International Conference on Innovative Computing and Communications: Proceedings of ICICC 2023, Volume 1 (Lecture Notes in Networks and Systems, 703) 9819933145, 9789819933143

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Lecture Notes in Networks and Systems 703

Aboul Ella Hassanien Oscar Castillo Sameer Anand Ajay Jaiswal   Editors

International Conference on Innovative Computing and Communications Proceedings of ICICC 2023, Volume 1

Lecture Notes in Networks and Systems Volume 703

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, Türkiye 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]).

Aboul Ella Hassanien · Oscar Castillo · Sameer Anand · Ajay Jaiswal Editors

International Conference on Innovative Computing and Communications Proceedings of ICICC 2023, Volume 1

Editors Aboul Ella Hassanien IT Department, Faculty of Computers and Information Cairo University Giza, Egypt Sameer Anand Department of Computer Science Shaheed Sukhdev College of Business Studies University of Delhi New Delhi, India

Oscar Castillo Tijuana Institute of Technology Tijuana, Mexico Ajay Jaiswal Department of Computer Science Shaheed Sukhdev College of Business Studies University of Delhi New Delhi, India

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-99-3314-3 ISBN 978-981-99-3315-0 (eBook) https://doi.org/10.1007/978-981-99-3315-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 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

We hereby are delighted to announce that Shaheed Sukhdev College of Business Studies, New Delhi, in association with National Institute of Technology Patna and University of Valladolid Spain has hosted the eagerly awaited and much coveted International Conference on Innovative Computing and Communication (ICICC-2023) in hybrid mode. The sixth version of the conference was able to attract a diverse range of engineering practitioners, academicians, scholars, and industry delegates, with the reception of abstracts including more than 3400 authors from different parts of the world. The committee of professionals dedicated toward the conference is striving to achieve a high-quality technical program with tracks on innovative computing, innovative communication network and security, and Internet of things. All the tracks chosen in the conference are interrelated and are very famous among present-day research community. Therefore, a lot of research is happening in the above-mentioned tracks and their related sub-areas. As the name of the conference starts with the word ‘innovation,’ it has targeted out of box ideas, methodologies, applications, expositions, surveys, and presentations helping to upgrade the current status of research. More than 850 full-length papers have been received, among which the contributions are focused on theoretical, computer simulation-based research, and laboratory-scale experiments. Among these manuscripts, 200 papers have been included in the Springer proceedings after a thorough two-stage review and editing process. All the manuscripts submitted to the ICICC-2023 were peer reviewed by at least two independent reviewers, who were provided with a detailed review proforma. The comments from the reviewers were communicated to the authors, who incorporated the suggestions in their revised manuscripts. The recommendations from two reviewers were taken into consideration while selecting a manuscript for inclusion in the proceedings. The exhaustiveness of the review process is evident, given the large number of articles received addressing a wide range of research areas. The stringent review process ensured that each published manuscript met the rigorous academic and scientific standards. It is an exalting experience to finally see these elite contributions materialize into three book volumes as ICICC-2023 proceedings by Springer entitled International Conference on Innovative Computing and Communications. The articles are organized into three volumes in some broad categories covering subject v

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matters on machine learning, data mining, big data, networks, soft computing, and cloud computing, although given the diverse areas of research reported, it might not have been always possible. ICICC-2023 invited three key note speakers, who are eminent researchers in the field of computer science and engineering, from different parts of the world. In addition to the plenary sessions on each day of the conference, ten concurrent technical sessions are held every day to assure the oral presentation of around 200 accepted papers. Keynote speakers and session chair(s) for each of the concurrent sessions have been leading researchers from the thematic area of the session. A technical exhibition is held during all the two days of the conference, which has put on display the latest technologies, expositions, ideas, and presentations. The research part of the conference was organized in a total of 26 special sessions. These special sessions and international workshops provided the opportunity for researchers conducting research in specific areas to present their results in a more focused environment. An international conference of such magnitude and release of the ICICC-2023 proceedings by Springer has been the remarkable outcome of the untiring efforts of the entire organizing team. The success of an event undoubtedly involves the painstaking efforts of several contributors at different stages, dictated by their devotion and sincerity. Fortunately, since the beginning of its journey, ICICC-2023 has received support and contributions from every corner. We thank them all who have wished the best for ICICC-2023 and contributed by any means toward its success. The edited proceedings volumes by Springer would not have been possible without the perseverance of all the steering, advisory, and technical program committee members. All the contributing authors owe thanks from the organizers of ICICC-2023 for their interest and exceptional articles. We would also like to thank the authors of the papers for adhering to the time schedule and for incorporating the review comments. We wish to extend my heartfelt acknowledgment to the authors, peer reviewers, committee members, and production staff whose diligent work put shape to the ICICC-2023 proceedings. We especially want to thank our dedicated team of peer reviewers who volunteered for the arduous and tedious step of quality checking and critique on the submitted manuscripts. We wish to thank my faculty colleagues Mr. Moolchand Sharma for extending their enormous assistance during the conference. The time spent by them and the midnight oil burnt is greatly appreciated, for which we will ever remain indebted. The management, faculties, administrative, and support staff of the college have always been extending their services whenever needed, for which we remain thankful to them. Lastly, we would like to thank Springer for accepting our proposal for publishing the ICICC-2023 conference proceedings. Help received from Mr. Aninda Bose, the acquisition senior editor, in the process has been very useful. New Delhi, India

