical information, we can deduct from this information that such mobile for social media attacks, smartphones provide a wide range of attack channels. Threats on user privacy, such as social engineering. In addition, when we set up a smartphone, it asks for permission to access private information in its applications. The device’s information if an assailant were to construct such a device, they would get the data from the app and put it into a spreadsheet. It might be used as a jumping off point for a social engineering project attack. For mobile app attacks, two separate attack scenarios might be used as a starting point. The other talked about how inter-app information sharing on smartphones can be sniffed and then utilised to break programme controls and permissions. In certain circumstances, the attacker merely plagiarises and deploys a popular smartphone application to carry out the attack [17–19].

7 Detecting/Stopping Social Engineering Attacks As previously said, there is no one-size-fits-all approach to implementing social engineering assaults; however, we may infer that the most basic strategy to defend oneself is to use common sense. It may be an attack if something appears suspicious. The following are some of the most prevalent signs of a social engineering attack: • Be wary of anyone who instils in you a strong sense of urgency in order to get you to make a hasty decision. • Someone who is requesting information that they should not have or already know. • Something that is not quite right. For example, if you receive notification that you have won the lotto despite never having entered it. In general, there are various actions that must be properly followed in order to prevent Social Engineering attacks. • If you feel someone is attempting to use social engineering to make you a victim, stop communicating with them. • If you receive a phone call from someone you do not recognise, hang up. • If he’s chatting with you on the internet and you do not know him, stop that. • If you are unsure about an e-mail, delete it. • If the attack is work-related, notify your help desk or information security staff as soon as possible away.

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8 Preventing Future Social Engineering Attacks You can protect yourself from future social engineering attempts by taking the following precautions: Make secure passwords for your accounts and do not share them with anyone else. You will never be contacted by an organisation asking for your password. It is an attack if someone does this. Do not be too open with your information. Every time you share something with others, you raise your risk of being attacked and give the attacker more information about you. Even minor details about yourself shared over time might be used to form a whole image of you. As a result, attackers will have an easier time tracking you down and convincing you to do exactly what they want. Contacts must be verified. Some companies, such as your bank, credit card company, mobile service provider, or others, may contact you for legitimate purposes, such as to obtain information or to resolve an issue. If you have any doubts about whether a request for information is legitimate, do not provide any sensitive information and instead consider going to an organisation and requesting the person’s name and extension number. Whilst it may seem inconvenient, taking this extra step to protect your identity and personal information is definitely valuable it.

9 Conclusions We provided a comprehensive summary of social engineering attacks in this study. To help with this, we built a comprehensive taxonomy of assaults, categorising them into attacking phases and variety of social engineering attacks, and illustrating that attackers employ a variety of channels to conduct social engineering attacks. They are mostly carried out by humans, but also by software and a variety of methodologies such as physical, technological, social, or socio-technology. Individual assault boundaries are relatively expandable and have not been technically exhausted in most instances, as is a complete understanding of social engineering abilities and social engineering attacks on mobile applications. Varieties of methods are used in social engineering attacks. Methodologies that are social and technical as a result of this, Stopping and effectively defending against socio-technical attacks requires user awareness of the threat. It is necessary to improve engineering attacks and their effectiveness.

References 1. Irani D, Balduzzi M, Balzarotti D, Kirda E, Pu C (2011) Reverse social engineering attacks in online social networks. In: International conference on detection of intrusions and malware, and vulnerability assessment, July 2011. Springer, Berlin, Heidelberg, pp 55–74

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2. Krombholz K, Hobel H, Huber M, Weippl E (2013) Social engineering attacks on the knowledge worker. In: Proceedings of the 6th international conference on security of information and networks, Nov 2013, pp 28–35 3. Krombholz K, Hobel H, Huber M, Weippl E (2015) Advanced social engineering attacks. J Inf Secur Appl 22:113–122 4. Koyun A, Al Janabi E (2017) Social engineering attacks. J Multidiscip Eng Sci Technol (JMEST) 4(6):7533–7538 5. Surendra R, Reddy BS, Chawla P, Soni K (2021) Cyber security in digital sector. In: 2021 international conference on artificial intelligence and smart systems (ICAIS), Mar 2021. IEEE, pp 703–710 6. Zetter K (2010) Google hack attack was ultra sophisticated, new details show 7. Schwartz M (2013) Microsoft hacked: joins Apple, Facebook, Twitter. Information Week. Retrieved from http://www.Informationweek.com/security/Attackacks/microsoft-hac ked-joins-apple-facebook-tw/240149323 8. Perlroth N (2013) Chinese hackers infiltrate New York Times computers. The New York Times 9. Hasan M, Prajapati N, Vohara S (2010) Case study on social engineering techniques for persuasion. arXiv preprint arXiv:1006.3848 10. Heartfield R, Loukas G (2015) A taxonomy of attacks and a survey of defence mechanisms for semantic social engineering attacks. ACM Comput Surv (CSUR) 48(3):1–39 11. Ivaturi K, Janczewski L (2011) A taxonomy for social engineering attacks. In: International conference on information resources management, June 2011. Centre for Information Technology, Organizations, and People, pp 1–12 12. Salahdine F, Kaabouch N (2019) Social engineering attacks: a survey. Future Internet 11(4):89 13. Tyagi SS (2021) Secure data storage in cloud using encryption algorithm. In: 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV), Feb 2021. IEEE, pp 136–141 14. Mehta S, Sharma A, Chawla P, Soni K (2021) The urgency of cyber security in secure networks. In: 2021 5th international conference on intelligent computing and control systems (ICICCS), May 2021. IEEE, pp 315–322 15. Mutreja M, Khandelwal K, Dham H, Chawla P (2021) Perception of IOT: application and challenges. In: 2021 6th international conference on communication and electronics systems (ICCES), July 2021. IEEE, pp 597–603 16. Gragg D (2003) A multi-level defense against social engineering. SANS Read Room 13:15 17. Rahman AM, Al Mamun A, Islam A (2017) Programming challenges of chatbot: current and future prospective. In: 2017 IEEE region 10 humanitarian technology conference (R10-HTC), Dec 2017. IEEE, pp 75–78 18. Pranav DS, Punj D, Dubey T, Chawla P (2021) Data mining in cloud computing. In: 2021 5th international conference on computing methodologies and communication (ICCMC). IEEE, pp 71–78 19. Bamba I, Singh J, Chawla P, Soni K (2021) Big social data and modelling frameworks. In: 2021 international conference on artificial intelligence and smart systems (ICAIS), Mar 2021. IEEE, pp 724–727

On-Device Emotional Intelligent IoT-Based Framework for Mental Health Disorders Godson D’silva, Jagprit Batra, Amit Bhoir, and Akanksha Sharma

Abstract There has been a massive surge in mental health cases around the world post COVID-19 pandemic, which can be defined as an urgent and dangerous world health threat. According to pre-COVID-19 estimates, depression and anxiety alone have cost the economy over US $1 trillion each year. Every dollar spent on evidencebased care for depression and anxiety results in a return of $5 as per the research conducted by WHO (Arnrich et al. in Methods Inf Med 49:67–73, 2010 [1]). Moreover, when a person is sick, they visit the doctor to perform some tests to determine a medical diagnosis. But when it comes to mental health, there is no test to determine the diagnoses, which makes mental health diagnosis challenging. The paper proposes on-device IoT framework to aid in the identification, assessment, treatment, and potentially alleviation of symptoms associated with mental health disorders. To determine and investigate the effectiveness of the proposed system used in the paper for diagnosis, treatment, follow-up, and mental health enhancement, we will be using the diagnostic and statistical manual of mental disorders 5 (DSM 5) (WHO in Depression and other common mental disorders: global health estimates. Geneva, 2017 [2]), an authoritative reference used for defining and categorizing mental health disorders. The proposed system will leverage the on-devices ML capabilities to identify the symptoms from raw heterogeneous sensor data like apple watch, Raspbian, textile t-shirts, smart mirrors, mobile phones, etc. And use HERE SDK to identify the person travel patterns. The proposed system includes features like emotional intelligent IoT, data privacy, model adaptability. Keywords Mental health · COVID-19 · Stress · DSM 5 · IoT · HERE SDK · On-device ML · Mental disorders · Diagnosis · Wearable devices

G. D’silva (B) · J. Batra · A. Bhoir · A. Sharma HERE Technologies, Mumbai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_50

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1 Introduction Mental health is the foundation of a person’s well-being and efficient functioning. It is more than the absence of a mental disorder; it is the ability to think, learn, and comprehend one’s emotions and the reactions of others. Until far, the majority of ubiquitous healthcare research has concentrated on pervasive computing for somatic ailments like diabetes, hypertension, heart failure, and obesity [1], with mental problems receiving less attention. It is crucial to remember that mental illnesses affect roughly 1/4th of the population at some point in their lives. Furthermore, these illnesses are global, affecting people of all ages in all countries and communities. As per a study by WHO, more than 500 million individuals worldwide suffer from mental health illnesses, with depression, schizophrenia, and dementia being the most common [2–6]. It is high time now that patients and therapists should have an ongoing support in assessing early warning indicators in an objective and timely manner in order to successfully implement this new type of therapy. This is where the Internet of things, ubiquitous computing, and deep learning may help [7]. Some of the existing pitfalls with this approach are like the current IoT devices have lots of intelligence but no emotional intelligence and ML decision-making models run on third-party servers, data privacy [8, 9]. In this paper, to determine and investigate the effectiveness of the proposed system used in the paper for diagnosis, treatment, follow-up, and mental health enhancement, we will be using the diagnostic and statistical manual of mental disorders 5 (DSM 5) [2], an authoritative reference used for defining and categorizing mental health disorders. The proposed system addresses the leading mental disorders such as bipolar disorders, depression, schizophrenia, and stress-related disorders categories by DSM 5. Changes in mental health can be reflected by analyzing data linked to behavioural, psychological, and social signals [2] which we will be sensing from the IoT devices. In this course of this study, we hope to reconcile studies with language and expert classification (i.e. based on the DSM 5) rather than using generic nonexpert criteria, as is common in technical efforts. The proposed idea is to develop an on-device emotional intelligent IoT framework to assist people to aid in the identification, assessment, treatment, and potentially alleviation of symptoms associated with mental health disorders.

2 Related Work New options for gathering physiological, behavioural, and social data from persons are being established as a result of the nature of IoT technology. MONARCA, a personal mobile monitoring device for patients with bipolar disorders, is a good illustration of this [10]. The system keeps a track of patients moods and also provides quick and easy access to about them to their doctors and family members, albeit

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it does not allow for substantiation on how to get the data, identify patterns, and remove insights. Another approach to treating manic-depressive disease (Bipolar I) is to utilize cell phones to continuously monitor people’s behavioural patterns such as current GPS coordinates to identify the location and mobility, activity levels, and social engagement, all of which can serve as possible markers of a mental state shift [11]. Psyche, in a similar vein, is a continuous monitoring system for treating and predicting bipolar disorder episodes, integrating both physiological and behavioural sensing and providing the user with an interface for personal and disease-wise data management [12]. Optimal is a sensing platform that predicts depression and stressrelated disorders in patients based on cognitive, motor, and verbal behaviour [13]. The smartphone sensing system called crosscheck addresses and predicts markers of schizophrenia levels using a combination of continuous mobile sensing and patient reports [14]. Although there is an increase in research and development of IoT-based systems and services for mental health care, there are still numerous hurdles to overcome. There are a variety of problems, some of them include IoT device performance and the capacity to handle and analyze large amounts of data to implement intelligent health services [15, 16]. The ability to forecast key events in patients with mental diseases like clinical depression in real-time is also a challenge [10]. Because various measurements of mental state and behaviour are being developed, the psychiatric features or digital biomarkers used to detect and assess any of these mental healthassociated illnesses may alter due to the rapid growth and acceptance of IoT-related technologies [17, 18].

3 Conceptual Framework The proposed idea is to create an on-device emotional intelligent IoT framework to help people in identifying, assessing, treating, and potentially alleviating symptoms associated with mental disorders.

3.1 Main Components There will be three-part to this framework: data sensing, sensor fusing, and emotional intelligent actions which are illustrated in Fig. 1. Data Sensing The heterogeneous sensing sensors like smart mirrors, watches, textiles, mobile, fridges, etc., will capture behavioural, psychological, and social signals [19] which indeed provides promising cues regarding mental health of an person. Behavioural data such as location, voice features, person physical activity, and engagement with smartphones or smart watches. Psychological data such as face emotions, heart

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Fig. 1 System architecture

rate variability (HRV), movement of eyes, body temperature, electrodermal activity (EDA). Social data such as level of engagement in a person’s interactions with others, social media usage and posts. All this data is detected by the sensors which are crucial for deciding the mental health of a person [8]. Sensor Fusing All this heterogeneous data collected from different sensors are sent to the central device which can be Jetson Nano, Raspberry Pi, or Arduino. HERE indoor positioning is used for understanding the travel pattern and locations of the user by using this in wearable devices or phones which work offline too. This raw data is clean and pre-processed, and they are fused in a structured format. Emotional Intelligent Actions The on-devices ML model does the prediction based on the collected data of the symptoms provided by the different sensors for the possibility of a disorder as per the DSM 5 standard [20–22]. The observations by the model are drawn by a stream of data over a period of time. The proposed system will take the necessary action in an emotionally intelligent manner taking into consideration the situation where the user is located, the right time, sensors data, severity of the disorder, etc. The possible actions will be in line with treatments or counselling sessions to further strengthen the observations. This observation will act as feedback to the on-device ML model which will have the domain adapting capabilities ensuring the privacy of the user.

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3.2 DSM Model Flow As per the DSM 5 manual, any person suffering from mental disorders has to have symptoms for more than 2 weeks, and based on this study, we have created an workflow for predicting a mental disorder as illustrated in Fig. 2. 1. 2. 3.

4.

A CRON job will run to get sensor data of 2 weeks from the data source and if JSON data for complete two weeks is found, it will move to next step. From all the JSON data gathered for 2 weeks, algorithm will try to filter data based on features per day and then use this to analyze its future in the next step. The gathered data of face, speech, HRV, and WESAD outputs is checked for stress and is combined into a new data which has stress fusion timestamps and will fetch the activity based on these timestamps. Along with activity will also try to get the mobility aspect of the person from using HERE indoor positioning and HERE SDK to understand the travel pattern and social behaviour.

Fig. 2 Flow diagram

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Pre-defined weights assigned to each model result are used to calculate if the person was stressed or not for the whole day. This is evaluated in the similar fashion on 2 weeks data, if it is found that person was in stress for more than 80% of time, the algo concludes that the person has a mental disorder and needs to visit a counsellor. The proposed system also includes emotional intelligent notifications which means scheduling the notification based on the person’s stress levels.

4 Experiments 4.1 Data Collection for Activity Prediction and HRV Prediction Using Smart Watch The sensor data used for activity prediction is the accelerometer data which is x, y, and z axis and for HRV prediction, SDNN data is used, which is collected from apple watch as illustrated in Fig. 3. The apple watch is connected to an iOS device through Bluetooth, and the proposed system uses HealthKit API to get this raw sensor data. This raw sensor data is send to the AWS IoT core through XMPP protocol. The AWS IoT receives this raw sensor data triggering the execution of activity and HRV TFLite model and Jetson Nano device, i.e. subscriber to IoT topic. The activity model has been trained to recognize actions such as riding a bike, walking, descending and ascending stairs, standing, and sitting. The HRV model predicts whether a person is stressed. As shown in Fig. 4, the model projected that the user was seated based on accelerometer data, and from the HRV data system determined that the user was not stressed. These predictions, together with raw sensor Fig. 3 Data capturing on apple watch

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Fig. 4 Activity and HRV model prediction

data, are combined into a single JSON file with the latest timestamp for further analysis and prediction of the user’s mental health.

4.2 Face and Speech Emotions Recognition Using Smart Mirror The face image and speech audio are captured by devices such as smart mirrors and smartphones. After that, the device uploads the files to the AWS s3 bucket and sends the object URL to AWS IoT topic. The master device in this case is a Jetson Nano which obtains these URLs from topic and downloads the face and speech data from the s3. After that, the Jetson Nano used speech and face emotion recognition TFLite models to do prediction over this downloaded data. The face emotion model predicts emotions such as happy, angry, and neutral. As illustrated in Fig. 5, a person is smiling, and the model correctly classified the emotion as happy with 0.98 accuracy. Whilst angry, sad, happy, fearful, and calm are all emotions that the speech emotion model has been trained to identify. The speech model can also recognize if the user is a male or a female. In Fig. 6, the speech model correctly detected an angry female emotion since the audio featured an angry female voice. These predictions are then merged into a JSON file with the most recent timestamp.

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Fig. 5 Facial emotion recognition model prediction

Fig. 6 Speech emotion recognition model prediction

4.3 WESAD Model Prediction To predict if the user is stressed, the WESAD model uses sensor data from two different devices. RespiBAN uses (ECG), (EDA), (EMG), respiration, body temperature, and three-axis acceleration and Empatica E4 uses blood volume pulse (BVP, 64 Hz), (EDA, 4 Hz), body temperature (4 Hz), and three-axis acceleration (32 Hz). The proposed system uses the test data values from the dataset to perform model prediction. It performs WESAD model inference on the received data to determine whether the user is stressed as seen in Fig. 7. This prediction, together with activity and HRV, is then saved in a JSON file.

Fig. 7 WESAD model prediction

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Fig. 8 DSM 5 model prediction

4.4 DSM 5 Model The DSM requires data over time to predict correctly. The proposed solution captures the symptoms daily and saves the model’s prediction of activity, HRV, face emotion, speech emotion, and WESAD into a JSON file with a timestamp. As per the discussed DSM 5 model flow, the disorder indicators are calculated and sent to the user in an emotionally intelligent manner. As shown in Fig. 8, the user found 95.57% of the time stressed in 2 weeks of data, and the system determined that the user may be suffering from a mental disorder.

5 Conclusion As we know, the rise of mental health cases in the world due to the COVID-19 pandemic can be described as a threat to our health worldwide. Moreover, as the pandemic passes by, the ongoing and necessary public health measures may make people experience situations related to poor mental health outcomes, like isolation and job losses. The mental disorder affects everyone irrespective of ages and backgrounds and thus understanding or targeting the symptoms which may signify a problem can result in successful treatment. When mental illness is left untreated, the symptoms can worsen with time and impact person’s well-being negatively. Some mental health disorders which involve observable changes in behaviour or do not impact or alter person’s judgement can benefit a lot from IoT technologies. However, the capabilities of IoT for mental health disorders have been hardly utilized. We proposed an on-device emotional intelligent IoT framework will help people in recognizing, evaluating, curing, and potentially relieving symptoms associated with mental health disorders at an early stage resulting into a happier well-being. In this work, we aim at bringing together studies with the terminology and expert classification (i.e. based on the DSM 5), in comparison with using common amateur criteria. Acknowledgements The researchers are using this opportunity to express deep gratitude to HERE Technologies for allowing us to perform industry relevant research and providing us the required resources for completing our study.

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References 1. Arnrich B, Mayora O, Bardram J, Tröster G (2010) Pervasive healthcare—paving the way for a pervasive, user-centered and preventive healthcare model. Methods Inf Med 49:67–73 2. WHO (2017) Depression and other common mental disorders: global health estimates. Geneva 3. Products—NHIS early release program—homepage, 31 Aug 2021. Centers for Disease Control and Prevention. www.cdc.gov/nchs/nhis/releases.htm 4. Hamel L, Kearney A (2020) KFF health tracking poll—July 2020. KFF, 14 Aug 2020. www. kff.org/coronavirus-covid-19/report/kff-health-tracking-poll-july-2020/ 5. Hadzic M, Chen M, Dillon TS (2008) Towards the mental health ontology. In: Proceedings of the IEEE international conference on bioinformatics and biomedicine (BIBM 2008), Philadelphia, PA, 3–5 Nov 2008 6. Miller BF, Coffey MJ (2021) Understanding suicide risk and prevention. Health Policy Brief, Jan 2021. https://doi.org/10.1377/hpb20201228.198475/full/ 7. Arnrich B, Osmani V, Bardram J (2013) Mental health and the impact of ubiquitous technologies. Pers Ubiquit Comput 211–213 8. Gutierrez LJ et al (2021) Internet of things for mental health: open issues in data acquisition, self-organization, service level agreement, and identity management. Int J Environ Res Public Health 18(3):1327 9. WHO (2001) The world health report: 2001: mental health: new understanding, new hope. World Health Organization, Geneva. http://www.who.int/whr/2001/en/ 10. Frost M, Marcu G, Hansen R, Szaántó K, Bardram J (2012) The MONARCA self-assessment system: persuasive personal monitoring for bipolar patients. In: Proceedings of the 2011 5th international conference on pervasive computing technologies for healthcare (PervasiveHealth) and workshops, Dublin, 23–26 May 2012 11. Grünerbl A, Oleksy P, Bahle G, Haring C, Weppner J, Lukowicz P (2012) Towards smart phone-based monitoring of bipolar disorder. In: Proceedings of the second ACM workshop on mobile systems, applications, and services for healthcare—mHealthSys’12, Toronto, ON, 6 Nov 2012. ACM Press, New York, NY, p 1 12. Paradiso R, Bianchi AM, Lau K, Scilingo EP (2010) PSYCHE: personalised monitoring systems for care in mental health. In: Proceedings of the 2010 annual international conference of the IEEE engineering in medicine and biology society (EMBC’10), Buenos Aires, 31 Aug–4 Sept 2010 13. Botella C, Moragrega I, Baños R, García-Palacios A (2011) Online predictive tools for intervention in mental illness: the OPTIMI project. In: Studies in health technology and informatics. IOS Press, Amsterdam 14. Wang R, Scherer EA, Tseng VWS, Ben-Zeev D, Aung MSH, Abdullah S, Brian R, Campbell AT, Choudhury T, Hauser M et al (2016) CrossCheck: toward passive sensing and detection of mental health changes in people with Schizophrenia. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing—UbiComp’16, Heidelberg, 12–16 Sept 2016 15. Babar M, Rahman A, Arif F, Jeon G (2018) Energy-harvesting based on internet of things and big data analytics for smart health monitoring. Sustain Comput Inform Syst 20:155–164 16. Wan J, Gu X, Chen L, Wang J (2018) Internet of things for ambient assisted living: challenges and future opportunities. In: Proceedings of the 2017 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC 2017), Nanjing, 12–14 Oct 2018 17. Dimitrov DV (2016) Medical internet of things and big data in healthcare. Healthc Inform Res 22:156 18. Glenn T, Monteith S (2014) New measures of mental state and behavior based on data collected from sensors, smartphones, and the internet. Curr Psychiatry Rep 16:523 19. Abdullah S, Choudhury T (2018) Sensing technologies for monitoring serious mental illnesses. IEEE Multimed 25:61–75 20. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders: DSM-5. American Psychiatric Association, Arlington, VA

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21. D’silva GM et al (2017) Real world smart chatbot for customer care using a software as a service (SaaS) architecture. In: 2017 international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (ISMAC). IEEE 22. D’silva GM et al (2017) Smart ticketing system for railways in smart cities using software as a service architecture. In: 2017 international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (ISMAC). IEEE

Energy Minimization of Cloud Computing Data Center Strategies, Research Questions: A Survey Abhineet Anand, M. Arvindhan, Naresh Kumar Trivedi, Ajay Kumar, and Raj Gaurang Tiwari

Abstract Large client models use cloud computing because it has several benefits, including minimizing cost of construction resources and its elasticity property which enables services to also be up or down to current demand. And order to provide cloud services, which meet all the requirements specified in service level agreements, there are so many challenges to still be overcome from the cloud-provider viewpoint (SLAs). As data centers absorb huge quantities of power, it is a major challenge in cloud computing to increase their energy efficiency. The main purpose of this systematic analysis is to present and analyze many algorithms in this cloud computer environment used to reduce the energy from data center and to compare solutions for research challenges. In the energy-conscious cloud applications of the data center, a new combination of the Virtual Machine Image Constructor (VMIC) is possible to evaluate component efficiency. In addition, the capacity to satisfy the required SLAs would ideally reduce energy on various host machines using VM implementations. Using less VM migration and PM shutdowns than a common heuristics method. The ongoing research work on the minimizing of energy strategies used in cloud computing is a comparative research study. Keywords Power management · Virtual machine image constructor · Data center · Cloud resource management · Utility functions · Energy-aware · Cloud computing A. Anand · N. K. Trivedi · A. Kumar · R. G. Tiwari (B) Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India e-mail: [email protected] A. Anand e-mail: [email protected] N. K. Trivedi e-mail: [email protected] A. Kumar e-mail: [email protected] M. Arvindhan Galgotias University, Greater Noida, UP, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_51

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1 Introduction In the case of large numbers of users, cloud computing provides application, platform, or infrastructure resources with complex and dynamically changeable needs. Cloud service suppliers need to take various decisions on resource management which follow multiple goals to meet the consumers’ needs in an economical way such as, for example, fulfilling service level agreements (SLAs), though reducing energy expenses. This paper concentrates upon the issue in the form of volatile working loads in order to allocate virtual machines (VMs) to physical hosts. In particular, this requires decisions based on the following categories, such as when to move VMs, where to move VMs, and which physical devices are to be turned off. VMs must be moved. In this situation, we try to optimize the benefit of an IaaS service provider through the trading of profits (including meeting SLAs) and expenses. These decisions can be taken with a view to fulfilling various goals [1]. This problem is not the first to tackle. Designing heuristic algorithms that make decisions about the complex allocation of workload, the use of energy needs to be considered when determining the position of VMs. On considering the heuristic approach, these documents aim to define criteria that can be useful for adaptation (which appears to include the detection of overloaded or underloaded hosts) and to then decide on redistribution in ways which take into consideration the projected energy use. Researchers emphasize on problems such as the detection when the load on a virtual machine indicates that adjustment can be helpful when designing and refining the heuristic [2]. This same functionality methodology to adaptive systems defines a utility function that determines the adaptive target and examines alternative adaptations in an optimization algorithm, which identifies adaptation maximizing usefulness. The utility of an assignment over a time interval t is defined as: Utilityfn(da, t) = Incoming data(da, t)−Energy Consumption(da, t). Such utility feature catches the service provider’s purpose. The service (a, t) returns the expected financial return on physical machines for a duration of time t during the report of virtual machines (VMs) (PMs). Under practice, this includes developing models to estimate the Revenue and Energy cost of a job over an interval t and selecting a search engine that enquires into the possibilities of alternative tasks. As a result, the utility approach captures the adaptation goal precisely and finds strategies that accomplish the objective. The utility-based approach has been applied to a number of applications, ranging from grid planning to flexible VM placement. [3] This paper summarizes the key contributions as follows: 1. 2. 3.

The useful solution to the issue of VM placement. A utility feature to estimate the benefit from adaptive VM placement with due regard to the effects of adaptation, energy consumption and SLA infringements. Build custom machine images.

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2 Description of the Useful Feature This utility role reflects the policy of self-management to create an autonomous cloud data center. The aim of the power supply feature is to optimize VM location by reducing energy utilization and SLA infringements (SLAVs). Utility(a, t) = Income(a, t) − (Estimated Energy Cost(a, t) +Estimated Violation Cost(a, t) + PDM Cost(a, t))

(1)

Cloud service providers have no requirement to make the regular modification of hardware or install specific software systems free of restrictions to the acquisition of cloud providers. You have free use of IaaS services for data savings, PaaS for computing activity, and SaaS applications for the Internet analysis of IoT data. Virtualization technology provides an exact way to reallocate physical resources in a cloud data center between various physical nodes. It develops standard VMs in physical nodes. The tasks are simultaneously sent to the same physical machine for various VMs (PM). The cloud saves sensed data based on a standard threshold value for processing and computational analysis. The task category is classified according to the computing method and a suitable cloud VM is allocated. But sudden data flows occur because of sensing region variations. The cloud system may also choose an appropriate VM for live migration in some variations. The sudden change in the sensory environment can result in resource lack, known as hunger or hunger. The VMs are migrated from one physical node to another in these situations. This leads to further VM migration and to resource waste, increased power usage, and intrusion. This leads to further VM migration. Therefore, we are introducing a collection and migration of VMs in cloud environments that are optimally energy consuming. The main aim of the technique is to provide a continuous IoT service environment without infringing service level agreements (SLAs) [4–6].

3 VM Resource Utilization Process with Physical Machine A vector regression technique called supporting regression approach was used to combine resource requirement prediction and the VM resource usage process in a SLA-conscious resource planning procedure. Through the execution of the PM compression step, the Sercon algorithm is critical in defeating the process of resource fragmentation. RF-aware migration doubles the estimated time for VM migration and is more successful than the Sercon approach to resource fragmentation. However, the time required for migration and the amount of energy consumed would not be included in this study. A heuristics-based algorithm was developed using the fit decrease and harmonic cardinality limitation approaches for successfully positioning VMs. This is not valid, however, for complex workloads. The method of live VM movements in accordance with the complex resource provision policies for the

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consolidation of VMs also does not achieve QoS. Thus, the energy consumption at the physical node is approximately 45% and the network charge of the data center is 15%. Whenever the use of the CPU is full, 70% of resources like memory or disk are wasted because the necessary resources are not available which lead to a high energy consumption. Moreover, the use of unbalanced resources does not have an adequate mechanism [7, 8]. The use of resources based on an anthracis algorithm is summarized to resolve the problem of VM resource balance. However, the issue of VM migration was not considered for resource hunger and the resource requirement task review. In the context of a combination of electricity use, a useful resource rate, and the output of the CPU process both during consolidation, a model energy consumption was implemented. This framework only takes into account the use of CPU resources. The energy consumption of other resource sources is not estimated (such as when the CPU is inactive) and certain instances are ignored (such as when the tasks need to claim high bandwidth with low CPU requirement). The main concept is to collect the same amount of resources. To prevent premature outcomes, the swarm randomly separates the categories and sub-group areas and often assigns parameters at random to each sub-group. The optimization algorithm is characterized as the number of resources that do not completely disclose the measure of load balancing. In different techniques, the cloud data center is confronted with unforeseen spikes, which contribute to unavailability of resources. The main parameters to define the respective target host are not considered in this method. The time and energy consumption of VM migration therefore does not meet the threshold value. These have little effect on processing time and poor efficiency. Different strategies for live VM migration based on use of resources for load balance were embraced [8, 9]. The energy exploited by the mth physical node or host is designated as ξm and estimated using Eq. (1). 

VM j

ξm = ITm × ξI +

ξ rjm

(2)

j=1

where ξ I represents the energy utilized by the mth node in an idle state and IT m is the idle time (in milliseconds) of the mth node, which is given in Eq. (2). ITm Makespan −

q h m VM  j  m=1 j=1 i=1

ψirjm

(3)

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4 Proposed Algorithm Here, in this section we explained about the sample image of AZURE cloud for creation of Virtual image creation. The following steps are carried out for some performance of image creation. “Name”: “Image Creation Role”, “IsCustom”: true, “Description”: “Azure Image Builder access to create resources for the image build”, “Actions”: [ “Microsoft.Compute/galleries/read”, “Microsoft.Compute/galleries/images/read”, “Microsoft.Compute/galleries/images/versions/read”, “Microsoft.Compute/galleries/images/versions/write”, “Microsoft.Compute/images/write”, “Microsoft.Compute/images/read”, “Microsoft.Compute/images/delete” ], “NotActions”: [ ], “AssignableScopes”: [ “/subscriptions/ < subscriptionID > /resourceGroups/ < rgName > “

4.1 Inputs for the Action “Resource-group-name”: Required. This is the resource group where the action creates a storage for saving artifacts needed for customized image. Azure image builder also uses the same resource group for Image Template creation. “Image-builder-template”: The name of the image-builder-template resource to be used for creating and running the Image builder service. Then you can give the full file path to that as well. E.g., _${{ AZURE.WORKSPACE}}/ vmImageTemplate/ubuntuCustomVM.json_ Note that in case a file path is provided in this action input, then parameters in the file will take precedence over action inputs. Irrespective, customizer section of action is always executed “Location”: This is the location where the Azure Image Builder(AIB) will run. E.g., “eastus2” The source images must be present in this location, so for example, if you are using

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Fig. 1 Microsoft Azure Virtual Image creation

Shared Image Gallery, a replica must exist in that region ( Fig. 1) This is optional if AIB template file path is provided in “image-builder-template” input “Build-timeout-in-minutes”: Optional. Time after which the build is canceled. Defaults to 240 “vm-size”: Optional. By default AIB uses a “Standard_D1_v2” build VM. [10–12]

4.2 Create and Configure a Source VM vaz group create –name myResourceGroup1 –location east. az vm create \ – – – – –

resource-group myResourceGroup1 \ name myVM \ image ubuntults \ admin-username azureuser \ generate-ssh-keys.

Image Version Creation az sig image-version create \ – resource-group myGalleryRG1 \ – gallery-name myGallery1 \ – gallery-image-definition myImageDefinition1 \

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Criteria

529 Sample numbers Energy consumption

Energy consumption 30

5.8 × 10−4

VMs migration

30

2.4 × 10−20

SLAV

30

4.5 × 10−5

– gallery-image-version 1.0.0 \ – target-regions “southcentralus = 1” “east = 1” \ – managed-image"/subscriptions/ < Subscription ID > /resourceGroups/ MyResourceGroup1/providers/Microsoft.Compute/virtualMachines/myVM”.

5 Result and Discussion Modules for the vital object transformation require data centers to reduce the total power provided while complying with SLAs. As described, each test set (day) is carried in six equal parts in the AZURE virtual image formation and the simulation is performed separately for each component. It should be noted that our VM Selection and Host Selection Policy has been enforced focused on the VM CPU forecast, provided by models in various days and parts submitted (Table 1).

6 Conclusion This paper mainly studies the method to make more energy efficiency in cloud computing data center environment. In recent year, we have energy efficiency which has come with popular design requirement for the cloud computing so that data center consumes less energy. When we summarize our work, then we absorb that every algorithm has its own advantages and disadvantages. Based on our survey, we have proposed the algorithm to increase the energy efficiency. On the analysis of the result, we observed that VM migration process reduced the energy consumption. SLAV reduces more energy consumption compare to Normal process and VM migration process. We have also discussed the configuration of source VM which will affect the consumption of energy by data center. While complying SLA, measure has been taken to meet the energy consumption optimization objective of cloud-based data center.

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References 1. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830 2. Kovalev V, Kalinovsky A, Kovalev S (2016) Deep learning with Theano, Torch, Caffe, Tensorflow, and deeplearning4J: which one is the best in speed and accuracy? Publishing Center of BSU, Minsk 3. Feurer M, Springenberg JT, Hutter F (2014) Using meta-learning to initialize bayesian optimization of hyperparameters. In: Proceedings of the 2014 international conference on meta-learning and algorithm selection, vol 1201, CEUR-WS, pp 3–10 4. Varoquaux G, Buitinck L, Louppe G, Grisel O, Pedregosa F, Mueller A (2015) Scikit-learn: machine learning without learning the machinery. GetMobile Mob Comput Commun 19(1):29– 33 5. Yi H, Jung H, Bae S (2017) Deep neural networks for traffic flow prediction. In: 2017 IEEE international conference on big data and smart computing (BigComp), IEEE, 2017, pp 328–331 6. Duy TVT, Sato Y, Inoguchi Y, Performance evaluation of a green scheduling algorithm for energy savings in cloud computing. In: 2010 IEEE International Symposium on parallel and distributed processing, workshops and Ph.d. forum (IPDPSW), IEEE, 2010, pp 1–8 7. Arvindhan M, Anand A (2019) Scheming a proficient auto scaling technique for minimizing response time in load balancing on Amazon AWS Cloud. In: International conference on advances in engineering science management and technology (ICAESMT)-2019 8. Anand A, Chaudhary A, Arvindhan M (2021) The need for virtualization: when and why virtualization took over physical servers. In: Advances in communication and computational technology, pp 1351–1359 9. Arvindhan M, Bhanu P (2020) Data mining approach and security over Ddos attacks. ICTACT J Soft Comput 10. Daniel A, Arvindhan M, Pandey AK, Privacy-preserving of cloud storage security. In: International conference on machine intelligence and data science applications-MIDAS2020–813 11. Partheeban N, Godlin Atlas L, Arjun KP, Sreenarayanan NM, Arvindhan M (2020) Evaluation of self-adaptive channel equalizer using EPLMS algorithm. J Comput Theor Nanosci 1(9):1546–1955 12. Shaw SB, Kumar JP, Singh AK (2017) Energy-performance trade-off through restricted virtual machine consolidation in cloud data center. In: 2017 International conference on intelligent computing and control (I2C2), IEEE, 2017, pp 1–6

Network Security and Telecommunication

Scope of Machine Learning in Mobile Wireless Sensor Networks Kavita Gupta, Sandhya Bansal, and Ajay Khurana

Abstract This work highlights the scope machine learning approaches in Mobile Wireless Sensor Networks. As Mobile Wireless Sensor Network faces numerous challenges in terms of energy conservation, data collection and aggregation, fault tolerance, QoS. Sink Mobility, etc. Machine learning is the branch of Artificial intelligence used to analyse data for making predictions, so as to get the optimized results. Here, work shows how the machine learning approaches can be used in sensor networks to improve network performance by extending lifetime, data collection and aggregation, handling mobility of sink node, QOS, fault tolerance, etc. Keywords Mobile wireless sensor networks · Machine learning · Artificial intelligence · Data aggregation · Data collection

1 Introduction The extensive growth of sensor networks leads to the usage of many more sensor nodes. The sensor nodes have some limitations of limited battery life that leads to the incorporation of new sensor nodes but replacement or deployment of sensor nodes is not an easy task at all times. Mobility of sensor nodes causes a faster energy depletion rate of sensor nodes. Energy efficiency in sensor networks is one of the major challenges for researchers. Various researchers have offered several K. Gupta (B) University Institute of Computing, Chandigarh University, Gharuan, India e-mail: [email protected] S. Bansal M. M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, India e-mail: [email protected] A. Khurana University School of Business, Chandigarh University, Gharuan, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_52

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approaches for energy-efficient routing by various approaches like clustering arrangement, routing approach, localization, etc. Few researchers also worked on offering energy saving approaches [1] using topological arrangements of sensor nodes [2]. Some of the researchers represent their findings using mathematical models [3] for energy conservation, localization [4, 5]. But few of the modelling approaches face challenges like environmental effects like temperature, weather, time location, etc. As the environment has a direct effect on the operating interface of sensor nodes [6]. Therefore locality of sensor nodes plays an important role in offering energyefficient networks by reducing the energy consumption rate. Energy saving in sensor networks is itself a big challenge as to achieve high performance with more number of sensor nodes are required that causes more energy consumption rate [7]. Although the wireless network offers significant advantages like feasibility in unreachable areas, military applications, and many more. Machine learning is a new era in the branch of Artificial Intelligence. The concept of machine learning gain popularity due to its self-automated capability. AI strategies can be used in WSN to offer better service in terms of data collection, Aggregation, and transmitting to the base station. Major application areas of sensor networks include sensing and following [8, 9]. This paper will highlight the applications of AI with MWSN to improve the data collection and aggregation, QoS, Anomaly detection, etc. approaches.

2 Mobile Wireless Sensor Network (MWSN) MWSN is an organized/unorganized collection of sensor nodes that senses the environment with its self-organizing and self-curing capability. The mobility of sensor nodes claims its space in the community of sensor networks. Although a static sensor network offers numerous advantages like military application, habitat monitoring, and military applications but offers limitations like limited battery life, localization, energy depletion, etc. Mobility of sensor nodes overcome the issues like localization, energy consumption rate list, just to list a few. MWSN’s infrastructure-less network makes it popular to overcome the problems faced in static Wireless Sensor Network (WSN). The major functional areas of MWSN comprise Base Station, Cluster Head, and member nodes. The base station is responsible for data collection from its member nodes. Efficient transmission schemes can be used to reduce the hop movement amongst the sensor nodes. Cluster Head is responsible for data collection from its member nodes. Various efficient hierarchical data collection schemes have been proposed by researchers. The selection of an appropriate cluster head is also a challenging issue. The researcher in [10] offers an efficient approach to cluster head election. The proposed approach selects the cluster head using specialized parameters residual energy level and remaining time of stay in the network. Although the node deployment process is the same for both static and mobile nodes. Mobility support sensor network handles the limitations of hardware constraints of limited power supply and other environmental constraints.

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MWSN offers several advantages like self-localization, self-healing, and association amongst sensor nodes. Along with these advantages, MWSN faces numerous challenges like leaving their respective cluster unattended. Data handling in mobility networks is one of the challenging tasks; Machine learning approaches can be used to overcome these issues to an extent. The next section gives an overview of machine learning and approaches of machine learning [11, 12].

3 Machine Learning Machine learning is one of the vibrant researches filed these days. It offers numerous soft computing approaches that can be used for data prediction and measuring [13]. Highlights deep learning models, used for energy-efficient data collection and fusion at the base station. This neural network model reduces the data collection overhead at the base station. The WSN’s can incorporate heterogeneous, various self-ruling, and reasonable just as negligible force base stations. AI offers the approaches used worldwide to improve the data overhead at data collection points. Machine learning approaches proved to be useful for the domains like biosciences, localization, address identification, etc. Usage of ML is not only limited to the mentioned areas but it is used at the industrial level also for the extraction of accurate and useful data. Artificial intelligence approaches are helpful for relevant data extraction in an efficient manner. The use of AI with sensor networks makes these network activities more efficient and active. Implementation of machine learning is very challenging in sensor networks due to some of the reasons mentioned below [14]: • Deployment of sensor nodes in unreachable areas where conventional networks are not feasible. • Sensor network architecture is predefined as it is specified by the scientists based on the type of data required. But AI approaches promise to give an optimal solution with unpredictability and also computation of resources used. • Data collection, as well as data processing like extraction of relevant data in sensor network, is accomplished by the base station so it is only overhead the base station. Approaches of Machine Learning Supervised Learning In this machine learning approach, machine learning is based on training. The machine is trained for specific trained input data, it produces particular output. This type of machine learning model is based on underlying patterns and the relationship between input data and output. In this type of learning training data is provided to machines by the supervisor. Results produced by the supervised learning area are accurate and well labelled. Supervised learning model follows the steps as given in Fig. 1.