Sameer Anand Ajay Jaiswal Convener, ICICC-2023

Contents

High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types . . . . Rabei Raad Ali, Lahib Nidhal Dawd, Salama A. Mostafa, Eko Hari Rachmawanto, and Mohammed Ahmed Jubair Securing the Networks Against DDoS Attacks Using Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Noor A. Hussein A Machine Vision-Based Approach for Tuberculosis Identification in Chest X-Rays Images of Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Vidya Chellam, Vivek Veeraiah, Ashish Khanna, Tariq Hussain Sheikh, Sabyasachi Pramanik, and Dharmesh Dhabliya Prediction of Patients’ Incurable Diseases Utilizing Deep Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Praveenkumar, Vivek Veeraiah, Sabyasachi Pramanik, Shaik Mahaboob Basha, Aloísio Vieira Lira Neto, Victor Hugo C. De Albuquerque, and Ankur Gupta

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Real-Time Control of Humanoid Robotic Arm Motion Using IT2FLC Based on Kinect Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saif F. Abulhail and Mohammed Z. Al-Faiz

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Blockchain-Based Access Control with Decentralized Architecture for Data Storage and Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Vanjipriya and A. Suresh

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Effective Strategies for Resource Allocation and Scheduling in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Jananee and A. Suresh

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A Brief Review Particle Swarm Optimization on Intrusion Detection System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. M. Nandana and Ashok Kumar Yadav CryptoDataMR: Enhancing the Data Protection Using Cryptographic Hash and Encryption/Decryption Through MapReduce Programming Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Siva Brindha and M. Gobi

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5G Wireless Network-Based Cybersecurity Analysis Using Software Defined Phy_HetNets and Boltzmann Encoder Convolutional Basis Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Manikandan Parasuraman, Ashok Kumar Munnangi, Sivaram Rajeyyagari, Ramesh Sekaran, and Manikandan Ramachandran Wearable Sensor Based Cloud Data Analytics Using Federated Learning Integrated with Classification by Deep Learning Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Ashok Kumar Munnangi, Sivaram Rajeyyagari, Ramesh Sekaran, Nashreen Begum Jikkiriya, and Manikandan Ramachandran Comparison of Decision Tree and Random Forest for Default Risk Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Usha Devi and Neera Batra Trusted Cloud Service Framework for Cloud Computing Security . . . . . 157 Sasmitha and A. Suresh HITR-ECG: Human Identification and Classification Simulation System Using Multichannel ECG Signals: Biometric Systems Era . . . . . . 171 Alaa Sabree Awad, Ekram H. Hasan, and Mustafa Amer Obaid Brain Disorder Classification Using Deep Belief Networks . . . . . . . . . . . . . 183 Rehana Begum, Ravula Vaishnavi, Kalyan Rayapureddy, and Gelli Sai Sudheshna Diabetic Retinopathy Detection Using Deep CNN Architecture and Medical Prescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Rajasekhar Kommaraju, Nallamotu Haritha, Patibandla Yugala, Mukkera Pushpa, and Sanikommu Yaswanth Reddy An Innovative Software Engineering Approach to Machine Learning for Increasing the Effectiveness of Health Systems . . . . . . . . . . . 207 Ananapareddy V. N. Reddy, Mamidipaka Ramya Satyasri Prasanna, Arja Greeshma, and Kommu Sujith Kumar Region of Interest and Feature-based Analysis to Detect Breast Cancer from a Mammogram Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 D. Saranyaraj, R. Vaisshale, and R. NandhaKishore

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Vehicular Ad Hoc Network: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Monika and Poonam Rani The Proposed Deep Learning Combined with Knowledge Graphs for Fake News Detections on Social Networks . . . . . . . . . . . . . . . . . . . . . . . . 253 Quoc Hung Nguyen, Le Thanh Trung, Thi Thuy Kieu Phan, Thi Xuan Dao Nguyen, Xuan Nam Vu, and Dinh Dien La Algorithm-Driven Predictive Analysis of Blue-Chip Stocks in the Murky Indian Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 A. Celina and K. Kavitha Survey on Crime Analysis, Forecasting, and Prediction Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Iqbal Singh Saini and Navneet Kaur Soft Computing and Data Mining Techniques for Dengue Detection: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Dilip Kumar Choubey, Robin Newton, Mukesh Kumar Ojha, and Santosh Kumar Extended HD-MAABE Scheme Supporting Delegation and Revocation for Cloud-based Organizational Data Sharing . . . . . . . . . 311 Reetu Gupta, Priyesh Kanungo, and Nirmal Dagdee Detection of Open Metal Sites in Metal–Organic Frameworks Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Eeshita Gupta, Devansh Verma, Shivam Bhardwaj, and Sardar M. N. Islam PCA-Based Feature Selection and Hybrid Classification Model for Speech Emotion Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Astha Tripathi and Poonam Rani ANFIS and Kernel Extreme Learning Machine to the Assessment and Identification of Seismic b-value as Precursor . . . . . . . . . . . . . . . . . . . . 355 Anurag Rana, Pankaj Vaidya, and Rohit Kumar Payroll Management Using Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Govinda Giri, Kunal Chakate, Sudhanshu Gonge, Rahul Joshi, Ketan Kotecha, Om Mishra, Deepak Parashar, and Preeti Mulay Centric Management of Resources in Modern Distributed Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 V. Sandhiya and V. M. Gayathri An Optimized Stochastic PCA Feature Selection Method to Enhance the Prediction of Credit Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Usha Devi and Neera Batra

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Enhancing Security of IoT Data Transmission in Social Network Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 R. Hemalatha and K. Devipriya A Study on Existing EHR Models Used for Validating the Clinical Records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Priyanka Sharma, Tapas Kumar, and S. S. Tyagi Usage of AI Techniques for Cyberthreat Security System in Android Mobile Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 Phaneendra Varma Chintalapati, Gurujukota Ramesh Babu, Pokkuluri Kiran Sree, Satish Kumar Kode, and Gottala Surendra Kumar Essential Amino Acids of Lectin Protein of Selected Pulses: A Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 Arti Chauhan, Nihar Ranjan Roy, and Kalpna Sagar A Survey on Human Behavioral Cybersecurity Risk During and Post Pandemic World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 Hanna Paulose and Ashwani Sethi Extraction of Patterns for Cervical and Breast Cancer Protein Primary Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Charan Abburi, K. S. Vijaya Lakshmi, Chimata Meghana, and K. Suvarna Vani The Implementation of Artificial Intelligence in Supply Chain . . . . . . . . . 497 Elisabeth T. Pereira and Muhammad Noman Shafique Analytical Investigation of Different Parameters of Image Steganography Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Ravi Saini, Kamaldeep Joshi, and Rainu Nandal Automatic Recognition of Speaker Labels Using CNN-SVM Scheme . . . 513 V. Karthikeyan, P. Saravana Kumar, and P. Karthikeyan Automatic Subjective Answer Evaluator Using BERT Model . . . . . . . . . . 531 Sanyam Raina, Heem Amin, Shrey Sanghvi, Santosh Kumar Bharti, and Rajeev Kumar Gupta Facial Feature Analysis for Autism Detection Using Deep Learning . . . . . 539 Anjali Singh, Mitali Laroia, Abha Rawat, and K. R. Seeja Software Testing Errors Classification Method Using Clustering Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553 Liubov Oleshchenko Optimization of Hybrid Renewable Resources (PV-Wind-Biomass) Using HOMER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 Chandan Singh and Shelly Vadhera