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Fig. 1 Supervised learning process

Supervised learning methods start with an input data matrix. Each row identified each observation and columns correspond to each variable or predictor. Machine learning algorithms identified not only a number but a data set in the input matrix. Inputs are specified with variable x 1 and various data types correspond to variable y1 . The value of y1 varies for classification and regression approaches. For regression, it must be numeric and for classification, it may contain null values in the data matrix. In the step of algorithm selection, few points keep in consideration like the speed of training, memory usage, prediction of new data, and transparency in the selection of data. The selection fit model is done based on various algorithms like Classification trees, K-nearest neighbours, Support Vector analysis, discriminated analysis, regression trees, classification ensembles or regression ensembles, etc. A characteristic function is used for the selection of the model. The next is the selection of a validation method to check the accuracy of the resulting fit model. Mainly three methods are used to examine the accuracy of the fitting model are as follows: • Examine the resubstitution errors like the Cross-validation Regression tree, Resubstituting error model, and test of ensemble quality. • Secondly, examining the cross-validation errors like cross-validate a regression tree, Test ensemble Quality, etc.

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• Lastly, examining the out-of-bag error for decision trees like Test Ensemble Quality, Bootstrap aggregation of regression tree, and bootstrap aggregation of the classification tree. In the next step of the supervised learning process, the accuracy of the fit model is identified till we get the satisfied results. The validation process gets accomplished by changing the specified parameters for better speed, accuracy, and less memory consumption. Model Parameter changing can also lead to the development of a compact model with accurate results. Alternative algorithms may be tried to get a suitable accurate model. In the last step, the above said Fit model can be used for the prediction of results or desired outputs. For Classification and Regression, Supervised learning is assumed to be a good approach. The supervised learning algorithms main include Neural networks, Random forest, Decision trees, K-nearest neighbours, Super vector machine [15]. These algorithms work efficiently for solving the localization problems [16, 17], routing problems [18, 19], target tracking [20], sensor blending [20, 21], and event detection problems [22]. Classification: Classification algorithms take the data as input and use machine learning to define new data points. These algorithms are categorized as a single and multiclass. The algorithms like Support vector machine, K-nearest neighbours, Artificial neural networks, Bayesian learning, Random forest, Decision trees are efficiently used to solve different issues and challenges in sensor networks. These algorithms are discussed in the following section: K-Nearest Neighbours: It is considered to be one of the simplest methods to solve classification and regression problems. This method specifies and considered the Euclidian distance. This method finds the missing samples from the featured space and decreases the dimensions. In sensor networks, K-nearest neighbour approach has been applied for data aggregation [23] and anomaly detection [24]. Support Vector Machine: This supervised learning approach classifies the data with coordination with individual observations. This approach is suitable for both nonlinear and linear problems [18] and suitable for large data sets. This approach is used in sensor networks for congestion control [25], like routing [18], localization [17, 26], connectivity issues [27], and fault detection [28]. Artificial Neural Networks: It is a mathematical model based on the imitation capability of the human brain for brain tasks. It is the collection of neurons that produces the output by processing the input data. The artificial neural network comprises three layers namely, the input layer, hidden layer, and the output layer. Input data is given using the input layer and processed by the hidden layer and finally, the output is produced by the output layer. Major application areas of Artificial neural networks includes node localization [17], data aggregation [19], routing [29, 30], etc. Deep Learning: This approach lying under the family of Artificial neural networks. It analyses the communication and information processing system human brain, then processes the data, translation and speech recognition, and decision making. This

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approach is used to handle many issues in WSNs like anomaly and fault detection, energy harvesting, data quality estimation, and routing [29]. Bayesian learning is a statistical approach to specify the relationship between datasets. This approach assumes different probability functions to determine the posterior probability. It also handles various issues of sensor networks like routing [18, 19], data localization [16, 17], aggregation [19], fault detection, connectivity, and coverage problems [31]. Decision Trees: This approach of supervised learning enhances the readability by using if then else rules. There are two types of nodes, one leaf node, and another decision node. This model predicts a class or target based on the decision rules and creates a training model inferred from training data. There are many advantages of decision trees like transparency, less ambiguity in decision making, and allows for a comprehensive analysis. Decision trees are applied in WSNs to handle various issues like connectivity [32], data aggregation [19], mobile sink, etc. Random Forest: It is also a supervised learning algorithm that has a collection of trees, and each tree gives a classification. This approach works on the two principles; first, it creates a forest classifier, then produces the results. This technique works efficiently for heterogeneous data with a vast number of data sets. It is used in WSNs to solve problems like MAC protocols [33] and sensor network coverage [34]. Semi-Supervised Learning: In Semi-supervised learning, output is predicted on the basis of available data sets. Firstly, unsupervised learning approach is used to form, and then supervised learning is approach is used to label the remaining data [15]. Practically it is expensive to gather input and output pair training data in this learning algorithm. Semi-supervised learning is applied in WSNs to solve various issues like fault detection, localization, data aggregation [18, 19] in WSNs. Regression: Regression is one of the most popular approaches of statistics. Prediction and forecasting are the two main purposes for using regression. It can also be used to define the relationship between dependent and independent variables. In the existing literature, the regression approach used makes the prediction for variables than observation. These regression approaches can be used in sensor networks for handling various issues like localization, data aggregation, and connectivity issues just to list a few. Unsupervised Learning In this learning approach, the output is not predicted based on specified labelled input. To use that data, it is required to find the hidden patterns where an unsupervised prediction model is used. Unsupervised learning approach could not be used for classification and regression problems as it produces no specific output for the given input. Unsupervised learning approaches are used for data sets of similar types of data presented in compressed format. The unsupervised learning approach is used for finding useful information from the data, for works on uncategorized or unlabelled data. Unsupervised learning approaches can also be useful to make the human think by their experience and this approach can also be used for when there is no specific

Scope of Machine Learning in Mobile Wireless Sensor Networks Fig. 2 Unsupervised learning

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output for specified input values. Unsupervised Learning algorithms mainly classified as: Clustering and Association (refers to Fig. 2). Clustering is defined as the method of grouping the items that are similar in their characteristics but have no similarity with the members of other groups. Clustering is done based on commonalities between the group of items. Association rules in machine learning are used to find the relationship between variables in data sets. It specifies the frequency of items existing in the given dataset. The unsupervised learning algorithms include Hierarchical clustering, K-means neighbours, Anomaly detection, Principal Component Analysis, Fuzzy c-means, etc. Hierarchical Clustering: in this learning approach data sets are arranged in hierarchical order. Data items that have some similarities, arranged in form of clusters. Hierarchical clustering is mainly suitable for MWSN’s routing algorithms. These can also be used for data collection from neighbouring sensor nodes than data aggregation and sharing in the cluster itself and the base station [35, 36]. This approach can also be used for synchronizing the routing in networks [37], handling mobility of sink [38], and energy consumption [15]. K-means Clustering: In this approach data is categorized in k clusters, a higher value of k refers to small groups and a smaller value of k refers to big groups. In the kmeans algorithm approach each cluster has its own centroid and data sets form the clusters based on Euclidian distance from the centroid. The mean of every cluster is recomputed as new centroids, and the process continues until the optimal cluster centroids are found. Principal Component Analysis: In this approach, large data sets are difficult to interpret. This approach reduces the dimensionality of data sets at the same time increases the interpretability so preserves the data loss. This approach used the data compression feature and filters the noisy data. PCA is applied individually on each cluster head to reduce overhead and also reduces the overflow in the buffer. There are many areas where PCA is used like Localization [39], fault detection [28], data aggregation [40, 41], and target tracking [42] just to list a few. Fuzzy c-means: This approach was proposed by using fuzzy set theory [43]. In this approach, clusters are defined by considering similarity parameters like intensity, distance, connectivity. This approach produces optimal results as compared to the k-means clustering approach. Its major application areas include pattern recognition,

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business intelligence, bioinformatics, etc. They are also mainly used in localization [18], connectivity problems, and mobility of sink.

4 Challenges in MWSN and Machine Learning In sensor networks, nodes are deployed in a hostile environment without any human intervention. But nodes in-network faces few challenges in terms of limited battery life, dynamic topology, node distribution, and connectivity, etc. These challenges are categorized as Functional challenges, Security, and future challenges. The upcoming section highlights these challenges: Clustering and Data Aggregation: MWSN follows hierarchical architecture and nodes are arranged in form of clusters. The cluster formation process executes itself and nodes organize themselves in nearby in their nearby clusters and start communication with their neighbours. Member nodes can directly transfer data to the base station, but the selection of optimal cluster head is the major challenge. Cluster head performs the tasks of data collection and aggregation from the member nodes [40, 41]. Energy consumption: Energy scavenging is assumed to be one of the best alternative approaches which work in an open environment. It is the process where environmental energy can be converted to electrical energy but the application of this approach is a major challenge in sensor networks [15, 41, 42]. Sink Mobility: In sensor networks, member nodes send the data to their sink node in a multi-hop as well as single-hop manner. Because of this energy consumption rate of nearby nodes is high and the concept of sink mobility came into existence. Mobile natured sink moves from one node to the next node to collect data from specified nodes. But it may happen nearby nodes sends the data to a specified node. Multiple specified nodes may cause network delay and are also cost-effective [44, 45]. Congestion Control and Routing: In Sensor networks, routing is the biggest challenge in terms of nodes density in the network, as well as mobility of nodes, is also the biggest challenge. Machine learning approaches help to select an optimal route to reduce the energy consumption rate [30]. Extending the energy level of sensor nodes offers a prolonged network lifetime. Security: Security is also the biggest challenge in the field of sensor networks. Security aspects of the network involve anomaly detection, quality of service (QoS), link quality management, etc. Various approaches had been proposed to secure networks from black hole attack, node replication attack, sinkhole attack, etc. [46, 47]. To meet QoS in sensor network data aggregation, query processing, unbalanced traffic, etc. are a few of the challenges that can be handled by machine learning approaches discussed in [32]. Fault detection is also one of the challenging tasks due to the application of sensor networks in a hostile environment. Energy depletion, node lifetime,

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topology change are a few of the issues that can be handled by machine learning approaches [17, 33, 35].

5 Conclusion and Future Scope Machine learning approaches can be used to handle various challenges like detection of temporal and spatial coherence, resource management for high computational tasks. Machine learning-based data compression and dimensional reduction approaches can be used to compress data as compared to traditional compression approaches to produce better results. MWSN works on hierarchical networks where data correlation is one of the major issues that need the attention of researchers. In a sensor network, the maximum amount of energy is exhausted due to the overloading of the sensor network and the increased density of nodes. Efficient machine learning approaches can be used to manage resources and allocation schemes. As the discussion is already done for the major issues like data aggregation, localization of nodes, sink mobility, the energy consumption rate of nodes, anomaly detection [30, 46] and QoS management are can be handled using the machine learning approaches.

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An Overview of Cybercrimes and Its Impact: Indian Scenario Ashvarya Chaudhary

Abstract The phenomenal rise in the field of communication and the digital world has resulted in possible issues in the domain of cyber law and its ramifications. Nothing comes without a price and disadvantages. Though it makes people’s lives easier by allowing them to do a variety of tasks with a single click of a button, it has also created new obstacles. Everything, including business, industries, government operations, and so on, comes to a halt without technology, particularly computers. Our economy and government, as well as various sorts of businesses, rely on computers for the efficient operation of their businesses and economic advancement, while criminals are actively involved in various types of cybercrime. As a result, the current study discusses a comprehensive knowledge and overview of cybercrime, particularly computer-related cybercrime, and its effects on numerous fields. Keywords Cybercrimes · Business · Consumer technology · Computer crime · IT Act

1 Introduction Our lives have changed dramatically as a result of digital technology and new communication methods. Computers are used to facilitate business transactions. Individuals and businesses are tremendously adopting PCs (Personal Computers) to create–transmit and retain information in electronic-form since it is less expensive1 . However, this technology has also ushered in a new wave of illegal activities known as cybercrime; it is also defined as “any illegal act fostered or enabled by a computer, whether the computer itself an object of a crime, an instrument used to commit a crime, or a repository of evidence related to a crime.” The world is seeing colossal transformation in commerce arena with the advent of Internet-based organization especially in the global-pandemic times. Goods and services are sold and purchased as a routine through various business houses. Online A. Chaudhary (B) University Institute of Computing, Chandigarh University, Chandigarh, Punjab, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_53

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payments, E-billing, or E-payments are very common these days. Clearly, a large segment of people relies on Internet for their day-to-day work directly or indirectly. But this system of Internet is slowly getting worse and leading to various real and tangible issues behind the financial crisis and the long-lasting impact on the economy as well as on the society [1]. The need for some rules for regulating the behavior of the society is required wherever people organize in groups or associations. These rules and regulations are formed to control the behavior of mankind in a civilized society and to maintain peace and order, known as “law” and disobedience of law may be termed as a “crime.”1 According to academics, there is a need to take the economic impact of cybercrime seriously by organizations, with the cost of cybercrime already reaching 0.89% of worldwide GDP, or $600 billion per year. Latest analysis on the economic impact of cybercrime by security firm “McAfee” and the “Center for Strategic and International Studies,” Europe has the biggest economic influence of cybercrime, estimated at 0.91% of regional GDP, compared to 0.83 percent in India. “The reality is that cybercrime is just an evolution of traditional crime and has a direct impact on economic growth, jobs, innovation and investment,” he said. “Companies need to understand that in today’s world, cyber risk is business risk” [2].

2 Cybercrime There are three main elements that make up a crime: a human being, mens rea (evil intent), and actus reus (loss of society at high level). Cybercrime is when such an act is committed or omitted in furtherance of an evil intent using computer technology. Even though the word “cybercrime” has been judicially defined in several Indian decisions, it is not specified in any act or statute issued by the Indian legislature. Cybercrime is an uncontrollable evil that stems from the misuse of modern society’s growing reliance on technology. The usage of computers and other related technologies in daily life is fast increasing, and it has evolved into a need that supports user convenience. It is an unlimited and immeasurable medium. Hackers are criminals who engage in these kind of illicit activity. Computer crime is another term for cybercrime [2]. According to Techopedia, “Online bank information-theft, identity-theft, online predatory crimes, and other unlawful computer access” are all examples of cybercrime. More serious crimes, such as cyber terrorism, are also a major source of concern. Although cybercrime involves a wide range of actions, it can be divided into two categories: • Computer network or device-related crimes.

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Prof. S.N. Mishra; Indian Penal Code, Central Law Publication, p. 2.

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• Cybercrime that involves the exploitation of computer networks to facilitate other criminal actions [2].

2.1 Types of Cybercrime The computer-related offenses are broadly as follows: 1.

2.

Dos Attack: A denial-of-service attack prevents legitimate users from utilizing a service. When a web server is inundated with requests for information, the system becomes overwhelmed. Although such attacks usually do not jeopardize data security, they do waste time and money [6]. Adware and Spyware: Adware and Spyware are often used together, and there is a thin line of difference between the two. They are often referred to the programs that get installed on our computer without and with our permission (perhaps permission being granted unwittingly). The former category is called spyware and the latter adware: • Adware generally comes with an uninstaller and can be easily removed from a system. • Spyware, in the contrast, installs itself surreptitiously and is difficult to remove without assistance. These apps can decimate computer’s resources, slow down Internet connection, track online activities, and even forcefully redirect browser [2].

3.

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Scareware or Fake Anti-virus Program: Often an unethical marketing practice is used to create anxiety, or perception of threat to convince an unsuspecting consumer into purchasing and downloading a software of limited or no benefit; sometimes, it is malware itself. This is known as Scareware or fake anti-virus program. Some forms of spyware and adware also use scareware tactics. This amounts to fraud and cheating as well. Ransomware: Ransomware (malware), or extortive malware that holds users’ data to ransom. Cyber Stalking, Spam, Spim, and Phishing: Spam, Spim, and Phishing are also common in the Cyber world. They are broadly as follows (Fig. 1); • Cyber Staking: Many characteristics of information technology, such as its low cost, simplicity of use, and anonymity, make it an ideal medium for phishing scams, child sexual exploitation, and, increasingly, a new problem known as “cyber-stalking.” Stalking is the act of following and watching someone for an extended period of time in an aggravating or threatening manner. It usually include persistent harassing or threatening activity, such as following a person, pop up at the home or building, making unwanted phone calls, leaving written messages, or vandalizing the property.

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• Spam: It is an email that has been sent to you without your permission. They are a threat. They should include an option to unsubscribe, or they should not be sent unless specifically requested. • Phishing: This is a deceptive method of obtaining confidential information. Phishing is a type of email scam in which unsuspecting consumers are sent official-looking e-mails that seek to trick them into divulging Internet passwords and user names. Typically, victims are lured to click on a link that leads to a hacked version of a company’s website.

3 Challenges in Cybercrime The following are some of the variables that continue to have an impact on the condition of cyber-security: • Low Awareness of Internal Employee: Internal employee awareness remains the first line of defense. However, few businesses engage in cyber-security awareness training and improvement. • Lack of Budget and Unsupportive Top Management: Budgets are frequently driven by business needs, and cyber-security is given a low priority. Support for cyber-security projects is frequently given poor importance by top management, which is another source of concern. This is mostly due to a lack of understanding of the dangers’ consequences. • Ineffective management of identity and access: This is the fundamental element of cyber-security. In a period where hackers appear to have the upper hand, all it takes is one hacked credential to get access to a corporate network. Despite considerable progress, there is still much to be done in this area. • The Rise of Ransomware: The recent case of malware attacks, viz. WannaCry and Petya, two recent computer assaults, have highlighted the growing threat of ransomware. Criminals are looking into alternative routes as more consumers become aware of the risks of ransomware attacks via email. To get around endpoint security software that focuses on executable files, some people are experimenting with malware that reinfects afterward, “some are starting to employ built-in tools and no executable malware at all long after a ransom is paid.” Authors of ransomware are increasingly employing strategies other than encryption, such as removing or modifying file headers. • Mobile devices and apps: As more businesses use mobile devices as their primary mode of communication, hackers will increasingly target them. Because mobile apps may be used to conduct financial transactions, the mobile phone has become a more appealing target, resulting in an increase in mobile malware. The potential of jailbreak and rooted smartphones being utilized for financial gain expands the attack surface. • Social Media: As the use of social media grows, hackers will have more opportunities to exploit it. Many users publish their information for all to see, which could be used to damage the user’s company [12].

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• Obscenity: It is commonly stated that if your youngster spends too much time alone on a computer, you should be concerned; the information available in cyberspace is as diverse as human thought. It also contains pornographic material and information. Such material has the potential to deceive an impressionable mind [11].

4 Challenge in Preservation and Retention of Information Nothing is private in the computer world. Everything can be discovered until the data is irreversibly lost or overwritten. It is critical that the information be kept safe [10]. Impact of cybercrimes on various systems • Impact on Society A crime-free society is just a myth, crime is an omnipresent occurrence that is an inextricable aspect of social existence, the question, “Why is there so much ado about crime?” may irritate some people. It is undeniable fact that crime is a social phenomenon; it is everywhere, civilized or uncivilized, and it is one of the most basic inclinations of all the activity. However, it is important to keep in mind that high crime rates are a source of societal concern not because of their nature, but because of the potential for social disruption [9]. • Impact on Socio-Eco-Political “Crime is a dynamical and comparative” phenomenon that is influenced by the existing societal order’s relative socio-political and economic transformations. Coincidentally, economic crime is at an all-time maximum. This reveals the indissoluble relationship between crime and other social phenomena, economic systems, and political establishment. In addition, one of the most important elements determining crime rates is the population. A positive association has been discovered between the increase in crime and the country’s population. Other factors impacting crime include the circumstances in a given location, the rate of urbanization, population migration from surrounding places, unemployment, wealth disparity, [computer literacy in the case of Cybercrime], and so on. Simultaneously, the economic structure of a given society has an impact on economic crimes. This indicates all definitions of crime are linked to socioeconomic and political variables. • Impact on Industry and cybercrimes Many businesses have been victims of cybercrime throughout the years, but they are unaware of it, as it is a sickness that kills from within. According to the survey, a representative sample of 59 large-sized enterprises in several economic sectors, despite having well awareness of the cyber-threat, cybercrime has significant financial ramifications for institutions and government bodies. The average annualized cost of cybercrime for 55 companies, according to the survey, is $4.9 million per year,

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ranging from $3.5 million to $32.5 million per year. In comparison with the previous year’s study, the total cost is considerable [8]. • Impact on Consumer Behavior Corporations must recognize the dangers to their online enterprises, have strategic consequences for their long-term success, and take appropriate steps to eliminate or considerably decrease these threats. These counter-measures, dubbed “cybersecurity,” were created to protect consumer’s privacy and information shoppingexperience. Need is there to develop models which will allow corporations “to study the effects of cybercrime on online consumer confidence” and to counter through “leveraging the benefits associated with the latest developments in cyber-security.” • Impact on Businesses Cybercrime is a broad term that encompasses a wide range of devious unlawful behaviors aimed at jeopardizing a company’s computer security. An electronic breakin aims to steal financial information from a company or its clients, deny service to the corporate website, or implant a virus that tracks the organization’s online activity in the future [7]. Need for Cyber Law With the increased misuse of technology, tough statutory rules are required to regulate illegal actions in the cyber realm and to defend the actual meaning of technology. The law that governs cyberspace is known as cyber law. Computers, networks, software, data storage devices (such as hard disks, USB disks, and so on), the Internet and even electronic gadgets like cell phones, Automated Teller Machines, and so on are all included in cyber space [6]. Cyber law covers the following topics: 1. 2. 3. 4.

Cybercrime Digital and electronic signatures Intellectual Property (IP) Privacy and Data Protection.

There are a number of reasons why traditional law finds it difficult to deal with cyberspace. The “INFORMATION TECHNOLOGY ACT, 2000” [ITA, 2000] was adopted by the “Indian Parliament” to protect the fields of “e-commerce, e-governance, ebanking, and cybercrime punishments and penalties.” The IT Amendment Act, 2008 [ITAA-2008] was enacted to amend the previous Act [3]. The principal goal of the Information Technology Act of 2000 is to give electronic trade legal legitimacy and to make it easy to file e-records with the govt. The IT Act also criminalizes a variety of cybercrimes and imposes harsh penalties (imprisonment up to 10 years and compensation up to Rs 1 crore) [4, 5]. Cybercrime has a great potential and, as a result, has a tremendous impact when it occurs. It is simple to perpetrate without requiring physical presence as it is global in nature, it has become a problem and risk to the criminal justice system, as well as vice versa. The international character of

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ICTs may preclude the application of strict legislation, posing a threat to the principle of criminal law. As a result, international laws and regulations, as well as reliance on technology, are essential in combating the crime race [11, 12].

5 Conclusion and Future Trends of Cybercrime in India Finally, it can be stated that the phenomenal development in Internet use around the world, particularly in India, has coincided with a significant increase in cybercrime, making India vulnerable to such crimes. These crimes are of global nature and not restricted to boundaries. There is different impact on different kinds of working systems which adds up to build an economy or a strong nation. We need to have strong and leak proof laws and regulation to curb the menace of cybercrimes. Efforts of the law-making agencies should be made to keep the crime under control. Therefore, the legislation must be covering each and every aspect of cybercrimes so that a vigilant and constant check can be made over the cybercrimes. It will be easier to join criminal organizations via the Internet than it was previously. Secret messages can easily be sent to a big group of people through the Internet without being noticed. Furthermore, genuine civil liberties could be used to advocate for not monitoring information technology. All of these factors make dealing with cybercrime more challenging. In the approaching era, improved social engineering attacks will be the norm. Attackers will progressively use social engineering approaches to get above technology security controls, fine-tuning their methods to take advantage of natural human tendencies.

References 1. 2. 3. 4. 5. 6. 7.

8. 9. 10.

Chatterjee I (2014) Law on Information technology. Central Law Publications Justice Yatindrasingh (2016) Cyber laws. Universal Law Publishing Co Mishra SN (2018) Indian Penal Code, Central Law Publication, edn Information Technology Act, 2000, Information Technology Act, 2000—Wikipedia Indian Penal Code, 1860, India Code: INDIAN-PENAL-CODE-1860 Das S, Nayak T (2013) impact of cybercrime: issues and challenges. Int J Eng Sci Emerg Technol 6(2). ISSN: 22316604 Gupta RK, India: An Overview of Cyber Laws vs. Cyber Crimes: In Indian Perspective, available at https://www.mondaq.com/india/privacy-protection/257328/an-overview-of-cyberlaws-vs-cyber-crimes-in-indian-perspective Williams GL (2006) Glanville Williams Learning the Law. In: Smith ATH (ed). Sweet & Maxwell Roshan, N., What is cyber Crime. Asian School of Cyber Law, 2008: Access at—http://www. http://www.asclonline.com/index.php?title=Rohas_Nagpal Govil J (2007) Ramifications of cyber crime and suggestive preventive measures. In: International conference on electro/information technology, 2007 IEEE. 2007,Chicago. pp 610–615

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11. Jones A (2004) Technology: illegal, immoral, or fattening? In: Proceedings of the 32nd annual ACM SIGUCCS fall conference. , ACM, Baltimore, pp 305–309 12. Majesty H (2010) Cyber Crime Strategy, S.o.S.f.t.H. Department, Editor. The Stationery Office Limited, UK, p 42

An Insight on Latest Technologies of Cyber Security Aditya Bansal, Raghav Goel, Shagun Sharma, Kanupriya Verma, Megha Bhushan, and Ashok Kumar

Abstract Cyber security plays an important role in protecting the financial as well as personal data of an individual. In the time of Internet availability, cyber-attack is the basic challenge to the user’s privacy and security. Cyber-crimes occur due to human errors and online stealing of user’s information, which can be avoided and protected by enabling the correct use of cyber security. It provides a way of securing Internet information, electronic systems, servers, mobile devices, and computers from malicious attacks. Hence, tools are required to provide security against cyber-attacks, i.e. phishing, Trojan horse, and malwares, to protect cyber information from unauthorized access and use. This review provides the information on latest techniques used in cyber security including merits, demerits, and future challenges along with the detailed description about cyber-attacks. Further, it will help researchers to build up more proficient solutions for cyber security. Keywords Cyber security · Cyber-attacks · Artificial intelligence · Blockchain · Machine learning

1 Introduction Social Media is a tool for extracting information about financing data, various health, and pharmaceutics issues [1]. It can be the biggest cause of leaking user privacy and A. Bansal · R. Goel · S. Sharma Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India e-mail: [email protected] K. Verma Thapar Institute of Engineering and Technology, Deemed to Be University, Patiala, Punjab, India M. Bhushan (B) School of Computing, DIT University, Dehradun, India e-mail: [email protected] A. Kumar School of Computer Application, Lovely Professional University, Phagwara, Punjab, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_54

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data. Due to which, false information can be conveyed. Most of the time social media has been identified as a significant cause of cyber-attacks, like creation of fake accounts by which a huge amount of fake information can be spread [2]. It is a challenging task to identify fake accounts. Most of the time cyber-attacks are caused by improper use of electronic gadgets and human errors for which various industries use cyber security for protecting data and privacy [3]. The attackers can hack a number of systems at a moment remotely [4]. Due to an increase in online systems, the number of cyber-attacks has also increased. Cyber-attacks have always been the biggest cause of concern for online applications and data storage systems. To avoid such challenges, there is a need for an anti-cyber-attack system which is also called cyber security. According to [4], the hackers can use a small bug which can spoil the complete private and confidential information. The account hacking is also easy for the hackers if the users have set weak passwords, installed the software with less security or outdated software. Outdated software is the easiest and preferred way to steal a user’s personal information [5]. To avoid such problems, third party clone codes can be used to protect the data against malware and insecure systems [6]. Cyber-attacks can also occur in Autonomous Vehicles (AVs), which are in high demand nowadays [7]. Many companies make huge investments on AVs; however, it is necessary that there should be no privacy and security compromise. The sensors which are used in the AVs are very sensitive to hostile changes. An integration of Artificial Intelligence (AI), cloud and cyber security is helpful to create highly secured sensors for data protection in AVs. Numerous companies are moving toward technology due to its high demand in the automobile industry [8]. Due to the technology, operations of various vehicles have been drastically changed from mechanical to computerized. Cyber security has given more advancements to AVs like reducing the amount of accidents, user friendly nature and data protection [9]. Further, the electric grid has experienced a substantial change toward smart grid for better security, sustainability, and customer experience [10]. It relies on two-way communication and exposes itself to public information networks to deal with the cyber-attacks. The operationalization of smart grid, numerous advanced monitoring, and control features have been enabled for the prevention of cyber-attacks [11]. In Denmark, Electric Access Control Systems (EACS) or digital systems are used for controlling the security portals [12]. A decade ago, EACS had used smart cards to avoid cyber-attacks, but they are not updated and replaced till now [13]. There are many advancements in cyber security like SIEMs and ELK (abbreviations missing); however, these are not secure enough against a number of cyber-attacks. Information security and privacy are not the only concern issues but also letting people know that how their data is being collected and used is also important. With the growth of digitization, there is an increase in health care which can be seen as a boon or bane due to the flourishing use of AI [14]. The advanced cyber secured healthcare systems are enabled with high protection from cyber-attacks [15]. Cyber-attacks can transform the best ongoing businesses into ash within a very short duration, and hence, it is required to enable the complete set of systems with advanced and highly secured frameworks. Cyber security can prevent such systems from a number of failures such as data stealing and information leakage. Due to an increase in AI, the demand

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for Internet of Things (IoT) system has also increased. IoT devices are very important in the robotic world due to highly efficient sensor functionalities. Nowadays, because of the lack of cyber knowledge people are facing attacks in the smart IoT devices [16, 17], which requires the need of cyber security. However, it is incapable of protecting the data and machines. Hence, machine learning (ML) has been combined with cyber security to understand the working of human made robotics and protect them from unauthorized access and usage [18]. There are various online scams which could happen due to the absence of using cyber security in the right way. Hence, to solve the online information leakage scam ML plays an important role which is well known for prediction of upcoming theft [19]. Many industries require cyber security to avoid nuclear power generation challenges and to decrease the cyber-attacks for managing the cyber systems. This survey paper consists of the complete information about the tools and techniques, which can be used to identify cyber security attacks, securing the healthcare data, autonomous vehicles data, unauthorized access to public information, financial data, to optimize smart grids, detecting and protecting from upcoming thefts in nuclear power generation. This review is structured as follow: Section 1 describes the introduction to cyber security, IoT, and AI. Various technologies used by cyber security are introduced in Sect. 2. The complete literature review has been written along with the merits and demerits of various cyber security technologies are described in Sect. 3. Finally, the conclusion and future scope are described in Sect. 4.

2 Background 2.1 Machine Learning in Security ML and deep learning techniques play an important role to determine the source of spreading the fake news, identifying human emotions and sentiments using various models [1, 20]. AI has been found as the base of ML, which helps in solving various issues such as maintaining privacy, securing user accounts, and data personalization over social media. But, ML is not enough to handle the cyber-crimes happening in IoT systems, clouds, and hardware systems. Hence, cyber security has come into existence for solving the matter of online theft. It comprises technical and nontechnical skills to understand the complete framework of online security management [3]. There are a number of systems which help in solving the challenges of cyberattacks [4, 17, 18, 21]. Numerous cyber-crime-based statistics are available which results in poor security due to hacking and Trojan horse attack on the systems. Cyber security technology called test protective helps in tracking the vulnerabilities in the software devices to avoid leakage of confidential data. There are various cyber hacking systems which can be implemented in smart transport systems [7, 22]. To

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solve such problems, AI can be combined with cloud-based services to maintain security and privacy. ML plays an important role when it comes to the challenges with advanced Internet of transportation systems. ML predicts the upcoming attacks on the online systems by using historical data. Cyber security helps in reducing the accidents by using a Cyber Security Management System (CSMS) structure which is built using dev-ops [5, 8]. Along with such features, there are many challenges with cyber security techniques such as firewall settings and difficulty to install the software.

2.2 Blockchain in Security With the growing technology, there is an increase in the usage of smart grids and smart cards, due to which security challenges have also increased [23]. To solve such problems, blockchain technology can be used to make a secure and private method [10, 12] by using its distributed architecture and immutability [23]. It keeps the whole network secure during online information and money transfer. Smart cards have been used in the hotel industries for locking and unlocking the rooms [12]. The emergence in cyber security has made the social animal very careless about their personal data and privacy [13], and therefore, it is required to maintain security during online sharing of information among organizations [14]. There are various technologies which can be combined and tested with cyber security to increase the level of securing data and hardware devices which are mentioned in Fig. 1. Data analytics and ML are found as the best tools used for sharing the information by keeping data security in mind. Another cyber security technology called CyExec is developed using virtual box and Docker to increase privacy and security in IoT devices [15].

Fig. 1 Technologies used in cyber security

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3 Literature Review Thuraisingham [1] has identified that social media plays a major role in connecting people all over the globe. It provided the pre-hand information about diseases and also identified the major reason for spreading fake information. The integration of AI and cyber security has changed the way of spreading information and protecting from cyber-attacks. Hall and Rao [3] have identified technical cyber defense as a less effective process to provide cyber security and to avoid cyber-attacks in social accounts by taking care of decision-making. Akram and Ping [4] have recognized cyber-crimes as the process of rising insecurity in every Internet-connected field. They have evaluated the vulnerabilities of the software systems by tracing the patch files at source levels, which helped to maintain security and avoided cyber-attacks. Thuraisingham [7] has presented IoT as a positive contribution in the industry of automobiles including air and water vehicles. AVs are also required to be protected as they contain sensor information. Hence, to secure such information, AI has been combined with cyber security which could help in securing the information of AVs. Schmittner et al. [8] have recognized cyber security as a vital segment of the automobile industry. The results conclude that the CSMS is mandatory for vehicle lifecycle and ecosystems. Zhuang et al. [10] have used cyber security and blockchain technology in the field of smart grid to store the information without the need of external parties. A survey was conducted to identify the people’s perspective and techniques for implementing blockchain-based applications on the smart grid. Tuele et al. [12] have identified that smart cards are mostly used in the industries but after a deep survey cards were recognized as sensitive to cyber-attacks. Carroll et al. [13] have found Internet and digital world as a part of daily life. Consuming such facilities can cause many risks about which the population is unaware. A study has been conducted to identify the human mind which resulted in people becoming conscious about their data security and privacy. Hautamäki and Kokkonen [14] have identified computer networks based healthcare systems as the most common and important element in the digitized world. These digital systems can cause severe hazards and, therefore, are very insecure. To avoid insecurity in such systems, the concept of cyber security has been classified in different levels to solve the security issues of information leakage among two parties. Shin and Seto [15] have concluded that the lack of knowledge of cyber security in the field of IoT can damage the confidentiality of information and businesses. It has been observed that web developers can exploit the knowledge about cyber security technology to develop a low cost safe and secure system. Kumar et al. [16] have used the concept of AI to deal with natural language processing for providing security to social media. Merat and Almuhtadi [24] have developed ML-based computer processes, which can be used to map multitasking systems. A paradigm called SHOWAN has been designed to map and model the behavior of cyber awareness. There are various latest cyber security technologies along with their merits and demerits as tabulated in Table 1.

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Table 1 The state of the art related to cyber security technologies, merits, and demerits Citation Technology used

Merits

Demerits

[1]

• Artificial intelligence

• Detection of user sentiments • Information on diseases • Detection of fake news

• Privacy challenges • Malicious software

[3]

• Non-technical skills • Soft skills

• Supports critical analysis • Lacks in solving skills demands of cyber security role • Problem solving skills

[4]

• DoS attack

• Helps overcome cyber • Component level security attacks granularity is vulnerable • Tracing vulnerabilities in computer system • Shows statistics of recent cybercrime

[7]

• Artificial intelligence • Cloud-based intelligent transport system

–

–

[8]

• Cyber security management system • Dev-Ops

–

–

[10]

• Blockchain • Smart grid

• Data protection • Controlling smart grid using cloud

• Improvement for data collection, storage and control execution in smart grid is needed

[12]

• Smart cards

–

• Remote duplication of smart cards • Reveals civil registration number

[14]

• Data network

• Useful for real life scenarios • Can be used nationally and internationally

• Requires testing with real data with classified levels • Systems often restrict connectivity

[15]

• Internet of Things • CyExec

• Detecting vulnerabilities • Attacks and defenses against IoT devices

• High cost of exercise system • Shortage of employees in managing the exercise system

[16]

Artificial intelligence

–

–

[19]

• Machine learning • Deep learning

• Recognition of online frauds

• Due to less dataset Deep learning cannot predict better

[23]

• Blockchain technology

• Immutable • Due to decentralized server information leakage is impossible

• Complex architecture

[24]

• Artificial intelligence • Cyber security • Machine learning

–

–

(continued)

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Table 1 (continued) Citation Technology used

Merits

Demerits

[25]

• Network support • Integration with hardware devices is easy

• Data security and safety issues

• IoT • Radio Frequency Identification Technology (RFIT)

4 Conclusion and Future Scope Cyber-crimes can cause fraud, destruction of information, loss of productivity as well as intellectual property and personal data theft. It can result in anxiety and fear in the human mind about security and privacy of their data. There are many attacks such as Trojan horse, Man-in-the-middle attack, browser hijack, polymorphic virus, and many more which have adversely affected the common man. Although there are many cyber security-based algorithms available, such crimes are still prevailing. Thus, there is a requirement to develop anti-cyber-crime tools and technologies which can avoid the virus injection in software along with hardware security. This review provides an overview on the latest cyber security attacks and approaches to avoid cyber-crimes. It discusses various techniques of cyber security followed by their merits and demerits, and future challenges. The combination of cyber security tools shown in Table 1 can perform better to avoid cyber-attacks rather than each tool alone. For the future scope, there is a need to develop a more secure network to avoid data leakage and privacy issues.