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Comparison of Machine Learning Models for Wind Power Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577 Bholeshwar and Shelly Vadhera SOLWOE—A Novel Way to Diagnose Depression Among Teenagers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 K. M. Anandkumar, V. Adarsh Srinivas, J. Jayasurya, and K. R. Lakshman River Water Level Prediction Using LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . 601 Nikita Garg, Srishti Negi, Ridhima, Shruthi Rao, and K. R. Seeja Hybrid Binary Dragonfly Algorithm with Grey Wolf Optimization for Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611 Sireesha Moturi, Srikanth Vemuru, S. N. Tirumala Rao, and Sneha Ananya Mallipeddi Comparison of Machine Learning Algorithms Based on Damage Caused by Storms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627 Deepak Dharrao, Sudhanshu Gonge, Rahul Joshi, Pratyush Vats, Shobhit Mudkhedkar, Aditya Padir, Naman Pandya, and Rajveer Singh A Comparative Study for Prediction of Hematopoietic Stem Cell Transplantation-Related Mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 Rishabh Hanselia and Dilip Kumar Choubey A Strategic Review on MIR Photodetectors: Recent Status and Future Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 Bhaskar Roy, Md. Aref Billaha, Ritam Dutta, and Debasis Mukherjee Smart Farming Monitoring Using ML and MLOps . . . . . . . . . . . . . . . . . . . 665 Yaganteeswarudu Akkem, Saroj Kumar Biswas, and Aruna Varanasi Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677 Amol V. Dhumane, Priyanka Kaldate, Ankita Sawant, Prajwal Kadam, and Vinay Chopade IoT-Based Home Automation System Using ESP8266 . . . . . . . . . . . . . . . . . 695 Jyoti Rawat, Indrajeet Kumar, Noor Mohd, Kartik Krishnan Singh Rana, Nitish Pathak, and Rajeev Kumar Gupta Analyzing Critical Stakeholders’ Concern in Indian Fruits and Vegetables Supply Chain Using Fuzzy-AHP . . . . . . . . . . . . . . . . . . . . . . 709 Rekha Gupta

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Analysis of Top Vulnerabilities in Security of Web-Based Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723 Jyoti Rawat, Indrajeet Kumar, Noor Mohd, Ayush Maheshwari, and Neelam Sharma Fault Tolerant Algorithm to Cope with Topology Changes Due to Postural Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737 Preeti Nehra and Sonali Goyal Analysis on Speech Emotion Recognizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 Yogesh Gupta Summarization of Research Paper into a Presentation . . . . . . . . . . . . . . . . 755 Neha Badiani, Smit Vekaria, Santosh Kumar Bharti, and Rajeev Kumar Gupta Novel Design of Conformal Patch Excited Four Element Biodegradable Substrate Integrated MIMO DRA . . . . . . . . . . . . . . . . . . . . 765 Rasika Verma and Rohit Sharma Early Kidney Stone Detection Among Patients Using a Deep Learning Model on an Image Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 779 Sharwan Buri and Vishal Shrivastava Determination of License Plate Using Deep Learning and Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795 Shiva Tyagi, Riti Rathore, Vaibhav Rathore, Shruti Rai, and Shruti Rohila Critique of Non-fungible Token (NFT): Innovation, Analysis and Security Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 Prayash Limbu and Rohan Gupta Face Mask Detection Using Transfer Learning and TensorRT Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 Paleti Nikhil Chowdary, Pranav Unnikrishnan, Rohan Sanjeev, Mekapati Spandana Reddy, KSR Logesh, Neethu Mohan, and K. P. Soman Skin Disease Recognition by VGG-16 Model . . . . . . . . . . . . . . . . . . . . . . . . . 833 Ankit Yadav, Vinay Sharma, and Jyotsna Seth Machine Learning Approach for Securing of IoT Environment . . . . . . . . 849 Amit Sagu, Nasib Singh Gill, Preeti Gulia, and Deepti Rani DDOS Attack in WSN Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . 859 Manu Devi, P. Nandal, and Harkesh Sehrawat Prediction of Thorax Disease in Chest X-ray Images Using Deep Learning Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873 Saranya Bommareddy, B. V. Kiranmayee, and Chalumuru Suresh

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Comparative Analysis of Machine Learning Algorithms for Medical Insurance Cost Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 885 Mahadasa Praveen, Gundu Sri Manikanta, Gella Gayathri, and Shashi Mehrotra Correction to: IoT-Based Home Automation System Using ESP8266 . . . . Jyoti Rawat, Indrajeet Kumar, Noor Mohd, Kartik Krishnan Singh Rana, Nitish Pathak, and Rajeev Kumar Gupta Correction to: Determination of License Plate Using Deep Learning and Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shiva Tyagi, Riti Rathore, Vaibhav Rathore, Shruti Rai, and Shruti Rohila

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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893

Editors and Contributors

About the Editors Dr. (Prof.) Aboul Ella Hassanien is the founder and head of the Egyptian Scientific Research Group (SRGE). Hassanien has more than 1000 scientific research papers published in prestigious international journals and over 50 books covering such diverse topics as data mining, medical images, intelligent systems, social networks and smart environment. Professor Hassanien won several awards including the Best Researcher of the Youth Award of Astronomy and Geophysics of the National Research Institute, Academy of Scientific Research (Egypt, 1990). He was also granted a scientific excellence award in humanities from the University of Kuwait for the 2004 Award and received the superiority of scientific—University Award (Cairo University, 2013). Also, he honored in Egypt as the best researcher in Cairo University in 2013. He also received the Islamic Educational, Scientific and Cultural Organization (ISESCO) prize on Technology (2014) and received the state award for excellence in engineering sciences 2015. Dr. (Prof.) Oscar Castillo holds the Doctor in Science degree (Doctor Habilitatus) in Computer Science from the Polish Academy of Sciences (with the Dissertation “Soft Computing and Fractal Theory for Intelligent Manufacturing”). He is a professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico. Currently, he is the president of Hispanic American Fuzzy Systems Association (HAFSA) and past president of International Fuzzy Systems Association (IFSA). Professor Castillo is also a chair of the Mexican Chapter of the Computational Intelligence Society (IEEE). His research interests are in type-2 fuzzy logic, fuzzy control, neuro-fuzzy and genetic-fuzzy hybrid approaches. He has published over 300 journal papers, 10 authored books, 40 edited books, 200 papers in conference proceedings and more than 300 chapters in edited books, in total 865 publications according to Scopus (H index = 60), and more than 1000 publications according to Research Gate (H index = 72 in Google Scholar).

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Editors and Contributors

Dr. Sameer Anand is currently working as an assistant professor in the Department of Computer Science at Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi. He has received his M.Sc., M.Phil. and Ph.D. (Software Reliability) from Department of Operational Research, University of Delhi. He is a recipient of “Best Teacher Award” (2012) instituted by Directorate of Higher Education, Government of NCT, Delhi. The research interest of Dr. Anand includes operational research, software reliability and machine learning. He has completed an innovation project from the University of Delhi. He has worked in different capacities in international conferences. Dr. Anand has published several papers in the reputed journals like IEEE Transactions on Reliability, International Journal of Production Research (Taylor & Francis), International Journal of Performability Engineering, etc. He is a member of Society for Reliability Engineering, Quality and Operations Management. Dr. Sameer Anand has more than 16 years of teaching experience. Dr. Ajay Jaiswal is currently serving as an assistant professor in the Department of Computer Science of Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi. He is a co-editor of two books/journals and co-author of dozens of research publications in international journals and conference proceedings. His research interest includes pattern recognition, image processing and machine learning. He has completed an interdisciplinary project titled “Financial InclusionIssues and Challenges: An Empirical Study” as Co-PI. This project was awarded by the University of Delhi. He obtained his masters from the University of Roorkee (now IIT Roorkee) and Ph.D. from Jawaharlal Nehru University, Delhi. He is a recipient of the best teacher award from the Government of NCT of Delhi. He has more than 19 years of teaching experience.

Contributors Charan Abburi Department of CSE, Siddhartha Engineering College, Vijayawada, VR, India Saif F. Abulhail College of Information Engineering, Al-Nahrain University, Baghdad, Iraq Yaganteeswarudu Akkem National Institute of Technology Silchar, Cachar, Assam, India Mohammed Z. Al-Faiz College of Information Engineering, Al-Nahrain University, Baghdad, Iraq Rabei Raad Ali National University of Science and Technology, Thi-Qar, Nasiriyah, Iraq Heem Amin Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

Editors and Contributors

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K. M. Anandkumar Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India Md. Aref Billaha Jalpaiguri Polytechnic Institute, Jalpaiguri, West Bengal, India Alaa Sabree Awad College of Basic Education, University of Anbar, Haditha, Iraq Gurujukota Ramesh Babu Department of CSE, Shri Vishnu Engineering College for Women(A), Bhimavaram, Andhra Pradesh, West Godavari District, India Neha Badiani Pandit Deendayal Energy University, Gandhinagar, Gujarat, India Shaik Mahaboob Basha N.B.K.R. Vidyanagar, India