References 1. B. Thuraisingham B (2020) The role of artificial intelligence and cyber security for social media. In: Presented at the 2020 IEEE international parallel and distributed processing symposium workshops (IPDPSW), May 2020. https://doi.org/10.1109/ipdpsw50202.2020.00184 2. Thakur K, Hayajneh T, Tseng J (2019) Cyber security in social media: challenges and the way forward. IT Professional 21(2):41–49. https://doi.org/10.1109/mitp.2018.2881373 3. Hall JL, Rao A (2020) Non-technical skills needed by cyber security graduates. In: Presented at the 2020 IEEE global engineering education conference (EDUCON). https://doi.org/10.1109/ educon45650.2020.9125105 4. Akram J, Ping L (2020) How to build a vulnerability benchmark to overcome cyber security attacks. IET Inf Secur 14(1):60–71. https://doi.org/10.1049/iet-ifs.2018.5647 5. Cazier J, Medlin B (2006) Password security: an empirical investigation into E-commerce passwords and their crack times. Inf Syst Secur 15(6):45–55. https://doi.org/10.1080/106589 80601051318 6. Backes M, Bugiel S, Derr E (2016) Reliable third-party library detection in android and its security applications. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016. Available: https://doi.org/10.1145/2976749.2978333

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7. Thuraisingham B (2020) Cyber security and artificial intelligence for cloud-based internet of transportation systems. In: Presented at the 2020 7th IEEE international conference on cyber security and cloud computing (CSCloud)/2020 6th IEEE international conference on edge computing and scalable cloud (EdgeCom), 2020. https://doi.org/10.1109/cscloud-edgecom49 738.2020.00011 8. Schmittner C, Dobaj J, Macher G, Brenner E (2020) A preliminary view on automotive cyber security management systems. In: Presented at the 2020 design, automation and test in Europe conference and exhibition (DATE), 2020. https://doi.org/10.23919/date48585.2020.9116406 9. Ren K, Wang Q, Wang C, Qin Z, Lin X (2020) The Security of Autonomous driving: threats, defenses, and future directions. Proc IEEE 108(2):357–372. https://doi.org/10.1109/jproc. 2019.2948775 10. Zhuang P, Zamir T, Liang H (2021) Blockchain for cybersecurity in smart grid: a comprehensive survey. IEEE Trans. Ind. Inf. 17(1):3–19. https://doi.org/10.1109/tii.2020.2998479 11. Goel S, Hong Y (2015) Security challenges in smart grid implementation. SpringerBriefs in Cybersecurity, pp 1–39. https://doi.org/10.1007/978-1-4471-6663-4_1 12. Teule JJ, Hensel MF, Buttner V, Sorensen JV, Melgaard M, Olsen RL (2020) Examining the cyber security of a real world access control implementation. In: Presented at the 2020 international conference on cyber situational awareness, data analytics and assessment (CyberSA), 2020. https://doi.org/10.1109/cybersa49311.2020.9139617 13. Carroll F, Legg P, Bonkel B (2020) The visual design of network data to enhance cyber security awareness of the everyday internet user. In: Presented at the 2020 international conference on cyber situational awareness, data analytics and assessment (CyberSA), 2020. https://doi.org/ 10.1109/cybersa49311.2020.9139668 14. Hautamaki J, Kokkonen T (2020) Model for cyber security information sharing in healthcare sector. In: Presented at the 2020 international conference on electrical, communication, and computer engineering (ICECCE), 2020. https://doi.org/10.1109/icecce49384.2020.9179175 15. Shin S, Seto Y (2020) Development of IoT security exercise contents for cyber security exercise system. In: Presented at the 2020 13th international conference on human system interaction (HSI), 2020. https://doi.org/10.1109/hsi49210.2020.9142678 16. Kumar N, Kharkwal N, Kohli R, Choudhary S (2016) Ethical aspects and future of artificial intelligence. In: Presented at the 2016 international conference on innovation and challenges in cyber security (ICICCS-INBUSH), 2016. https://doi.org/10.1109/iciccs.2016.7542339 17. Mangla M, Kumar A, Mehta V, Bhushan M, Mohanty SN (2022) Real-life applications of the internet of things challenges, applications, and advances. Apple Acad Press, 2022. [E book] Available: https://doi.org/10.1201/9781003277460 18. Verma K, Bhardwaj S, Arya R, Islam MSU, Bhushan M, Kumar A, Samant P (2019) Latest tools for data mining and machine learning. Int J Inno Tech Exp Engi 8(9S):24–28. https://doi. org/10.35940/ijitee.I1003.0789S19 19. Sharma S, Ramkumar KR (2020) A comparative analysis on applications, tools and techniques of deep learning. J Crit Rev 7(15):1542 20. Nalavade A, Bai A, Bhushan M (2020) Deep learning techniques and models for improving machine reading comprehension system. IJAST 29(04):9692–9710. http://sersc.org/journals/ index.php/IJAST/article/view/32996 21. Sahrom Abu M, Rahayu Selamat S, Ariffin A, Yusof R (2018) Cyber threat intelligence—issue and challenges. Indonesian J Electr Eng Comput Sci 10(1):371. https://doi.org/10.11591/ijeecs. (v10.i1. pp371–379) 22. Sharma S, Nanda M, Goel R, Jain A, Bhushan M, Kumar A (2019) Smart cities using internet of things: recent trends and techniques. Int J Inno Tech Exp Engi 8(9S):24–28. https://doi.org/ 10.35940/ijitee.I1004.0789S19 23. Sharma S, Kumar A, Bhushan M, Goyal N, Iyer SS (2021) Is blockchain technology secure to work on? In Blockchain AI technol Ind Internet Things., pp 66–80 IGI Global. http://doi:10. 4018/978-1-7998-6694-7.ch005

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24. Merat S, Almuhtadi W (2015) Artificial intelligence application for improving cyber-security acquirement. In: Presented at the 2015 IEEE 28th Canadian conference on electrical and computer engineering (CCECE), 2015. https://doi.org/10.1109/ccece.2015.7129493 25. Datta P, Sharma B (2017) A survey on IoT architectures, protocols, security and smart city based applications. In: Presented at the 2017 8th international conference on computing, communications and networking technologies, ICCCNT 2017. https://doi.org/10.1109/ICCCNT.2017. 8203943

Progressive Web Apps (PWAs)—Alternate to Mobile and Web Sarwar Ali, Chetna Grover, and Renu Chaudhary

Abstract With the advancement in technology in each counting second new solutions to new problems comes into the picture and PWA are one of them which came as an alternative to the current leading technologies like android and Web, combining features of both the technology and overcoming the shortcoming of both the technologies. Progressive Web Apps are web apps that are built using the Service Workers API which can work offline by intercepting HTTP requests and delivering cached responses. The concept of progress is an approach that takes advantage of the capabilities of the environment instead of having rigid requirements. PWA is built without requiring the installation of a native app and works seamlessly across various platforms. Many Android Web browsers support Service Workers, which is a standard component of the PWA landscape. This paper aims to analyze the current state of support for PWA features in Web Views and how it fits into the overall web experience. Keywords Progressive web apps · Install ability · In-app browsers · Information technology · Service worker · App Shell · Native android application

1 Introduction In 2015, Google Chrome engineer Alex Russell and designer Frances Berriman coined the term progressive web apps to describe apps that take advantage of the latest features of modern browsers. Progressive Web Apps (PWA) is a new technology that aims to overcome the limitations of mobile browsing and native apps [1]. They are web-based user experiences that are dependable, quick, and engaging. They are started by clicking on an icon on the device’s home screen, similar to how native applications are launched. PWAs appear on your screen right away, regardless of whether or not you have internet access. They provide support for the splash screen by sending push notifications. The service worker (a collection of APIs) works in S. Ali (B) · C. Grover · R. Chaudhary HMRITM, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_55

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Fig. 1 Visuals of PWAs

the background, allowing developers to programmatically cache and preload files, as well as manage data through a mechanism known as push notifications [2]. Service Worker is a threaded module that is responsible for providing generic entry points via which PWA may perform background tasks. PWAs have a linkable URL that is both responsive and secure. PWAs are web apps that use a combination of web and native app technologies and patterns to make use of both web and native app capabilities. Web applications are more accessible than native apps. For example, visiting a website is easier and faster than installing an app, and you can easily share web apps by providing a link. In 2014, the number of people using mobile devices to access the internet exceeded those using desktop computers [3]. This demonstrates how vital it is to make your online apps mobile-friendly today more than ever. To overcome the constraints that the web as a platform imposes on mobile devices, companies frequently feel the need to build native or hybrid apps. Figure 1 shows the visuals of how the structure of a PWA looks like. These programs are typically downloaded from app stores and have extensive access to device hardware via platform-specific APIs [4]. These programs may be downloaded from the respective operating system’s app store and operated in a native environment, with all of the capabilities that a native app has. When these programs are removed from their natural context, they are unable to provide this experience due to browser limitations. Progressive Web Applications (PWA was able to solve some of these issues. By eliminating app stores, boosting page performance, employing voice search, utilizing phone capabilities, and keeping the UX consistent, PWA increased conversion rates. Web App Manifest, Push Notifications, and Add to Home Screen capability are all popular features. Safari currently only supports Web App Manifest and Add to Home Screen, and it does not enable web push notifications. Other main browsers, on the other hand, provide all of these capabilities.

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2 Characteristics of PWAs Each technology available today consist of a lot of characteristics which make that technology unique from other. Figure 2 diagrammatically list down all the characteristics which PWA offers us apart from it that explanation of all the features and characteristics PWA provides are as follows.

2.1 Discoverability Not an app or an app store, but a search engine—is where 93% of online activity begins. One of the many reasons we provide search engine optimization services to our clients is this [5]. The web’s superpower is its reach and discoverability, and search engines like Google and Bing, not app stores, are the most popular way to connect with clients. It is hard to send direct traffic to a native app’s landing screen, especially if the client has not completed the 6–8 steps required to install the app. However, you may use search or sponsored channels to deliver massive amounts of traffic to any landing page. Without the need for complicated installs, it is simple to share by URL.

2.2 Offline Work Capability Progressive web apps are not just about speed. They can also operate even if the user is offline or has a sluggish or unstable internet connection [6]. Service workers: the

Fig. 2 Characteristics of PWAs

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technology behind that capability enables the app to save items offline and handle network requests to retrieve them from the local cache. It leads directly to another advantage: reducing the amount of data required to execute the program.

2.3 Speed Because high-performing sites engage and keep visitors better than low-performing sites. Performance is critical to the success of any online experience [7]. When it comes to attracting customers to use your software, speed is crucial. A visitor’s bouncing rate increases by 123% as page load times grow from one second to 10 s. The load event does not bring the performance to a halt. Users should never be concerned about whether or not their actions, such as pressing a button, were recorded. The scrolling and movement should be as fluid as possible [8]. Performance has an impact on the whole experience, from how people perceive the app to how well it works.

2.4 Cross-Platform Progressive Web Apps (PWAs) are cross-platform applications. Progressive web applications are truly platform agnostic [9]. That’s because they start as websites and then evolve into apps. The best part is that service workers, the fundamental technology underpinning PWA, are supported by all modern browsers. Chrome has the benefit of being the first to market with the most established support narrative, but Microsoft, Firefox, Samsung, and even Apple are catching up fast [10]. The majority of them offers add to home screen feature. Even if they do not, you may still encourage them to bookmark the page. Using iOS’s conventional mobile web app capability, add the site to the home screen on iPhone and iPad.

2.5 Smaller Disk Footprint Native apps have a large disk footprint. As a result, they use a large amount of the limited storage space available on mobile devices [11]. On the other hand, a few hundred kilobytes of storage are required for progressive Web apps. This varies depending on the number of pictures needed, but proper image size and caching rules limit even those to a bare minimum. This implies customers will have more space to shoot photos, films, and other “fun” activities [12].

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2.6 Responsive to Any Screen size Users may use PWA on any screen size, and all of the content is available regardless of the viewport size [13]. Users may use the program in many sizes, even on the same device, because devices exist in a range of sizes. As a result, it is crucial to ensure that not only does your content fit within the viewport but that all of your site’s features and content are accessible across all viewport widths [14].

2.7 Secure PWA uses the HTTPS protocol to encrypt the data we send, making it more difficult to intercept and alter [15]. Furthermore, people regard HTTPS as a guarantee of a publisher’s security and dependability, and Google rewards users who utilize it with higher search rankings.

3 Technologies and Concepts Each application availing in the market consist of a set of technologies and concepts which is used to build its architecture which ultimately let it develop and scale.

3.1 Web App Manifest The web app manifest is a JSON file that informs the browser how to act when a user installs your web app on their mobile device. Even though the file primarily includes generic information about your app, it is critical in making your web app seem and feel like a native program [16]. The name of the application, the icons it should use, the start URL it should start at when the app is activated, and other details are all included in a typical manifest file [17]. The majority of browsers will automatically propose that your visitors install your app if specific requirements are satisfied. Chrome requires a manifest to display the Add to Home Screen popup.

3.2 Service Workers Instant page loading, an app-like experience, an increase in mobile conversions, offline browsing, and push notifications are just a few of the benefits of PWAs. Service Workers deserve a large portion of the credit. It is a technical term for

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Fig. 3 Working of Service Workers

an API that caches key assets and files to enable offline experiences [18]. As a result, even if the network is down or unstable, PWA will continue to function. Intercepting requests and managing server answers are other responsibilities of service workers. Service workers are independent of the application they are associated with since they execute separately from the main thread. When the app is not active, the service worker can receive push messages from a server [19]. This allows the app to show push notifications to users even when the browser is closed. Service worker goes through three steps in its lifecycle—Registration, Installation, and Activation. Figure 3 diagrammatically shows the working of service workers.

3.3 APP Shell An application shell (or app shell) architecture is one technique to create a Progressive Web App that loads consistently and quickly on your users’ displays, much like native apps [20]. The app’s “shell” is the bare minimum of HTML, CSS, and JavaScript necessary to power the user interface, and when cached offline, it may provide consumers with rapid, dependable performance on subsequent visits [21]. The application shell is not loaded from the network every time the user comes to the site [22]. The network is only used to provide the necessary information. An application shell is a good choice for single-page apps with JavaScript-heavy designs. The dynamic content for each page is then loaded using JavaScript [23]. Without a network, an app shell is beneficial for quickly delivering some early HTML on the screen. Figure 4 shows how the architecture of an App Shell looks like.

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Fig. 4 Architecture of App Shell

3.4 Transport Layer Security (TLS) TLS is the industry standard for safe and reliable data transmission between two apps. The data’s integrity necessitates the use of HTTPS and the installation of an SSL certificate on the server [24]. The Transport Layer Security (TLS) protocol is supported by all PWAs. HTTPS is required for progressive web apps because it provides a better user experience. They also necessitate the use of a registered service worker, which requires the use of HTTPS. HTTPS unlocks the potential of the current web; without it, your website is limited to a restricted range of features because TLS prevents snooping and assures that the dialog between the client and the server does not tamper.

4 Required Technical Comparisons (Progressive Web Apps, Native, Hybrid, and Web Apps) Customers want advanced features like offline support, push notifications, and other native app-like functionality on their devices, but responsive websites could not provide them [25]. These elements are essential for increasing consumer engagement and e-commerce conversions. As a result, several online firms are constantly transforming their responsive websites to Progressive Web Apps (PWAs). While a progressive app looks and acts like an app, it is a website. It is exclusively designed for specified screen widths utilizing CSS, HTML5, or JavaScript. When the screen size is determined, the PWA kicks in, presenting the user with a tailored version of the website when they visit [26]. This implies that depending on the smartphone and

572 Table 1 PWA versus responsive website

Table 2 Progressive Web App vs Hybrid App

S. Ali et al. Capabilities

Progressive web apps Responsive website (PWA)

Platform

Cross-platform

Web

Installable

Yes

No

Mode

Offline/online

Online only

Push notifications Yes

No

GPS enabled

No

Yes

Capabilities

Progressive web apps ( Hybrid app PWA)

Development cost

Low

High

Indexed by Google Yes

No

Linkable

Yes

No

Requires update

No

Yes

Performance

Fast UI

Low performance

tablet screen sizes in common usage, several versions of the same website/web app may be required. Here is a bunch of ways to distinguish the two apart (Table 1): A hybrid app is a blend of native and web app aspects [27]. They enable programmers to write code in a single language that can operate on a variety of operating systems. Built with web technologies (JavaScript, HTML, and CSS), but instead of being shown in the user’s browser, they are operated from within a native app with its browser. PhoneGap (also known as Cordova), Appcelerator Titanium, and Ionic are examples of hybrid mobile app systems. A platform is not required to construct a hybrid app, but it is advantageous because it takes care of bridging the gap between native and JavaScript APIs. Here is a bunch of ways to distinguish the two apart (Table 2). Progressive Web Apps are web-based solutions that combine the functionality of a website with that of a native app. To put it another way, they are websites that replicate the behavior of a Native App, resulting in an app-like experience [28]. Native Apps are designed to operate on mobile devices and offer better performance and a wider range of functionality [29]. These apps are for certain operating systems (typically produced independently for Android and iOS) or even individual handsets. In fact, by definition, practically every software you download from Google Play or Apple’s App Store is a native solution. Here is a bunch of ways to distinguish the two apart (Tables 3 and 4):

Progressive Web Apps (PWAs)—Alternate to Mobile and Web Table 3 PWA versus native app

Table 4 PWA versus native app reasons to choose PWA for your web and mobile apps

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Capabilities

Progressive web apps Native app (PWA)

Platform

Cross-platform

Platform-specific

Indexed by Google Yes

No

Linkable

Yes

No

Requires update

No

Yes

Usability

Offline usability

High performance

Cost

Low

High

Features

PWA

Native app

Responsive web

Multi-platform capability

Yes

No

Yes

Updates required No

Yes

No

Push notifications

Yes

Yes

No

Offline usability

Yes

No

No

Faster UI

Yes

No

No

Easy sharing

Yes

No

Yes

Low cost to build Yes

No

No

5 Challenges to Overcome 5.1 Not Universally Supported The major PWA enablers, such as service workers, push notifications, and add to the home screen, are presently unavailable on iOS [30]. However, this is still a work in progress. Fortunately, most current browsers now include support for service workers, which are the backbone of most PWA functionalities.

5.2 Missing Native Device Features Several native device functionalities are still lacking [31]. App development becomes much more difficult when you do not have the necessary tools to work within. To solve this, Google has launched “Project Fugu,” a new project. Its goal is to provide a standardized WebAPI for each native device functionality that the browser presently lacks [32].

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5.3 Problems in the UX The PWA is currently being worked on, as is its compatibility with various (mobile) browsers and OS systems [33]. Since Apple does not support all of the features of a PWA, the user experience will be constrained in some circumstances [34]. It is not feasible to utilize Bluetooth or Siri or experience Face ID or Touch ID at this stage. Missing development features might also result in a poor user experience [35].

5.4 Limited Legitimacy Because PWAs lack a central download store, they lack the sense of legitimacy and trust that native applications from the Play Store/App Store provide [36].

5.5 High Battery Consumption Because PWAs are built-in high-level web code, phones must work harder to read the code thus, consuming more energy than native applications [37]. If they need to extend their battery life, users who observe a drain in their battery power are more inclined to utilize this type of program [38].

6 Conclusion and Future Scopes As technology advances, there are more and more compelling reasons to adopt PWAs. Large social networking sites, for example, are a fantastic example of something that might work well as a PWA. PWAs like Twitter Lite and Pinterest show how one can make a mobile web experience virtually as good as your native app, which is useful in countries where connectivity is scarce or expensive. PWA has many of the characteristics that we expect from a web or mobile application, such as speed, dependability, and user experience [36]. PWA development is simple for developers because they do not have to worry about languages or frameworks. When compared to native app development, PWA requires a fraction of the time. Progressive Web Apps provide several evident and potential benefits: There is no need to download or install them. They cover a wide range of topics and handle data with care. PWAs are also potential options for app development. When it comes to PWAs, there are no limitations. Apple has so far refused to fully support the app format, limiting the user experience compared to an Android device. This technology will play a significant role in the future. Like any other new technology, PWA will take time to grow and evolve—developers and consumers will not adopt it quickly.

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As a result, none of the concerns constitute cause for alarm. Google is confident that many of these will get handled in the future.

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An Analysis of Legal and Cybersecurity Issues in Internet of Things Gagandeep Kaur, Rishabh Malhorta, and Vinod Kumar Shukla

Abstract Nowadays, the technologies in the world developing a lot. Daily new technologies or new versions of existing technologies have been popping up with some benefits to the world. Just like artificial intelligence which has changed the life of people, similarly Internet of Things (IoT) helps the citizens to work more efficiently and to grow more. IoT linked with other technologies have changed the nature of work for the organizations and living style of human beings. Hence, the meaning of IoT in simple words is network of physical objects which means the physical devices/objects embedded with other technologies for the purpose of connecting and exchanging information/data with other devices and systems over the Internet. IoT plays a beneficial role in every sector such as education, agriculture, healthcare and it has been deeply interwoven in the lives of peoples and societies. Benefits aside, exchanging of data and connecting to the Internet, also connecting to potential cyber threats/crimes, where cyber security concept/law come into picture. In this paper the authors have discussed the evolution and meaning of Internet of Things, challenges involved, security in IoT and its legal implications with special references to Indian laws. Keywords Internet of Things · Data security · Data privacy · Cybersecurity · Indian laws · Information technology act

G. Kaur (B) · R. Malhorta University of Petroleum and Energy Studies, Dehradun, India e-mail: [email protected] R. Malhorta e-mail: [email protected] V. K. Shukla Department of Engineering and Architecture, Amity University, Dubai, UAE © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_56

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1 Introduction Internet of Things not new in the market as it can be traced back in nineteenth century when it was started. However, that was consider as a development phase but with the time and lots of efforts by researchers, scholars and entrepreneurs gained a good popularity in the world today. IoT in connection with other technologies developed/established/invented out of the mind devices which have been provided lots of benefits to the people in different fields of work. Especially, it has gain the demand in healthcare sector, which can be seen during pandemic how IoT-based devices helps the patients as well as doctors to diagnose COVID-19 (coronavirus). For example, these IoT devices able to monitor virus-infected patients by interconnected networks and devices. Also, the citizens relied on the system of communication to protect people against the spread of the novel coronavirus [1]. It has been observed that IoT and its related applications provided numerous benefits to the citizens in different sectors in different forms. However, as the data being shared all over the internet and with other devices plays a sensitive and important role. Hence, security concept in IoT comes into picture. People who all are accessing IoT related applications or organizations applying IoT devices for their consumers benefits concern with the security of their information or other types of attack that can be happen while accessing to devices such as cyber-attack. In order to make sure the integrity of information and other concern, safety measures must be there and adhere to the guidelines or rules provide by the government or by the international organization [2]. However, in India the laws are very vast in nature but to deal specifically any law or act not being proposed by the government. There is a draft policy on Internet of Things which was being introduced in 2015 with the aim to develop connected and smart IoT-based system for our country’s economy, society, environment, and global needs. As this policy not is being enacted as a law or act, hence the strictness on the organizations using IoT applications is very lenient. This clearly states the lack of the legal laws in India and it is a major challenge which needs to be resolved with clarities [3]. Hence, in this paper the author will discuss in detail of the concept of IoT with the challenges involved and security concerns with Indian legal perspective.

2 Concept of Internet of Things 2.1 History The concept of Internet of Things (IoT) can be traced back in nineteenth century when it was termed by Kevin Ashton. In the early 1980s, first Internet appliance was established, i.e., Coca Cola Machine at the Carnegie Melon University which has inspired a lot of inventors all over the globe to create their own connected appliances [4]. After the development of World Wide Web (www) in 1989—a Global Positioning System became available for commercial use, the governmental system of satellites

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Fig. 1 Some major events before and after Internet of Things

was complemented by the privately held ones, providing the future IoT systems with basic communications which developed an opportunity for the inventors to establish interconnected devices with ease. However, the period of 1990–2000 was considered a development phase as lots of researchers was trying to study more about IoT and its uses. By the span of time around mid of 2004, it has gain popularity in the market, when it was observed how the IoT-based devices helping to develop the lives of people by huge publishers, newspapers, and magazines started to mention IoT in their articles. Before and after invent of the term “Internet of Things” several events occurred which are being described in Fig. 1 [5]. Like other countries, India was also growing in IoT and related industries. As it was applied in many sectors of industries especially the healthcare sector was rapidly expanding, enhancing connectivity and altering the doctor-patient relationship with the help of IoT enabling remote and real-time monitoring of patients. Similarly, the benefits can be seen in other sectors as well with some advancement [6]. After the break of pandemic around the globe in 2020, world was been struggling by the novel severe respiratory syndrome coronavirus 2 by striving to control the unprecedented spread of the virus and develop a vaccine [7]. Several efforts made to find a treatment or control the spread of the COVID-19 but not shown any acceptable results, having a high demand for global monitoring of patients with symptomatic and asymptomatic COVID-19 infection, IoT technology has received significant attention in the healthcare domain where it plays an important role in different phases of various infectious diseases [8].

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Table 1 Definitions by different organizations Organization Name

Definition

Gartner

It is defined as the network of physical devices that contain embedded technology to communicate and sense or interact with their internal states or the external environment [11]

IBM

It is the concept of connecting any device (so long as it has an on/off switch) to the Internet and to other connected devices [12]

Oracle

Describes the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet [10]

PWC

The IoT refers to the connection of all kinds of devices to the Internet and to each other [13]

Aeris

The Internet of Things (IoT) refers to a system of interrelated, Internet-connected objects that are able to collect and transfer data over a wireless network without human intervention [14]

2.2 Definition In layman term, Internet of Things means physical devices or objects which are embedded with sensors or technologies which help to connect and exchange data with other devices and systems over the Internet/communications network [9, 10]. Various organizations and researchers have tried to define the meaning of Internet of Things (IoT), but there is no globally accepted or universal definition. Table 1 shows the list of some definitions based on different organizations. Walt Mossberg (2014) described IoT as “a constellation of inanimate objects by a built-in wireless connection, to be monitored, controlled and connected to the Internet”. It “refers to the connection of everyday things to the Internet and so on, the goal is to provide users with smart, efficient information” [15].

2.3 Challenges When it comes to developing Internet of Things (IoT) objects within the laboratory, network connectivity is not a major problem. With only a few devices supported by the server, the connection has very low latency and no hassles. Simultaneously sending the same IoT system to a global scale with thousands or millions of users accessing it, is completely different. The Internet is not just one network, there are many considerations such as mobile towers, connections, fire protection walls, and proxy servers that can cause problems with the connection. There are lots of challenges in IoT but the major challenges have been designed in Fig. 2 [16].

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Fig. 2 Challenges

3 Cybersecurity in IOT 3.1 Security Issues in IoT As the definition of IoT itself describe that all devices are interlinked with internet helps to communicate and exchange of data which gives a huge rise of potential cyber threats, where security aspect comes into play in IoT to keep the things and CIA trait secure. Hence, in simple terms, security in IoT means the act of securing Internet of Things devices and networks they are connected [17]. As the information being scattered/shared through Internet, with the rise of technology new forms of attack can be conducted by the attackers with the help of Internet and can affect the organizations, human lives badly. For example, a person using an IoT-based device at home and suddenly that deices got hacked; the attacker can do anything with that device to perform any type of offense. There are lots of issues involved when it comes to security in IoT, but major problems have been drawn in Fig. 3 [18].

Fig. 3 Major security issues

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Fig. 4 Legal challenges in India

3.2 Legal Challenges in India Apart from security issues as mentioned in Fig. 3, there are some other challenges as well when it comes to legal perspective. One of the major concerns is lack of specific laws in India. Along with laws, another very important concern is Data Privacy and Data Protection, as data plays a vital role in exchange of information over the internet and hackers/attacker look for the same. Figure 4 provides the brief detailed list of legal challenges in India [19].

3.3 Existing Indian Laws As establishing or developing of IoT devices or exchanging of information over the Internet with other devices must require proper governance. There are several laws/acts/rules in India which governance lots of industry or technical stuff with proper guidance. However, in relation to cyber law in India—The Information Technology Act 2000 with its rules provides a proper guidance in dealing with cyber space and its connected things. The Government of India released a framework on Internet of Things in 2015 with the aim to promote the formation of an IoT ecosystem and development of IoT products particular to Indian needs in areas such as agriculture, health, water quality, and natural disasters. Governments around the globe are also awakening to the potential of machine-to-machine (M2M) communication in solving metropolitan concerns/problems, and are constantly exploring new ideas to keep pace with changing technological trends. Table 2 laid down the existing major Indian laws dealing with Internet of Things [9, 17].

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Table 2 Major existing Indian Laws Laws/Act/Rules

Section/Article Description

Information Technology Act, 2000

Section 43A

A company that does not and does not maintain reasonable security procedures and strategies regarding sensitive personal information or data held, owned or operated by a PC device owned, controlled or operated by, is subject to a fine to the person concerned

Indian Contract Act

Section 10

An e-contract can form a valid and binding relationship between the parties, if it fulfills the essentials of a valid contract

Indian Telegraph (Amendment) Rules, 2017

Guidelines for mandatory testing and certification prior to sale, import or use in India of IoT devices

IoT Policy 2015

Aim to develop connected and smart IoT-based system for our country’s economy, society, environment, and global needs

Information Technology (Reasonable Rule 3, 5 and 8 Security Practices and Procedures and Sensitive Personal Data or Information) Rules 2011

Defining Sensitive Personal Information (SPI) Any Body Corporate required to obtain consent prior to collection of sensitive personal data regarding the use of that data Any Body Corporate or person on its must have complied with reasonable security practices and procedures If they have implemented such practices and have in place codes that address managerial, technical, operational, and physical security control measures which could follow the IS/ISO/IEC 27,001 standard or another government approved and audited standard

4 Conclusion It is of the view that Internet of Things plays an important role in various fields and has made work easier. Various definitions were made by different researchers, there is no universally accepted definition, and hence, in simple terms it means the physical objects embedded with other technologies and exchange of information over the Internet to connected devices. Countless benefits achieved with the help of IoT devices not only to the individuals but also the organization. Besides the benefits there are some major challenges involved such as data privacy, security, data storage, laws,

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and much more. Various measures been provided by the government of India and national and international organizations. In 2015, the Indian government proposed a draft framework on “Internet of Things” with the purpose to facilitate national and global participation of industry, promoting standards around IoT technologies developed in the country which does not resolve the major issues of privacy, data security or laws.

5 Future Works There are numerous challenges involved related to information transferring from IoT devices through internet or processing the information collected by IoT devices. There is no specific framework to deal with these challenges/issues. However, there are some important points which can be applied to overcome the problems/issues/challenges that are: 1.

2.

3.

4. 5. 6.

7.

When an IoT devices collecting personal information of consumer for the purpose of profiling, there must a consent from the consumers approving for the same. Whenever the information collected by IoT devices and transfer the same for processing those information by any organization, the consent must be taken by them indicating the same and also rights for their should be made clear to consumers as per GDPR. The draft of Internet of Things Policy 2015 only discuss the development or enhancement needs to be made in different sectors as laid down in the policy but it does not talk about the security techniques to be adopted to protect against cybersecurity threats. The Information Technology Act considered as a first Indian Cyber Law does not cover the term “IoT” or “IoT devices” which needs to be inserted. Government not only need to draft a policy but a stringent law which will made necessary for organizations to adhere the same. Government while framing any policy or law keep in mind about the security measures or remedies of occurring any uncertain events and whom to report for the same. There should no conflicting of laws with existing legislations while framing new and specific framework on Internet of Things.

References 1. Pittaway D (2020) The use of IOT during COVID-19. Retrieved from https://www.iotforall. com/use-of-iot-during-covid-19 2. Kumar A (2018) Internet of Things—The Bright Wave of the Future. Retrieved from https:// www.toolbox.com/tech/iot/blogs/internet-of-things-the-bright-wave-of-the-future-121818/

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3. Internet of Things. Retrieved from https://www.nielit.gov.in/aurangabad/node/17004/ 4. Lueth KL (2014) Why the Internet of Things is called Internet of Things: Definition, history, disambiguation. Retrieved from https://iot-analytics.com/internet-of-things-definition/ 5. Vardomatski S (2018) The history of IOT: A comprehensive timeline of major events, infographic. Retrieved from https://hqsoftwarelab.com/blog/the-history-of-iot-a-comprehensivetimeline-of-major-events-infographic/ 6. Gandharv K (2021) What’s driving India’s IOT market? Retrieved from https://analyticsind iamag.com/whats-driving-indias-iot-market/ 7. Zhang SX, Wang Y, Rauch A, Wei F (2020) Unprecedented disruption of lives and work: health, distress and life satisfaction of working adults in China one month into the COVID-19 outbreak. Psychiatry Res 288:112958 8. Christaki E (2015) New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence 6(6):558–565 9. Maurya K (2019) Internet of things (IOT)—Indian Legal Perspective. Retrieved from https:// www.communicationstoday.co.in/internet-of-things-indian-legal-perspective/ 10. What Is the Internet of Things (IoT)? Retrieved from https://www.oracle.com/internet-of-thi ngs/what-is-iot/ 11. Internet of Things (iot). Retrieved from https://www.gartner.com/en/information-technology/ glossary/internet-of-things 12. Clark J (2016) What is the Internet of Things, and how does it work? Retrieved from https:// www.ibm.com/blogs/internet-of-things/what-is-the-iot/ 13. The Essential 8 emerging technologies internet of things. Retrieved from https://www.pwc. com.au/pdf/essential-8-emerging-technologies-internet-of-things.pdf 14. What is Iot? Defining the Internet of Things (IoT). Retrieved from https://www.aeris.com/in/ what-is-iot/ 15. Simon T (2017) Chapter seven: Critical infrastructure and the internet of things. Cyber Secur Volatile World 93 16. Jose J (2018) Internet of Things. Penguin Random House 17. Sahay G (2020). Internet Of Things (IoT)—Policy And Challenges In India. Retrieved from https://www.mondaq.com/india/telecoms-mobile-cable-communications/992 586/internet-of-things-iot-policy-and-challenges-in-india 18. Langkemper S. The most important security problems with IOT Devices. Retrieved from https:// www.eurofins-cybersecurity.com/news/security-problems-iot-devices/ 19. Malhotra S (2020) Application and legal challenges of the Internet of Things. Retrieved from https://blog.ipleaders.in/application-legal-challenges-internet-things/

Incorporation of Secure Channel Communications Over Multi-tenant Database Rati Shukla, Rahul Shrivastava, Shivaji Sinha, Anurag Mishra, and Vikash Yadav

Abstract The Enterprise IT industry is undergoing a paradigm shift—with the help of cloud computing which is one of the main reasons. The popular IT giants such as Google, IBM, Microsoft, and Amazon have started their cloud computing infrastructure. Software as a Service (SaaS) has been one of the major business models to provide cost saving Enterprise services to small and medium enterprises (SME). In SaaS cloud provider provides their services to their customer’s on rental basis which is usually very less as compared as paying for licensed applications. Multitenancy which is an important feature of cloud computing provides a concept named multi-tenant database; is a relational model-based database architecture where single instance of the database servers multiple customers called Tenants. The application is planned to virtually partition its data and configuration and tenant is provided with a customized virtual application. However, the customers are often reluctant to store their highly confidential data using multi-tenant database in the fear of their information being exposed to other tenants either due to some application bug or any passive

R. Shukla (B) Motilal Nehru National Institute of Technology, Prayagraj, Uttar Pradesh, India e-mail: [email protected] R. Shrivastava Sanganan IT Solution, Noida, Uttar Pradesh, India e-mail: [email protected] S. Sinha JSS Academy of Technical Education, Noida, Uttar Pradesh, India e-mail: [email protected] A. Mishra ABES Engineering College, Ghaziabad, Uttar Pradesh, India e-mail: [email protected] V. Yadav Department of Technical Education, ABES Engineering College, Ghaziabad, Uttar Pradesh, India A. Mishra GLA University, Mathura, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_57

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or active attack on the database. So, here we define a highly secured model of a multitenant database protected by different encryption algorithms. Every stream of data that travels through the channel is encrypted using AES encryption and is stored in database in encrypted form. Further, the key generated by AES algorithm has been encrypted using RSA encryption and stored in database data. The particular key corresponds to a particular User ID, So, even if the data if exposed to some unauthorized user due to some application bug that will not be decrypted. Further, role-based access control (RBAC) defines the roles of different tenants and sub-tenants using the application. Keywords AES encryption · RSA encryption · SAAS · Multi-tenant · RBAC

1 Introduction Traditional Enterprise business applications require use of software and hardware that are usually very expensive and complex. It requires a complete bunch of experts to install, configure, test, run, secure, and update them. Why cloud? The answer lies here— With the help of cloud computing, one can easily does remove all types of worry regarding managing hardware and software, which is the responsibility of an experienced vendor. In the case of shared infrastructure, one only needs to pay for what they need. The upgrades are automatic, and scaling up or scaling down is also very easy. A cloud application can easily be accessed using a browser. You can simply log in, customize the app, and start using it. The conversation around cloud computing has however, shifted. It is no longer just a delivery system for IT services; cloud is increasingly seen as a growth engine for business. Cloud computing provides best low costing business solutions to small and medium enterprises. And applications can easily be shifted from traditional models to cloud-based models. However, maintenance could be a nightmare if not handled carefully. When you choose cloud servers and storage solutions for your private and hybrid clouds, you are building an infrastructure that delivers on the promise of cloud. Multi-tenancy cloud computing is a business concept used by most Software as a Service (SaaS) implementations [11]. Multi-tenancy-Three stages of market incorporation include the ideals of multitenancy (Fig. 1): 1. 2. 3.

Layer of data center Layer of Technology Layer of Functionality.

Infrastructure and application-layer customer convergence with multi-tenancy are the newest additions to cloud infrastructure topology architecture. This integration is primarily aimed at cost reduction and the creation of highly flexible SaaS software, which they do by violating the criteria for security and consumer segregation. Such

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Fig. 1 Azure architecture

designs are not suitable for applications where users store confidential data, personal data, credit card details, etc. [7–10].

2 Literature Survey Multi-tenancy: it is an architecture where a single instance serves a number of tenants at a time. There are three different types to manage a multi-tenant data— 1. 2. 3.

Individual databases. Shared database, different schemas. Shared database, shared schema.

Which technique is to be chosen depends in the economic considerations, security considerations, number of tenants and size of each tenant. Here, the last technique is of our interest. Shared database, shared schema: in this technique the database contains a set of tables that store the data of all the tenants. No separate database is deployed for every tenant. Also, a single instance is created to serve all the requests from the client. Traditional applications can be easily converted to multi-tenant at low cost with less effort. But care should be taken to build a strong reliable architecture otherwise, a poor architecture could lead maintenance nightmare [1]. The architecture considerations are: Application stability: We can define multi-tenant application as for long initiation failure could lead to total disruption affecting all the tenants. Resource Limitations and Throttling: The application limits the possibilities to occur frequencies. It

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provides you with a feature to provide you the geographical location to the deployed place. SLA: the application provides different service levels for different subscription levels of the service. Authorization and authentication: own customized schemes for authorization and authentication could be used, or the existing systems like Gmail, Microsoft account could be used.

3 Problem Formulation The compromise with the security issues in a multi-tenant applications is one of the main reason why companies are reluctant to make use of such services. For example, in a multi-tenant database there is a container database and a pluggable database. To a single container database a number of pluggable databases are attached as and when required. The container database contains the schema of the database and the pluggable database contains the schema customized for every tenant and the data being stored itself. The technique is similar to Oracle 12c [12]. In a multi-tenant database two major challenges faced when this technique is used: 1.

2.

There is a potential possibility that one of the machines might control what its neighbors are doing in this form of configuration, burrowing into the underlying networks to circumvent the security of the software layer, because a single computer delivers services to several different virtual machines. There is a potential possibility that one of the machines in this sort of configuration might control what its neighbors are doing, burrowing into the underlying infrastructure as a single computer delivers services to several different virtual machines to circumvent protection introduced on the software layer. It is quite a possibility that may there can be some data breach due to some of bad decisions of implementations or may be that can be generated due to some type of bug. People involved in using infrastructure are generally aware of this vulnerability. These two above risks could prohibit users from acquiring multi-tenant services focused on the cloud. So, protection is really important in such applications. The given scheme in the paper tries it best to enhance the security of an all dimensionally emerging new technology that is going to be the future of the Enterprise IT industry.

Objectives There are a few major objectives of Security in Multi-tenant databases: 1. 2. 3. 4. 5.

Third party software should not be used to get the coded data on receiver machine. We should adapt encryption technologies in such a manner that every data packet should travel from these channels only. Accession should be password enabled. Keys generation should be automated and should be free from dependencies. The trust-factor among users should be high.

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4 Methodologies Used and Proposed The paper proposes a design of the multi-tenant database that has been secured using AES and RSA encryption algorithms.

4.1 Schema The database is a multi-tenant database. It provides services to multiple tenants to whom the environment looks like being used by them only. The application can be customized for every tenant according to its needs. (1)

The schema proposed is 1.

2. 3.

There are a number of organizations (tenants) named x, y, z using multitenant database on a cloud to store information, all the organizations are involved in similar business types. The organizations provide multi brand online shopping services. The organizations need to maintain database of (the different tables being created): • Details of their employees. • The shippers and the suppliers with whom the company set ups shipping and supplying contracts for a given time period. • The customers who purchase products (the customers can purchase product from any organization). • The orders being booked by customers. • The products being sold and the products being shipped and supplied.

(2)

Admin: There exist two types of admin

1.

2.

(3)

The admin of the organization developing the given cloud service. Such an admin can manage the tenants, add or delete tenants to the application and can view the tenant’s basic details. The second type of admin is the authority being appointed at the end of every tenant, who can manage the different users of that particular tenant, will authorize registration of every user to the application and can customize the application accordingly. How users are differentiated:

There exists a tenant id for every tenant. The tenant id (unique for every tenant) and user id (e-mail id here, unique for every user) both together are used to differentiate users.

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Scalability of multi-tenant database:

New tenants can be added to the application by the admin anytime. A particular user like the shipper and the supplier may be dealing with a number of organizations. Therefore, shippers and suppliers can use the service to see details of orders relevant to every organization. A particular shipper or supplier can deal with a maximum of five organizations only (can be customized according to the needs). The employees however, work for a single organization only. Every time a new user among employees, shippers or supplier’s registers for a particular tenant they are being authorized by the local admin at the tenant end. (5)

Extensibility of Multi-Tenant Database:

Every table of the database includes some extra columns which can be customized according to the needs of a particular tenant as and when required. This adds to the extensibility of the database.

4.2 Advanced Encryption Standard (AES) Encryption The Standard of Advanced Encryption (AES) is a U.S. Specification of the National Institute of Standards and Technology (NSIT) for the encryption of electronic data, as provided in 2001. [1] AES is based on the Rijndael cipher developed by Joan Daemen and Vincent Rijmen, two Belgian cryptographers. The NIST selected the Rijndael (pronounced ‘rain doll’) algorithm in late 1999 as the proposal that best met the design criteria of safety, versatility, efficiency of implementation, and simplicity. AES and Rijndael are two distinct terms (Fig. 2). Performance of AES: The AES algorithm encrypts at the rate to 5320 bytes/sec in ECB mode on a P-4 2.4 GHz machine [2, 3].

4.3 Ron Rivest, Adi Shamir, and Leonard Adleman (RSA) Encryption RSA is a public key cryptosystems first published in 1977. It is commonly used to secure data transmission (Fig. 3). Implementation: • It uses a public key used for encrypting the message and a private key used for decrypting the message. The public key is known to everyone. • A typical size of n is 1024 bits [4]. The block size must be less than or equal to log2 (n), in practice block size is i bits, where 2i < n < = 2i+1 The algorithm

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Fig. 2 AES encryption flowchart

Fig. 3 Key generation formulation

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generates two large prime numbers. The public key consists of the modulus n and the public exponent e. The private key consists of the modulus n and the private exponent d. Secrecy need to be followed for the purpose of the prime factors [5] (Fig. 3).

4.4 Role-Based Access Control RBAC restrict the access of the system for the users. There are three terms defined under this: 1. 2.

Role assignment: each user is assigned a role, and can perform functions only defined under that role. Role authorization: the user must be authorized to perform given role, and the authorization must be validated at the time of login. Permission authorization: a user can exercise permission only if the permission is authorized for the user’s role.

The system (Fig. 4) being proposed as a cloud service involves multiple organizations which store their data on the cloud. And in return expect high security of their information. Every single stream of data flowing through the channel is being encrypted using AES encryption. To every new user registered, a key is generated by AES corresponding to that particular user at the time of registration and is stored in database in encrypted form using RSA encryption. When a user sings up, the user is authorized for its user id and password by checking into the database and a session is granted to the user. At the time a user requests from the server, the key generated by AES is being decrypted using RSA’s private key, and AES’s key is further used to decrypt the requested user data. Therefore, in case of some application bug or an attack on the database the data is still safe in encrypted form. The data can only be

Fig. 4 Designed architecture

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decrypted using key generated by AES. It is assumed that it is impractical to decrypt a massage on the basis of the cipher text plus knowledge of the encryption/decryption algorithm. In other words, we do not need to keep the algorithm secret; we need to keep only the key secret [6]. Therefore, the key of AES is also encrypted to enhance security.

5 Conclusion The proposed architecture provides a cloud-based multi-tenant SAAS application. The application designed ensure high commitment by the cloud company and a safe, secure and reliable cloud to multi-tenants cutting the major cost involved in purchasing licensed products and services and paying for the space on the server for storing data. The database has been secured using AES encryption. The architecture proposed has put its best efforts to provide a business model for small and medium enterprises (SME) to grow. Many traditional applications can also be easily converted to multi-tenant type.