Institute

of

Science

and

Technology,

Neera Batra Maharishi Markandeshwar (Deemed To Be University), MullanaAmbala, Haryana, India Rehana Begum Lakireddy Balireddy College of Engineering, Mylavaram, India Shivam Bhardwaj Boston University, Boston, USA Santosh Kumar Bharti Pandit Deendayal Energy University, Gandhinagar, Gujarat, India Bholeshwar NIT Kurukshetra, Kurukshetra, India Saroj Kumar Biswas National Institute of Technology Silchar, Cachar, Assam, India Saranya Bommareddy VNR VJIET, Hyderabad, India G. Siva Brindha Coimbatore, Tamil Nadu, India Sharwan Buri Computer Science & Engineering, Arya College of Engineering & I.T., Jaipur, India A. Celina SRM Institute of Science and Technology, Kattankulathur, India Kunal Chakate Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India Arti Chauhan School of Engineering, GD Goenka University, Gurugram, India Phaneendra Varma Chintalapati Department of CSE, Shri Vishnu Engineering College for Women(A), Bhimavaram, Andhra Pradesh, West Godavari District, India Vinay Chopade Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India Dilip Kumar Choubey Department of Computer Science and Engineering, Indian Institute of Information Technology Bhagalpur, Bihar, India

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Editors and Contributors

Paleti Nikhil Chowdary Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India Nirmal Dagdee Shivajirao Kadam Institute of Technology & Management, Indore, India Lahib Nidhal Dawd Computer Techniques Engineering Department, Dijlah University College, Baghdad, Iraq Victor Hugo C. De Albuquerque Federal University of Ceará, Fortaleza, Brazil Manu Devi Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Haryana, India Usha Devi Maharishi Markandeshwar (Deemed To Be University), MullanaAmbala, Haryana, India K. Devipriya PG & Research Department of Computer Science, Tiruppur Kumaran College for Women, Tiruppur, Tamil Nadu, India Dharmesh Dhabliya Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, India Deepak Dharrao Symbiosis Institute of Technology, Pune, India Amol V. Dhumane Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India Ritam Dutta Poornima University, Jaipur, Rajasthan, India Nikita Garg Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India Gella Gayathri Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, Andhra Pradesh, India V. M. Gayathri Department of Networking and Communications, SRM Institute of Science and Technology, Chennai, India Nasib Singh Gill Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India Govinda Giri Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India M. Gobi Coimbatore, Tamil Nadu, India Sudhanshu Gonge Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India Sonali Goyal Department of Computer Science and Engineering, MMEC, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, India

Editors and Contributors

xix

Arja Greeshma Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India Preeti Gulia Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India Ankur Gupta Vaish College of Engineering, Rohtak, India Eeshita Gupta Bharati Vidyapeeth’s College of Engineering, Delhi, India Rajeev Kumar Gupta Graphic Era Hill University, Dehradun, Uttarakhand, India; Pandit Deendayal Energy University, Gandhinagar, Gujarat, India Reetu Gupta School of Computer Science and Information Technology, DAVV, Indore, India; Lal Bahadur Shastri Institute of Management, New Delhi, India Rekha Gupta Lal Bahadur Shastri Institute of Management, New Delhi, India Rohan Gupta Department of Electronics and Communication Engineering, University Institute of Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India Yogesh Gupta School of Engineering and Technology, BML Munjal University, Gurugram, India Rishabh Hanselia Department of Computer Science and Engineering, Indian Institute of Information Technology Bhagalpur, Bihar, India Nallamotu Haritha Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India Ekram H. Hasan College of Basic Education, University of Anbar, Haditha, Iraq R. Hemalatha PG & Research Department of Computer Science, Tiruppur Kumaran College for Women, Tiruppur, Tamil Nadu, India Noor A. Hussein College of Information Technology, University of Babylon, Hilla, Iraq Sardar M. N. Islam Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia M. Jananee Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamil Nadu, India J. Jayasurya Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India Nashreen Begum Jikkiriya Department of Artificial Intelligence and Machine Learning, Malla Reddy University, Hyderabad, Telangana, India Kamaldeep Joshi UIET MDU Rohtak, Rohtak, India