References 1. Bezemer C, Zaidman A (2010) Multi-tenant SaaS applications: maintenance dream or nightmare? IWPSE-EVOL ’10 2. McCaffrey J, AES keeping data secure with, MSDN magazine. http://msdn.microsoft.com/enus/magazine/cc164055.aspx 3. Stalling W (2006) Ch-5, 5.2, Cryptography and network security, 4th edn. Pearson 4. A-K Al Tamimi, Performance analysis of data encryption algorithms. http://www.cs.wustl.edu/ ~jain/cse56706/ftp/encryption_perf/ 5. Stalling W (2006) Ch-9, 9.2, Cryptography and network security, 4th ed. Pearson 6. Tripathi A, Yadav V (2021) Multi-objective ANT lion optimization algorithm based mutant test case selection for regression testing. J Sci Ind Res 80(7):582–592 7. Stalling W (2006) Ch-2, 2.1, Cryptography and network security, 4th ed. Pearson 8. Betts D, Homer A, Jezierski A, Narumoto M, Zhang H (2013) Developing multi-tenant applications for the cloud on windows azure. Microsoft patterns & practices 9. Suresh KS, Prasad KV (2012) Security issues and security algorithms in cloud computing. Int J Adv Res Comp Sci Softw Eng 2(10) 10. Securing multi-tenancy and cloud computing, white paper, Juniper networks. https://www. yumpu.com/en/document/view/6799330/securing-multi-tenancy-and-cloud-computing-jun iper-networks 11. Aulbach S, Grust T, Jacobs D, Kemper A, Rittinger J (2008) Multi-tenant databases for software as a service: schema-mapping techniques. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data (SIGMOD ’08). Association for Computing Machinery, New York, NY, USA, pp 1195–1206. https://doi.org/10.1145/1376616.1376736 12. Oracle multitenant; oracle database 12 c, an oracle white paper, June 2013

Review of Detection of Packets Inspection and Attacks in Network Security Sai Kalki Jajula, Khushboo Tripathi, and Shalini Bhaskar Bajaj

Abstract In this paper, different types of attacks and their malicious act behind the scenes are presented. The solutions to defend the victim against attacks are given. In today’s scenario when hackers typing away incessantly on keyboards, juggling multiple computers to take down a group of individuals, the user needs to focus on identification and solution from such misbehavior acts. Therefore, the paper includes the detection through acquiring knowledge about packets in networks, functionality of malicious attacks, deep packet inspection (DPI) mechanism together CIA triads and further prevention from new attacks by knowing the behavior of all kind of attacks. Keywords Network attack · Packet inspection

1 Introduction In networking, a bundle is a little portion of a bigger message. Information sent over PC organizations, like the Internet, is partitioned into packets. These packets are then recombined by the PC or gadget that gets them. Assume a client needs to stack a picture. The picture document does not go from a web server to the client’s PC in one piece. All things being equal, it is separated into packets of information, sent over the wires, links, and radio floods of the Internet, and afterward reassembled by the client’s PC into the first photograph. A typical packet consists of three segments: The header part, the payload part and the trailer. The header part, as it can interpret, contains information about the receivers’ and senders’ addresses. This information is S. K. Jajula (B) · K. Tripathi · S. B. Bajaj Department of Computer Science, Amity University, Gurugram, India e-mail: [email protected] K. Tripathi e-mail: [email protected] S. B. Bajaj e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_58

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crucial as it helps in the decision-making process during the transport of the packet. The second part of a packet, i.e., the payload, contains the main information that is to be carried forward. Suppose a request is sent to view Google and receive the response on the same in HTTPS. The request and the response at layer 3 were sent via packets. Since not all of the information can be transferred via a single packet, data from the upper layer gets segmented into multiple packets and is then sent with the respective header, payload, and trailer parts. The trailer part of the packet contains a few bits of information that helps the receiver to inform that it has received all the information (in other words the entire segmented packet has been received at the end).

2 Introduction to Deep Packet Inspection (DPI) Deep packet inspection [1] is a type of data analysis method that inspect the data being sent over a computer network in detail. DPI detection methods: • Port-Based Detection • Signature Matching • Heuristic and behavior Analysis.

2.1 Port-Based Detection In the real world, do not keep all the ports open and use only specific ports for communication purposes. So, in port-based detection, the DPI tool checks the port. If it finds packets that are associated with ports and which are not cleared for communication, then it blocks such communication.

2.2 Signature-Based Detection Signature-based detection is the most effective way to detect threats. A signature is just a hash value of a malicious file that has already been detected before. What the DPI tool does is that it creates a hash value of the data of each packet that is being transmitted and checks it with the existing database of signatures. If the signature matches, then it drops the packet.

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3 Network Attacks 3.1 Network Attacks Against Confidentiality Attackers can use many methods to compromise confidentiality. After data entry, the attacker can read sensitive data such as passwords or card numbers, if network traffic is not encrypted. Pros and Cons of Network Attack Cyber kill Chain: Cyber kill chain [2] is a series of steps based on which an attack is carried out. It helps with understanding and combating different types of attacks. We have divided the cyber kill chain into seven phases each representing a specific action that takes place during an attack as mentioned in Table 1. An attacker identifies the victim, creates a victim-specific weapon, sends it through an appropriate channel, and then starts exploiting the victim in 1st four Phases The attacker installs a backdoor, takes over the victim’s machine, and finally achieves the set objective in below last three phases. Wanna cry ransomware attack: War driving is perfect alternative when a wireless network attack looks almost impossible. The word comes from a telephone hacking technique used in the 1980s—war composition. The war composition is to dial all mobile numbers during a specific sequence looking for modems. This strategy was so effective that it continues to be used by many security professionals and malicious hackers. Just like the war dialing, war composition is probably going to be around for users and eventually be helping unscrupulous attackers and the security profession [3]. The shortcoming of those items started a longing for more dependable arrangements. In any case, war drivers have not totally dropped the work of WNIC-based programming since it stays important in present day programs. Workflow of Wanna cry ransomware attack is given in Table 2. Table 1 Seven phases of Cyber Kill Chain Reconnaissance

Research, identification, and selection of targets

Weaponisation

Pairing remote access malware with exploit into deliverable payload

Delivery

Transmission of weapon to target

Exploitation

Once delivered, the weapon’s code is triggered, exploiting vulnerable applications or systems

Installation

The weapon installs a backdoor on a target’s system allowing persistent access

Command and control Outside server communicates with the weapons providing “hands pon keyboard access” inside the target’s network Actions on objective

The attackers works to achieve the objective if the intrusion, which can include exfiltration or destruction of data, or intrusion of another target

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Table 2 Steps involved in Wanna cry ransomware attack

Ethical War Drivers. The malevolent use of the conduct of war has given the full practice a bad name. The prevalence of network attacks raises questions about the motivation of “ethical war drivers”. While some companies have confidence to locate a war driver from the outside and have them transported by security that very same reasoning makes their cordless networks vulnerable [9, 10]. Cordless innovation permits an organization to expand well past the stopping space of a place of business. The Bottom Line on War Driving. War driving is a well-known action performed by each sort of individuals. While war drivers do consolidates malevolent individuals effectively looking for remote organizations to assault, they generally incorporate authentic clients searching out signals as a part of their organization commitments. As innovation progresses, greater security experts are probably going to carry out a kind of war driving as a component of their normal administration system. Packet Capture: Packet Capturing (Packet Sniffing) could be a style of organization assault where the aggressor catches the data parcels (commonly Ethernet outlines) in movement. When the data is caught, the aggressor can peruse the touchy information like passwords or card numbers, if the organization traffic is not encoded. The premier broadly utilized bundle catch programming is Wire-shark. Wardriving: Wardriving [4] is the act of discovering Wi-Fi networks from a moving vehicle. It involves slowly driving around a location with the goal of locating Wi-Fi signals. This could be accomplished by a personal or by two or more people, with one person driving and attempting to find wireless networks. Wardriving is likewise pretty much as straightforward as searching with the expectation of complimentary Wi-Fi utilizing [11] a cell phone inside a vehicle. Nonetheless, the definition generally applies to equipment and programming arrangement explicitly intended for finding and recording Wi-Fi organizations. Password Attacks: Passwords are something that creates a boundary to prevent unauthorized people from accessing our information. As long as there is a barrier

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Table 3 Keylogger incident response Keylogger incident response Preparation

The attacker has already exploited and installed keylogger app and the back doors. The security team must be trained to handle such incidents

Detection and analysis

Perform the analysis to identify the infected machines

Containment, eradication and discovery

Scan or reimage if required to remove the machine from the network

Post incident activity

Identifying the email/link that leads to such malicious software to be downloaded in the machine (RCA)

between attackers and information, attacks will be carried out to obtain the information. A secret key assault is essentially where an aggressor attempts to take or break a secret phrase. This is potentially the most broadly perceived individual and corporate components for datum breaks. These are based attacks are wont to hack the passwords of customers of a true PC to recognize access. Keylogger Attacks: A keylogger might be a program that runs inside the foundation of a PC, logging the client’s keystrokes. After a client enters a secret phrase, it is put away inside the log made by the keylogger and sent to the assailant. Indeed no one would actually install such software if the attacker sent it with a name, for instance, “KeyloggerAtack.exe”. So, to lure the victim, the attacker usually advertises it with an extremely tempting scheme, for instance, “You have been selected the winner out of one million people in a lottery of $X,XX,XXX. To receive the money, kindly register yourself using the link.” All it takes is a click to install the software and get that miscreant inside the device as given in Table 3. Port Scanning and Ping Sweeps: Port Scanning could be a combination of association attack, where the attacker endeavors to get the organizations running on a true PC by sifting the TCP/UDP ports. Here the attacker endeavors to choose relationship with the TCP/UDP ports to look out which ports are open on a goal PC. Right after seeing which TCP/UDP ports are open, the attacker can sort out which organization is running on a genuine PC and which item is running on a goal PC. Then, the assailant can attack and hack the genuine PC organizing shortcoming in the thing [12]. Dumpster Diving: Dumpster driving is investigating through organization dumpsters for any data that might be valuable for an aggressor for assaulting the organization. Model is searching for worker names, Software application item data, network framework gadget make and models and so forth. Phishing and Pharming: Phishing is a starter to hack fragile information (ordinarily money related information like bank user id/secret key MasterCard nuances, etc.), by sending unconstrained messages with fake URLs. Pharming is another association attack planned for redirecting the traffic of 1 site to a substitute site.

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Social Engineering Social Engineering is fairly attack during which someone with shocking shrewd capacities moves others toward revealing information about network that may be adjusted take data.

3.2 Network Attacks Against Integrity A trustworthiness assault is likewise called an information honesty danger is an assault that endeavors to ruin information. It is a purposeful assault most regularly done by malware which erases or alters the substance of a cell phone’s location book or schedule. Man-in-the-Middle Attack (MitM) A man-in-the-middle attack (also known as an MitM attack) [5] is one of the best attacks to understand attacks against integrity. A man-in-the-center assault is a sort of organization assault where the assailant sits between two gadgets that are imparting to control the information as it moves between them. From the name, one can clearly understand that in this attack, a third person, who listens to the conversation between two parties, carries out the attack against integrity. The conversation can be between two servers, two persons, or a server and a client [13].

3.3 Network Attacks Against Availability Availability in networking is nothing but having information/service available to authorized people at all times. Consequences of attack against availability Denial of Service (DoS) Attacks DoS Attack [6, 7] type is an attack to an association server with gigantic number or organization requests with it which cannot manage. DoS can make the server crash the server and genuine customers are denied the assistance. Distributed Denial of Service (DDoS) Attacks Distributed denial of service (DDoS) [7] attacks are mostly just like DoS attacks in core functionality, originating from many assaulting PCs from various geological locales. The difference is that the victim is targeted by multiple systems, instead of just one. This makes it more lethal than a standard DoS attack. SYN Flood Attacks SYN flood attacks [8] are the type of attacks where attacker sends many TCPSYN packets to initiate a TCP connected, but never send a SYNACK pack back. TCP Three-Way Handshake Before it proceed directly to the videos to learn about SYN flood attacks, the TCP three-way handshake, the main core

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concept on which SYN attacks are based. A SYN flood attack can be carried out in a DoS as well as a DDoS manner.

4 Conclusion and Future Scope The purpose of study in this paper is to give glance on concept of packet inspection and highlighting different attacks used to break integrity, confidentiality and availability of a packet in the network. This research paper can be used to grab knowledge in packet inspection and all the attacks till today which significantly can widen areas of possible types of attacks. Resulting a strong firewall, network security and VPN can be implemented while designing the network as per the requirement.

References 1. Bujlow T, Carela-Español V, Barlet-Ros P (2015) Independent comparison of popular DPI tools for traffic classification. Comput Netw 76(0):75–89. https://doi.org/10.1016/j.comnet. 2014.11.001 2. Ahn B, Kim T, Choi J, Park S-W, Park K, Won D (2021) A cyber kill chain model for distributed energy resources (DER) aggregation systems. In: 2021 IEEE power energy society innovative smart grid technologies conference (ISGT), Washington, DC, USA, 2021, pp 1–5. https://doi. org/10.1109/ISGT49243.2021.9372209 3. Kao D, Hsiao S (2018) The dynamic analysis of WannaCry ransomware. In: 2018 20th international conference on advanced communication technology (ICACT), Chuncheon, Korea (South), 2018, pp 159–166. https://doi.org/10.23919/ICACT.2018.8323682 4. Wu D, Arkhipov DI, Zhang Y, Liu CH, Regan AC (2015) Online war-driving by compressive sensing. IEEE Trans Mob Comput 14(11):2349–2362. https://doi.org/10.1109/TMC.2015.238 8475 5. Callegati F, Cerroni W, Ramilli M (2009) Man-in-the-middle attack to the HTTPS protocol. IEEE Secur Priv 7(1):78–81. https://doi.org/10.1109/MSP.2009.12 6. Sun YL, Han Z, Yu W, Ray Liu KJ (2006) Attacks on trust evaluation in distributed networks. In: 2006 40th annual conference on information sciences and systems, Princeton, NJ, USA, 2006, pp 1461–1466. https://doi.org/10.1109/CISS.2006.286695 7. Tabatabaie Nezhad SM, Nazari M, Gharavol EA (2016) A novel DoS and DDoS attacks detection algorithm using ARIMA time series model and chaotic system in computer networks. IEEE Commun Lett 20(4):700–703. https://doi.org/10.1109/LCOMM.2016.2517622 8. Degirmencioglu A, Erdogan HT, Mizani MA, Yılmaz O (2016) A classification approach for adaptive mitigation of SYN flood attacks: preventing performance loss due to SYN flood attacks. In: NOMS 2016—2016 IEEE/IFIP network operations and management symposium, Istanbul, Turkey, 2016, pp 1109–1112. https://doi.org/10.1109/NOMS.2016.7502971 9. Gupta N, Jain A, Saini P, Gupta V (2016) DDoS attack algorithm using ICMP flood. In: 2016 3rd international conference on computing for sustainable global development (INDIACom), New Delhi, India, 2016, pp 4082–4084 10. Ranchhodbhai PN, Tripathi K (2019) Identifying and improving the malicious behavior of rushing and blackhole attacks using proposed IDSAODV protocol. Int J Recent Technol Eng 8(3):6554–6562 11. Jain A, Tripathi K (2019) Malicious detection using secure mutual trust based routing on an intrusion detection system in WSN. Int J Recent Technol Eng 8(3):3144–3150

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12. Jain A, Tripathi K (2018) Biometric signature authentication scheme with RNN (BIOSIGRNN) machine learning approach. In: Proceedings of the 3rd international conference on contemporary computing and informatics, IC3I 2018 d, 2018, pp 298–305, 9007284 13. Midha S, Tripathi K (2020) Remotely triggered blackhole routing in SDN for handling DoS. In: Lecture notes in networks and systems 2020, vol 116, pp 3–10

Route Optimization for Waste Collection Minal Sahu, Purvi Sharma, Hitesh Kumar Sharma, Tanupriya Choudhury, and Bhupesh Kumar Dewangan

Abstract The vehicle routing problem is a synonym used in the enclosure of transport, distribution, and outsourcing to optimize routes. Route planning techniques are one of VRP’s major errands: planning to seek an optimal way on a map from a starting point to a destination. We strive to achieve a GIS-based transport system that provides the easiest, fastest, and shortest route to reach the hub. In this paper, we discuss the description of the different route planning algorithms and then explain their efficiency comparison and analysis when Municipal Corporations implement them throughout the existing road network for use in the waste management framework. Along with Haversine formula, we choose Dijkstra, the most well-known shortest path algorithm and traveling salesman problem. Keywords Vehicle routing problem (VRP) · GIS · Dijkstra shortest path algorithm · Traveling salesman problem · Haversine formula

1 Introduction Optimized route selection is a complex but required process of taking in consideration of all possible defined routes linking the starting source to the final destination and looking at the attached cost of each course by identifying the best value route. Route optimization for waste management is essential and challenging [1]. We intend to M. Sahu University of Glasgow, Glasgow, UK P. Sharma Cognizant Technology Solutions, Bengaluru, India H. K. Sharma (B) · T. Choudhury School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, India e-mail: [email protected] B. K. Dewangan Department of CSE, O. P. Jindal University, Raigarh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_59

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collect the waste from each location later gather it to a hub based on the radial distance. The Municipal Corporation vans will traverse these hubs in the shortest path manner by picking one specific algorithm for each category to be put on to contrast the execution of the specified algorithms according to the evaluation [2, 3]. Optimized route (shortest path) algorithm given by Dijkstra’s helps to find the minimal cost route between source and destination [4]. Considering these kind of algorithms helps to find the best route on minimum cost by considering different edges and calculating cost at each node [5]. Thus, the software selects the best path from source A to destination B with the least heuristic cost by comparing it with the traveling salesman problem using Haversine formula. These two algorithms individually and combined give the best performance analysis to the Municipal Corporation to be followed by the vans. The application of this project will let the Government focus on “Swachh Bharat Abhiyan” in its most efficient way [6]. We propose efficient waste management and transportation strategy that curtails the waste collection and transportation route by reducing total expenditure. The current inquest is to study and develop algorithms to save our resources and boost the system’s efficiency by reducing cost.

2 Literature Review In metropolitan cities with a muddled road network, people who traverse the path daily want to plan for the fastest or most affordable road map to their destinations [7]. Such a quest requires an illustrious knowledge of the public transport network. At the point where the Nagar Nigam garbage truck begins its way from one location to the next, the system will represent routes in adjustable mode. Many techniques have been proposed by various researchers. Authors have proposed graph-based and tree-based traversal algorithms to find the shortest path between source and destination [8]. With the help of finding shortest path, it helps to reduce the time and cost of traveling from source to destination. As we have noticed significant increment in fuel and time of travel from one point to another points, therefore there is a need of finding solution of this real-time problem is the need of almost all travelers of this era [9]. In this paper, we have tried to propose an algorithm which is graph based for waste collection from several societies to minimize the increasing expenditure [10]. India, being a developing country, is a huge setback. Poor waste management leads to poor quality of life. It can be utilized as a potential source of energy. It even creates a good source of employment opportunities [11, 12]. The paper has a motive toward the optimal route establishing for waste collection within societies. It aims to propose a possible path from source to destination using various approach optimizers. The latest tools and techniques which have advanced algorithmic features can help to overcome from this difficulty and provide a solution that will help to reduce time, cost, and efforts by providing a shortest route. Thus, it helps to provide the better solution that aids government policy in cleaning India to daylight. Therefore, road

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transit increases the benefit and reduces the cost time lagging process in the road network routing. Chen [13] in his research work proposed a geometric-based route optimization solution for minimizing the cost of traveling. The same kind of approach can be used in maximum waste collection covering a minimum path. Authors in [14] in their work proposed a genetic-based algorithm to find the solution for minimum path or shortest path algorithm. Genetic algorithms can be used to here to maximize the waste collection with minimum fuel consumption by taking the pre-defined shortest route [15].

3 Algorithms Used The shortest path problem is to discover a route between 2 vertices in the graph so that the total weight of the edges is minimal. Single-source and all-pair are two main types of shortest path algorithms. Both algorithms work in a distinct and best way [13, 15]. All-pair algorithms require more time to run because they include longer traversal, with added complexity [16, 17]. All the shortest path algorithms return values that can be used to determine the shortest path. • Single—shortest destination—problem with paths: Get the shortest path from every other node to the destination node. • Single-pair shortest path problem: For allowed vertices, u and v get the briefest way from u to v. We decide the single-source issue with source vertex u, in expansion, no calculations that run asymptotically quicker than the most excellent singlesource calculations within the most exceedingly lousy case are known for this issue. • All—pairs shortest—paths problem: Find the shortest path from both spots, u to v, for each team of vertices u and v. The running source algorithm can explain this issue from each vertex. Still, it is usually resolved quicker, and its arrangement is essential [18, 19]. Analysis of different algorithms is presented in Table 1.

4 Problem Statement About 377 million urban people produce 62 million tons [11] of municipal solid waste per annum. Still, only 43 million tons (MT) of the waste are collected, 11.9 MT is processed, and 31 MT is deposited in landfill sites. Absence of endeavors by Municipal authorities, the management of garbage has become a constant issue, even though the most significant part of any government expenditure, i.e., more than 60% of the costs in solid waste management systems, is spent on the collection and

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Table 1 Tabular analysis Algorithms for finding shortest path (Dijkstra’s algorithm)

This algorithm is Time complexity T (n) used to find the of the Dijkstra shortest path algorithm is O(n2 ) between source and destination. Cost matrix is considering in calculating the minimum cost path and finding the combination of minimal number of nodes from start to end node

Haversine formula

The great circle distance between two points on a sphere is calculated by this formula, considering their longitudes and latitudes. The law of Haversines alludes to the sides and points of spherical triangles

Traveling salesman problem

A salesman who Time complexity T (n) must move of TSP is O(n2 * 2n) between N cities is represented in the problem. As long as he visits each one on his trip, and finishes where he was at first, the order in which he does so is something he does not care about

Formula is: d=2 sin-1(sin2 (lat2-lat1)/2 + cos (lat1) cos (lat2) sin2 (long2-long1)/2)1/2

transportation process including laboring cost, the high price of fuel and machinery, and equipment maintenance [20, 21]. Waste collection and transportation are the linkage between the waste generators and the waste management system, and this contact needs to be discreetly managed to ensure a resolved scenario. Therefore, the shortest route for waste collection and transportation can reduce expenditures exponentially.

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Fig. 1 Clustering of localities

5 Proposed Work This paper presented the all-path routing algorithm, which calculates the entire possible paths in the network. The advantage of all-path routing algorithms is the identification of all possible ways in a network that may provide an option for the existence of alternate paths and the availability of multiple courses for sharing of the load. We propose a different garbage collection technique; we will form a cluster of society based on the distance between them, which will be calculated by Dijkstra’s algorithm, thus helping to find the shortest distance between the nodes. As a result, the garbage collector vehicle begins its route from a small hub (represented by a black circle) to pick up the trash from each society and arrives at the same hub by the shortest path possible with the help of a traveling salesman’s problem shown in Fig. 1. Likewise, garbage is collected at other smaller hubs; other vehicles from the central hub (represented in the orange circle) will collect garbage from all smaller hubs with the help of a traveling salesman’s problem.

6 Methodology The following steps are conducted to achieve the objective of the proposed work (shown in Fig. 2): 1

It gathered the latitudinal and longitudinal coordinates to determine the location of all areas through GPS [2].

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Fig. 2 Route of vehicle

2 3 4

5

6

Computation of the distance between 2 coordinates using Haversine formula in miles/km. Save distance data between 2 societies in the form of a matrix in the file, which is to be connected to code. Creating a small hub (represented by the black circle) for a cluster of societies will be formed based on colonial distances from a small hub to an individual organization with the help of Dijkstra’s algorithm. Traveling salesman problem will be applied to these hubs so that the garbage collector truck will start the path from the small hub and efficiently collect garbage from each society and revert to the hub. Likewise, garbage is collected at other smaller hubs with the help of the TSP algorithm, which will be applied to each hub to get the optimized route

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Now, other vehicles from the central hub (represented in the orange circle, i.e., Nagar Nigam) will collect garbage from all smaller hubs with the help of a traveling salesman’s problem and arrive back at the central hub, i.e., Nagar Nigam. We visit each locality in the TSP pattern and gather the waste to a hub. Later on, applying Dijkstra’s and choosing the best-optimized path to reach Nagar Nigam.

7 Result and Discussion There are so many algorithms which have been proposed to propose a minimum cost path for a traveler from the source to destination. Each algorithms uses the cost weight associated with each edge between two corresponding nodes. In this paper, we proposed an algorithm combination to solve the problem of route optimization for waste management. This work is crucial since it provides a solution to the real-life problem of Nagar Nigam. Upraising the economy promotes the national policy, aim, and objective in cleanliness. If we see results by manual scenario, the distance covered will be 29.7 km, while if we apply our strategy and tsp algorithm, we will get a distance of 15.6 km. This would help us save 47% of the path and would undoubtedly help to reduce transportation costs and fuel to a greater extent. As a result, shown in Figs. 3 and 4, the benefit and cost of road transit increase and reduce overhead enchanters on the routing over road to enforce the Swachh Bharat Mission and sanity in India. This will reduce the environment’s budget to invest in several other possibilities, cover every possible location, and make India a reality.

Fig. 3 TSP region

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Fig. 4 Manual scenario

8 Conclusion Cost of waste collection and transportation as more than 60% of the costs in different countries are due to laboring cost, the high price of fuel, and machinery and equipment maintenance. “The PM of India initiated Swachh Bharat Abhiyan,” but the clean India Mission is still a dream to achieve. We are developing shortest path alternatives using algorithms, i.e., Dijkstra and traveling salesman problem to boost the system’s efficiency by reducing the expenditure and extra negative miles is our objective. Consequently, our project reflected in escalating the current system’s efficiency, minimizing the usage of resources such as fuel, reducing empty miles, less pollution, traffic, feasible and adaptable. Many shortest distance path solutions are available; however, their real-life implementation is vigorously required in countries like India and others with excellent outputs and feasible for the public. Subsequently, we may use IoT devices to notify the server about the litter in an area. Any issue raised may notify the respective authority, and the former route would be generated. Efficiency may be increased by using A*, considering the routing problem keeping awareness of street network hindrances, restrictions, and environmental conservation. Alternatively, other fields such as ambulance or food delivery will also utilize this.

References 1. Chabini I (1998) Discrete dynamic shortest path problems in transportation applications. MIT, USA 2. Ogheneovo EE, Seetam E (2016) A heuristic graph-based shortest path algorithm for optimizing routing problems

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3. Chiu DKW, Lee OKF, Leung HF, Au EWK, Wong MCW (2005) A multi-modal agent-based mobile route advisory system for public transport network. In: Proceedings of the 38th annual Hawaii international conference on system sciences 4. Mwemezi JJ, Huang Y (2011) Optimal facility location on spherical surfaces. New York Sci J 4(7):21–28 5. Reid MR (2011) Shortest distance between two points on earth 6. Chopde NR, Nichat M (2013) India landmark-based shortest path detection by using A* and Haversine formula. Int J Innov Res Comput Commun Eng 1(2):298–302. (G.H. Raisoni College of Engineering and Management, Amravati) 7. Mathur A, Jakhotia M, Lavalekar A, Magar N (2014) Shortest path finding algorithms for real road network. 3(10). (Computer Department, VIIT, Pune University) 8. Das S, Bhattacharyya BK (2015) Optimisation of municipal solid waste collection and transportation routes. Waste Manage 43:9–18 9. Lahiry S, Nagendra H (2016) India’s challenges in waste management. Interrogating sustainable cities 10. Shilpa (2016) An efficient all path routing algorithm. Int J Sci Eng Res 11. Ogheneovo1 EE, Seetam E (2016) A heuristic graph-based shortest path algorithm for optimizing routing problems. Am J Eng Res 5(11) 12. Kumasi, Ghana (2017) Shortest route optimisation for emergency service. 6(9) 13. Chen DZ (1996) Developing algorithms and software for geometric path planning problems. ACM Comput Surv 28 (4es):18. https://doi.org/10.1145/242224.242246 14. Sharma HK (2013) E-COCOMO: the extended cost constructive model for cleanroom software engineering. Database Syst J Board 3 15. Han S, Tabata Y (2002) A hybrid genetic algorithm for the vehicle routing problem with controlling lethal gene. Asia Pacific Manage Rev 7(3) 16. Budget 2019: Pollution control in focus, Environment Ministry gets Rs 2,954 crore: https://eco nomictimes.indiatimes.com/news/economy/policy/budget-2019-pollution-control-in-focusenvironment-ministry-gets-rs-2954-crore/articleshow/70095085.cms 17. Dewangan BK, Agarwal A, Venkatadri M, Pasricha A (2018) Autonomic cloud resource management. In 2018 5th international conference on parallel, distributed and grid computing (PDGC). IEEE, pp 138–143. https://doi.org/10.1109/PDGC.2018.8745977 18. Agarwal A, Venkatadri M, Pasricha A (2019) Design of self-management aware autonomic resource scheduling scheme in the cloud. Int J Comput Inf Syst Ind Manage Appl 11:170–177 19. Choudhury T, Pasricha A (2020) Cloud resource optimisation system based on time and cost. Int J Math Eng Manage Sci 5(4):758–768. https://doi.org/10.33889/IJMEMS.2020.5.4.060 20. Taneja S, Karthik M, Shukla M, Sharma HK (2017) AirBits: a web application development using microsoft azure. ICRDSTHM-17) Kuala Lumpur, Malasyia 21. Choudhury T, Pasricha A, Chandra Satapathy S (2020) An extensive review of cloud resource management techniques in industry 4.0: Issue and challenges. Softw Pract Exp. https://doi.org/ 10.1002/spe.2810 22. Dewangan BK, Agarwal AV, Pasricha A (2019) A self-optimisation based virtual machine scheduling to workloads in cloud computing environment. Int J Eng Adv Technol (IJEAT) 8(4):91–96 23. Sharma HK, Kumar S, Dubey S, Gupta P (2015) Auto-selection and management of dynamic SGA parameters in RDBMS. In: 2015 2nd international conference on computing for sustainable global development (INDIACom), 2015, pp 1763–1768 24. Jain A, Choudhury T (2020) GAP: hybrid task scheduling algorithm for the cloud. Revue d’Intelligence Artificielle 34(4): 479–485. https://doi.org/10.18280/ria.340413

Impact of Dispersion Schemes and Sensing Models on Performance of Wireless Sensor Networks Mini, Ashok Pal, and Tanupriya Choudhury

Abstract With a view to assort the difficult tasks in distinct domains of pharmaceutical, technical, horticultural, WSNs are spreading speedily. In WSNs, the remarkably practical and novel research domains are coverage and dispersion schemes. In WSNs dispersion schemes precisely control the functioning of the networks. In disorderly dispersion employment of enormous amount of sensors mended the authenticity and scalability. For how much duration a physical area is supervised by sensors is known as coverage. For trespassers investigation in conserved or sensitive regions barrier coverage is primly measured. In WSNs recent research concentrates on coverage, connectivity, dispersion schemes, localization, and power saving methods. Therefore this paper presents an advanced analysis of WSNs depending upon distinct aspects such as count of sensors, dispersion schemes, sensing range models. Keywords Wireless sensor networks · Coverage · Dispersion schemes · Sensing range models

1 Introduction A versed formation of a wireless sensor network has turned out as a popular field in research during these days. A sensor is an instrument which replicates and discovers Mini (B) Research Scholar, Dept. of Mathematics, Chandigarh University, Gharuan (Mohali), Punjab, India e-mail: [email protected] Assistant Professor, S.A.Jain College, Ambala City, Haryana, India A. Pal Professor, Dept. of Mathematics, Chandigarh University, Gharuan (Mohali), Punjab, India e-mail: [email protected] T. Choudhury (B) Informatics Cluster, School of CS, University of Petroleum and Energy Studies (UPES), Bidholi Campus, Dehradun 248007, Uttarakhand, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_60

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certain kind of input through both hylic and natural circumstances like pressure, heat, light, etc. An electronic indication which is transferred to a controller to monitor furthermore is usually the output of the sensor. A mechanism of appliances which can convey the data collected through an observed zone from wireless links can be delineated as wireless sensor network. From various nodes and along with a gateway the datum is forwarded and to other mechanisms the datum is combines as wireless Ethernet. WSNs are a wireless mechanism which contains of base stations and numeral of nodes. To audit hylic or natural circumstance as sound, pressure, temperature these networks are employed and co-operatively surpass datum from the network to major locale. In physical pursues, like forest detection, cattle tracking, flood detection, prediction, and weather prediction and also in financial operations such as seismic activity forecasting and observing these networks are employed. Military applications like tracking and climate supervising applications employ these networks. Sensor nodes from sensor networks are employed to the area of interest and are distantly monitored by a consumer. By employing these networks enemy tracking, safety detection are also accomplished. Health applications like monitoring and checking of doctors and patients employ these networks. In the area of transportation like tracking of traffic, dynamic vehicle routing administration, and tracking of parking lot, etc. employ these networks commonly. Quick critical reaction, technical procedure tracking, computerized building climate control, eco system and location tracking, secular designed health monitoring, etc. employ these networks.

2 Assortment in WSN WSNs are distributed into distinct aspects such as kinds of sensors, dispersion schemes, sensing models, kinds and algorithms of coverage, power saving techniques. Depending upon the environment and node kind, kinds of sensors are again partitioned. Deterministic and random dispersion are the mainly dispersion schemes. Basically there exists two kinds of sensing range models first is deterministic sensing range model and second is probabilistic sensing range model. Coverage can be furthermore partitioned into whole area coverage and fractional area coverage. By employing sleep/wakeup schemes, reduction of datum, radio module, battery repletion energy efficiency can be done [1]. Depending upon distinct aspects comprehensively WSN categorization are designed in Fig. 1.

3 Depending upon Environment Distinct Kinds of WSNs Rely on the surroundings the types of networks are concluded in order that these can be positioned underwater, underground, on land, and so on. Distinct kind of WSNs comprises.

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Fig. 1 Classifications of WSN in terms of sensor categories, deployment strategy, coverage, and sensing models

3.1 Terrestrial WSNs Terrestrial WSNs are adequate of communicating base stations skillfully, and comprise hundreds to thousands of wireless sensor nodes diffused in arranged or unarranged mode. In an unarranged manner, the sensor nodes are arbitrarily assigned inside the destined field which is dropped through a certain plain. The arranged mode takes optimum placing, grid placing, and 2-D, 3-D placing frameworks. The battery potency is confined in this WSN. Yet, as a complementary source, the battery is fitted along solar cells. The potency maintenance of these WSNs is obtained by employing short duty cycle process, reducing lateness, and optimum routing, and so on. Investigation by sensors is verified in simulation environment. For power saving TDMA protocol along with temporary communication of sensors had been suggested for better outcomes in terrestrial areas [2]. In terrestrial WSN for power savage A-ECM had been proposed and presents preferential outcomes in realistic scenarios [3]. Proper packet size has a significant role in WSN. For terrestrial WSN what should be its size is discussed [4].

3.2 Underground WSNs In view of prevalence, assistance and material expenses considerations observant arrangement the underground wireless sensor networks are much valued than terrestrial WSNs. To audit underground circumstances the WSNs mechanism comprise varied sensor nodes which are invisible in the ground. Up ground supplementary sink nodes are positioned to broadcast intimations through the sensor nodes to the base station. The underground wireless sensor networks broadcasted into underlay are complicated to revive. The sensor battery nodes outfitted with a definite battery capacity are crucial to revive. Further in view of advanced stratum of depletion and

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signal deficiency the underlay atmosphere forms wireless communication an out dare. In underground WSN what should be the techniques of wireless broadcasting and designing of these techniques are elaborated [5]. Radiation, reflection, absorption and scattering are the major causes of path loss of transferred signal. Two models which are already in existence to solve these issues are analyzed compared [6].

3.3 Underwater WSNs Aside from 70% of the earth is absorbed with water. These networks comprise varied sensor nodes and vehicles radiate underwater. In view to collect datum through these sensor nodes self-governing underwater vehicles are employed. An out dare of underwater transmission a prolonged dissemination interruption and bandwidth and sensor deficiencies underwater, WSNs are outfitted with a definite battery which cannot be changed or recharged. The challenge of energy preservation for underwater WSNs contains the advancement of underwater transmission and networking problems. In UWSN techniques dealing with existing issues such as optimal size of packet, connectivity, localization, environmental factors are discussed in detail [7]. Uniqueness, obstacles, threats, safety structures of UWSN are introduced [8]. Routing related problems and advanced techniques related to localization issue are introduced [9]. By considering all exiting complicated factors in UWSN power saving routing protocol has been presented [10].

3.4 Multimedia WSNs To permit following and supervising of occurrence in the mode of multimedia, like as audio, video, and imaging multimedia wireless sensor networks have been recommended. These networks comprise little expense sensor nodes outfitted with microphone and cameras. In order to shorten the datum, datum recover and connection these nodes are linked with one another by a wireless connection. The challenges among multimedia WSN contain huge power consummation, big bandwidth necessities, datum conversion, and restricted methods. Furthermore, for the matters to be conferred appropriately and regularly multimedia contents need wide bandwidth. Issues and needs related to existing structuring of routing in MWSN are presented. Existing routing extrications related to structure and optimization objectives are assorted into major parts [11]. Competency and constraints related to existing routing techniques are discussed and comparison in existing techniques has been done [12]. New method depending upon clustering in order to collect real time data is proposed in which sensors with greatest power are chosen as cluster heads with a aim to gather data and transfer this data to base station in order to save low power nodes [13].

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3.5 Mobile WSNs This network comprise an aggregation of sensor nodes which can be communicated with the hylic circumstances. The mobile nodes may reckon sense and transfuse. In comparison to static sensor networks the mobile sensor networks are more potential. The benefits of MSN in comparison to static wireless sensor network comprise best and elevated extensions, developed energy competence, preferable system efficiency, and so on. Along these there exist a few restrictions of WSN. WSN acquires insufficient repository measures and moderate transforming power. It fabricates in lower transmission range and uses enough power. It needs least energy restraint protocols, contains batteries along with a definite life time. In it inactive devices confer least energy. In MWSN, there exists so many methods and techniques in literature which can be employed to improve the coverage after dispersion of sensors and mathematical formulation related to these techniques are presented [14]. In order to provide optimal outcomes along with savage of power node selection schemes are introduced and validated in simulation scenario [15]. One of the major issues in wireless sensor networks that is localization issue is introduced and the existing techniques in literature to resolve this issue is assorted and new directions to extricate this issue is discussed [16]. In order to calculate nodes next position using its previous state by considering rate of motion, kind of motion, direction of sensors a localization technique is presented in which dispersion strategy and count of nodes are ignored [17].

4 Dispersion System of Sensors On the pre requisite of deployment strategy, usage and deployment region methods of sensor node placement can be categorized. Relying on the dispersion strategy, sensor nodes may be deterministic or random. To comprehend the designated domain of interest sensors are disseminate over the auditing area in random dispersion. When human interference is not practically feasible and when the knowledge about the domain is not formerly known then random dispersion is consistent. When there is a random dispersion of the sensor nodes some portion of the sensor domain may be disorderly or densely shrouded. Connectivity, supervision and failure investigation get much critical when the sensors are densely shrouded. If every pair of sensor node reveals and interchanges its datum with each sensor nodes by which entire connectivity is attained. Gathering of accurate datum for observation is the tenet at the back of WSN employment that is of two kinds: Event Driven and ondemand. In event driven processing process is activated from the sensor node that discovers and informs about event to the monitoring station starts the process and sensor node reacts to their exaction in on requisition processing. Observation of fire in forest is an instance of an event driven reporting system and an inventory management is an instance of on-demand reporting system. A deterministic sensor approach

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matey atmosphere, whereas a random sensor deployment is employed in secluded domains or military applications or emergency regions. In places with a confined sensor region deployment of deterministic sensor node is feasible. Due to deployment of nodes is done physically and use of few sensor nodes, expenses is being least vastly. Advanced techniques, algorithms, methods to extricate major issue of dispersion in WSN are presented and compared consecutively and assorted into two main categories deterministic and random [18]. In order to gain optimal coverage with least number of nodes major four coverage methods namely, meta-heuristics, grid, geometry, force techniques are introduced. Assortment of coverage, realistic issues in dispersion of nodes, kinds of sensing models are introduced [19]. For the placement of mobile sensor nodes distinct methods have been offered by the researchers that asserts credible conduct with optimum usage of resources. WSNs are mainly employed in adverse atmospheres like volcanoes, flooded areas, and Deep Ocean where for post dispersion management human interference is not feasible therefore attempts are being forged to increase its competency and tenability. Distribution of nodes can be done in systematic or indiscriminate manner. For inaccessible, uncertain or wild ranged areas indiscriminate distribution from the sky is utmost appropriate. For large domains like forests, war zones, disaster suffering domains and wildlife reservoir which need full analysis the term “open area” is employed. Though, it can be employed to describe small domains unveiled to open sky alike enemy halts, that need amid coverage and are not accessible physically. For environment monitoring distinct dispersion schemes and analyzing of probable reasons for existing issues and threats. In order to recognize issues, classify and explain root causes various techniques and mechanisms are discussed [20]. Various underwater acoustic sensor networks applications along with dispersion schemes and localization algorithms depending on advantage and disadvantage are presented [21]. Distinct mechanisms on dispersion such as artificial bee colony, particle swarm optimization, genetic algorithm, artificial immune system are presented and juxta positioned on various factors such as complicacy, scalability, domain of interest, suitability [22]. On the basis of application domain distribution may be categorized as open domain or indoor deployment. In unveiled domains where circumstances are fierce and domain to be examined is especially wide open region distribution is related to the employment of sensor network while indoor distribution is related to finite regions like buildings. Virtual force driven deployment schemes break the rule of physics to find the direction of MSN in view to disperse homogeneously within a candidate domain. In re-establishment of MSN to geometrically computer domains pre computed relocation point-based deployment schemes assign diverse algorithms in view to disperse them homogeneously in a candidate domain. In candidate domain for homogeneously dispersion of MSNs dispersion regimes employ both of the regimes in distinct situations. In candidate domain at any point few events may take place randomly and occurrence of these events even for one time is of much magnificence. Investigation of these types of events needs full coverage of the candidate domain. This kind of coverage can be attended by blanket distribution of nodes value distribution of nodes. Barrier distribution of nodes ascertains segregation by encircling the whole candidate domain with sensor nodes.