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Editors and Contributors

Rahul Joshi Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India Mohammed Ahmed Jubair Department of Computer Technical Engineering, College of Information Technology, Imam Ja’afar Al-Sadiq University, AlMuthanna, Iraq Prajwal Kadam Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India Priyanka Kaldate Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India Priyesh Kanungo School of Computer Science and Information Technology, DAVV, Indore, India P. Karthikeyan ECE Department, Velammal College of Engineering and Technology, Madurai, India V. Karthikeyan ECE Department, Mepco Schlenk Engineering College, Sivakasi, India Navneet Kaur Department of Computer Science and Engineering, Chandigarh University, Mohali, India K. Kavitha SRM Institute of Science and Technology, Kattankulathur, India Ashish Khanna Maharaja Agrasen Institute of Technology, Delhi, India B. V. Kiranmayee VNR VJIET, Hyderabad, India Satish Kumar Kode Department of CSE, Shri Vishnu Engineering College for Women(A), Bhimavaram, Andhra Pradesh, West Godavari District, India Rajasekhar Kommaraju Department of Information Technology, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India Ketan Kotecha Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India Gottala Surendra Kumar Department of CSE, Shri Vishnu Engineering College for Women(A), Bhimavaram, Andhra Pradesh, West Godavari District, India Indrajeet Kumar Graphic Era Hill University, Dehradun, Uttarakhand, India Kommu Sujith Kumar Lakireddy Mylavaram, Andhra Pradesh, India

Bali

Reddy

College

of

Engineering,

P. Saravana Kumar ECE Department, AAA College of Engineering and Technology, Sivakasi, India Rohit Kumar Arni University, Kangra, HP, India

Editors and Contributors

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Santosh Kumar Department of Computer Science and Information Technology, Siksha ‘O’ Anusandhan University, Bhubaneswar, India Tapas Kumar Department of Computer Science Engineering, Faculty of Engineering, MRIIRS, Faridabad, India Dinh Dien La Ha Giang Department of Information and Communication, Ha Giang, Vietnam K. R. Lakshman Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India K. S. Vijaya Lakshmi Department of CSE, Siddhartha Engineering College, Vijayawada, VR, India Mitali Laroia Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India Prayash Limbu Department of Computer Science Engineering, University Institute of Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India KSR Logesh Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India Ayush Maheshwari Graphic Era Hill University, Dehradun, Uttarakhand, India Sneha Ananya Mallipeddi Department of Computer Science and Engineering, Narasaraopeta Engineering College, Guntur, India Gundu Sri Manikanta Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, Andhra Pradesh, India Chimata Meghana Department of CSE, Siddhartha Engineering College, Vijayawada, VR, India Shashi Mehrotra Faculty of Engineering, College of Computing Science and IT, Teerthankar Mahaveer University, Moradabad, India Om Mishra Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India Neethu Mohan Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India Noor Mohd Graphic Era Deemed to Be University, Dehradun, Uttarakhand, India Monika Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India Salama A. Mostafa Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia Sireesha Moturi Department of Computer Science Narasaraopeta Engineering College, Guntur, India

and

Engineering,

xxii

Editors and Contributors

Shobhit Mudkhedkar Symbiosis Institute of Technology, Pune, India Debasis Mukherjee Brainware University, Kolkata, West Bengal, India Preeti Mulay Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India Ashok Kumar Munnangi Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India P. Nandal Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, GGSIP University, Delhi, India Rainu Nandal UIET MDU Rohtak, Rohtak, India G. M. Nandana Department of Computer Science and Engineering, ASET, Amity University, Noida, India R. NandhaKishore Technical Lead, Ascendas IT Park, Trane Technologies, Tharamani, Chennai, Tamilnadu, India Srishti Negi Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India Preeti Nehra Department of Computer Science and Engineering, MMEC, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, India Aloísio Vieira Lira Neto Federal Institute of Ceará, Fortaleza, Brazil Robin Newton Department of Computer Science and Engineering, Indian Institute of Information Technology Bhagalpur, Bihar, India Quoc Hung Nguyen University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, Vietnam Thi Xuan Dao Nguyen University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, Vietnam Mustafa Amer Obaid College of Medicine, University of Anbar, Ramadi, Iraq Mukesh Kumar Ojha Depertment of Electronics and Engineering, Greater Noida Institute of Technology, Greater Noida, UP, India Liubov Oleshchenko National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine Aditya Padir Symbiosis Institute of Technology, Pune, India Naman Pandya Symbiosis Institute of Technology, Pune, India Deepak Parashar Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India