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5 Sensing Models Distinct kind of WSNs comprises. Each sensor consists of a particular range up to which it can sense the area and depending upon this range Rs, every sensor consists of a coverage area. In WSN, total coverage of an area can be determined by combining all the covered areas by each sensor node and range of communication among sensors depend upon the radio coverage Rc. In observed area, if a particular portion exists where no active sensor node occurred then we say that coverage holes are occurring in that particular area. The sensing models are assorted into deterministic and probabilistic sensing models. Network coverage is calculated using both deterministic and probabilistic models and comparison is also done by considering both models [23].

5.1 Binary Sensing Range Model In it, a sensor node only observes the incident which occurs in the range of sensing. Any point which is absent inside the domain is not entertained. Consider Rs as the sensing radius of every node which is showed as a uniform circle it is showed by the Eq. (1).  p=

1, if d(i, s) ≤ Rs 0, Otherwise

(1)

5.2 Probabilistic Sensing Model In it strength of a sensor node reduces along with the expansion of the distance. These kinds of models are more practical than deterministic models. Distinct aspects such as noise, weather, presence of obstacles, affect the functioning of probabilistic sensing models. Probabilistic range models are classified into distinct kind of models like Log-normal shadowing, Elfes, Rayleigh fading model. Enhancement of coverage after primal dispersion is done by employing probabilistic sensing range models. Mathematical equations are extricated by using probabilistic sensing range models [24]. Coverage is calculated by considering and incorporating shadowing effects in a circular region and comparison is done with deterministic sensing range model for same scenario [25]. To calculate the strength of received signals which is necessary for transfer of data packets log-normal model is employed [26]. Log-normal model is employed to introduce an effectual technique in order to find out the locale with greatest reception ratios in transitional domain [27]. Coverage is calculated in a circular region by employing both binary and Elfes sensing range models and binary sensing range model outperforms than other [28]. Prediction of network lifespan

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and estimation of packet loss rates in two different scenarios that is in single-hop and multi-hop is done by employing Elfes sensing range model [29]. An analytical model for estimating coverage probability in the presence of multipath fading along with shadowing is calculated depending upon received signal strength. Further, the coverage probability derivations obtained using Rayleigh fading and log-normal shadowing fading are validated by node deployment using Poisson distribution [30].

5.3 Shadow-Fading Sensing Model In it disturbances by environmental factors, obstacles are acknowledged. Sensing ability of a sensor node is not same in each direction. In one direction it can sense the larger area than the area sensed by the same node in other direction. In it probability that an event is occurring at a distance d from the center of the sensor node is measured by Eq. (2).  Pdet = Q

10γ log10 (d/rs ) σ

 (2)

where η denotes path loss exponent r s denotes sensing range and σ denotes fading.

5.4 Elfes Sensing Model In this model, probability that a sensor node will detect an event which is occurring at a distance d from the center of sensor node is given by Eq. (3).

Pdet

⎧ d ≤ R1 ⎨ 1, γ = e−λ(d−R1 ) , R1 < d < RMAX ⎩ 0, d ≥ RMAX

(3)

where R1 denotes the sensing uncertainty by sensor. λ and μ is device oriented parameter, RMAX denotes maximum sensing range. Binary sensing range model can be derived from Elfes sensing model while R1 = RMAX, R1 = 0, γ = 1, then Eq. (3) reduces to Eq. (4).  P=

e−λd , 0 ≤ d < RMAX 0, d ≥ RMAX

(4)

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6 Coverage In WSN, one of the primary concerns is coverage issue. An intruder tracing is major objective in the barrier coverage as they get in the guarded region or pass through the border area. Accomplished coverage may be delineated as network lifespan by introducing the characteristics as sensing capability and energy consummation by sensing nodes. It is often delineated as how much extent of the sensing region is observed. On the application needs coverage rely upon whether it requires whole region coverage or partial region coverage. Numeral of sensors is needed in whole region coverage. Partial region coverage is distinct from full region coverage.

6.1 Full Coverage In wireless sensor network, to check the domain is the major purpose of area coverage and in it each portion of area is observed by one note on more than one of these extreme motive is a lifespan of domain coverage in the network. Time span amid initial of network functioning and the coverage necessity is life span of the domain coverage. Datum handling and expansion also affect the network lifespan and absorb much power.

6.2 Partial Coverage Whole coverage needs great expenditure and many complications, in few operations, it is not effective. Partial coverage can be employed in such situations and it promises coverage inside a definite extend. It is also acknowledged as a coverage, p-coverage, q-coverage. In WSN, in order to amplify the network lifespan and to backlog power partial coverage is primarily employed. There exists two variants of partial coverage namely point and directional coverage which are explain further. Wrapping of a collection of points is major objective of point coverage. Every point in the point coverage is analyzed by one or more than one sensor, so as to every sensor in the sensing extend observes entire spot.

7 Conclusion In this we observe that in Wireless Sensor Networks major existing problems like distribution systems, sensing models and coverage were investigated. In the area of wireless sensor network, this paper can also be employed as basic knowledge. In this research it was observed that in designing ensuing methods connectivity and

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coverage are fragile problems which can be presumed and routing notions can also be considered for constructing ensuing extrications.

References 1. Amutha J, Sharma S, Nagar J (2020) WSN strategies based on sensors, deployment, sensing models, coverage and energy efficiency: review, approaches and open issues. Wirel Pers Commun 111(2):1089–1115 2. Bayrakdar ME (2020) Energy-efficient technique for monitoring of agricultural areas with terrestrial wireless sensor networks. J Circ Syst Comput 29(09):2050141 3. Ahmad A, Javaid N, Imran M, Guizani M, Alhamed AA (2016) An advanced energy consumption model for terrestrial wireless sensor networks. In 2016 international wireless communications and mobile computing conference (IWCMC). IEEE, pp 790–793 4. Yigit M, Yildiz HU, Kurt S, Tavli B, Gungor VC (2018) A survey on packet size optimization for terrestrial, underwater, underground, and body area sensor networks. Int J Commun Syst 31(11):e3572 5. Sardar MS, Xuefen W, Yi Y, Kausar F, Akbar MW (2019) Wireless underground sensor networks. Int J Performability Eng 15(11):3042 6. Huang H, Shi J, Wang F, Zhang D, Zhang D (2020) Theoretical and experimental studies on the signal propagation in soil for wireless underground sensor networks. Sensors 20(9):2580 7. Awan KM, Shah PA, Iqbal K, Gillani S, Ahmad W, Nam Y (2019) Underwater wireless sensor networks: a review of recent issues and challenges. Wirel Commun Mob Comput 2019 8. Yang G, Dai L, Wei Z (2018) Challenges, threats, security issues and new trends of underwater wireless sensor networks. Sensors 18(11):3907 9. Khalid M, Ullah Z, Ahmad N, Arshad M, Jan B, Cao Y, Adnan A (2017) A survey of routing issues and associated protocols in underwater wireless sensor networks. J Sens 2017 10. Rani S, Ahmed SH, Malhotra J, Talwar R (2017) Energy efficient chain based routing protocol for underwater wireless sensor networks. J Netw Comput Appl 92:42–50 11. Shen H, Bai G (2016) Routing in wireless multimedia sensor networks: a survey and challenges ahead. J Netw Comput Appl 71:30–49 12. Bhandary V, Malik A, Kumar S (2016) Routing in wireless multimedia sensor networks: a survey of existing protocols and open research issues. J Eng 2016 13. Usman M, Jan MA, He X, Nanda P (2016) Data sharing in secure multimedia wireless sensor networks. In 2016 IEEE Trustcom/BigDataSE/ISPA. IEEE, pp 590–597 14. Mohamed SM, Hamza HS, Saroit IA (2017) Coverage in mobile wireless sensor networks (M-WSN): a survey. Comput Commun 110:133–150 15. Sheng Z, Mahapatra C, Leung VC, Chen M, Sahu PK (2015) Energy efficient cooperative computing in mobile wireless sensor networks. IEEE Trans Cloud Comput 6(1):114–126 16. Chelouah L, Semchedine F, Bouallouche-Medjkoune L (2018) Localization protocols for mobile wireless sensor networks: a survey. Comput Electr Eng 71:733–751 17. Alaybeyoglu A (2015) An efficient Monte Carlo-based localization algorithm for mobile wireless sensor networks. Arab J Sci Eng 40(5):1375–1384 18. Aznoli F, Navimipour NJ (2017) Deployment strategies in the wireless sensor networks: systematic literature review, classification, and current trends. Wirel Pers Commun 95(2):819–846 19. Priyadarshi R, Gupta B, Anurag A (2020) Deployment techniques in wireless sensor networks: a survey, classification, challenges, and future research issues. J Supercomput 1–41 20. Beutel J, Römer K, Ringwald M, Woehrle M (2010) Deployment techniques for sensor networks. In: Sensor networks. Springer, Berlin, pp 219–248 21. Tuna G, Gungor VC (2017) A survey on deployment techniques, localization algorithms, and research challenges for underwater acoustic sensor networks. Int J Commun Syst 30(17):e3350

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22. Kumar M, Gupta V (2016) A review paper on sensor deployment techniques for target coverage in wireless sensor networks. In: 2016 international conference on control, instrumentation, communication and computational technologies (ICCICCT). IEEE, pp 452–456 23. Hossain A, Biswas PK, Chakrabarti S (2008) Sensing models and its impact on network coverage in wireless sensor network. In: IEEE region 10 and the 3rd international Conference on Industrial and Information Systems, 2008, pp 1–5 24. Pal A, Mini (2021) Estimating the coverage performance of a wireless sensor network considering boundary effects in thepresence of sensor failure. EVERGREEN Joint J Novel Carbon Resour Sci Green Asia Strategy 8:601–609 25. Pal A, Mini (2021) Coverage sensitivity analysis of a wireless sensor network with different sensing range models considering boundary effects, materialstoday: In: Proceedings 26. Mitra SK, Naskar MK (2011) Comparative study of radio models for data gathering in wireless sensor network. Int J Comput Appl 27(4):49–57 27. Chen Y, Terzis A (2011) On the implications of the log-normal path loss model: an efficient method to deploy and move sensor motes. In: Proceedings of the 9th ACM conference on embedded networked sensor systems, pp 26–39 28. Mini AP, Lal G (2021) Influence of sensing model and sensor failure on the coverage performance of wireless sensor networks. Design Eng 13751–13759 29. Chakraborty A, Rout RR, Chakrabarti A, Ghosh SK (2013) On network lifetime expectancy with realistic sensing and traffic generation model in wireless sensor networks. IEEE Sens J 13(7):2771–2779 30. Kumar S, Lobiyal DK (2013) Sensing coverage prediction for wireless sensor networks in shadowed and multipath environment. Sci World J 2013

A Research Perspective of VANET Applications: A Review Payal Kaushal, Meenu Khurana, and K. R. Ramkumar

Abstract There is a need to develop an Intelligent Transportation System (ITS) that can manage the traffic flow without causing any issues. An increasing number of vehicles, with a variable network density, results in difficulty for the development of ITS. Vehicular Ad Hoc Network (VANET) makes it possible to communicate the information among the vehicles by using roadside units deployed along with the roadsides, application unit, and on board unit deployed within the vehicle. By creating a virtual link between the vehicles, it will be easy to make communication possible among the vehicles. It would help the transportation system to communicate all informational messages among the vehicles. VANETs are included in Sustainable Goals by the US government, and it is also considered as a challenging goal for India as well. This paper presents a survey on the basics of VANET, its components, communication among vehicles, characteristics, and applications classified based on the safety and comfort of the traveller. Keywords VANET · RSU · DSRC · WAVE spectrum · ITS

1 Introduction Vehicular Ad Hoc Network (VANET) is a subcategory of Mobile Ad Hoc Network (MANET) and is increasing its importance in the research field in today’s world [1]. To form an Intelligent Transportation System (ITS), a vehicle should interact with other vehicles and Road Side Units (RSUs). There are many challenges like P. Kaushal (B) · M. Khurana · K. R. Ramkumar Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India e-mail: [email protected] M. Khurana e-mail: [email protected] K. R. Ramkumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_61

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scalable network, high mobility, changing network topology, uncontrollable network size, traffic congestion, and network security which are faced while implementing VANET scenario [2, 3]. In this paper, we first gave an introduction to VANETs along with their components, communication methods, and characteristics, Sect. 2 gives literature studied, Sect. 3 explains different dedicated short-range communication, Sect. 4 explains applications for safety and comfort of the traveller, and at last, Sect. 5 concludes the work. Strategy1: Research papers from the last one and a half-decade have been added to this survey. Strategy2: All the papers were studied with the aspects of components, communication, characteristics, and applications of VANETs.

1.1 Components of VANETs In VANETs, communication is possible through the following components [4, 5]: Road Side Unit (RSU): RSUs are deployed along the roadside. Communication between the RSUs and vehicle helps the driver as well as the passengers travelling in the vehicle to extract required information while travelling. On Board Units (OBU): OBU helps the vehicle to communicate with the other vehicle on the infrastructure and pass appropriate information for travelling. Application Unit (AU): AU makes communication possible within the vehicle. This application can be used by the driver of the vehicle or the passengers travelling in the vehicle.

1.2 VANET Communication Due to the variable speed of the vehicle, communication between them cannot be always direct, so VANETs follow Dedicated Short-Range Communication (DSRC) [6]. It permits the vehicle to communicate even if the vehicles are at a faraway distance [7, 8]. The communication between the vehicles in VANETs is known as vehicle-to-vehicle (V2V) communication. The communication between vehicles and the infrastructure along the road is known as vehicle-to-infrastructure (V2I) communication [9]. Figure 1 represents communication between different roadside units in blue dashed lines, V2V communication in red dashed lines, and V2I communication in green dashed lines.

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Fig. 1 VANETs scenario

1.3 Characteristics of VANETs VANETs and MANETs share many characteristics but some distinct features are elaborated below [6, 10, 11]: High Mobility: It is the primary feature of VANETs, as compared to MANETs. Vehicles can move in the same or different directions because they are not fixed. The movement of vehicles depends on road topology and its layout. Vehicles have to find their route by obeying traffic lights and road signals. VANETs can handle the communication between the mobile nodes. Dynamic network topology: The movement of the vehicle is completely unpredictable. The varying speed of the moving vehicles and the selection of multiple paths define the dynamic topology of VANETs. Road safety and traffic efficiency: Due to day by day increase in the number of vehicles, it is becoming difficult to manage the traffic flow. VANET provides a direct communications facility among vehicles to transmit information. Traveller comfort: VANETs provide information about traffic flow, warning messages about accidents to the driver and information about shopping malls and fast food, music to the travellers at the same time.

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Wireless communication: Due to the unpredictable movement of the vehicle on the road network, the data transmission is done via wireless communication channels in VANETs. Network Density: Due to random movement of vehicle, network density varies. For heavy traffic network, density is high and for light network, density is low.

2 Literature Studied Yousefi et al. [1] outlined the application areas for the safety and comfort of the traveller, challenges faced while implementing VANETs. Zeadally et al. [2] surveyed some of the recent research areas like security, broadcasting, Quality of Service (QoS), and routing in VANETs. A review was made by the author on wireless access standards for VANETs. The author discussed the deployment of VANETs in different countries like the US, Japan, and the European Union. Simulators were discussed along with their benefits and limitations. Challenges like scalability, reliability, robustness, and security for implementing the VANET scenario were outlined. Arif et al. [3] surveyed applications of VANETs for extracting passengers’ information for relaxation and comfort, by managing traffic and reducing air pollution. VANETs facilitate by providing a direct link between different vehicles for sharing information. This results in reduced overhead in communications. Security challenges like message dissemination, authentication, and reliable message transmission were discussed concerning an intelligent transport system. Shrivastava and Vishwamitra [12] studied and compared routing protocols like AODV, DSDV, and OLSR based on different Quality of Service (QoS) parameters like throughput, packet overhead, and packet loss ratio. The simulator used was NS3. The simulation result showed that AODV performed better than other routing protocols. Kumar et al. [13] described architecture and different standards used for VANETs. Different possible applications, features, and implementation of VANETs in the real-world were also given. The author explained the need for VANETs to reduce the number of road accidents. Yogarayan et al. [14] described the basic of VANETs and different types of communication that occurs in VANETs. Different perspectives about the present and future state of VANET along with its challenges were also given. Krundyshev et al. [15] suggested particle swarm optimization (PSO) algorithm to ensure network noise immunity, node availability but did not solve security issues in the network. The proposed defensive mechanism was evaluated through simulation. Saeed et al. [16] used fuzzy modelling to check the impact of conventional parameters over cognitive parameters, and minimize the error rate by using artificial neural networks. Cognitive memory was used for retrieving previous route experiences.

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3 Dedicated Short-Range Communication DSRC helps in accomplishing ITS. The key role of ITS is to control traffic, based on real-time data from different RSUs and the vehicles. More efficient ITS will result in a reduction of carbon emissions. For communication, IEEE 802.11p standard is used by car companies. IEEE802.11p is a protocol architecture used to assure road safety and manage road traffic by sharing the location, acceleration, and speed of the vehicle [17, 18]. Table 1 represents literature focusing on features of the IEEE 802.11p protocol. DSRC is a standard proposed by the Federal Communication Commission (FCC), in 1999. V2V and V2I communication both are supported by DSRC and it uses 75 MHz of bandwidth in the 5.9 GHz frequency (5.850–5.925 GHz) [22]. It is a standard that allows communication between vehicles [24]. Out of 75 MHz, initial 5 MHz is reserved for the guard band. DSRC spectrum is divided into seven channels of 10 MHz each. In this, each channel is reserved for some function. Channel 178 (5.885–5.895 GHz) is reserved for safety communication and is known as a control channel. It is used for sending the most critical alarming messages. Channel 172 and channel 184 which are at the edge of the spectrum use a total of 20 MHz bandwidth and are reserved for advanced accident prevention and public safety. Channel 174, channel 176, channel 180, channel 182 are the service channel for regular communication [25, 26] (Table 2). DSRC architecture is proposed by IEEE and it follows the OSI model [24]. Table 3 shows the name of each layer along with the protocol used by it. IEEE 802.11p is adopted by the physical and MAC layer. IEEE 1609.4 is adopted by MAC sub-layer Table 1 IEEE 802.11p analysis/survey Contribution

[19]

[20]

[21]

[22]

[23]

DSRC analysis

✓

✓

X

✓

✓

PHY analysis

✓

✓

✓

X

X

MAC analysis

✓

X

X

X

✓

ITS analysis

✓

X

X

X

X

Scope of literature review

2010–20

2006–19

2000–18

2006–15

2008–17

Table 2 WAVE spectrum Frequency (GHz)

Channel

Usage

5.850–5.855

Guard band

5.855–5.865

CH172

Critical safety of life

5.865–5.885

CH174/CH176

Service channel

5.885–5.895

CH178

Control channel

5.895–5.915

CH180/CH182

Service channel

5.915–5.925

CH184

High power public safety

632 Table 3 DSRC layers

P. Kaushal et al. Name of the layer

Protocol

Application layer

IEEE 1609.1

Security services

IEEE 1609.2

Transport layer

IEEE 1609.3

Network layer LLC sub-layer

IEEE 802.2

MAC sub-layer extension

IEEE 1609.4

MAC sub-layer

IEEE 802.11p

PHY layer

extension IEEE 802.2 is adopted by LLC sub-layer. IEEE 1609.3 is adopted by the network and transport layer. IEEE 1609.2 is adopted for security services. IEEE 1609.1 is adopted by the application layer [17, 19].

4 Applications of VANETS VANETs impart communication among vehicles, infrastructure, and roadside units. V2V and V2I communications provide safety and comfort applications to the traveller. In this, the vehicles are equipped with network devices such as Global Positioning System (GPS) receivers, electronic sensors so that all the traffic-related information could be collected and transferred to the other vehicles present nearby or the infrastructures [26]. Applications of VANETs can be categorized as comfort and safety of the traveller.

4.1 Comfort Application Comfort applications are the applications that help in the enhancement of the driver as well as passengers’ comfort level. These are non-safety applications. Information related to climate change, the location of nearby restaurants, or petrol pumps comes under this category. It can be categorized as [27]: Remote Vehicle Personalization Diagnostics: Users can implement personalized vehicle settings or even upload vehicle diagnostics information through AU. Internet Access: RSU can also work as a router, and provide an Internet facility to the driver or the passenger. Digital Map: Through RSU, the passenger or the driver can download the map of regions according to their convenience.

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Real-Time Video Relay: Passenger or driver waiting for someone or having rest can also use the facility of on-demand movie through application unit which is further connected to RSU. Value-Added Advertisement: This service allows the passenger to watch advertisements given by the nearby shopping store. Petrol pump or restaurant-like information is also shared via RSU.

4.2 Safety Applications Safety applications refer to the applications which help in enhancement for the protection of the traveller on the road network. Improvement in traffic flow by lane changing warning, emergency video streaming is considered under this category. For safety applications, information related to traffic is collected through OBU, and then, it is processed and broadcast in the form of messages to the other vehicles and infrastructures through RSU. This application utilizes V2V and/or V2I communications. It can be categorized as [28]: Public Safety: In the case of any accidental emergency, public safety applications guide the drivers. It helps the emergency response team in reducing its travel time by guiding optimized routes. A frequency of 1 Hz is used by public safety applications within the range of 300–1000 m. Public safety applications are: a. Emergency Vehicle Warning: Warning messages regarding any ambulance, fire brigade vehicle is passed to the driver through RSU and nearby vehicles. So that these vehicles can reach their destination without breakage. A broadcast message is passed via RSUs which has information like emergency vehicle speed, route, direction, etc. to the entire vehicle nearby. b. Emergency Vehicle Signal Preemption: Through VANETs, the traffic infrastructure is designed in such a way that if any emergency vehicle is about to reach any red-light signal, then a message is passed through RSU for the change in traffic signal to green so that the emergency vehicle can cross without any delay and it reduces the chances of accidents. c. Post-Crash Warning: If an accident occurs on some route, then the affected vehicle transmits the message to the nearby vehicle on the same route. This is done via V2V and V2I communication to either change the path or have a delay in travelling time. Sign Extension: It notifies the drivers about the signs given on the roadside to prevent the accident. A frequency of 1 Hz and a communication range between 100 and 500 m is utilized for the sign extension. The sign extension applications are [29]: a. Low-Bridge Warning: OBU is equipped near the upcoming bridge so that it can send an alert message about the height of the bride to the vehicles. So that decision can be made by the driver of the vehicle whether to pass through the bridge or not.

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b. In-Vehicle AMBER Alert: If the law enforcement authorities find out that any particular vehicle is tangled in any doubtable crime, then an AMBER message is transmitted to vehicles in that area excluding a suspected vehicle so that the other people could be protected. c. Low Parking Warning: If any vehicle tries to enter a low parking area, then a warning message will be passed to that particular vehicle about the shortage of free space through RSU. Information from Other Vehicles: Information from the other vehicles helps in smooth traffic flow, ensure traffic safety, traffic assistance for this the frequency between 2 and 50 Hz is used within the range of 50–400 m by vehicle and RSUs. Information from other vehicles can be: a. Cooperative Forward Collision Warning: To avoid a collision at an accident location, a warning message is communicated through V2V communication to the nearby vehicles. Warning messages include the exact location of the accident, options for alternate routes, etc. This is done for smooth traffic flow. b. Pre-Crash Sensing: Information is collected from vehicle sensors applied at OBU and RSU to predict a situation, where an accident is about to happen. c. Road Condition Warning: By using the features like OBU and RSU, information regarding road conditions is transferred in advance to the vehicle travelling on the route and if the upcoming road’s condition is not good, the warning message is transmitted This application is very useful in rural areas, where road surfaces are not smooth. d. Emergency Brake: To avoid any accidents in smoky or foggy weather, a warning message is transmitted to the vehicles travelling on the road for sudden hard braking. This is done by using communication among the vehicles and RSU. Intersection Collision Avoidance: This system reduces traffic incidents by analysing the estimation of the possibility of an incident to happen. This frequency of 10 Hz with a communication range of 200–F300 m is used. The intersection collision avoidance can be classified as: a. Warning for Violating the Stop Sign: After calculating the distance between the vehicle and the stop signal, this application sends a warning message to the vehicle approaching near the signal. This reduces the violation of stop signals. This is done by extracting information about the vehicle from RSU and adjacent vehicles. b. Intersection Collision Warning: The information of the road intersection is collected through RSU, and then, the probability of the incident to happen is predicted. For intersection collision warning, the data is collected by the sensors deployed through the application unit; it includes speed, location, and traffic flow information. c. Pedestrian Crossing Information: Through the sensors deployed on RSU, the information about the pedestrian crossing the road is collected and passed to

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the nearby approaching vehicles. This information is passed to the approaching vehicle so that the speed of the vehicle could be reduced accordingly and an accident could be avoided.

5 Conclusion and Future Scope VANET is the most demanding and promising research field in wireless communication systems due to its security and safety-related services for travellers. In this paper, a detailed discussion of VANETs is done with the perspective of their components, characteristics, communication, and application related to the safety and comfort of the traveller. In India, the government has even started implementing electronic toll plazas for further improvement by collecting toll electronically. The United States has even added VANETs as the sustainable goal for decreasing the death rate due to road accidents. There will be many challenges for implementing VANETs in future. The adoption of VANETs in India is the greatest challenge. This study would provide new research directions for future researchers.

References 1. Yousefi S, Mousavi MS, Fathy M (2006)Vehicular ad hoc networks (VANETs): challenges and perspectives. In: ITST 2006-2006 6th international conference ITS telecommunication proceedings pp 761–766 2. Zeadally S, Hunt R, Chen YS, Irwin A, Hassan A (2012) Vehicular ad hoc networks (VANETS): status, results, and challenges. Telecommun Syst 50(4):217–241 3. Arif M, Wang G, Zakirul Alam Bhuiyan M, Wang T, Chen J (2019) A survey on security attacks in VANETs: communication, applications and challenge. Veh Commun 19(1):1–36 4. Schoch E, Kargl F, Weber M, Leinmüller T (2008) Communication patterns in VANETs. IEEE Commun Mag 46(11):119–125 5. Poonia RC (2018) A performance evaluation of routing protocols for vehicular ad hoc networks with swarm intelligence. Int J Syst Assur Eng Manag 9(4):830–835 6. Kaushal P, Khurana M, Ramkumar KR, Gandhi V (2020) A study on routing protocols of vehicular ad hoc network. Int J Adv Res Eng Technol 11(12):2672–2686 7. Sharma S, Nanda M, Goel R, Jain A, Bhushan M, Kumar A (2019) Smart cities using internet of things: recent trends and techniques. Int J Innov Technol Explor Eng 8(9):24–28 8. Mangla M, Kumar A, Mehta V, Bhushan M, Mohanty S (2021) Real-life applications of the Internet of Things: challenges, applications, and Advances. CRC Press, Taylor and Francis Group 9. Xia Y, Qin X, Liu B, Zhang P (2018) A greedy traffic light and queue aware routing protocol for urban VANETs. China Commun 15(7):77–87 10. Al-Sultan S, Al-Doori MM, Al-Bayatti AH, Zedan H (2014) A comprehensive survey on vehicular ad hoc network. J Netw Comput Appl 37(1):380–392 11. Sharma S, Kaushik B (2019) A survey on internet of vehicles: applications, security issues and solutions. Veh Commun 20 12. Shrivastava PK, Vishwamitra LK (2021) Comparative analysis of proactive and reactive routing protocols in VANET environment. Meas Sens 16(4):100051

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13. Kumar V, Mishra S, Chand N (2013) Applications of VANETs: present and future. Commun Netw 05(01):12–15 14. Yogarayan S, Razak SFA, Azman A, Abdullah MFA, Ibrahim SZ, Raman KJ (2020) A review of routing protocols for vehicular ad-hoc networks (VANETs). In: 2020 8th International conference information communication technology ICoICT 15. Krundyshev V, Kalinin M, Zegzhda P (2018) Artificial swarm algorithm for VANET protection against routing attacks. In: Proceedings of IEEE industrial cyber-physical systems ICPS, pp 795–800 16. Saeed Y, Ahmed K, Zareei M, Zeb A, Vargas-Rosales C, Awan KM (2019) In-vehicle cognitive route decision using fuzzy modeling and artificial neural network. IEEE Access 7(1):20262– 20272 17. Li Y (2015) An overview of the DSRC/WAVE technology. 15(3):9–21 18. Nzouonta J, Rajgure N, Wang G, Borcea C (2009) VANET routing on city roads using real-time vehicular traffic information. IEEE Trans Veh Technol 58(7):3609–3626 19. Arena F, Pau G, Severino A (2020) A review on IEEE 802.11p for intelligent transportation systems. J Sens Actuator Netw 9(2):1–11 20. Harkat Y, Amrouche A, Lamini ES, Kechadi MT (2019) Modeling and performance analysis of the IEEE 802.11p EDCA mechanism for VANET under saturation traffic conditions and error-prone channel. AEU-Int J Electron Commun 101:33–43 21. Cao S, Lee VCS (2020) An accurate and complete performance modeling of the IEEE 802.11p MAC sublayer for VANET. Comput Commun 149(9):107–120 22. Shuhaimi NI, Heriansyah, Juhana T (2016) Comparative performance evaluation of DSRC and Wi-Fi direct in VANET. In: Proceedings-2015 4th international conference instrumentation, communication information technology biomedical engineering icICI-BME 2015, pp 298–303 23. Fitah A, Badria A, Moughit M, Sahel A (2018) Performance of DSRC and WIFI for intelligent transport systems in VANET. Proc Comput Sci 127:360–368 24. Kenney JB (2011) Dedicated short-range communications (DSRC) standards in the United States. Proc IEEE 99(7):1162–1182 25. Yin J et al. (2004) Performance evaluation of safety applications over DSRC vehicular ad hoc networks. In: VANET-Proceedings first ACM international workshop vehicular ad hoc networks, pp 1–9 26. Taha MMI, Hasan YMY (2007) VANET-DSRC protocol for reliable broadcasting of life safety messages. In: ISSPIT 2007-2007 IEEE International symposium signal processing and information technology, pp 104–109 27. Ahmad I, Noor RM, Ali I, Imran M, Vasilakos A (2017) Characterizing the role of vehicular cloud computing in road traffic management. Int J Distrib Sens Netw 13(5):1–14 28. Sheikh MS, Jun L, Wang W (2019) A survey of security services, attacks, and applications for vehicular ad hoc networks (VANETs). Sens Rev 19(16):1–40 29. Khurana M, Ramakrishna C, Panda SN (2020, Feb) Antenna diversity scheme for multipath mitigation in vehicular adhoc networks on urban roads. In: Indo-Taiwan 2nd international conference on computing, analytics and networks, Indo-Taiwan ICAN 2020-proceedings, pp 317–324

Remote Authentication of Fingerprints Using Meaningful Visual Cryptography Surajit Goon, Debdutta Pal, and Souvik Dihidar

Abstract Nowadays, fingerprint recognition plays a vital role in digital security. We can authenticate any person using a central remote server. Fingerprints have the unique characteristics to identify any person uniquely. In this paper, we propose a technique by which we can remotely transmit any fingerprint securely. Meaningful visual cryptography (VC) scheme is used to implement this method. In VCS, secret image (here fingerprint) is divided into multiple shares, and each of these shares is integrated with some natural images and then transmitted using different network paths. Our model follows the 2 out of 2 secret sharing schemes. The major drawback of any VCS is loss of contrast in the output reconstructed image. Meaningful shares make this more difficult to retrieve the secret image. Our model uses some filtering techniques to overcome the problem and obtain the original fingerprint. Keywords Visual cryptography · Halftone image · Meaningful shares · Fingerprints · Similarity score

S. Goon (B) · D. Pal Department of Computer Science and Engineering, Brainware University, Barasat, West Bengal, India e-mail: [email protected] D. Pal e-mail: [email protected] S. Dihidar Department of Computer Science, Eminent College of Management and Technology, Barasat, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_62

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1 Introduction Identity of a person can be established using biometry [1]. Fingerprints are used to authenticate any individual by uniquely identifying that person. Each and every person has some unique biological characteristics of their fingerprint patterns. Biometric features of any fingerprints are always different from other [2]. Our approach stores the original fingerprint database on a server, and if a person wants to authenticate himself via fingerprint from any remote location or client office, the same will generate two different shares using VCS. Following the generation of these shares, two cover images are used to conceal the original two shares, which are then transmitted via different network paths to the server, where the server applies some filtering techniques to the merged images and checks for a match using its database. In Sect. 2, we discuss about the literature reviews. In Sect. 3, we briefly describe our proposed model, and in Sect. 4 shows the implementation of the same. We discuss the results in Sect. 5. Finally, in Sect. 6, we summarize our findings and future directions.

2 Literature Review 2.1 Visual Cryptography Cryptography is one of the most used techniques to protect biometric templates [3]. Visual cryptography was introduced by Naor and Shamir [4], and this is a simple technique by which we can generate multiple shares from secret image in such a way that to reveal the secret image at decryption end we need only k numbers of shares among the total n numbers without applying any computational power. We simply stack those k shares one after the other, and the secret will be revealed via the human visual system [5]. In case of 2 out of 2 visual cryptographic scheme, two sub-pixels are generated for each original pixel P. Figure 1 represents the shares of a white and a black pixel. The choice of selecting white or black pixel is randomized. When we overlapped these two shares, we can determine the pixel P. Bayer [6] used an order dithering technique to convert a gray image to an equalsized binary image. Floyd and Steinberg [7] described an error diffusion technique to convert the original gray image into halftone image. Nakajima and Yamaguchi [8] suggested a 2 out of 2 EVCS to transform the gray image into meaningful shares.

2.2 Fingerprint Recognition Flow-like ridges of human fingers are called fingerprints. Jain et al. [9] described that fingerprints are formed due to the initial conditions of the embryonic mesoderm.

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Fig. 1 Illustration of 2 out of 2 VCS with 2 sub-pixel construction

Fingerprint recognition systems are popularly based on ridge endings and ridge bifurcations. Both these structures are combined called minutiae [10]. Minutiae extraction and matching are two basic operations used in fingerprint recognition systems.

3 Proposed Method In our proposed method, we developed a model in which fingerprints are remotely recognized using a server database. Nowadays, large corporations have numerous branches or client offices in various cities. Any employee may be posted or visit several sites over a period of time. In such a case, it is not feasible to carry out multiple fingerprint registration processes. In our system, user or employee only has to register their fingerprints once, and that data will be stored in server database (Fig. 2). After scanning any fingerprint, it immediately went through the meaningful VCS process, which generates two distinct shares, which were then transmitted to the server via different network paths. In Fig. 3, the shares were superimposed and resized on the server, and the overlapping image was filtered. Then server searches for the successful match with its database, if found notify the client, authentication is successful; otherwise, authentication failure notification will be generated.

Fig. 2 Fingerprint registration process

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Fig. 3 Proposed model

4 Implementation Rao et al. [11] described fingerprint protection using visual cryptography. VCS itself degrade the quality of image, so it is very difficult to use this technique in fingerprint matching. Moreover, meaningful VCS required more emphasis on the quality of the decrypted images.

4.1 Encryption Process Step 1 At client office, fingerprint is scanned through biometric device. Step 2 Convert the fingerprint into halftone image using Floyd–Steinberg algorithm. Step 3 Apply 2 out of 2 VCS to create share 1 and share 2. Step 4 Share1 and share2 are integrated into cover image 1 and cover image 2, respectively, to generate meaningful share 1 and meaningful share 2. Step 5 Transmit both meaningful share 1 and 2 using separate network path to the server.

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4.2 Decryption Process Step 1 After receiving both meaningful share 1 and meaningful share 2, server generates the overlapped secret image by stack them together. Step 2 Applying morphological functions to reduce the noise and enhance the contrast. Step 3 Check to see if the fingerprint matches the one in the employee database. Step 4 Depending on the match result, an authorization notification is sent to the client. Figure 4 shows the encryption results where two meaningful shares are generated and also the decryption results, as well as the resized and filtered output. We used ‘strel’ object which is a morphological element used in dilation and erosion, to filter the output fingerprint. It is a flat structuring element which has binary valued neighborhood, and in this structure, true pixels are taken into account but false pixels are rejected. Figure 5 gives a basic idea about the fingerprint matching techniques.

Fig. 4 From top left to right a original fingerprint, b share1, c share 2, d cover image 1, e cover image 2, f meaningful share 1, g meaningful share 2, h overlapped image and i) final output after filtering and resizing

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Fig. 5 Finger print matching process

5 Result We first created the fingerprint database by extracting each and every fingerprint minutiae and stored the template in server. After filtering and resizing the meaningful VCS generated fingerprint, we again extract the minutiae from it and then compare it with the existing database. FMR: False match rate. Proportion of fraudulent attempts that are declared to match another object’s template. FNMR: False non-match rate. Proportion of actual tries which might be falsely declared not to suit a template of the identical object. EER: Equal error rate. The point where FNMR=FMR. Figure 6 plots the FMR, FNMR and EER of our system against the matching rates with similarity scores. Fingerprint matching result is shown in Fig. 7. In our experiments, we considered 0.48 as the matching percentage score to detect fingerprint.

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Fig. 6 Similarity score plot

If the similarity score is greater or equal to 0.48, then system will authenticate the person but if the similarity score is less than 0.48, then the authentication fails. Table 1 gives, for each and every sample fingerprints, the similarity score is more than 0.48 for the identical fingerprints. Other imposter fingerprints are scored less than 0.48.

6 Conclusion We have implemented a model where fingerprints can be remotely authorized using meaningful visual cryptography. So, there is no need to register any employee at multiple client offices, only one registration at server side is enough. Our system not only transmits the fingerprint information securely, it also hides the original shares behind some natural images so that it will not attract any intruders or hackers. Because meaningful visual cryptography always produces images with contrast loss, it is very difficult to construct an output fingerprint image which will work in any fingerprint recognition systems. These system losses some data due to the implementation of meaningful shares but still it is a successful one because using the output image, most biometric systems can identify and authorize any person successfully. In the future, other biometric matching systems and authorizations can be implemented through this VCS.

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Fig. 7 Fingerprint recognition result Table 1 Similarity scores of fingerprint samples Fingerprint samples

Original fingerprint

Minutiae extraction

VCS output

Similarity score

Sample 1

0.7098

Sample 2

0.7396

(continued)

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Table 1 (continued) Fingerprint samples

Original fingerprint

Minutiae extraction

VCS output

Similarity score

Sample 3

0.61865

Sample 4

0.95455

References 1. Ross A, Othman A (2011) Visual cryptography for biometric privacy. IEEE Trans Inf Forensics Secur 6(1):70–81 2. Bansal R, Sehgal P, Bedi P (2011) Minutiae extraction from fingerprint images-a review. IJCSI 8(5) 3 3. Soutar C, Roberge D, Stoianov A, Gilroy R, Vijaya Kumar BVK (1999) Biometric encryption. In: ICSA guide to cryptography, vol 22. McGraw-Hill 4. Naor M, Shamir A (1994) Visual cryptography. In: Proceedings of Euro crypt 1994. Lecture notes in Computer Science, vol 950, pp 1–12 5. Goon S (2019) Major developments in visual cryptography. Int J Eng Adv Technol 9(1S6) 6. Bayer B (1973) An optimum method for two-level rendition of continuous-tone pictures. In: IEEE international conference on communications, pp 11–15 7. Floyd RW, Steinberg L (1976) An adaptive algorithm for spatial grey scale. Proc Soc Inf Disp 17:75–77 8. Nakajima M, Yamaguchi Y (2002) Extended visual cryptography for natural images. J WSCG 10(2):303–310 9. Jain A, Hong L, Pankanti S, Bolle R (1997) An identity authentication system using fingerprints. Department of Computer Science, Michigan State University, USA, pp 1–66 10. Dey S, Karmakar SK, Goon S, Kundu P (2019) A survey on fingerprint pattern recognition. Int J Res Granthaalayah 7(8):496–506 11. Rao YVS, Sukonkina Y, Bhagwati C, Singh UK (2008) Fingerprint based authentication application using visual cryptography methods (improved id card). In: TENCON 2008–2008 IEEE region 10 conference, pp 1–5. IEEE

A Novel Approach to Ensure the Security of Question Papers Using Visual Cryptography Surajit Goon, Debdutta Pal, and Souvik Dihidar

Abstract In the recent COVID-19 pandemic situation, physical distributions of University question papers to the affiliated colleges are very difficult. Though online distributions of question papers are a good alternative, they may be vulnerable in respect of security concerns. Hackers may intervene in the process and question papers may be stolen or altered. In this paper, we used K-out-of-N visual cryptography techniques to secure the question papers in between the transmissions. Before creating the shares, we applied an extra password protection to the original question paper using AES encryption. At the college examination center, end multiple shares are received by the separate stakeholders, who were ultimately combined together to create the decrypted question paper only after having the password provided by the head of the Institution. Keywords Visual cryptography · Halftone image · Error diffusion · AES

1 Introduction Traditional question paper distribution of University examinations is greatly affected by the recent COVID-19 pandemic. Due to the several restrictions imposed by the various Government authorities makes this distribution very tough. Online transmission of the same is an excellent alternative. But there lie many threats to the system with respect to security. Any intruders or hackers may steal or alter the question S. Goon (B) · D. Pal Department of Computer Science & Engineering, Brainware University, Barasat, West Bengal, India e-mail: [email protected] D. Pal e-mail: [email protected] S. Dihidar Department of Computer Science, Eminent College of Management & Technology, Barasat, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_63

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papers. We applied password protection to the original question paper using AES encryption in our approach. After that, we applied visual cryptography schemes and generated N number of shares from the original encrypted question papers. Now University will send K−1 number of shares to the colleges. One or more examiners including the college examination controller will receive one share each through emails. Head of the Institute will also get the password separately from the University through email. At the time of examination, University will send the Kth share from its rest shares to the exam controller. Now, the exam controller will generate the decrypted question paper after getting the password from the head of the Institution and stacking K number of shares including his own share and University share. We discuss the literature survey in Sect. 2. In Sect. 3, we describe the proposed architecture and Sect. 4 discuss about the implementation of the same. In Sect. 5, we discuss about the results and finally in Sect. 6, we conclude with future scopes.

2 Literature Review Various scholars around the world have researched the connected secret sharing problem, and its physical aspects, such as pixel expansions, various scholars around the world have researched the connected secret sharing problem and its physical aspects such as contrast, pixel expansions, contrast and color. For example, Naor and Shamir [1] demonstrated threshold VCS with flawless black pixel reconstructions. Atenies et al. [2] gave bulding of VCS for the general access structure. In addition, Eisen et al. [3] advocated the creation of threshold VCS for given whiteness levels of restored pixels. In general, an Extended Visual Cryptographic Scheme (EVCS) accepts a secret message and its original shares as inputs and shares of output that meet the following three criteria: (1) The secret image can be recovered by any qualified subset, (2) except for the secret picture’s size, any banned subset of shares cannot receive any information about the secret image and (3) all the shares are meaningful images [4]. The proposed approach, which is based on blue-noise dithering principles, employs the void and cluster algorithm [5] to encode a secret binary image into halftone shares that carry important visual information. In symmetric cryptography, the transmitter and receiver share a single key. The most popular symmetric algorithm is Advanced Encryption Standard (AES) [6]. It is more secure and efficient than its previous algorithms [7]. It was first introduced in Federal Information Proceeding Standard (FIPS) 192 [8, 9].

3 Proposed Method The original question paper was first converted into a halftone gray image using Floyd–Steinberg’s [10] error diffusion technique. Now University will create N

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Fig. 1 Proposed model

number of shares using K-out-of-N VCS immediately after applying AES encryption to the original question paper. Then K−1 shares are sent to the examiners, exam controllers and head of the Institutions. Head of the Institution also gets the AES key from the University. Just before starting of the examinations, University shares the Kth share to the exam controller or head of the Institution. Then all the K numbers of shares are stacked together, and applying the AES key to the overlapped image, we get the output question paper. Figure 1 describes the proposed model.

4 Implementation We experimented K-out-of-N sharing algorithm on the original question paper, where a total number of shares N = 4 and the number of shares for K value is 3. Before VCS, we applied a key to the original question paper image using AES encryption. Figure 2 shows the graphical interface of the encryption end. It will generates N (4) numbers of shares which are shown in Fig. 3. At decryption end, we can only get the original image by stacking K (3) or more shares. If the value of K is less than required (in our case K = 3), we will get a partial image. After choosing any number of shares among all the generated shares, the

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Fig. 2 a Encryption–decryption window and b encryption end GUI

Fig. 3 From top left to right: a original image, b halftone image, c share 1, d share 2, e share 3 and f share 4

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Fig. 4 a Decryption with the correct key and b decryption with the incorrect key

reconstructed image and the decrypting graphical user interface are shown in Fig. 4. Figure 4a shows the successful execution with the correct key, and Fig. 4b shows the successful implementation with wrong key.

5 Results After comparing the decrypted image with the original question paper image using MSE, PSNR and SSIM, we established that the conversion quality was excellent. The correlation between two images is calculated using the Pick Signal Noise Ratio (PSNR).

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Table 1 Decryption results with the correct key Original image

Decrypted image with correct key

MSE

PSNR

SSIM

0.0022

85.6426

0.9912

Table 2 Decryption results with the incorrect key Original image

Decrypted image with incorrect key

MSE

PSNR

SSIM

13,787.69

6.7359

0.0250

  PSNR = 10 ∗ log 2552 /MSE

(1)

The squared difference between the original and decrypted images is the mean square error (MSE). MSE = 1/(M ∗ N )

 

2

I (i, j ) − I ∼ (i, j )

(2)

The structural similarity index (SSIM) is a metric that quantifies how much image quality is lost during decryption. In our experiments where the question paper was decrypted using the correct key, we got MSE value of 0.0022, PSNR value greater than 85 dB and SSIM value of 0.9912. These results indicate that our decrypted question paper image quality is excellent. Table 1 gives the above results. When the question paper decrypted using an incorrect key, we got the MSE value of 13,787.69 and PSNR value of less than 7 and SSIM value of less than 0.03 which indicates that the image could not be recognized and the decryption process ends without revealing the secret. Table 2 gives the results of our experiment with an incorrect key.

6 Conclusion We’ve created a concept in which physical question papers distributed by the Universities can be completely replaced using visual cryptographic schemes. It can greatly reduce the University examination departments’ distribution cost and also provide

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end-to-end security measures between Universities and its examination stakeholders. Results can be more accurate in future experiments by introducing some optimization techniques.

References 1. Naor M, Shamir A (1994) Visual cryptography. In: Proceedings of Euro crypt 1994 Lecture notes in computer science, vol 950, pp 1–12 2. Ateniese G, Blundo C, Santis A, Stinson DR (2001) Extended capabilities for visual cryptography. Acm Theor. Comput Sci 250:143–161 3. Eisen PA, Stinson DR (2002) Threshold visual cryptography schemes with specified whiteness levels of reconstructed pixels. Des Codes Crypt 25(1):15–61 4. Goon S (2019) Major developments in visual cryptography. Int J Eng Adv Technol 9(1S6) 5. Kandar S, Maiti A, Dhara BC (2011) Visual cryptography scheme for color image using random number with enveloping by digital watermarking. Int J Comput Sci Issues (IJCSI) 8(3):543 6. Hasib AA, Haque AAMM (2008) A comparative study of the performance and security issues of AES and RSA cryptography. In: 2008 third international conference on convergence and hybrid information technology, pp 505–510 7. Kofahi NA, Al-Somani T, Al-Zamil K (n.d.) (2003) Performance evaluation of three encryption/decryption algorithms. In: 46th Midwest symposium on circuits and systems, pp 790–793 8. Joan Daemen VR (1999) AES proposal: Rijndael version 2, AES submission. http://csrc.nist. gov/CryptoToolkit/aes/rijndael/Rijndaelammended.pdf. 9. Daemen J, Rijmen V (2013) The design of Rijndael: AES-the advanced encryption standard. Springer Science & Business Media 10. Floyd RW (1976) An adaptive algorithm for spatial gray-scale. In: Proc Soc Inf Disp, Vol 17, pp 75–77

NO PHISHING! Noise Resistant Data Resampling in Majority-Biased Detection of Malicious Websites Arghasree Banerjee , Kushankur Ghosh , Rahul Sen, Aritro Chakraborty, Sudipta Roy Chowdhury, and Sankhadeep Chatterjee

Abstract Considering the fatality of malicious websites, the current literature supports the application of machine learning-based architectures to perform automatic classification of websites. Irregular distribution of the data often results in a majority-biased classification and eventually decreases a model’s classification performance. In this paper, we addressed the problem in detecting malicious websites by employing Tomek Link-based resampling techniques to avoid the sampling of noisy examples. The experiment is conducted with three popular classification techniques and compared with different oversampling and undersampling frameworks. The fatal effect of the irregular class distribution in malicious website detection is further experimentally justified with the increasing level of irregularity. Keywords Malicious web domain · Phishing · Class imbalance · SMOTE · Tomek Link

1 Introduction The online cyber-attacks or phishing conducted through a website tends to obtain sensitive details about a user by gaining complete trust. These websites follow the visual appearance of any genuine website that is trusted by a wide range of people. R. Sen University of Engineering and Management, Kolkata, India A. Banerjee (B) · K. Ghosh Department of Computing Science, University of Alberta, Edmonton, Canada e-mail: [email protected] A. Chakraborty · S. R. Chowdhury Department of Computer Science & Engineering, University of Engineering & Management, Kolkata, India S. Chatterjee Department of Computer Science & Technology, University of Engineering & Management, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_64

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The attacker anonymously sends the website link to the user with fake attractive offers [1]. Trusting it to be a genuine one, when the user tries to log in to the website his personal details like email, home address, phone number, and bank account access details get exposed to the criminal operating the page. In the recent literature, we find that the traditional approaches like the PageRank and Black List fail in various scenarios due to its inflexible nature of identifying websites [2, 3]. To overcome the challenges, researchers suggested a machine learning-based automated approach to understanding the pattern of malicious pages based on the existing known patterns of the website. Machine learning has been proved to be effective in domains like gravitational waves detection [4], content generation in games [5], link prediction [6], and hip dysplasia diagnosis [7]. The recent research in the domain of phishing website identification mostly explores robust machine learning models in the context of Uniform Resource Locator (URL) patterns. In one study [8], Zouina and Outtaj proposed a hybrid SVM-based framework by using popular similarity indexes. The approach was found to be effective; however, Nlevelshtein obtained an error rate of 40%. In one approach by Jain and Gupta [9], the model was developed by using both URL and HTML source code. In a recent study, hybrid deep neural networks (DNN) were proposed by using genetic algorithms (GA) in feature selection [10]. Subasi and Kremic [11] found AdaBoost classifier to be the optimal classifier in their comprehensive experiments in detecting Phishing websites. This motivated us to experiment AdaBoost in our experiment. The current literature lacks concrete studies on the effects of imbalanced data in detecting fake webpages. Few studies tested algorithms like SMOTE [12, 13] and Borderline-SMOTE [14] but failed to demonstrate the change in a classifier’s performance in classifying fake websites. In this paper, we experimentally demonstrate the fatality of imbalanced data by showing the deflection in a model’s performance. We further tested state-of-the-art models with Tomek Link-based resampling frameworks on three popular machine learning models and illustrated a comparison by testing four other resampling frameworks. Through this paper, we make the following contributions: I.

Addressed the class imbalance problem in detecting fake websites using machine learning from URLs II. Showed the deviation in the performance of machine learning models with the increase in the degree of disproportion among the labels III. Employed Tomek Link-based un-noisy resampling solution to undertake the prediction of the websites IV. Compared popular models and resampling techniques The rest of the sections of the paper is segregated as follows: In Sect. 2, we discuss about the methodology proposed in the paper along with the description about the data. Section 3 illustrates our experimental findings. Finally, we conclude our research in Sect. 4.

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2 Methodology 2.1 Data The data set [15, 16] is constructed on 111 features that help to determine the legitimacy of a website. The construction is done by collecting 30,647 data from Phishtank website and 58,000 data from Alexa ranking website. Further enhancement in the data is made by adding 27,998 community-labelled legitimate URLs. The data distribution among the classes attained an imbalance ratio (ImR) of 1.89. This is calculated as a ratio between the counts of majority and minority instances in the data set.

2.2 Resampling Based on Tomek Links Avoiding the noise generation by SMOTE is our primary motive of applying Tomek Links. An example that is projected in the distribution of the opposite class can be termed as a noise. A Tomek Link can be visualised as a link between a majority μi , such that and a minority instance [17]. A minority example ⎤ μi ∈ M, is assumed ⎡ to be a z-dimensional vector where μi = a1 , a2 , a3 . . . az . Similarly, a majority example m j belonging to a majority set Δ , assumed to be a z-dimensional vector can be made to form a Tomek Link with μi if there exists no other m x that is closer to μi . A majority sample forming a link is considered a noise, and it is excluded from training. In case of undersampling (Tomek-U), we downsample the Δ by excluding the instances forming a link. The excluded instances are treated as noise. During oversampling, we follow the approach proposed by Chawla et al. [18]. We generate a set of artificial samples S = [∅1 , ∅2 , ∅3 . . . ∅C ] where based on the value of the amount of generation Oq , C can be defined as: C=

Oq · |M| 100

Each of the minority sample is considered to create a set of its k closest Euclidean neighbours N. N can be defined as: N = [n 1 , n 2 . . . n k ] The synthetic sample is generated as: ∅l,z = μl,z + Q · θl,z A neighbour for each μl is selected randomly can be defined as:

Oq 100

times to calculate the θl,z . θl,z

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θl,z = n z − μl,z After balancing the data set with the generated examples, we exclude the majority and minority samples forming a link.

3 Experimental Analysis The experiment is conducted by using three popular classifiers: K-nearest neighbour (KNN), AdaBoost, and decision tree (DTC). All the results are documented based on a tenfold cross-validation approach. Apart from Tomek link-based resampling like SMOTE-Tomek and Tomek-U, further comparison is conducted by applying techniques like SMOTE, random oversampling (ROS), and random undersampling (RUS). The experiment is conducted in two separate sections. In the first section, we show decrease in performance of machine learning models with the gradual increase in ImR. In the second section, we illustrate the performance deflection after applying resampling. Considering τp , τn , f p , and f n as true positive, true negative, false positive, and false negative values, the performance is recorded based on the following metrics [19, 20]: / GM = FM =

2

τp τn · τn + f p τp + f n

2 · τp 2 · τp + f p + f n

3.1 Effects in Classification with the Increasing Degree of Imbalance In Figs. 1 and 2, the X-axis represents the ImR and the Y-axis represents GM and FM. A biased classification towards the majority class occurs as a result of the disparity between the [21–24]. In this set of experiments, we measure the trend of wrong classification of websites by increasing the disparity. After manually balancing the data set, we randomly delete data from the minority class and hence, increasing the ImR. It is found that each of the classifiers records a prominent degradation in the performance.

NO PHISHING! Noise Resistant Data Resampling in Majority… 0.87

659

0.94

0.95

0.935

0.945

0.93

0.94

0.925

0.935

0.92

0.93

0.86 0.85 0.84 0.83 0.82 0.81 0.8 0.79 0.78

1

1.5

2

(a)

2.5

0.915

1

1.5

2

(b)

2.5

0.925

1

1.5

2

2.5

(c)

Fig. 1 Deflection in GM with the increase in ImR: a KNN, b AdaBoost, and c DTC

Fig. 2 Deflection in FM with the increase in ImR: a KNN, b AdaBoost, and c DTC

3.2 Results In Table 1, we document the results obtained on the original imbalanced data set. The upweighting of the minority samples is conducted in a similar way as proposed by Banerjee et al. in [25]. In Figs. 3 and 4, the deflection of GM and FM value (represented in the Y-axis) with SMOTE-Tomek by applying different classifiers is depicted through graphical representation. It is observed that the average value of GM increases with the increase in the oversampling quantity. In Fig. 3a, the best GM value is obtained at 86% oversampled quantity with KNN classifier. In Fig. 3b the optimal G-mean value is obtained at 77% oversampled quantity with AdaBoost Classifier and in Fig. 3c the best value is obtained at 75%. GM value is observed at 87% oversampled quantity with DTC. In contrast to the GM value, the FM value is observed to be decreasing on average with the increase in the oversampling quantity. The highest FM score with KNN classifier is observed

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Table 1 Performance on the original data set

Model

GM

FM

KNN

0.8614

0.8217

AdaBoost

0.9295

0.9085

DTC

0.9494

0.934

0.935

0.874

0.9515

0.934

0.872

0.951

0.933

0.87

0.9505

0.932

0.868 0.931

0.866

0.95

0.93

0.864

0.9495

0.929

0.862 9%

59%

0.928 9%

(a)

59%

0.949 9%

(b)

59%

(c)

Fig. 3 Deflection in GM scores with SMOTE-Tomek: a KNN, b AdaBoost, and c DTC 0.8775

0.937

0.956

0.877

0.9365

0.9558

0.8765

0.936

0.9556

0.876

0.9355

0.9554

0.8755

0.935

0.9552

0.875

0.9345

0.955

0.8745

0.934

0.9548

0.874

9%

59%

(a)

0.9335

9%

59%

0.9546

9%

(b)

59%

(c)

Fig. 4 Deflection in FM with SMOTE-Tomek: a KNN, b AdaBoost, and c DTC

at 24% oversampled quantity which is depicted in Fig. 4a. In Fig. 4b, the best FM value is obtained at 69% oversampled quantity with AdaBoost classifier, and the best FM score with DTC is observed at 79% oversampled quantity which is depicted in Fig. 4c. In Table 2, we recorded the performance obtained after fully balancing the data set using resampling techniques.

NO PHISHING! Noise Resistant Data Resampling in Majority… Table 2 Results obtained after using resampling techniques

661

Resampling

Model

GM

FM

SMOTE

KNN

0.873

0.8757

AdaBoost

0.9343

0.9352

DTC

0.9499

0.9541

KNN

0.8721

0.8743

AdaBoost

0.9324

0.9336

DTC

0.9511

0.9556

KNN

0.8716

0.8726

AdaBoost

0.9347

0.9325

DTC

0.9479

0.9532

KNN

0.8649

0.8764

AdaBoost

0.9321

0.9378

DTC

0.9501

0.9537

KNN

0.8629

0.8623

AdaBoost

0.9364

0.9343

DTC

0.949

0.9495

SMOTE-Tomek

ROS

Tomek-U

RUS

4 Conclusion In this paper, we address the majority-biased behaviour of the classifiers due to the imbalance between the count of the number of data representing phishing and nonphishing websites in the training data. The performance degradation while detecting phishing websites with the increase in ImR is comprehensively illustrated. The plots for each of the models are found to undergo a decrease in each step of the experiment. The proposed Tomek Link-based solution is found to give reliable results compared to techniques like ROS and RUS. The DTC model is found to undertake good classification even in imbalanced condition. The highest GM of over 95% was recorded by DTC for Tomek link-based sampling techniques.

References 1. Rao RS, Pais AR (2019) Jail-Phish: an improved search engine based phishing detection system. Comput Secur 83:246–267 2. Alsariera YA, Elijah AV, Balogun AO (2020) Phishing website detection: forest by penalizing attributes algorithm and its enhanced variations. Arab J Sci Eng 45(12):10459–10470 3. Yang L, Zhang J, Wang X, Li Z, Li Z, He Y (2021) An improved ELM-based and data preprocessing integrated approach for phishing detection considering comprehensive features. Expert Syst Appl 165:113863 4. Corizzo R, Ceci M, Zdravevski E, Japkowicz N (2020) Scalable auto-encoders for gravitational waves detection from time series data. Expert Syst Appl 151:113378

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5. Summerville A, Snodgrass S, Guzdial M, Holmgård C, Hoover AK, Isaksen A, Nealen A, Togelius J (2018) Procedural content generation via machine learning (PCGML). IEEE Trans Games 10(3):257–270 6. Rossi A, Barbosa D, Firmani D, Matinata A, Merialdo P (2021) Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans Knowl Discov Data (TKDD) 15(2):1–49 7. Zonoobi D, Hareendranathan A, Mostofi E, Mabee M, Pasha S, Cobzas D, Rao P, Dulai SK, Kapur J, Jaremko JL (2018) Developmental hip dysplasia diagnosis at three-dimensional US: a multicenter study. Radiology 287(3):1003–1015 8. Zouina M, Outtaj B (2017) A novel lightweight URL phishing detection system using SVM and similarity index. HCIS 7(1):1–13 9. Jain AK, Gupta BB (2018) Towards detection of phishing websites on client-side using machine learning based approach. Telecommun Syst 68(4):687–700 10. Ali W, Ahmed AA (2019) Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting. IET Inf Secur 13(6):659–669 11. Subasi A, Kremic E (2020) Comparison of adaboost with multiboosting for phishing website detection. Proc Comput Sci 168:272–278 12. Pristyanto Y, Dahlan A (2019, November) Hybrid resampling for imbalanced class handling on web phishing classification dataset. In: 2019 4th international conference on information technology, information systems and electrical engineering (ICITISEE), pp 401–406, IEEE 13. Prayogo RD, Karimah SA (2020, October) Optimization of phishing website classification based on synthetic minority oversampling technique and feature selection. In: 2020 International workshop on big data and information security (IWBIS), pp 121–126, IEEE 14. Zhang J, Li X (2017, December) Phishing detection method based on borderline-smote deep belief network. In: International conference on security, privacy and anonymity in computation, communication and storage, Springer, Cham, pp 45–53 15. Vrbanˇciˇc G, Fister I Jr, Podgorelec V (2020) Datasets for phishing websites detection. Data Brief 33:106438 16. https://data.mendeley.com/datasets/72ptz43s9v/1 17. Batista GE, Bazzan AL, Monard MC (2003, December) Balancing training data for automated annotation of keywords: a case study. In: WOB, pp 10–18 18. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority oversampling technique. J Artif Intell Res 16:321–357 19. Liu Z, Japkowicz N, Wang R, Liu L (2020) A sub-concept-based feature selection method for one-class classification. Soft Comput 1–16 20. Banerjee A, Ghosh K, Sarkar A, Bhattacharjee M, Chatterjee S (2021) Effects of class imbalance problem in convolutional neural network based image classification. In: Advances in smart communication technology and information processing: OPTRONIX 2020, Springer Singapore, pp 181–191 21. Banerjee A, Ghosh K, Chatterjee S, Sen D (2021, March) FOFO: fused oversampling framework by addressing outliers. In: 2021 International conference on emerging smart computing and informatics (ESCI), pp 238–242, IEEE 22. Ghosh K, Banerjee A, Bhattacharjee M, Chatterjee S (2021) Improved Twitter sarcasm detection by addressing imbalanced class problem. In: Advances in smart communication technology and information processing: OPTRONIX 2020, Springer, Singapore, pp 135–145 23. Ghosh K, Banerjee A, Chatterjee S, Sen S (2019, October) Imbalanced twitter sentiment analysis using minority oversampling. In: 2019 IEEE 10th international conference on awareness science and technology (iCAST), pp 1–5, IEEE 24. Chatterjee S, Das AK, Ghosh K, Banerjee A, Bhattacharjee M, Banerjee S (2021) Performance improvement of artificial neural networks by addressing class overlapping problem. In: Advances in smart communication technology and information processing: OPTRONIX 2020. Springer, Singapore, pp 229–237 25. Banerjee A, Bhattacharjee M, Ghosh K, Chatterjee S (2020) Synthetic minority oversampling in addressing imbalanced sarcasm detection in social media. Multimedia Tools Appl 79(47):35995–43603

A Review on Analysis and Development of Quantum Image Steganography Technique for Data Hiding Sonia Thind and Anand Kumar Shukla

Abstract Data security is very important aspect of security to protect the information from being tampered and unauthorized use. Data can be protected in both ways whether data is placed at one place or during transmission of information. There are two ways to protect the data, i.e., Cryptography and Quantum Steganography. For the security and privacy of sensitive information on the internet, current information security techniques basically depend on the classical cryptography techniques. Recent advancements in Quantum Steganography methods challenge the classical cryptographic systems. In Quantum Steganography technique media or characters are hided in other Medias like image in other image, text in an image, sound in image, image in a video, etc. This paper give a review of many research papers which presents how secure communication can be achieved by using quantum image steganography technique for data hiding, by highlighting objective, methodology, analysis, findings and limitations of the papers on that area. Keywords Quantum-cryptography (QC) · Steganography · Data hiding

1 Introduction Today in technological world, where data and information is an important aspect of computer communication, it is also important to protect the information from being tampered. Data can be protected in both ways whether data is placed at one place or during transmission of information. There are two ways to protect the data, i.e., Cryptography and Quantum Steganography. Cryptography is a technique which is used to convert the data characters into un-readable format and then sent it to the receiver, here intruder know about the existence of cipher text not about the plain text S. Thind (B) · A. K. Shukla University Institute of Computing, Chandigarh University, Gharuan, Mohali, Punjab, India e-mail: [email protected] A. K. Shukla e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_65

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[1]. Whereas, with the advancements in quantum communications [2–3, 4, 5, 6, 7] concealing of information using quantum techniques has attracted the researchers and experts from all around the world. In Quantum Steganography [8, 9, 10, 11, 12, 13] technique media or characters can be hide in other medias like image in other image, text in an image, sound in an image, image in a video, etc. For the security and privacy of sensitive information on the Internet, current information security techniques basically depend on the classical cryptography techniques. Recent advancements in Steganography methods challenge the classical cryptographic systems.

1.1 Steganography Process Steganography process consists of the following elements [14]: Secret message—Original secret message characters that you want to conceal in the original image (cover media). Stego-key—It is used to hide information in the Steganography. Cover media—It is used to hide the characters inside it. For example: image, audio, video, etc. Encoding algorithm—Secret message is encoded by using encoding algorithm. Stego-media—Medium used after concealing the secret information in the cover medium by using encoding algorithm and Steganography key. Decoding algorithm—Secret message is extracted by using extraction algorithm. In Steganographic communication system there are two parties involved, i.e., sender and receiver. Further the communication process can be divided into hiding of information and extraction of information. Secret data is hided in cover medium by using Quantum algorithm. After embedding the secret message, cover image and information are collectively called stego image. After that, sender sent this stego image to the receiver. Secret message is extracted by using extraction algorithm by the receiver. An intruder may monitor this public channel in order to detect the presence of any communication. Here key, i.e., Stego-key is used in the Steganography process to hide the secret message (Fig. 1).

2 Related Work Wang et al. [15] designed a quantum image data hiding algorithm (LSQb) which was based on NEQR technique. In this algorithm secret data was hided by embedding the qubit stream of message in the last qubits of image cover. Also, data was searched using frequency area which raised the data security of image cover. Quantum image Fourier frequency domain LSQb data hiding protocol was also discussed. Wei et al. [12] presented a protocol for quantum channels (noisy). Noisy channels were not affecting that protocol anymore and it was also able to send n

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Fig. 1 Steganography process

qubits secretly in public communication quantum channel. Message can be extracted without publishing the cover data. Projective measurement operator was used to read the cover information. That’s why it was impossible for the intruder to detect any communication has taken place. In this way proposed protocol has good security. Protocol security also depends upon the uncertainty of quantum measurements as compared to other protocols. Secret message was extracted by using the measurement results of sender’s POVM. In this way consumption of communication was reduced. As in this protocol POVM operators was used for message extraction and projective operators for message extraction, original message was extracted with the probability of 2/3. That was the imperfection of the proposed protocol. Qu et al. [16] proposed a watermark algorithm which was based on QUALPI for better protection of quantum image copyright. Proposed protocol gives good practicability for the design of quantum circuits for the embedding process and extraction process of an image. Two log-polar sampling properties were effectively used in that algorithm, i.e., scale invariance and rotation. By using scale invariances quantum image was extracted with good robustness. Stego image was introduced to many attacks like flip, translation, rotation and scaling. Results showed that the proposed technique have good performance on transparency, capacity and robustness. Liu et al. [17] solved the security flaws that were presented in Zhu et al.’s quantum key agreement technique. Four Pauli operations was utilized in order to conceal two bits as compare to the original operations that conceal one bit, then proposed safe and effective N party key agreement quantum method. In order to avoid eavesdropper’s flip attack, communication channel inspection by using decoy photons was included. In order to secure from collusion attack post-measurement method was used. Analysis result showed that presented protocol guaranteed the security, correctness, fairness and privacy of quantum key agreement. El-Latif et al. [18] presented a steganography technique in order to conceal a covert image into cover image. The covert image was first concealed by a gate (C-NOT) to

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reveal the safety of concealed information. The concealed quantum information was hided in the cover picture by making use of two most and least significant qubits. Also, a water marking technique was also presented to conceal a gray picture in the carrier picture. The map (Arnold’s cat) was used to scramble the water marked picture. Only water marked quantum picture and the secret stego-key were enough to retrieve the hided image. The presented innovation was demonstrated by an example of sharing patient’s medical images among the two hospitals. The results showed that the presented techniques had good embedding capacity, visual quality and high information security. Mukherjee et al. [13] presented a new steganographic technique in spatial domain by using various operations to give two layered protection for concealing data bits. Presented technique was not only had high data embedding capacity, but also able to withstand the various statistical attacks. Also, presented technique hided data bits per image pixel in each image section and in audio section at the cost of no loss and alteration of the message cover medium value. Qu et al. [19] proposed a new steganography approach which was depend on a good data hiding method. For ease, data hiding method was named as EMD embedding technique. In this embedding technique, each group of the image pixel of the image carrier consist of N image pixels and every top secret digit of the data belong to ((2N + 1)-ary) notation, here N denoted as system factor. Data hiding method was that only one image pixel of the image pixel carrier group was either maximized by 1 or reduced by 1. Experimental results proved that the new approach had better performance on security, imperceptibility, embedding capacity and efficiency. Qu et al. [14] presented a steganographic approach that was depend on the image-expansion technique and the Grover-search method. Before introducing an expansion method to make the super position of many copies of image of the equal size as the image carrier, the proposed algorithm was used the quantum log-polar image (QUALPI) representation. After that, secret message was embedded into one image copy by using specific encoded angle rotation. Grover-search algorithm was used extract the embedded secret data accurately. Due to non-cloning and quantum uncertainty theorem, proposed algorithm was not only achieved good security and imperceptibility, but also had large pay load due to coding scalability of algorithm. Mukherjee et al. [20] proposed a high embedding capacity method. Data hiding was not only restricted to pixel pairs of high difference. Pixel pair of low difference was also measured in order to maximize the data capacity. There are many data embedding procedures which depend up on the image pair’s contrast. Before embedding process secret data was encrypted first and image pixel pairs were selected in non-sequential way, making the proposed technique safer. Experimental results were carried out on two hundred images and it was found that the data embedding by the presented method was visually imperceptible and was endure many types of attacks. The strength of the proposed work was indicated by StirMark analysis. When performance was compared with the other methods, it was proved that presented technique had high PSNR value with the enhanced data capacity (Table 1).

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Table 1 Methodologies used in various research papers S. No.

Author/Year

Used techniques

Used parameters

Findings

Research gap

1

[15] Wang et al. (2015)

1. Least significant Qubit (LSQb) 2. Quantum fourier transform

1. Frequency domain of an image was used 2. Quantum fourier transform

Proposed method had better visual quality

Quantum image visual quality was not 100% good

2

[12] Wei et al. Positive operator valued measure (2015) and projective measurement operator used for data embedding and extraction

Secrecy, security and capacity

High secrecy, security and capacity

Secret message was extracted with the probability of 2/3. That was the imperfection of this research

3

[16] Qu et al. 1. QUALPI (2017) 2. Quantum water mark protocol 3. Least Significant Qubit

Log-polar data samplings, i.e., scale invariance and rotation

High performance on transparency, robustness and data capacity

Does not deal with complex attacks

4

[17] Liu et al. 1. Quantum key (2018) agreement 2. Bell states 3. Paulisoperation

1. Correctness 2. Security 3. Privacy 4. Faireness of Quantum key agreement

Better performance, in efficiency, privacy and correctness

Cloud secure storage of private data becomes a big concern

5

[18] Abd-El-Latif et al. (2018)

1. NEQR representation model 2. Arnold image scrambling 3. Quantum image processing and watermarking

1. Payload capacity 2. Security analysis 3. Visual quality

1. Excellent visibility 2. High embedding capacity

Secret message was not as secured as compared to other proposed schemes

6

[20] Mukherje et al. (2018)

1. Image Steganography 2. Audio Steganography 3. Galois-field 4. Symmetric bivariate polynomial

1. Visual perceptibility 2. Quantitative analysis of MSE, PSNR, SNR, BER, NCC, UIQI

1. High embedding capacity 2. Capable of withstanding statistical attacks

Proposed method was for lossless image and audio only

(continued)

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Table 1 (continued) S. No.

Author/Year

7

Used techniques

Used parameters

Findings

[19] Qu et al. 1. Quantum (2019) image Steganography 2. Exploiting modification direction

Imperceptibility measured by PSNR and histogram analysis

1. PSNR values Embedding of the images capacity need were much to be improved higher 2. Visual effect was much better

8

[14] Qu et al. 1. Quantum (2019) image-expansion 2. Grover-search algorithm and QUALPI

1. Imperceptibility Achieve good imperceptibility 2. Security and security 3. Capacity

New protocol can be made for embedding the Quantum image

9

[13] Mukherjee et al. (2020)

1. Visual perceptibility analysis 2. Quantitative analysis include BER, MSE, PSNR, UIQI, NCC

New protocol can be made for embedding the quantum image

1. Image Steganography 2. PVD-based embedding 3. Data hiding in low and high contrast pixel pair

High PSNR with enhanced capacity and security

Research gap

3 Proposed Objectives 1. To analyze the existing quantum image steganographic techniques in order to hide a information in an image. 2. To develop a new quantum image steganographic technique for data hiding and extraction. 3. To compare and validate the proposed technique with other existing techniques by using evaluation measures like PSNR, MSE, imperceptibility, security and embedding capacity, etc.

4 Research Methodology The proposed research methodology is represented in Fig. 2.

5 Scope and Limitations Scope 1. Proposed work may be used to send patient information or data hidden in medical images such as CT scan, MRI.

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Develop a new steganographic algorithm for embedding secret information into an image carrier

Expand the image and select an image carrier to hide the information

Send Stego media (image + secret information) through the communication channel

Develop a new stenographic algorithm for the extraction of information from the stego image

Evaluate the efficiency of the proposed technique using evaluation measures

Apply the proposed technique for a particular application

Fig. 2 Flow diagram of research methodology

2. Proposed work may be used for both Cryptography + Steganography, i.e., hybrid Steganography. 3. By using various Quantum computation approaches proposed method can be extended for performance enhancement. 4. It can be used for Military purposes. Limitations 1. Terrorists can use for criminal activities. 2. It can be used for intruder to attack the privacy of social media. 3. Pictures, videos and other cover medium used to hide the message can be plagiarized by the intruder.

6 Conclusion To protect the information from being tampered and unauthorized access, data can be protected in both ways whether data is placed at one place or during transmission of information. With the advancements in quantum communications concealing of information using quantum techniques has attracted the researchers and experts from all around the world. In Quantum Steganography technique media or characters are hided in other Medias like image in other image, text in an image, sound in image, image in a video, etc. This paper give a review of many research papers which presents how secure communication can be achieved by using quantum image steganography

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technique for data hiding, by highlighting objectives, methodology, findings, research gaps and limitations of the papers.

References 1. Marwaha P, Marwaha P (2010) Visual cryptographic steganography in images. In: Second international conference on computing, communication and networking technologies, 978-14244-6589-7/10. IEEE 2. Deng FG, Li XH, Long GL (2016) Quantum secure direct communication. J Beijing Normal Univ Natural Sci 52(6):790–799 3. Du J et al. (2002) Experimental realization of quantum games on a quantum computer. Phys Rev Lett 88(13). Art. no. 137902 4. Li J, Chen XB, Xu G, Yang YX, Li ZP (2015) Perfect quantum network coding independent of classical network solutions. IEEE Commun Lett 19(2):115–118 5. Chen XB, Dou Z, Xu G, Wang C, Yang YX (2014) A class of protocols for quantum private comparison based on the symmetry of states. Quantum Inf Process 13(1):85–100 6. Xu G, Chen XB, Duo Z, Yang YX, Li Z (2015) A novel protocol for multiparty quantum key management. Quantum Inf Process 14(8):2959–2980 7. Qu Z, Keeney J, Robitzsch S, Zaman F, Wang X (2016) Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks. China Commun 13(7):108–116 8. Cao Y, Zhou Z, Sun X, Gao C (2018) Coverless information hiding based on the molecular structure images of material. Comput Mater Continua 54(2):197–207 9. Meng R, Rice SG, Wang J, Sun X (2018) A fusion steganographic algorithm based on faster R-CNN. Comput Mater Continua 55(1):1–16 10. Nie Q, Xu X, Feng B, Zhang LY (2018) Defining embedding distortion for intra prediction mode-based video steganography. Comput Mater Continua 55(1):59–70 11. Mihara T (2012) Quantum steganography embedded any secret text without changing the content of cover data. J Quantum Inf Sci 2(1):10–14 12. Wei ZH, Chen XB, Niu XX, Yang YX (2015) The quantum steganography protocol via quantum noisy channels. Int J Theor Phys 54(8):2505–2515 13. Mukherjee N, Paul G, Saha SK Burman D (2020) A PVD based high capacity steganography algorithm with embedding in non-sequential position. Multimedia Tools Appl 79(1):13449– 13479 14. Qu Z, Li Z, Xu G, Wu S, Wang X (2019) Quantum image steganography protocol based on quantum image expansion and grover search algorithm special section on recent advances in video coding and security. IEEE 7:50849–50857 15. Wang S, Sang J, Song X, Niu X (2015) Least significant qubit (LSQb) information hiding algorithm for quantum image. Measurement 73(9):352–359 16. Qu Z, Cheng Z, Luo M, Liu W (2017) A robust quantum watermark algorithm based on quantum log-polar images. Int J Theor Phys 56(11):3460–3476 17. Liu WJ, Xu Y, Yang CN, Gao PP, Yu WB (2018) An efficient and secure arbitrary N-party quantum key agreement protocol using bell states. Int J Theor Phys 57(1):195–207 18. Abd-El-Latif AA, Abd-El-Atty B, HossainMS, Rahman MA, Alamri A, Gupta BB (2018) Efficient quantum information hiding for remote medical image sharing special section on information security solutions for telemedicine applications. IEEE 6:21075–21083 19. Qu Z, Liu W, Wang X, Cheng Z (2019) A novel quantum image steganography algorithm based on exploiting modification direction. Multimedia Tools Appl 78(8):7981–8001

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20. Mukherjee N (Ganguly), Paul G, Saha SK (2018) An efficient multi-bit steganography algorithm in spatial domain with two-layer security. Multimedia Tools Appl 77(3):18451–18481 21. Liao X, Wen QY, Sun Y, Zhang J (2010) Multi-party covert communication with steganography and quantum secret sharing. J Syst Softw 83(10):1801–1804

Forecasting the Growth in Covid-19 Infection Rates Soumi Dutta, Abhishek Bhattacharya, Prithwidip Das, Shayan Pal, Ratna Mandal, Ahmed J. Obaid, Wen Cheng Lai, Ambuj Kumar Agarwal, and Ben Othman Soufiene

Abstract The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. Keywords Covid-19 · Polynomial regression · Prediction analysis · Support vector machines S. Dutta (B) · A. Bhattacharya · P. Das · S. Pal · R. Mandal Institute of Engineering and Management, Kolkata, India e-mail: [email protected] A. Bhattacharya e-mail: [email protected] R. Mandal e-mail: [email protected] A. J. Obaid Faculty of Computer Science and Maths, Kufa University, Kufa, Iraq e-mail: [email protected] W. C. Lai National Taiwan University of Science and Technology, Taipei, Taiwan e-mail: [email protected] A. K. Agarwal Chitkara University, chandigarh, Punjab, India B. O. Soufiene University of Sousse, Sousse, Tunisia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_66

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1 Introduction The virus SARS-CoV-2 or Severe Acute Respiratory Syndrome associated Corona virus was first discovered in Wuhan, China. It was around October–November, 2019 when the first trace of the virus was found. The people infected had symptoms like nasal congestion, diarrhea, muscle pain, sore throat, fever with loss of taste and smell being the identifying factors and resembled diseases such as pneumonia but with time changed to more severe life threatening conditions such as hypoxia, multiorgan dysfunction and breathing difficulties which led to death. The symptoms varied from person-to-person but where seen between 7 and 12 days after getting infected. The Covid-19 virus is an airborne disease and can spread when an infected person sneezes or breathes can transmit to others or from person-to-person contact. The only preventive measures that can be taken to protect oneself from the infection is to use sanitizers and maintain proper hygiene, wear masks or face guards, social distancing, self-isolation and vaccination. Biotechnical advancements and research have paved the way for development of the vaccine in a short amount of time. The RNA, viral vector and protein sub-unit vaccines are being distributed across nations to cure and prevent further spread of the virus. But a certain caveat in this endeavor is the high population growth and the population density which is causing it to spread rapidly. The development of vaccines is also limited to only a few nations and distribution is slow when compared to the rapidly growing infection rates. To understand the situation, our aim in this research is to draw a comparative analysis of the situation before the vaccination phase and after the vaccination phase and to see how it changed after the vaccinations started worldwide. Then using machine learning algorithms like SVM and the implementation of statistical methods like polynomial regression, we built two prediction models, one based on polynomial regression and the other on SVM and compared the two to find the better model which could help us predict the rate of infections in the future so that we can prepare accordingly and distribute resources in areas with high infection rates to circumvent the impact and reduce the spread.

2 Methods and Algorithms In this chapter we will describe the different methods and algorithms we have used for our research.

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2.1 Polynomial Regression It is a statistical method used to understand the relationship between two or more variables where there is a dependent variable and an independent variable modeled as a nth degree polynomial. It is commonly represented as: y = b0 + b1 x1 + b2 x 2 + b2 x 3 + . . . + bn x n

(1)

where b0 is the bias, b1 , b2 ,…bn are the weights in the equation and n is the degree of the polynomial. It is mostly used in cases where there is a non-linear relationship between the values of x and the corresponding conditional mean of y, which is denoted by E(y—x).

2.2 Support Vector Machines It is a supervised machine learning algorithm used to analyze data for classification and regression. The underlying framework of SVM is based on the Vapnik–Chervonenkis theory, making it very robust and accurate. The models built using the algorithm assigns new values to different category, making it a non-probabilistic binary linear classifier. The algorithm first creates a best decision boundary, called the hyperplane which separates a n-dimensional space in to classes where new data points can be put in the appropriate categories. SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors. Then, we perform classification by finding the hyperplane that differentiates the two classes very well.

3 Literature Survey Many research works have been published which have focused on finding the prediction rates, like [1] using prediction models based on logistic regression using metrics like demography and laboratory tests showed very insightful results in low-risk derivation population but failed in high-risk external validation populations. To overcome these shortcomings other authors have used [2] feature selection methods like Gradient Boost feature selection and attribute reduction methods, found 18 indexes which are of the highest significance for the general diagnosis of Covid-19 like WBC, Eosinophil count and others which might help to increase accuracy in prediction of Covid-19 cases. Authors using [3] multivariate logistic regression model helped find underlying factors like T-lymphocyte subsets which provided important insight on a patient’s condition during treatment. People with a strong immune system have also been observed to have high recovery rates.

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Another approach is to modify the standard SIR model [4–6] with the inclusion of mobility data as the incidence of the Covid-19 spread is concave in the number of infection cases. This shows, in the long run, the number of cases to grow exponentially at the start of the contagion period and then become stable in the long run. The result has been verified with the growth of infection rates of different countries. To overcome the inefficiencies of the SIER model, [7] the authors developed the e-ISHR model which is a 3 stage model based on the SIER model. The 3 stages are staged mechanism, time delay mechanism and hospital system, these metrics made the model superior than the existing SIER model. The authors [8] also implemented an enhanced version of SIR Epidemic Model known as SIRD which is built by creating optimal parameter values and also the number of deaths due to the pandemic are taken in to account which is able to predict trends but there is an extent of unreliability as it is highly susceptible to external changes. The [9] mathematical models Gompertz, Logistic and a computational model, Artificial Neural Network is compared to predict the growth of infections in Mexico, the Gompertz model predicted the most cases with great accuracy. Some individual traits and factors affecting the spread of Covid-19 are studied [10] like the number of regenerations, incubation period and the average days to cure. The findings were then used to build a model where the Monte Carlo method is used and divided in to four states, latency, illness, health, or death. Then, for propagation simulation, different Gaussian distributions are set up and lastly the Particle Swarm optimization algorithm is used to optimize the model. Using this model the future trends were predicted of the existing epidemic data, and found that imposing controls would have important impact on the epidemic. After doing a [11] comparative analysis the authors found that multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS models showed the best results in predicting future trends of the Covid-19 outbreak. But this could further be improved by integrating machine learning and SEIR models for better accuracy and lead times. The authors [12] used three models namely, Elman neural network, long shortterm memory (LSTM), and support vector machine (SVM) with fuzzy granulation. Out of these, SVM and the Elman neural network can be used to predict the trends of infection rates, deaths and recovered cases and LSTM is more beneficial and effective in the prediction of the cumulative confirmed cases. Using a Hybrid AI model [13] consisting of the NLP module and the LSTM network in the ISI model is found to accurate in predicting an average infection time of 5.5 days which is in line with the trend. In another comparison [14] the authors compared Regression-based, Decision tree-based and Random Forest-based models for prediction of the Covid-19 trajectory, out of these Random Forest Model performed better than the other two. Ref. [15], while most of the mathematical modeling are based on SusceptibleExposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models and the AI implementations are done using Convolutional Neural Network (CNN) on CT scan and X-ray images. While, both have been very useful and reliable, still a lot of work still remains on the diversification of the datasets and other

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models also need to be explored. On a similar note, another study [16] noted that most predictive models fail to capture one or two crucial metrics which give result to wrong dimensions. However, the Jehi diagnostic model and the 4C mortality score have shown promising results.

4 Experiment Methodology For our research we used the dataset published by the World Health Organization (WHO). We tried to visualize the data using python and its associated libraries like matplotlib, seaborn, etc. The required columns are selected from the dataset and the data is represented accordingly. From the above knowledge, we wanted to see how the future trends in the infection rates would change overtime. To do so, we tried to implement a model using polynomial regression and another model using SVM. Then, we compared between the two to check which provides better prediction and accuracy when measuring the future growth in infection rates in the long run. While building the model we only considered the cases of India, but this research can definitely be expanded to predict the infection rates of other countries as well. The dataset contained the data of different countries with a lot of attributes. To remove any deviations, redundant fields and attributes were removed and the data is cleaned. The “dates” and “new cases” field values are then taken in separate array variables. The dataset is then split, 75% is taken for training and the rest 25% is use for testing the models. To implement polynomial regression, we used Eq. (1) with the sklean built-in package available in python. The output is then fed to linear regression to obtain the final output. Then we gathered the prediction results and found the Mean Absolute Error (MAE), Mean Squared Error (MSE) and the R2 scores [17–19]. Next, we built the SVM model (2.2). We used the built-in function of the python library “SVM confirmed predict (future forecast)” to predict the future forecasts and also found the MSE, MAE and R2 scores.

5 Results and Findings To understand the situation caused by the pandemic a bit better, we divide it in to two phases, the pre-vaccination phase and the post-vaccination phase so that we can see the difference.

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5.1 Pre-vaccination Phase Figure 1, shows that the rate of infected cases is highest in the United States, followed by India, Brazil, Russia and then United Kingdom. Higher infection rates are also directly related to the population of a country and the cases which are reported. In certain cases, the infected cases are not reported and those do not get noted. With a higher population the chances of infection increases rapidly if proper measures are not taken. Figure 2 shows the new cases that are reported on a daily basis. The US has the highest number of new cases reported closely followed by India, Brazil and other countries. Though these numbers seem small compared to a country’s population but the characteristics of Covid-19 show that it spreads rapidly among the masses and overtime can lead to heavy loss of lives. Figure 3 presents a dynamic where people infected with Covid-19 have recovered even before getting vaccinated. The number of recovery is highest in Brazil then, US, India, Russia and others. If proper measures are taken and the healthcare system is efficient of a country then recovery in such large numbers is a possibility. But a lot of other factors need to be taken in to consideration like age, demography, other ailments, etc. before a conclusion could be reached.

Fig. 1 Country-wise trend of total infection rates

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Fig. 2 Amount of new cases country-wise

Fig. 3 Amount of recovered cases country-wise

5.2 Post-vaccination Phase In Fig. 4, we can see the USA has administered the most number of vaccines which can also be verified from Fig. 5, where the availability of Moderna and Pfizer vaccine is the highest. Secondly, for China, the Wuhan vaccine and Sinovac vaccine has been given to their citizens in large scale leading to higher vaccinations and higher availability of the Sinovac vaccine. A similar case is also seen for the UK and India where AstraZeneca and Covishield or the vaccines developed by Oxford University is given to people, leading to higher manufacturing and more number of doses distributed among the masses. But in some countries like, Brazil, Turkey, Russia, Israel and others have only been able to vaccinate a small percentage of their population. This can be due to a lot many reasons like low population, low infection rates and others. Another reason for low vaccination rates is also the availability of vaccines. Some countries like the USA, India, UK, Germany and Russia have been able to make

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Fig. 4 Total vaccinations done country

their own vaccine and are distributing it to the masses and other nations as well. But most countries lack the proper infrastructure and financial capacity to manufacture vaccines on their own and have to rely on the goodwill of other nations from where they can buy the vaccines. This causes a drastic difference in the availability of vaccines in different countries. Also, countries which have developed their vaccines cannot immediately start the vaccination drive unless an approval is given from the authorities regarding the safety of the vaccine. Thus, from the above graphical representations of the data one can interpret that even though vaccines are available and are being given to people but the rate is very slow and the distribution across nations is very less. A combined effort is needed such that the vaccines can be given to people in a large scale across nations. But the doubt still remains whether the rate of vaccination can overcome rate of the number of infections so that more number of people can be vaccinated before they get infected and thus reduce the infection rate. For such an analysis, we took the cases of four countries, namely, India, Brazil, UK and the USA.

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Fig. 5 Availability of vaccines per country-wise

In Fig. 6, the blue line shows the total number of people who are vaccinated, the red line shows the infected cases and the total number of deaths are marked by the green line. For all the countries that have been considered, the vaccination rate is very high and is continually growing. In the above cases, the death rate compared to the rate of vaccination and the rate of infection is very low in contrast to their population. For countries like Indian and Brazil, even though the vaccination rate is high, the rate of infection is also growing so proper measures need to be taken to mitigate the infection rates. In countries like the UK and USA, due to higher vaccinations and other precautionary measures, the infection rate has become stable whereas the vaccination rate is continually increasing as more people are being vaccinated. Soon, it can be expected that the infection rates will become stable or get lower as more and more people are vaccinated.

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(a) India

(b) Brazil

(c) United Kingdom

(d) United States of America

Fig. 6 Comparison of the total cases, total vaccinations and total deaths

5.3 Prediction of Future Trends in Infection Rates On comparing between the model built using polynomial regression and the one built using SVM, we found the MAE, MSE and R2 Scores (Table 1). Lower MSE, MAE and R2 scores, are shown by the model developed using polynomial regression than the SVM-based model. The comparison graphs shown in Fig. 7, clearly define that the infection rate prediction performed by the model built using polynomial regression is more accurate, performs better and the predicted values are closer to the real data than the model built using SVM in case of predicting long term trends. Table 1 Comparison of SVM and polynomial pegression model Method

MSE

MAE

R2

SVM

3,211,586,736.1745872

55,710.35267423788

−307.1917008960262

15,789.30295590436

−1.924182505404389

Polynomial regression

314,075,512.40164834

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(b) Prediction using SVM

Fig. 7 Comparison of infection prediction rates between polynomial regression and SVM

6 Conclusions After analyzing the above data and charts, we can conclude that even though the vaccination drive has begun, it is moving very slowly and the availability of vaccines is not very high currently. The virus is also starting to mutate and is spreading rapidly due to lack of implementations of proper hygiene, sanitization, face masks and other precautions. To improve such a situation more number of vaccines need to produce so that it reaches all the people and is more accessible Country’s such as India, UK, USA, Russia and others who have developed the vaccines should provide it to other countries who have not yet been able to produce an effective vaccine. This step will provide a cure for Covid-19 across the globe and also strengthen international relations. It is very important to see the trend, and forecast the infection and vaccination rates for better judgements. On comparing the prediction models, the polynomial regression-based model performed better than the model built using SVM. This can be put to real world scenarios and tests to see how the model fares and externally validate the results.

7 Applications and Areas of Further Research The real world data are subject to change in time depending on the change in virus strains, immigration, adoption of preventive measure and many other factors which cannot be controlled. Even though we have tried to put forth our conclusions, the real world model can be perfected even more by combining other mathematical models and machine learning models taking more factors in to consideration while building more advanced models to provide more accurate prediction charts. Our research and analysis can be used further to employ predictions of the spread of the virus and

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also vaccinations across nations and allocate resources accordingly. In addition, both preventive measures and public awareness can help mitigate the spread of the virus.

References 1. Zhang H, Shi T, Wu X, Zhang X, Wang K, Bean D, Dobson R, Teo JT, Sun J, Zhao P, Li C, Dhaliwal K, Wu H, Li Q, Guthrie B (2020) Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK (4/27/2020) 2. Peng M, Yang J, Shi Q, Ying L, Zhu H, Zhu G, Ding X, He Z, Qin J, Wang J, Yan H, Bi X, Shen B, Wang D, Luo L, Zhao H, Zhang C, Lin Z, Hong L, Zhang L, Xu J, Lu R, Hu B, Hong C, Xu X, Chen J, Liu C, Chen Y, Cai Y, Zhu Q, Jiang J, Yang L, Yu S, Wu X, Zheng Z, Fong S, Zhao Q, Chen S, Huang S, Li J (2020) Artificial intelligence application in COVID-19 diagnosis and prediction (2/17/2020) 3. Liu Q, Fang X, Tokuno S, Chung U, Chen X, Dai X, Liu X, Xu F, Wang B, Peng P (2020) Prediction of the clinical outcome of COVID-19 patients using T lymphocyte subsets with 340 cases from Wuhan, China: a retrospec tive cohort study and a web visualization tool (3/18/2020) 4. Liu M, Thomadsen R, Yao S (2020) Forecasting the spread of COVID-19 under different reopening strategies. Sci Rep 10:20367 5. Dutta S, Saha N, Ghosh S, Das AK (2019) Clustering model for microblogging sites using dimension reduction techniques. IGI Global, Int J Info Syst Model Des (IJISMD) 6. Dutta S, Chandra V, Mehra K, Das AK, Chakraborty T, Ghosh S (2018) Ensemble algorithms for microblog summarization. IEEE Intell Syst 33(3):4–14. https://doi.org/10.1109/MIS.2018. 033001411 7. Li S, Song K, Yang B et al (2020) Preliminary assessment of the COVID-19 outbreak using 3-staged model e-ISHR. J Shanghai Jiaotong Univ Sci 25:157–164 8. Singh S, Raj P, Kumar R, Chaujar R (2020) Prediction and forecast for COVID-19 outbreak in India based on enhanced epidemiological models. In: 2020 Second international conference on inventive research in computing applications (ICIRCA), pp 93–97. https://doi.org/10.1109/ ICIRCA48905.2020.9183126 9. Torrealba-Rodriguez O, Conde-Gutie´rrez RA, Hernaandez-Javier AL (2020) Modeling and prediction of COVID-19 in Mexico applying mathematical and computa tional models. Chaos, Solitons Fractals 138:109946, ISSN 0960 10. Li L, Yang Z, Dang Z, Meng C, Huang J, Meng H, Wang D, Chen G, Zhang J, Peng H, Shao Y (2020) Propagation analysis and prediction of the COVID-19. Infect Dis Model 5:282–292, ISSN 2468-0427 11. Ardabili SF, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi- Koczy AR, Reuter U, Rabczuk T, Atkinson PM (2020) COVID-19 outbreak prediction with machine learning. Algorithms 13(10):249 12. Hao Y, Xu T, Hu H, Wang P, Bai Y (2020) Prediction and analysis of corona virus disease 2019. PLoS ONE 15(10):e0239960 13. Du S, Wang J, Zhang H, Cui W, Kang Z, Yang T, Lou B, Chi Y, Long H, Ma M, Yuan Q, Zhang S, Zhang D, Xin J, Zheng N (2020) Predicting COVID-19 using hybrid AI model (3/13/2020) 14. Majhi R, Thangeda R, Sugasi RP, Kumar N (2020) Analysis and prediction of COVID-19 trajectory: a machine learning approach. J Public Affairs e2537 15. Mohamadou Y, Halidou A, Kapen PT (2020) A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Appl Intell 50:3913–3925 16. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E et al (2020) Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 369:m1328. https://doi.org/10.1136/bmj.m1328

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17. Dutta S, Ghatak S, Dey R, Das AK, Ghosh S (2018) Attribute selection for improving spam classification in online social networks: a rough set theory based approach. Social Network Analysis Mining, Springer 18. Dutta S et al. (2019) Community detection based tweet summarization. In: Abraham A, Dutta P, Mandal J, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Advances in intelligent systems and computing, vol 813. Springer, Singapore. https:// doi.org/10.1007/978-981-13-1498-8_70 19. Dutta S, Das AK, Dutta G, Gupta M (2019) A comparative study on cluster analysis of microblogging data. In: Abraham A, Dutta P, Mandal J, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Advances in intelligent systems and computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_77

Exoplanet Hunting Using Machine Learning Nitin Tyagi, Prakriti Arora, Renu Chaudhary, and Jatin Bhardwaj

Abstract With the rapid expansion in the field of aeronautical engineering and its technology, the unseen and unknown area is now in our view and vision. The planets which are light years distant from our galaxy are now visible due to such advancement in the field of astronomy. In basic terms, Exoplanets may be defined as gigantic planets revolving around a star in an unknown possibly habitable galaxy. Due to the rise in temp of Earth, it becomes necessary to identify and gather information about another habitable planet. Such critical data is needed to be processed using machine learning and its models. Therefore, the models are trained and implemented from scratch to provide meaningful information from a humongous set of data. This paper illustrates the implementation and functioning of advanced algorithms to categorize whether the mass found is a planet or debris with the help of flux variation of the stars. Keywords Exoplanet · SVM · PCA · Machine learning · SVC · Gaussian filter · SMOTE

1 Introduction The technological advancement has led us to discover masses beyond the sight of our solar system. These masses are so far from us that they cannot be distinguished whether they are rocks or planets by a normal telescope. So, we need to use a special telescope called Kelpler’s telescope for the purpose of identification of these objects. And hence we need to use the concept of flux and transit and with the help of obtained values, the prediction of Exoplanet has been performed [1]. Suppose there is a large mass with a gravitational pull as close to that of the sun with powerful emission of light intensity (namely A). Therefore, the small planets with lower gravitational pull and lower emission intensity (B) would be revolving around that gravitational pull. So, if the planet B comes in between the object A and the observer (c), the intensity of the received light would drop which will indicate the N. Tyagi (B) · P. Arora · R. Chaudhary · J. Bhardwaj HMRITM, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_67

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Fig. 1 Transit method

presence of an orbiting body around the huge mass. Once, the planet B crosses our field of vision, the intensity will again become huge as shown in Fig. 1. Therefore, all the possible data about such mass is collected and then during the data organization phase we apply PCA to reduce and remove the unnecessary data which will obstruct the generation of desired output [2]. Then the data is levelled using normalization and encoded using label encoder. Techniques such as Gaussian filter is used to remove noise from the dataset and standardization is used for rescaling the data.next, to identify and categorize the nature of trend whether it is cyclic or not, visualization using matplotlib and seaborn is done. The next revolution in the field of aeronautical is the successful determination of a promising Exoplanet. Recently, Kepler 442B has been discovered and is said to be much more habitable than other known planets [3]. The rest of the paper is organized in the following manner: Sect. 2 illustrates the major techniques of data transformation. In Sect. 3, Algorithms behind Exoplanet Hunting have been described. In Sect. 4, we have discussed the findings of the work during Exoplanet hunting [4]. Finally, the conclusion and future scope of the paper has been presented in Sect. 5.

2 Techniques of Data Transformation Some important data transformation techniques used in Exoplanet Hunting are Normalization, Feature Scaling, SMOTE, etc.

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2.1 Normalization Normalization is a techniques of data transformation with the aim to update the data distribution to the normal distribution, which changes the shape of the distribution. Normal distribution, often called the Gaussian distribution or the bell curve, is a specific statistical distribution in which the number of samples below and above the mean are the same, the mean is equivalent to the median and the data points are concentrated near the mean [5]. The technique is applied on the numeric columns of the dataset and does not affect the variation in the range of values, crucially used to cut down redundant data and verifies that the data is related.

2.2 Gaussian Filter Gaussian filter is a low pass filter accustomed to reduce random noise, i.e. the high frequency components from the data and also to blur specific areas of an image. The versatile Gaussian filter is applied to remove noise defining the probability distribution, to smoothen an image and in mathematics [6]. We have used the Gaussian filter to remove the distorted noisy data. The filter basically depresses the noise by suppressing the high frequency elements.

2.3 Feature Scaling In general, the data contains features that extremely differ in magnitudes, range and units. Hence, Feature Scaling is applied feature-wise in the data transformation step to optimize the range of the numeric features within a specific scale. There are a number ways of performing scaling to a dataset, like Min–max, Unit Vector, Power Transformer, Quantile Transformer, Robust, Max Abs and Standard Scaler. In our conduct, Standard Scaler was used to set the range of the numeric features. Fundamentally, Standard Scalar scales each feature of the dataset to unit variance, removing the mean [7].

2.4 PCA PCA, Principal Component Analysis which is widely used in Exploratory Data Analysis and predictive models like Exoplanet Hunting. It is mainly used for minimizing the overfitting problem, converting high dimensional data points to low dimensional data points [8]. It is a statistical procedure which transmutes the data points of the

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correlated features to a group of linearly uncorrelated features with the help of orthogonal transformation. The new modified features are called the Principal Components. In our conduct, after applying PCA on our dataset, 57 principal components were inferred [9]. The Principal Component Analysis technique is based on mathematical concepts of variance and covariance and chiefly projects the low dimensional data to the high dimensional data.

2.5 SMOTE SMOTE refers to Synthetic Minority Over-sampling Technique, which is an oversampling strategy returning the synthetic samples of the minority class, applied on an imbalanced classification problem. The over-sampling is majorly applied to classifier models when one of the classes of the feature runs short of the data points as compared to the other classes of the feature. The chief objective behind performing the SMOTE technique is to impute synthetic samples of data points instead of imputing duplicate, mean, median or mode values to the minority classes [10]. It is used to synthetically balance the imbalanced data. SMOTE works by selecting alike samples and modifying them, one feature at a time and eventually replacing them by a random value confined within the difference of the neighbouring data points [11].

3 Algorithm Behind Exoplanet Hunting Basic Algorithm used in Exoplanet Hunting is Support Vector Machine (SVM). Support Vector Machine is a classic Supervised Machine Learning Algorithm used for Regression problems, outlier detections and Classification problems. SVM is mostly used for binary classification as in our case classification of 2-groups (namely exoplanet and non-exoplanet) was implemented. It is used over other classification algorithms since it does not demand much computation and provides significant accuracy [12]. SVM algorithm begins with plotting each data item as a point in a n-dimensional space where n stands for number of features in the data. As the algorithm loads in the data, plots the data items and then returns a hyperplane which separates the 2 groups which need to be classified. In an n-dimensional metric space, a hyperplane is a n-1 dimensional subset of that space which splits the space into two separate parts. To detect the right hyperplane, it first checks for a hyperplane which accurately segregates the data and then it checks for the maximum possible distance between the hyperplane and any data point [13]. Now, Kernel function is the trick of this algorithm performing complex transformations at an ease. The kernel inputs low dimensional data points and returns high dimensional data points. The same function is used as a parameter in the SVM algorithm and can have linear, polynomial, rbf(gaussian) or sigmoid as values, mostly used for non-linear data. The SVM Algorithm is considered to be fast and reliable which works aptly when there’s

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less data to work upon and tries to put together a boundary such that the distance between two categories is the broadest possible [14]. LinearSVC Algorithm and SVC Algorithm with sigmoid kernel were implemented to conduct the experiment. The SVC algorithm and LinearSVC algorithms are just different implementations of the SVM algorithm. SVC is a non-parametric supervised clustering algorithm, an extension of the Support Vector Machine in Classification model. Also, as already mentioned SVM works for binary class problems, on the contrary SVC is used for multi-class problems. Primarily, SVC is just a different implementation of SVM whilst SVM is a wrapper around the libsvm library, supporting various kernels. Like SVM algorithm, SVC algorithm also supports kernel function. The algorithm when applied with the sigmoid kernel returns good accuracy. Thus, for the model, the SVC algorithm with sigmoid kernel was used [15]. LinearSVC is nothing more than a faster implementation of SVC with a linear kernel. LinearSVC is presumed to have a linear kernel, hence does not support kernel function as a parameter and also lacks various other parameters of SVC. SVC implements One-versus-one classification whilst LinearSVC implements oneversus-rest strategy [16]. One of the prominent reasons for using this algorithm is that it completely fits the given data and returns a hyperplane which classifies the data points. Once you get the hyperplane categorizing the data, you can input features to your classifier model and get the output as the predicted class [17].

4 Research Work In the search of a new habitable planet, model to innovate exoplanets. Exoplanets are the planets revolving around a star other than our sun. In this project, niche technique which was focussed on to detect the exoplanets is based on flux fluctuation. A planet, which can be an exoplanet, revolves around the star and might block some of the starlight and as the planet orbits around a dip in the brightness of the star was observed [18]. The starlight was measured as flux, for each star, the flux has been recorded and if there is a fluctuation in the flux, it is likely that an exoplanet is revolving around that star. Considering this, the data has been collected. After the data is cleaned and transformed, it is classified between label 1 and label 2 (i.e. Exoplanets and nonexoplanets) using the LinearSVC Algorithm [19]. The Research work consists of 5 sections as follows.

4.1 Data Collection and Understanding The Kepler’s data consisting of flux variation of various stars from the Kaggle website uploaded by NASA was downloaded for the project. For the first step, we try to understand the structure of the data and look for any anomalies, missing data points and data type mismatch in our dataset. For our convenience, the target variable Label

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was encoded, from (2 and 1) to (1 and 0), i.e. 1 symbolizes that the star consists of exoplanets whilst 0 symbolizes non-exoplanets [20]. Subsequently, checking the frequency count of the target variable with the function.value_counts(), 5050 was the frequency of 0 label and 37 of 1 label; hence the data was highly imbalanced, most of the data consists of non-exoplanets. Hence, in further steps we will apply SMOTE (Synthetic minority over-sampling technique) to balance our data [21].

4.2 Data Pre-processing Data Pre-processing is one of the most crucial steps, any Supervised Machine Learning Model is accurately implemented when the data is well pre-processed [22]. Since, the data was highly imbalance the following techniques to clean and transform the data were used [23].

4.2.1

Normalization, Gaussian Filter, Feature Scaling

Transforming the data with the techniques Normalization, Gaussian Filter and Feature Scaling as explained above. Figures 2, 3 and 4 represents the data after normalization, Gaussian filter and Feature Scaling [24].

Fig. 2 Data after applying normalization

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Fig. 3 Data after applying Gaussian filter

Fig. 4 Data after applying feature scaling

4.2.2

Dimensional Reduction Using PCA

For the further transformation of the data, reducing the number of dimensions using Principal Component Analysis technique as shown in Fig. 5. After that it was inferred that there are 53 principal components, with which we can preserve almost 98.8 or 99% of the data [25].

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Fig. 5 Analysing the principal components

4.2.3

Data Balancing with SMOTE (Resampling)

Since, it’s already noted that the data is unbalanced, after applying Synthetic Minority Over-sampling Technique (SMOTE) the frequency of label 1 was 5050 (equal to the frequency of label 0) from 37.

4.3 Data Visualization Visualizing the data portrays a clear vision about our dataset [26]. The data was visualized at every stage of the experiment. To analyse the fluctuation in the original data before pre-processing, a histogram was plotted with the help of matplotlib representing the distribution of flux values for the first 4 planets [27]. Secondly, the data was analysed to check flux variation for random individual star data points using the plotly library [28]. Then implemented the same for both the labels as shown in Figs. 6 and 7.

4.4 Methodology We started the project by downloading the latest Kepler data, consisting of the flux values of various stars and a Label feature, representing the presence or absence of

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Fig. 6 Analysing flux variation for non-exoplanet stars

an exoplanet. Then, we went ahead with the conduct by exploring the structure and features of the data and eventually encoded the target variable (Label) and realized that the data is highly imbalanced [29]. With the help of the plotly library we analysed the flux variation of randomly chosen individual stars from the data [30]. Also, Gaussian histograms were plotted for label 0 and 1 separately analysing the range of flux variation for the data consisting of exoplanets as well as the data consisting of non-exoplanets. Then the train and test data were split, removing the label feature from each train-test pair so as to compare to the original data after pre-processing [31]. Next, the data was pre-processed with various techniques as mentioned previously. Applied Normalization, Gaussian filter and scaling to standardize the data. Further performed PCA for dimensionality reduction and SMOTE for balancing the data. Ultimately fitting the model first with LinearSVC Algorithm, then SVC with sigmoid kernel. Hence, summarizing the metrics with the help of confusion matrices [32].

4.5 Results A number of metrics are used to measure accuracy of a model. In this model, Classification Report as well as Confusion Matrix to illustrate metrics of each model

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Fig. 7 Analysing flux variation for exoplanet stars

were used. With the Classification report, we can clearly interpret the classified and misclassified data points [34]. However, the confusion matrix precisely predicts the right and wrong values based on the target variable. Figure 8 elucidates the metrics for the LinearSVC Algorithm [35]. From the Fig. 8, it was inferred that the accuracy of the model is 98% whilst recall score is 0.0. Hence, we cannot interpret any exoplanet with this model [36]. Eventually SVC algorithm with sigmoid kernel was implemented and following classification report and confusion matrix was obtained [37]. From the Fig. 9, we can exactly predict 5 exoplanets with the precision of 60 and 100% recall score [38].

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Fig. 8 Classification report and confusion matrix using LinearSVC model

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5 Conclusion and Future Scope Exoplanet hunting has been in the spotlight for space exploration for a long whilst. With the advanced tools of space exploration, traditional methods cannot be a skilful task and might vary subject to the investigators. In this project and study, we put forward a model based on Linear SVC and SVC with Sigmoid Kernel Algorithms put together with data transformation techniques for an efficient Exoplanet Detection method. When the LinearSVC algorithm was applied, we inferred 98% precision with a 0.0 recall score and were unable to detect any exoplanet. On the other hand, when SVC algorithm with the sigmoid kernel was applied, we concluded 60% accuracy with a 100% recall score and could identify 5 exoplanets. In the wake of the above experiment, we can extend it by combining the data provided by TESS and Kepler (K2) which will focus on the images captured by the satellites besides flux variation. The image further can be analysed with Convolutional Neural Networks to confirm the correctness of the above experiment. The research work and the model prepared for Exoplanet hunting is likely to be a boon for the scientists in apprehension of the planetary systems. The study can be represented as a second confirmation step to check for the presence of an exoplanet. Although, conventional methods used for the same might provide more accuracy. The objective of our conduct is not confined to planet detection yet somewhat believes in spotting hydrocarbons and elements in the planet’s surroundings. In due course, the research aims to substantiate new habitable planets in the galaxy.

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Technical Review and Data Analysis of Expert System Development Rashmi Pandey, Anand Kumar Pandey, Krishn Kumar Joshi, and Ravi Rai

Abstract In this modern world, expert systems are one of the well-known research areas of artificial intelligence. Most of the expert systems are highly responsive, reliable, easily understandable and high performance result oriented. This paper contains the design methodology, classification and development strategy of an expert system. Here we try to present some abstract analysis, structure of knowledge acquisition process with in the domain of expert system. We tried to review data analysis and express such kind of beneficial model that not only defend the innovative attributes of expert system but also described some of their constraint. Keywords Expert system (ES) · Machine learning · Data analysis · Artificial intelligence · Big data

1 Introduction Every organization or industry is focusing on expertise modeling using individual expert system which is linked with big data. System review and data analysis are the mechanism for identify all the components, processes, contents and prominent entities of the proposed system. This activity is done by an expert person known as content review analyst. In this modern world, most of the organizations, industries and corporate offices appointed content review analyst. Content review should be performed by an analyst using machine learning approach in a systematic manner on structured big data as per the requirement. The main objective or goal of the content review process is to encourage the learning activity, knowledge extraction, professional development and increase the productivity of the expert system.

R. Pandey · A. K. Pandey (B) · K. K. Joshi ITM University, Gwalior, India e-mail: [email protected] R. Rai ITM Gwalior, Gwalior, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_68

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An expert system (ES) is kind of computer system that have the assessment and decision making ability in certain situation. Most of the users of modern computer system prefer to use different tactics and strategies to improve the decision making skills relevant to ES or artificial intelligence system [1]. In this modern technical world every researcher is talked about machine learning, data science, artificial intelligence and expert system (ES). Most of the content review analyst reviews the content of ES in four steps: ● Selecting Appropriate contents: The data analyst use his system knowledge and experience to certify the appropriate contents that should be presentable and informative according to analyzed results. ● Literature Search: The analyst should use his literature survey skills to identify the most current topics, with explanation and suitable arguments of ES. ● Formatting and layouts: The data analyst has to verify and validate the formatting and layout contents of the system with predefined guidelines. ● Media and Links: Here we will make sure that the data media in the contents is properly link to each other.

2 Expert System and Its Classification An ES is a kind of artificial intelligence-based computer system with ability of machine learning and deep learning for certain operations. As per our content review analysis of ES, it can be classified into four groups: The rule-based ES, Fuzzy logicbased expert system, Frame-based ES and the expert system based on Neural Network [2]. We have also analyzed about model structure of all above types of expert system. ● The rule-based ES: It is also known as production system, in these kinds of systems the knowledge is represented as a set of principles, rules and prototypes. It is also known as earliest kind of ES which is most commonly found for data analysis. The framework for this type of ES is usually consisting of three main components: The database, production rules and control strategies. The suitable example for rule based ES is domain specific expert system that applied different rules to make decisions. ● The fuzzy logic based expert system: Also known as Fuzzy Expert System (FES), it uses fuzzy logic instead of Boolean logic. It is a kind of regulation-based structure of artificial intelligence with a compilation of correlated functions and rules with specific motive about data analysis. Fuzzy logic-based methodologies compile fuzzy set theory with fuzzy statistical parameters, data reasoning for ES and mostly used in operation research, modeling and simulation and in optimization techniques. ● The framework based expert system: At first famous American mathematician and computer scientist of MIT Marvin Lee Minsky introduced the theory of data structure that describes a concept “Frame” that used to illustrate the modern instance. These kinds of ES describe the layer viewpoint of the system. Most

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of the framework consist the appropriate extent such as: application interface, content metrics, assessment techniques, data flow and tool support that could be the essential components of ES. The framework oriented ES uses the structure in the record to hold the exact issues of input and output recent information during the implication engine. The individual frame can belong to numerous sub frames at the same time, it can also inherits the attributes and properties of main frame. ● The Neural Network-Based Expert System: The neural network models are nonlinear models used to recognize blueprint in records and the association between unprocessed and processed values. The neural network obtains information by design using knowledge illustrations. Whatever the unprocessed values we have and from which we need a neural network to be capable to mine or predict required information [3]. The ES provides the suitable examples and anticipation of result, the neural network-based learning algorithm continuously adjust the relevant distribution of the networks, accomplish the constant result after analysis.

3 The Expert System Methodology ES are machines that think and reason as an expert would in a particular domain with artificial intelligence. The research in an ES is determined for the designing, planning and implement for such kind of automated and deep learn-based computer programs that can imitate, emulate and able to decision making for human activities. The basic requirement for developing appropriate system and proper representing for the ES using suitable methodology is still very challenging task [1]. For different types of ES methodologies can be little bit differ as per the situation, but most of the ES uses common structure. The structure of an ES has essentially of six components: The user, user interface, knowledge base, decision maker, explanation system, knowledge base editor and an expert as shown in Fig. 1. The user can be end user or analyst who interacts with the knowledge base system with the help of appropriate user interface. The suitable user interface is requisite for dealings between user and knowledge base system. Different ES can have different types of user interfaces. Knowledge base is kind of big data or database which includes all kind of data, information and facts that available for inference engine to take decision [3]. The factual information, knowledge and structured data need to be pre-processed for decision maker. The explanation system describes all those rules, principles, policies, logics and protocols which are used to apply on knowledge base during processing and mining the information by expert algorithm [4]. The explanation system is also work just like the inference engine, which responsible for data analysis-based activities. When we go through the predefined and self-driven type of ES, then the concept of explanation system can be optional [5].

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Fig. 1 Structure of an expert system

The knowledge base editor is a kind of embedded application that helps to structure the contents of knowledge base. It also used to design some parameters for knowledge base properties. ES structure-based models are computer systems that improve the decision making capability of human experts.

4 Development Technology for Expert System Since 80 decades, the concept of an ES and techniques of artificial intelligence use in various applications. No one ES can be developed, without any proper information, knowledge database, strategic planning and appropriate management and control [6]. Every ES is developed and designed to extract knowledge by applying data mining and data analysis using knowledge extraction process as shown in Fig. 2. During the process of ES development various types of expert resources are required, such as domain expert, knowledge engineers and end users. Developing an ES relies on the techniques and tools developed in the ground of AI. Every ES

Fig. 2 Knowledge extraction process

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Fig. 3 Steps of development process of ES

development process is an iterative and step by step process shown in Fig. 3. The strategy of development process followed by top to bottom approach. The knowledge acquisition, knowledge engineering, knowledge data base, knowledge elicitation and knowledge presentation are the basic components of every development process of ES. As you can see, the development process of ES follows top to bottom approach and strictly follows step by step process. Until the specific and suitable problem for ES will not be identified the next step design the system cannot be initiated. After design the system, the expert will develop the rules, policies and prototypes to obtain the domain knowledge from the ES [7]. In the next step the knowledge engineer will use the suitable types of test cases to analysis the prototypes. After completion of development of ES, we have to keep the record of all the documentation in proper way and also train the user to use it as per the guidelines. During maintenance, we have to remain the knowledge base up to date by expected reviews.

5 Conclusion In the field of AI there are so many types of methodologies like rule-based, prototypebased, knowledge-based, etc., that have been used in the development of ES for over five decades. Each methodology has their own pros and cons and number of steps for development also. Most of the ES are developed, uses and analyzed toward the real problem solutions. During formalization the process of development of ES all kind of selected knowledge base, knowledge engineering has been applied to acquire

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the suitable and expert knowledge from the ES. In this paper we have discussed the analysis and review of development of ES and its classification.

References 1. Bratko I, Mozetic I, Lavrac N (1989) Kardio: a study in deep and qualitative knowledge for expert systems. MIT Press, Cambridge, MA 2. Chastikov AP, Gavrilova TA, Belov DL (2003) Development of expert systems. CLIPS environment. SPb: BHVPetersburg, 608 3. Giarratano JG (2002) Expert systems principles and programming, PWS Publishing, USA 4. Pandey AK, Pandey R (2015) Role of multi agent system methodology in system design. In: IEEE international conference IndiaCom-2015 at BVICAM, New Delhi 5. Minksy M (1975) A framework for representing knowledge. Psychol Comput Vis 73:211–277 6. Liao SH (2005) Expert system methodologies and applications – a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–103 7. Sharma D, Choudhury T, Dewangan BK, Bhattacharya A, Dutta S (2021) A recommendation system for customizable items. In: Hassanien AE, Bhattacharyya S, Chakrabati S, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Advances in intelligent systems and computing, vol 1286. Springer, Singapore. https://doi.org/10.1007/978981-15-9927-9_45 8. Pandey AK, Pandey R (2019) Data modeling and performance analysis approach of big data. In: ELSEVIER-SSRN digital library in the international conference SUSCOM-2019

Self-Driving Car Using Machine Learning Rishabh Kumar, Tarun Sharma, Renu Chaudhary, and Vibhor Singh

Abstract In current years self-driving cars have come to be most of the maximum actively mentioned and researched topics. By all definitions, those systems as a robot revolution belong to the robotics discipline, in spite of the truth that humans normally assign them to a particular area of the car industry. Replicating the complicated assignment of human driving by autonomous systems poses limitless engineering challenges, concerning the broader discipline of robotics, along with surroundings perception, choice making and control. Here we discuss all the types of autonomous cars invented and their most important technology used in it. And most importantly we discuss Tesla technology and the way they develop self-driving cars with the help of machine learning and giving cameras a vision using open CV, deep learning, etc. Also find how cameras are best in place of Lidar in autonomous cars. Keywords Autonomous car · Lidar · Artificial intelligence (AI) · Camera vision · Machine and deep learning · Neural network

1 Introduction An autonomous car is a driverless vehicle that has the technology to navigate through traffic, measure and analyses its surroundings with sensors, cameras, and GPS. Current use is that the driverless car (also called self-driving car) is currently in its early stages of use. Some companies currently involved in the manufacture and use of driverless car technology such as Google, Tesla, Uber [1]. Google self-driving car, in 2014, google revealed their own version of an autonomous vehicle, before they had been testing the technology on other types of car. Uber’s autonomous taxi, in 2016 Uber came out with self-driving technology attached to different types of cars for their taxi service. Testing took place in Philadelphia [2]. As shown in Fig. 1 different car uses different technology such as google uses Lidar, tesla uses machine learning and deep learning. R. Kumar (B) · T. Sharma · R. Chaudhary · V. Singh HMRITM, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_69

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Fig. 1 Autonomous car using different technology

Autonomous car possible uses are that it will make it possible for teens, seniors, and disabled people to communicate to places. Right now, over 35000 people die each year in India from car crashes and about 1,000,000+ are resentful [3]. Since human error causes 90% of auto accidents, self-driving cars, generated by computer systems can reduce such error. It can also save a lot of money because passengers will save driving time and they will be extra fuel active [4]. But Self-driving will also have legal, safety, social concern such as Autonomous cars require no driver, so who is to be in the case of an accident, since driverless cars will be run by a computer, there will be electronic security issues, such as hacking, social issues will arise with this new technology as it will take time for people to adopt advanced automated driving. Also people are worried about the security of the autonomous vehicle. Self-driving car can be hacked just like any other vehicle. But some people argue that threads from the hacker are not a large issue [5]. Ethical implications that happen with self-driving cars are such as when the cars are programmed to follow traffic laws. If the accident is occurring, sometime it is safer to break a law in that situation. Human can decide to break a law in an accident; programmed car cannot. Poses the question: who is morally responsible for the crushes, people who designed the vehicle. Still a lot to consider with those involvement. Social implication such as people will be in constant interaction with driverless car once they become prominent on the streets. Cars will have to interact with: Passengers in the car, pedestrians and other human drivers. At second section there is a discussion about how Lidar is doomed and why it can only be affordable to rich, third section is about disadvantages of using Lidar and what we use in place of Lidar and last but not the least discussion about questions concerning on autonomous vehicle [6].

2 Drawback of Lidar It manner that they are high-priced sensors which might be extraneous. It seems that meet an entire rich amount of high-priced afterward. Such as, one additions is poor, nicely at the moment you have got an entire bunch of them, it is ridiculous [7]. Instead of this Tesla makes use of cameras. Tesla’s vehicle’s presently use numerous reasserts of facts to collect independent riding: radar, GPS, maps, ultrasonic sensors, and more. But now no longer Lidar like a number of Tesla’s leader competitors. Elon

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Musk formerly defined that he perspectives Lidar as a crutch for self-riding motors. For Tesla, cameras are the keys to the destiny and its CEO sees a destiny while cameras will permit Tesla to look via the maximum unfavorable climate situations [8]. Andrej Karparthy, the Senior Director of AI at Tesla, took the level and defined that the sector is constructed for visible popularity. Lidar systems, he said, have a difficult time interpreting among a plastic bag and a rubber tire [9]. Large scale neural community education and visible popularity are vital for level four and level five autonomy, he said “In that feel, Lidar is certainly a shortcut,” Karparthy said “It sidesteps the essential problems, the vital hassle of visible popularity, this is vital for autonomy. It offers a fake feel of progress, and is in the end a crutch. It does give, like, certainly speedy demos! [10].” Uber, Waymo, Cruise, and numerous others use the generation of their self-riding generation stack. As proponents of the generation, they factor to Lidar’s cappotential to look via difficult climate and mild situations higher than present cameras. They are high-priced. And regularly hungry for power. That’s in which Tesla’s answer round cameras comes in [11]. As shown in Fig. 2 Technology focused by Tesla. The agency specific its contemporary era self-riding pc that works with all present Tesla motors. Once the software program is ready, it will permit all Teslas to power autonomously with their present sensor set—as a minimum that’s what the agency says—and that sensor set does not encompass Lidar. Instead, the sensors internal Tesla motors lean on a neural community that’s educated through facts accumulated through all Tesla motors. “Everyone’s education the community all of the time,” Musk said. “Whether autopilot is on or off, the community is being educated. Every mile that’s pushed for the auto that’s hardware 2 or above is education the community.” The

Fig. 2 Tesla used technology

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ensuing facts is form of frightening, however probably now no longer as frightening as counting on Lidar [12].

3 Basic Argument in Opposition to Lidar Mainly these arguments in opposition to Lidar such as cost, impractical in poor Weather, Potentiality Hungry, and nasty. Some of the arguments are discussed below:

3.1 Lidar is Pricey Each preliminary expenses and protection expenses. Even Aleven though Google’s Way forward in mobile has been capable of lessen, fee of Lidar through 90% from $75 k (in the year 2009) to $7.5 k (in the year 2017), want since 3 Lidar structures way that Lidar may be not able to offer a little-fee robot taxi limber one of two benefit sizable marketplace riddling as soon as possible. In fact, Way forward in mobile’s 5th technology vehicle brought in the year March 2020, multiplied the wide variety of Lidar detector end up 3 to four, in addition growing expenses. Waymo denied to be obvious approximately the fee of its motors, however an approximate end up 2017 shows an independent vehicle prepared with Lidar would possibly fee $250 k, at the same time as a greater current essay approximate the fee at approximately $180 k. In agreement, Tesla’s $38 k prototype 3, along a $10 k all-inclusive-oneself using (FSD) also, so far has all of the pc hardware and detector important for independent using. All we’re looking forward to is a model of Tesla’s unquiet community software program this is 10 times more secure than a human pilot. In addition, we should additionally report for the fee of protection. Since Lidar makes use of shifting elements, it is far less difficult for it to interrupt or go wrong and, thus, greater pricey to keep. Radar has no shifting elements and is reasonably priced to restore. The law of Wright’s say to us that each one up to the minute technology ultimately drop in fee, however we accept as true with that lengthy earlier than Lidar drops sufficient in fee to compete with Tesla’s low-fee robo-taxi fleet, Tesla will have so far got received the race.

3.2 Lidar Has Problems in Terrible Climate Conditions Because Lidar makes use of lasers so one can degree distance, Lidar is not able to paintings nicely in terrible climate conditions, which include more rain, snowfall, and haze-while radar nevertheless works in those conditions. Lidar is basically sightless in terrible climate. For this reason, independent motors the usage of Lidar nevertheless must use radar to force in such terrible climate conditions, in addition including to

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independent car expenses. If it great works in top climate, like Waymo’s independent using software that work in sure elements of reborn, Arizona, and why have Lidar at fully? As cameras can so far infer distance and can notice very greatly in good climate.

3.3 Lidar is Electricity Empty Wanting greater electricity than different detector. Out of all detector, Lidar calls for the maximum electricity, which has the impact of significantly lowering the vehicle’s using span. Additional common recharging and better charging expenses will be devastating to a low-fee robot-taxi agile and a main turn-off for consumers, who do not need the more fee and nuisance value.

3.4 Lidar is Unpleasant While this does not rely from an engineering or protection attitude, it does rely from a purchaser’s attitude while deciding on a vehicle. There is not any manner round how unpleasant Lidar sensors appearance while they are located atop a vehicle [13].

4 Few Essential Questions Concerning Autonomous Vehicle There are few essential questions which are concerning the Autonomous vehicle. Some of are listed below.

4.1 What Precisely Are We Debating? The automobile’s “imaginative and prescient” or simply the car “sight” This whole Lidar versus cameras discussion is essentially approximately the automobile’s detector–how will the automobile see the sector around it. how? So this is not always essentially a ‘Lidar versus Cameras’ discussion, which may be visible everywhere in the www via a easy Google search. The contrast need to be approximately the entire package deal of detector every employer makes use of: In different language, how does Tesla’s complete collection deal of detector (cameras, radar, ultrasonics) examine to different organizations and their complete package deal of detectors (LiDAR, cameras, radar, perhaps ultrasonics)? That is the discussion [14].

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4.2 For “Imaginative and Prescient,” How Does Lidar Examine to Different Detector? Each detector has its very own advantage and disadvantage which are discussed below.

4.2.1

Light Detection and Ranging (Lidar)

The essential assignment of Lidar is for computable distance. Brand new Lidar era is always upgrade, in order that more recent Lidars are gaining digital digicam-like imaginative and prescient similarly to their distance-measuring abilities, however essentially, Lidar changed into advanced for distance-measuring. At that time, the primary negative aspects of Lidar (cited above) are: (1) its excessive value, (2) its incapability to degree distance via heavy rain, snowfall, and smog, and (3) its hideousness [15].

4.2.2

Radio Detection and Ranging (Radar)

Like Lidar, radar’s essential assignment is for estimating distance, however it makes use of radio waves in place of light or lasers. The benefits of radar are: (1) its less value, (2) its capability to degree distance via heavy rain, snowfall, and smog, and (3) its capability to be hidden from a kind of view. Merely due to radar abilities to degree interval via terrible weather, even automobiles with Lidar will nevertheless want radar for preliminary rollout. However, the greatness of the statistics generated through Lidar is a ways advanced to radar, as may be visible less (despite the fact that more recent radar structures are becoming higher and higher, however at better value). However, even the better decision intensity statistics of Lidar has already emerge as out of date while device mastering is carried out to digital digicam statistics [16].

4.2.3

Cameras

Cameras are glaringly the maximum essential supply of “imaginative and prescient” for the automobile, however in addition they have disadvantage: (1) cameras cannot see via barriers like smog, snowfall, and more rain, and (2) cameras cannot degree distance like Lidar given that that is not always their purpose, despite the fact that computer systems may be skilled to degree distance via AI and device mastering.

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Ultrasonics

Lidar, cameras, and radar are totally negative at analyzing gadgets which might be closest, so Tesla makes use of ultrasonic detector to assist with object detection at much least eight meters (equal to 26 ft). A. Robotics is growing excessive-decision 4-Dimension imaging radar in an effort to enhance radar’s notion while as equal to previous or old radar technologies. But also, the value and first-class of those new radars are not known at this factor until the cameras may be tell to degree distance thru sensual networks (as Tesla has already done this); in this type of case, the cameras may be almost as suitable as Lidar at work out distance. Clearly, no detector is good enough through itself, that is why all organizations use an aggregate of sensors. Each detector generally the “first-rate” in a single area: Lidar is first-rate at calculating distance in normal, radar is first-rate at estimating distance in terrible weather, cameras are first-rate at notion/photo reputation, and ultrasonics are first-rate at calculating close by gadgets (much less than 8 m/26 ft) [17].

4.3 Is LiDAR Important for a Self-sustaining Automobile’s “Imaginative and Prescient”? Nope—if cameras can understand how to degree interval closely each employer solutions yes, besides Tesla and the small-recognized Comma. AI. closely all organizations fee Lidar due to the fact it is miles the first-rate at distance-measuring and detecting barriers, so Lidar is assumed to be important for protection causes. However, if a using pc prepared with cameras, radar, and ultrasonic detector may be skilled to degree distance simply in addition to Lidar–then Lidar is a unnecessary and pointless detector (in addition to being high-priced, vain in terrible weather, energy hungry, and ugly). This is the loss of life toll for Lidar. If interval and notion may be done with sufficient statistics, there’s not anything that Lidar should do this cameras should now no longer. Tesla has done this with its complete-self using pc (FSD) and via deep mastering and AI. This success has been defined in more than one places: A. Karpathy, director of AI at Tesla, explains how measuring intensity without Lidar has been done in the course of his Tesla Autonomy Day presentation Multiple engineers, inclusive of one which bought his employer to Tesla, authored this instructional paper: “similar Lidar Point Clouds with Deep detector Cloning” Derrick M. has written, “Research Guide for bottom approximation with Deep Learning [2].”

4.4 What is Important for Self-sustaining Using? “Vision” AND “Brains” Vision alone (via sensors) is not always sufficient for selfsustaining using. A automobile should have the first-rate and maximum high-priced

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sensors with inside the global, however it cannot force itself until it has a properly skilled “mind” to interpret what it is miles “seeing” (notion/photo reputation) and to choose “the way to answer” (synthetic brainpower). In different words, detector feed statistics to the using pc for interpretation. Then having fed statistics to the pc, sensors have served their goal. The first-class of the automobile’s self-sustaining using now relies upon at the first-class of the pc’s notion and synthetic intelligence. Sensors are the primary step, however now no longer the very last step in selfsustaining using [18]. We can examine this to 2 drivers: (i) a youngster with 20/20 imaginative and prescient without a using revel in, vs. (ii) a sixty years older with forty five years of using revel in, who is in want of latest glass and is using to the attention doctor. Shall the youngster force higher [19]? Shall the youngster’s imaginative and prescient gain result in higher using than a 60 year vintage with weakened imaginative and prescient? Of course, in some unspecified time in the future the 60 year vintage’s imaginative and prescient might be so terrible that he cannot force safely. But the factor is: Does best imaginative and prescient result in best using? No. Proper interpretation of conditions and using revel in may be greater vital than best imaginative and prescient [4]. This is in which Tesla’s gain is powerful–its statistics collecting and its AI device mastering. Tesla has skilled the “mind” of its automobiles to be a ways advanced to its rivals. Even opiliones economic analyst C. Rusch acknowledges this: “While we hold to have misgivings approximately dangers associated with TSLA now no longer subsuming Lidar into its automobiles yet, we agree with the mastering cycles use through having over 1 [million] automobiles on the street is an exquisite gain [20].” An self-sustaining automobile with best imaginative and prescient is not worthy for the street if it does now no longer have a properly-skilled “mind” to disturb what it sees and to understand the way to nicely answer [21]. And this type of mind can best be taught via big quantities of various and actual global statistics [22].

4.5 What Is Needed for a Self-sustaining Automobile to Have the First-Rate “Mind”? Data, big quantities of various and actual global statistics a self-sustaining automobile ought to be taught with a big quantity of statistics due to the fact AI receives smarter the greater statistics it is miles fed [23]. Varied statistics due to the fact the automobile ought to learn how to address facet instances, intense conditions [24]. Just using round in circles will gather “far driven,” however that revel in is vain. Or, best mastering from expressway using is inadequate to educate the automobile’s mind for using on busy town roads with plenty of on foot person, puppies being walked, and people on bicycle [25]. And actual global statistics (in place of simply simulations) due to the fact the actual global is “stupid” and human being engineers without a doubt cannot consider all of the eventualities an automobile would possibly stumble upon with inside the actual global [26]. As E. Musk has said, “In a simulation, you are

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essentially grading your very own homework. If you already know you are going to simulate it, you may really resolve for it. But as Andrej has said, you do not realize what you do not realize. The global could be very bizarre and it has hundreds of thousands of nook instances” (Tesla Autonomy Day). We cannot understand all the hundreds of thousands of nook instances until it is able to be accumulated from automobiles that force with inside the actual global [27]. Tesla’s statistics gain is unprecedented due to the fact Tesla attracts statistics without spending a dime from its current fleet of 1 million + automobiles. No different employer has such giant quantities of actual global statistics. And different organizations are not even inclined to confess their statistics disadvantage. Waymo’s self-sustaining automobiles can paintings properly in a surprisingly managed surroundings this is so far pre-mapped (like their robo-taxis in Phoenix, Arizona), however that does not resolve the “mind” problem [28]. An obsessive cognizance on Lidar handicaps Waymo, besides with inside the maximum perfect of managed and pre-mapped environments. Thus, selfsustaining automobiles with Lidar ought to use the equal strategies as Tesla to acquire complete autonomy, namely, statistics collecting which results in photo reputation and Artificial intelligence processing to make suitable and secure using decisions [29]. What is sincerely important for secure self-sustaining using is not always Lidar; what’s in the end important is the statistics to deal with the 0.00001% of intense facet instances that might imply the distinction among existence and loss of life. Circumstances like creation sites, animals, and particles on the street or particles flying with inside the air. But regardless of all such statistics, the automobile’s using pc nevertheless desires to be taught and its software program up to date continuously– that is precisely what Tesla does with its duplication statistics facts engine [30].

5 Conclusion and Future Scope The branch of robotics science in recent years is so much developed that now scientists are also focused on cars that drive by itself or also known as self-driving cars. But to do so they had to clear many challenges on real world problems that take place on the roads. With the help of machine learning and deep learning we can achieve this. By rejecting Lidar, Tesla is targeting an advanced “mind” via statistics collecting. Tesla will resolve self-sustaining using at a decrease value, with decrease energy consumption (as a result giving the automobile more range), and with more protection through education its automobiles to deal with the 0.00001% of intense and threatening facet instances. And the best manner this type of “mind” is completely skilled is through amassing a big quantity of various and actual global statistics. Only Tesla has this type of statistics gain due to the fact its current fleet of 1 million automobiles feeds Tesla a big quantity of various and actual global statistics as Tesla is very big company they had many resources for gathering data. Self-driving car will have very rich future scope as Driverless car will become more common. Uber will be probably continue using them for their taxi services, commercial trucks could become autonomous. They would affect the job market as many people are employed driving vehicles.

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More people would be needed for the manufacturing, design, and programming of self-driving vehicles. With all these we have the possibility of preventing 90% road accident occurs from human fault.

References 1. Wolmar C (2021) The long journey of the driverless car. Artif Intell. https://doi.org/10.5772/ intechopen.93856 2. Manchanda C, Rathi R, Sharma N (2019) Traffic density investigation and road accident analysis in India using deep learning. In: 2019 International conference on computing, communication, and intelligent systems (ICCCIS). https://doi.org/10.1109/icccis48478.2019. 8974528 3. A google self-driving car may have been at fault in an accident (2016). Phys Today. https://doi. org/10.1063/pt.5.029619 4. Sharma N, Kaushik I, Singh N, Kumar R (2019) Performance measurement using different shortest path techniques in wireless sensor network. In: 2019 2nd International conference on signal processing and communication (ICSPC). https://doi.org/10.1109/icspc46172.2019.897 6618 5. R, PM (nd) (2021) Self-driving autonomous car. Int J R Appl Sci Eng Technol. Retrieved October 20, 2021. https://www.academia.edu/43807174/Self_Driving_Autonomous_Car 6. Grover M, Verma B, Sharm N, Kaushik I (2019) Traffic control using V-2-V based method using reinforcement learning. In: 2019 International conference on computing, communication, and intelligent systems (ICCCIS). https://doi.org/10.1109/icccis48478.2019.8974540 7. Self-driving cars: what can we realistically expect? (n.d.). Retrieved October 20, 2021. https:// www.cedengineering.com/userfiles/Self-Driving%20Cars%20-%20What%20Can%20We% 20Realistically%20Expect-R1.pdf 8. Limitations of self-driving cars. Staver accident injury lawyers, P.C. (2016, July 12). Retrieved October 20 2021. https://www.chicagolawyer.com/blog/limitations-of-self-driving-cars/ 9. Porter L, Stone J, Legacy C, Curtis C, Harris J, Fishman E, Kent J, Marsden G, Reardon L, Stilgoe J (2018) The autonomous vehicle revolution: implications for planning/the driverless city?/autonomous vehicles–a planner’s response/autonomous vehicles: opportunities, challenges and the need for government action/three signs autonomous vehicles will not lead to less car ownership and less car use in car dependent cities–a case study of Sydney, Australia/planning for autonomous vehicles? questions of purpose, place and pace/ensuring good governance: the role of planners in the development of autonomous vehicles/putting technology in its place. Plan Theory Pract 19(5):753–778. https://doi.org/10.1080/14649357.2018.1537599 10. Car T (2020) Why LiDAR is doomed. Volt equity. Retrieved October 20, 2021. https://www. voltequity.com/article/why-lidar-is-doomed 11. Sharabok G (2020) Why Tesla won’t use Lidar. Medium retrieved October 20, 2021. https:// towardsdatascience.com/why-tesla-wont-use-lidar-57c325ae2ed5 12. Timothy B, Lee (2019) 11:45 am U. T. C. (2019, August 6) Elon Musk: anyone relying on Lidar is doomed experts: maybe not. Ars Tech Retrieved October 20, 2021. https://arstechnica. com/cars/2019/08/elon-musk-says-driverless-cars-dont-need-lidar-experts-arent-so-sure/ 13. Rosenband DL (2017) Inside Waymo’s self-driving car: my favorite transistors. In: 2017 symposium on VLSI circuits. https://doi.org/10.23919/vlsic.2017.800850 14. Hong JW, Cruz I, Williams D (2021) Ai, you can drive my car: how we evaluate human drivers versuss. self-driving cars. Comput Human Behav 125:106944. https://doi.org/10.1016/j.chb. 2021.106944 15. Scribd (n.d.). Inf (2017) 115 peterszikora WP. Scribd. Retrieved October 20, 2021. https:// www.scribd.com/document/486277775/inf2017-115-PeterSzikora-WP

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Impacts on Health Frameworks of Big Data Analytics: A Review Naresh Kumar Trivedi, Abhineet Anand, Ajay Kumar, Umesh Kumar Lilhore, and Raj Gaurang Tiwari

Abstract Due to its volume, varied complexity, and high dynamics of data sources in health organisations, the health industry has been affected and improved from the presence of big data. Though the usage of big data analytic methods, instruments, and digital platforms is applied in a variety of fields, they have promising research guidelines for medical organisations to implement and deliver new cases of use for potential health applications. As evidenced by pioneering research initiatives, the success of medical applications in big data is dependent solely on the architecture and on the deployment of related tools. New research has been undertaken to derive specific healthcare frameworks, providing diverse analytical data capabilities for handling data sources, from electronic records to medicinal images. We have presented several analytical avenues from a variety of stakeholders in the patient-centred healthcare system. About underlying data sources and the analysis capabilities and application areas, we also reviewed different big data frameworks. Moreover, the involvement of big data instruments in the development of the health ecosystem is also presented. Keywords Health care · Big data · Telemedicine · Analytical avenues · Big data frameworks

N. K. Trivedi · A. Anand · A. Kumar · U. K. Lilhore · R. G. Tiwari (B) Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India e-mail: [email protected] N. K. Trivedi e-mail: [email protected] A. Anand e-mail: [email protected] A. Kumar e-mail: [email protected] U. K. Lilhore e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_70

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1 Introduction In various verticals of the health system, one of the potential fields is big data analytics and its consequences [1]. Typical characteristics of big data included volume, velocity (flood data), and variety (data existence from numerous sources with diverse formats) [2]. Data sets in the healthcare sector are frequently categorised as structured information: data with a predefined type, format, and data structure. Examples in the field of health care include hierarchical diseases, symptom, diagnostic data, laboratory results, information on patients, history of admission, and medicines and clinical services available for invoicing. (i) Semi-structured data: data arranged with a minimum and self-describing character. For example, data produced by equipment like sensors to monitor patients’ behaviour effectively incorporate such data. (ii) Unstructured data: data without intrinsic structures that may include human-language medical prescriptions, clinical correspondence, literature on the biomedicine, summaries of discharge, and so on. There is, however, a tough and daunting work to explore healthcare data for diverse stakeholders (clinicians, patients, hospitals, etc.) due to the wider range of data available in different sources (structured, unstructured, and semi-structured). Moreover, effective platforms in data processing, better data-gathering technology, smart analyses, storage, and visualisation approaches should be advocated to achieve new information and an efficient decision-making support approach for health-related problems to achieve value from existing 3 V (the fourth ‘V’) of big data [3]. Extensive data can cover a variety of medical and health duties including clinical decision support, health monitoring, and public health administration [4]. The fast development of the electronic record of patients’ health (EHR), integrating with ICT-based mobile Health, electronic health, and social intelligent health-related devices has steered to new healthcare frameworks being established to promote the accuracy of medical treatments and personal care. A recent study shows that extensive healthcare solutions have been developed with support for various levels of layered services using architectural frameworks. Efficient analytics of different health data source sources for detecting causal linkages and patterns of interest among the huge data sources is the basic notion for the construction of these framework frameworks [5]. These frameworks support health issues from the sickness-centred framework to the patient-centred paradigm, resulting in active patient participation. In this paper, we present major advancements to integrate data analytics, monitoring, and visualising approaches in the development of a health framework over recent years as a final solution to enhanced healthcare systems. The activity of data processing plays an important part in turning raw data into usable knowledge because of the prevalence of the complex data structure, the volume of information, and related ambiguity among large data sources. Medical data are complicated but are strongly interdependent and hence demand highly sophisticated and well-established domain tools and technologies to facilitate data complexity, identify interconnections among different health characteristics, and choose target variables for health analytics. In the healthcare computer environment, different complications exist, such as managing stream-oriented data

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from omnipresent instruments for the monitoring of patients, integrating different sources of data to produce predictive models, format building, and medical image compression. Furthermore, mostly data cleansing, integration activities, and content synthesising procedures ultimately convert it into knowledge rely heavily on the utilisation of large data technologies at many stages of the data analysis life cycle. With this in mind, a systematic evaluation is underlined to highlight the main elements of various big data instruments that play a vital part in building a health framework [6]. The paper was organised in the following way: Sect. 2 has identified the influence of large-scale data analysis on the health system from the standpoint of different stakeholders. Section 3 will systematically explore several advanced research initiatives to construct health frameworks together with their advantages and disadvantages. It also underlines the involvement of several big data applications and tools in the delivery of healthcare solutions. Section 4 gives the final observations.

2 Big Data’s Impact on Healthcare The main value of big data in the business segment has been used effectively to report consumer behaviour patterns for developing creative corporate services and solutions. Big data involvement acts as an analysis and machine learning platform in the field of health care, for example, for implementing treatment plans, for personal medical care [7]. Sustainable solutions are available for each. The big data from the business segment were analysed by healthcare big data for different features and values. Instead of volume, speed, and variety, they redesigned the features of the medical large data in three features: silo, safety, and diversity [8]. Silo is the legacy database containing information on public health preserved at the premises of stakeholders such as hospitals. The security component involves additional care in the maintenance of medical data. This variation shows that different forms of health data, including structured, unstructured, and semi-structured [9]. Pragmatic changes in the perspectives of the players in achieving different healthcare solutions parties involved in big data analysis, and their respective technologies have taken place in the health sector [10]. To identify new information sources such as social networking sites, telemetry, and wearable technology, as well as the impact of big data on medical services, moreover, an evaluation of legacy sources of data such as health history, diagnostics data sets, clinically test data, efficaciousness of drug indices, and so on. If amalgam of these sources of data and analysis are applied, they provide health researchers with vital information to find new health solutions [11]. Given the relevance of these players in constructing a big data health ecosystem, the next part provides an insight into the effective sources of big data.

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2.1 Stakeholders of Healthcare and Big Data This segment explores how various big data resources address the requirements of major (patients, medical professionals, clinical operators, pharmaceuticals, and healthcare insurance). It also provides appropriate methods for analysing important interesting data patterns from identified data resources. Patients The patient population constantly demands a wide choice of medical services with an individualised suggestions at cheap costs [12]. They can supplement their knowledge of medicine by using digital social media networks, clinical forums, and a doctor’s clinical diagnostic. These sources of big data allow patients to interact with other people, including disease, side effects, hospitalisation, medical information, and diagnostic reports [13, 14]. Telemedicine may be used to meet your health demands by patients that could not visit hospitals. The platform can be a huge database to capture important health signals such as fever, heartbeat, blood pressure, and send them to a central repository to trigger regular health warnings [15, 16]. Medical Professionals The large volume of information created by patient diagnostic and therapeutic programmes contributes to the identification by healthcare providers of the true insight into the development of their therapy. Several big data sources are developed mostly by health systems through treatment plan implementation. Classification category, test outcomes, clinical reports, data on medical imaging, and sensors that fetch patient information in various contexts are included. They cover numerous diseases and clinical services. If the clinical disease repository (CDR) is deemed to be such data, this improves public health monitoring and provides a faster reaction by effectively analysing the patterns of diseases. Moreover, it offers major advantages such as allowing clinicians, for example, the integration of wearable devices in healthcare applications, to track drug uses, and monitoring of the patient’s health at all stages [17]. Operators of the Hospital To successfully manage the experience of a patient and to optimise the available resources, hospital operators rely intensely on results from large data sources. Models based on prediction and prescription analysis with the know-how of data scientists are built to measure the strength of correlations amid patient satisfaction indices and the services provided. In addition, resource assigned and optimisation technology may successfully be used to meet the labour requirements in different hospital parts based on accessible large data. The location awareness data are used by the strategic hospital operators to assess if various departments should be co-located to optimise the utilisation of expensive medical equipment. The development of the descriptive models would also be easier to improve the services available based on the postprocessing data supplied by text messages, e-mails, and follow-up calls [18].

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Pharmaceutical and Clinical Scientists The impact of big data on pharmacological and clinical research shows a medical change. The utilisation of omics and large clinical data assisting in designing of models to understand bio-drug processes for better predictive methods [19]. The high rate of success in achieving efficient medication designs is characterised. Effective healthcare analysis of several big data sources enables pharmaceutical company’s measure results of developed medicines in compact and lesser studies [20]. Insurance Companies Big data development in health care gives new analytical routes for health insurance. As a result, new health plans can be offered with minimal premium costs for frequently occurring geographically-based disorders. Supporting suitable client health plans based on numerous characteristics, such as age, sex, and history of your family, income, and the nature of your job enables the insurer as well as the client to benefit. Analysing unstructured in the history of claims using predictive modelling approaches allows the insurance company to predict real claim patterns and uncommon outliers to minimise abuse costs. The gross data in combination with IoT promotes the deployment by the insurers, using real-time data analytics on the customer behaviour, of innovative new business opportunities like insurance use. Mobile IoT plays an important part in changing health care by changing business models and working processes, enhancing productivity, and offering client’s experience [16, 21].

3 Big Data Frameworks for Healthcare Current studies have tried to promote different health framework frameworks to handle a vast volume of different data sources to identify important patterns and trends. This section discusses the structure of big data and emphasises its importance in the field of health care. Raghupathi and Raghupathi proposed an application of the architectural framework for healthcare systems employing big data analytics. The framework includes the data source, transformation, big data platform, and analytical layer. This layer is focused on sources from within and outside of the healthcare system. The layer transforms data into a big data platform through several data storage consist of middleware and data storing processes, such as extraction, transformation, and downloading processes [22]. The patient’s central personalised healthcare framework was presented by Chawla and Davis by using combined filtering. It identifies patient commonalities and provides individuals with individualised risk profiles. Combined filtering is a method for forecasting user preferences by correlating users’ known preferences with their comments. The medical history of individual patients was compared in the proposed context with all other medical histories of patients based on indicated similarities

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such as work, symptoms, test results, family medical history, and demographic information. Select a pool of comparable patients and use similitude calculation for the prediction of diseases [23]. Kim tried to establish a large-scale analysis framework using omnipresent healthcare systems. The context analyses vital accelerometer indicators for medical services. Vital signs are permanently unstructured data from time-series not recorded in traditional databases. Breathing data and mobility data were essential signs such as electromagnetic cardiogram signals (ECG). The framework established an open system environment to allow data and device interoperability. The framework was enlarged to extract the functional values of raw data from essential indications by adding algorithms and pile them for real-time analysis [24]. Fang have extensively examined the implications of computer issues in medicinal and health big data computing. They recommended a ‘Health Computing Pipeline Framework’ framework which integrates a succession of processes to draw on relevant patterns of large-scale health care. Process pipelines (identification of the data, like electronic health records, sources of clinical, and laboratory findings) and (extraction of interpretation) is included (identification of cost-effective storage infrastructures for health analysis) (Use the basis for good decisions in the health era of big data). Other study guidelines on concerns of information heterogeneity, including structured and unstructured health data, and the complexity of data available, privacy concerns, and visualisation of the patterns is investigated in conjunction with the suggested framework [25]. Youssef proposed a framework in the mobile cloud environment for big data analytics. The following components are included in the framework: healthcare systems integration, interconnectivity, accessibility, and data exchange for physicians, patients, and pharmaceutical developers (i) Component of the cloud that is responsible for delivering patient data and health services. (ii) HER—liable for combining separate data of patients from diverse sources, including the hospital, pharmacy, and laboratories. (iii) Security component—ensures security and privacy protection through the application of encryption and validation procedures. (iv) Data analytics component—use a variety of analytical approaches to identify novel models within the EHR database. (v) Components of the Care Delivery Organisation (CDO)—the numerous health organisations in the various sites [26]. Lin, who supported big data health framework on the cloud, focused on the necessity for self-care options for emergency patients. Hadoop, as well as distributed search, is part of this solution. The Hadoop cluster is able to store and indexing patient files and for executing simultaneous and scalable user requests, and the online cluster has been created [27]. The focus of Legaz-Garca et al. work is on the significance of semantic interoperability between clinical data. They noticed that a component in the health service was the lack of interaction between clinical models and the clinical record. They introduced a Web-Ontology-based framework for the semantic integration of electronic health data (OWL) [28, 29].

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Sarkar and Elgammal have developed a cyber-physical healthcare system. A framework for intelligent health care that incorporates sensors, cloud computing, the Internet of Things, and predictive analysis. The following are. (i) Data sensing, removing, and integrating connection layer (ii) Data storage layer—for the storage of structured, unstructured, and cloud information (iii) Big data analysis and processing layer—for diverse processing of data. The suggested architecture is good for a range of applications, including patient data analytics, patient management, genetic analysis, and increased monitoring of patients. Their work encompasses numerous ICT benefits in the domain of intelligent health care, like Internet of Things, sensor technology, cloud, and big data analysis. On the other hand, the framework is not capable of analysing complicated data sources like photos and streams [30, 31].

4 Conclusion Framework-based methods constantly accommodate the diverse needs of many healthcare stakeholders. New research into health care has changed the way that enormous amounts of patient data are handled, from health records to X-rays. This study examines several efforts in the establishment and analyses important results of health frameworks. A description of the researchers’ contributions highlights the data source used, analytic methodologies chosen, and other characteristics. In the conclusion, there is also a detailed examination of the implications of several big data tools in the development of the healthcare system.

References 1. Andreu-Perez J, Poon CCY, Merrifield RD, Wong STC, Yang G-Z (2015) Big data for health. IEEE J Biomed Heal Inform 19(4):1193–1208. https://doi.org/10.1109/JBHI.2015.2450362 2. Casas DM, González JÁT, Rodríguez JEA, Pet JV (2009) Using data-mining for short-term rainfall forecasting. In: Distributed computing, artificial intelligence, bioinformatics, soft computing, and ambient assisted living, pp 487–490 3. Ola O, Sedig K (2014) The challenge of big data in public health: an opportunity for visual analytics. Online J Public Health Inform 5(3):223. https://doi.org/10.5210/ojphi.v5i3.4933 4. Liang Y, Kelemen A (2016) Bayesian state space models for dynamic genetic network construction across multiple tissues. Stat Appl Genet Mol Biol 15(4):273–290. https://doi.org/10.1515/ sagmb-2014-0055 5. Sharma I, Tiwari R, Rana HS, Anand A (2018) Analysis of mahout big data clustering algorithms. Adv Intell Syst Comput 624:999–1008. https://doi.org/10.1007/978-981-10-59032_105 6. Tantan C, De Management TE, Hammouda B, Understanding the barriers to knowledge sharing in the French healthcare system: an exploratory assessment of physician’s perspectives 7. Al-Jarrah OY, Yoo PD, Muhaidat S, Karagiannidis GK, Taha K (2015) Efficient machine learning for big data: a review

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8. Jee K, Kim G-H (2013) Potentiality of big data in the medical sector: focus on how to reshape the healthcare system. Healthc Inform Res 19(2):79–85. https://doi.org/10.4258/hir.2013.19. 2.79 9. Isard M, Budiu M, Yu Y, Birrell A, Fetterly D (2007) Dryad: distributed data-parallel programs from sequential building blocks. SIGOPS Oper Syst Rev 41(3):59–72. https://doi.org/10.1145/ 1272998.1273005 10. Forouzanfar MH et al (2016) Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet 388(10053):1659–1724. https://doi.org/10.1016/S0140-6736(16)31679-8 11. Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks 12. Mancini F et al (2014) Whole-body mapping of spatial acuity for pain and touch. Ann Neurol 75(6):917–924. https://doi.org/10.1002/ana.24179 13. Mounia B, Habiba C (2015) Big data privacy in healthcare moroccan context. Proc Comput Sci 63:575–580. https://doi.org/10.1016/j.procs.2015.08.387 14. Kupwade Patil H, Seshadri R (2014) Big data security and privacy issues in healthcare. In: 2014 IEEE International congress on big data, pp 762–765. https://doi.org/10.1109/BigData. Congress.2014.112 15. Sharma N, An A, Husain A (2020) Cloud based healthcare services for telemedicine practices using internet of things. J Crit Rev 7(14):2605–2611. https://doi.org/10.31838/jcr.07.14.510 16. Dimitrov DV (2016) Medical internet of things and big data in healthcare. Healthc Inform Res 22(3):156–163. https://doi.org/10.4258/hir.2016.22.3.156 17. Sharma I, Tiwari R, Anand A (2017) Open source big data analytics technique. Adv Intell Syst Comput 468:593–602 18. Silva AF, Tavakoli M (2020) Domiciliary hospitalization through wearable biomonitoring patches: recent advances, technical challenges, and the relation to Covid-19. Sensors 20(23). https://doi.org/10.3390/s20236835 19. Klemperer EM, Hughes JR, Wu et al. (2017) Commentary on: do very brief reduction interventions increase quitting among smokers not ready to quit? Addiction 112(11):2041–2042. https://doi.org/10.1111/add.13970 20. Merelli I, Pérez-Sánchez H, Gesing S, Agostino DD (2014) Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives. Biomed Res Int 2014:134023. https://doi.org/10.1155/2014/134023 21. Bali AS, Ramesh M (2021) Governing healthcare in India: a policy capacity perspective. Int Rev Adm Sci 87(2):275–293. https://doi.org/10.1177/00208523211001499 22. Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Heal Inf Sci Syst 2:3. https://doi.org/10.1186/2047-2501-2-3 23. Chawla NV, Davis DA (2013) Bringing big data to personalized healthcare: a patient-centered framework. J Gen Intern Med 28(3):S660–S665. https://doi.org/10.1007/s11606-013-2455-8 24. Kim S, Kim D, Cho SW, Kim J, Kim J-S (2014) Highly efficient RNA-guided genome editing in human cells via delivery of purified Cas9 ribonucleoproteins. Genome Res 24(6):1012–1019. https://doi.org/10.1101/gr.171322.113 25. Fang X et al (2016) Evaluation of the microbial diversity in amyotrophic lateral sclerosis using high-throughput sequencing. Front Microbiol 7:1479. https://doi.org/10.3389/fmicb. 2016.01479 26. Youssef MAFM et al (2014) Gonadotropin-releasing hormone agonist versus HCG for oocyte triggering in antagonist-assisted reproductive technology. Cochrane Database Syst Rev 10:CD008046. https://doi.org/10.1002/14651858.CD008046.pub4 27. Trappey AJC, Trappey CV, Wu C-Y, Fan CY, Lin Y-L (2013) Intelligent patent recommendation system for innovative design collaboration. J Netw Comput Appl 36(6):1441–1450. https://doi. org/10.1016/j.jnca.2013.02.035 28. Bhattacharya A et al (2022) Predictive analysis of the recovery rate from coronavirus (COVID19). In: Tavares JMRS, Dutta P, Dutta S, Samanta D (eds) Cyber intelligence and information

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Upgrading Search Link Priority by Content Analysis Ayushi Prakash, Sandeep Kumar Gupta, and Mukesh Rawat

Abstract As all of the valuable information related to different domains are available in the form of hypertext in different web resources. Search engine provides a medium to retrieve this information according to the need of the user. There are various search engines available such as Google search engine, Alta, Vista, Yahoo Search Engine, etc., which provides an interface to access the web resources listing the search result according to the most relevant of the user query is an issue. In this chapter, a new technique is suggested which take the search result of Google search engine and tried to rearrange the search results in increasing order according to the relevancy with the search query. Normalized term frequency of the keywords lying in the particular web documents is used to calculate the rank of a web page. Keywords Normalization · Ranking · Recall · Precision · Search engine · Content

1 Introduction 1.1 Searching Search engine is basically software for searching or we can say system of organization which is map out to find out the WWW or Internet cast about in structured and in an organized way, with the help of query engine.

A. Prakash (B) · S. K. Gupta Department of Computer Science Engineering, Dr Kedar Nath Modi University, Tonk, Rajasthan, India e-mail: [email protected] M. Rawat Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, U.P, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. Dutta et al. (eds.), Emerging Technologies in Data Mining and Information Security, Lecture Notes in Networks and Systems 491, https://doi.org/10.1007/978-981-19-4193-1_71

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1.2 Tools Available for Searching Search engine is made up of tools that are based on web that enables different type of users to get information on World Wide Web. Search engines as in like Yahoo, Google, MSN, etc., are so helpful if in case users want to get relevant information along the web. Generally, search engine works in different mathematical formulae to get the appropriate results.

1.3 Need of Algorithm that Gives the Best Results According to the User Query Apart from mathematical formula, search engines use different algorithms and with the help of these algorithm that works basically on key elements of web pages as for example titles, content and words density shows ranking for where the results placed on the page. For making results more relevant, these algorithm modified and revised constantly. In this paper, such a methodology is suggested that tries to reorder the search results fetched by the search engine that are more relevant to the user query terms. Words of the query are method are matched with the documents corresponding to a search result, and the search links are reorder in according to the matching between the query terms and content of the search web documents. The process of content analysis and reordering of search results is mentioned in the below sections: A. Query processing and dictionary creation. B. Calculation of score of search results. C. Normalization score of search results.

2 Background History 2.1 Information Retrieval Accessing the right and relevant information in less time or we can say fast searching of relevant data is very important nowadays and the way by which we can get the desired information or access the relevant information is known as information retrieval [1]. There are some certain recovery programmes also known as information recovery programmes which is a part of accessing of information on web. Getting right information related to relevant topic from a large database containing all kind of data, metadata [2], directories, index, etc., is the known as retrieving information. Search can be depend on different criteria, sometimes based on metadata or sometimes on the whole text as well. We can show this process in Fig. 1.

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User

User’s Query

Search-Mechanism

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Fig. 1 Process of information retrieval

2.2 Search Engine A web search engine [3] is created to find out the information on the WWW. The search outcomes are normally given out in a line of results frequently referred to as Search Engine Results Pages (SERPs). The information may be a proficient in web pages, images, information, and other types of files. Some Search Engines (SE) also excavates data available in databases or open directories. Unlike web directories, which are maintained only by human editors, SE also maintains real-time information by running an algorithm on a web crawled. A search engine operates in the following manner: (a) Crawl on web, (b) Indexing of web, and (c) search interface (SI) Structure of SE In lieu of searching the entire documents, the search engine [4] exploring the existing database to find the catchword. Afterwards by use of software, it searches the plated information stored in the database with the help of web crawler. That is composed of two processors: Frontend processor (FEP) and backend processor (BEP). There are two main kinds of SE that have developed: a. SE with predefined keywords: A system that keeps predefined keywords that human beings have programmed widely. b. SE with generation of inverted index Another system that produce an “Inverted Index” [5] by exploring and observing texts it placed. This second form depends more specifically on the computer itself to do the maximum work. We can take overview of the frontend and backend process with the help of shown Fig. 2.

3 Proposed Methodology See Fig. 3.

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Fig. 2 Generation of inverted index

User

Query Interface

Query

1. Query PreProcessing Defined Terms

Submission to WWW Search Result

2. Dictionary Creation 3. Scoring 4. Normalized Score 5. Final Result

Fig.3. Work flow of content based ranking of web documents

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4 Model Description 4.1 Query Pre-processing In proposed model, we have given a novel way to identify and search the optimized form of the term to be indexed to achieve the best retrieval performance, with the help of query pre-processing on the query given by USER through query interface. There is the stepwise task involved in query pre-processing like Tokenization/Segmentation using Python, Stemming and Lemmatizes, Parts of Speech, Parsing, Context Analysis, Sentiment Analysis, etc. (Fig. 3).

4.2 Dictionary Creation Dictionary creation is a process to store the data from the Word Net, to identify the relevant words related to the query strings. Word Net is a virtual depository for the English language, which was created by Princeton and is part of the NLTK corpus. Word Net alongside the NLTK module to find the definition and explanation of words, synonyms, antonyms, and more. Let’s cover some examples. Many natural language processing require a large lexical database of word relations. The dictionary has much more consistent structures that can be exploited by a computer to recognize word relations. Dictionary creation may also contribute to the identification of definite groups of relation instances that are difficult to search from general texts (Table 1). Bring in wordnet: Fromnltk.corpusimportwordnet. Use of word “process” to get synsets here: syns = wordnet.synsets(“process”). with a sample_synset: print(syns[0].name()) plan.n.01. print(syns[0].lemmas()[0].name()). Explanation_synset: print(syns[0].definition()) To get the results, we use series of instructions Specimen of the term print(syns[0].examples()) Table 1 Example of dictionary creation Synonyms

Beneficial

Upright

Charmingly

Elegantly

Antonyms

Shoddy

Average

Bad

Weak

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Now, how we detect Synonyms and Antonyms to a word? Find the list of Synonyms and Antonyms: Synonyms_11 = [] Antonyms_1 = [] forsyninwordnet.synsets(“beautiful”): for Linsyn.lemmas(): synonyms.append(l.name()) if L antonyms(): antonyms.append(l.antonyms()[0].name()) print(set(synonyms_1)) print(set(antonyms_1))

4.3 Scoring the Results and Normalization In the below algorithm, keywords of the search result documents matched with each of the dictionary word. If the keyword found in the dictionary list, its score is calculated in the form of total occurrences of that particular keyword in the search document. But this procedure gives weightage to the word which is most frequently occurring in the document. To normalize these things, a normalized score is calculated. In which, each calculated score of term [6] of the document is divided by the average score of the whole search document. And new ranking [7] of the search documents is calculated by arranging the search documents according to their decreasing ranks. Input: Search results S.No.Doc IDURL 1.SR1 https://en.wikipedia.org/wiki/Op-system 2.SR2 https://whatis.techtarget.com/definition/ML-System-AI 3. SR3 https://edu.gcfglobal.org/en/computerbasics/understanding-ML 4. SR4 https://www.tutorialspoint.com/operating_system/os_Intro.htm D = {k1 , k2 , ….,kn }, where D is a dictionary Step 1: S = {Ki where Ki e SRj , 1