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Manikandan Parasuraman Department of Computer Science and Engineering, JAIN (Deemed-to-Be University), Bangalore, Karnataka, India Nitish Pathak BPIT, GGSIPU, New Delhi, India Hanna Paulose Guru Gobind Singh College of Engineering and Technology, Gurukashi University, Bhatinda, India Elisabeth T. Pereira University of Aveiro, Aveiro, Portugal Thi Thuy Kieu Phan University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, Vietnam Sabyasachi Pramanik Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, India Mamidipaka Ramya Satyasri Prasanna Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India Mahadasa Praveen Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, Andhra Pradesh, India S. Praveenkumar Madurai Kamaraj University, Madurai, India Mukkera Pushpa Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India Eko Hari Rachmawanto Department of Informatics Engineering, Dian Nuswantoro University, Semarang, Indonesia Shruti Rai Ajay Kumar Garg Engineering College, Ghaziabad, India Sanyam Raina Pandit Deendayal Energy University, Gandhinagar, Gujarat, India Sivaram Rajeyyagari Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Kingdom of Saudi Arabia Manikandan Ramachandran School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India Anurag Rana Shoolini University, Solan, HP, India Kartik Krishnan Singh Rana DIT University, Dehradun, Uttarakhand, India Deepti Rani Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India Poonam Rani Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India Shruthi Rao Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India Riti Rathore Ajay Kumar Garg Engineering College, Ghaziabad, India

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Vaibhav Rathore Ajay Kumar Garg Engineering College, Ghaziabad, India Abha Rawat Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India Jyoti Rawat VertexPlus Technologies Limited, Jaipur, Rajasthan, India Kalyan Rayapureddy Lakireddy Balireddy College of Engineering, Mylavaram, India Ananapareddy V. N. Reddy Department of Information Technology, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India Mekapati Spandana Reddy Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India Sanikommu Yaswanth Reddy Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India Ridhima Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India Shruti Rohila Ajay Kumar Garg Engineering College, Ghaziabad, India Bhaskar Roy Brainware University, Kolkata, West Bengal, India Nihar Ranjan Roy School of Engineering and Technology, Sharda University, Greater Noida, India Kalpna Sagar KIET Group of Institutions, AKTU, Ghaziabad, India Amit Sagu Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India Iqbal Singh Saini Department of Computer Science and Engineering, Chandigarh University, Mohali, India Ravi Saini UIET MDU Rohtak, Rohtak, India V. Sandhiya Department of Networking and Communications, SRM Institute of Science and Technology, Chennai, India Shrey Sanghvi Pandit Deendayal Energy University, Gandhinagar, Gujarat, India Rohan Sanjeev Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India D. Saranyaraj Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Vengal, Chennai, Tamilnadu, India

Editors and Contributors

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Sasmitha Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Tamil Nadu, India Ankita Sawant Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India K. R. Seeja Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India Harkesh Sehrawat Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Haryana, India Ramesh Sekaran Department of Computer Science and Engineering, JAIN (Deemed-to-Be University), Bangalore, Karnataka, India Jyotsna Seth Department of Computer Science and Engineering, Sharda University, Greater Noida, India Ashwani Sethi Gurukashi University, Bhatinda, India Muhammad Noman Shafique University of Aveiro, Aveiro, Portugal; University of Buner, Buner, Pakistan Neelam Sharma MAIT, GGSIPU, New Delhi, India Priyanka Sharma Department of Computer Science Engineering, Faculty of Engineering, MRIIRS, Faridabad, India Rohit Sharma Department of Electronics and Communication, SRM Institute of Science and Technology, Ghaziabad, India Vinay Sharma Department of Computer Science and Engineering, Sharda University, Greater Noida, India Tariq Hussain Sheikh Shri Krishan Chander Government Degree College, Poonch, India Vishal Shrivastava Computer Science & Engineering, Arya College of Engineering & I.T., Jaipur, India Anjali Singh Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India Chandan Singh National Institute of Technology Kurukshetra, Kurukshetra, India Rajveer Singh Symbiosis Institute of Technology, Pune, India K. P. Soman Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India Pokkuluri Kiran Sree Department of CSE, Shri Vishnu Engineering College for Women(A), Bhimavaram, Andhra Pradesh, West Godavari District, India

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V. Adarsh Srinivas Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India Gelli Sai Sudheshna Lakireddy Balireddy College of Engineering, Mylavaram, India A. Suresh Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamil Nadu, India Chalumuru Suresh VNR VJIET, Hyderabad, India S. N. Tirumala Rao Department of Computer Narasaraopeta Engineering College, Guntur, India

Science

and

Engineering,

Astha Tripathi Department of Computer Engineering, Netaji Subhas University of Technology, New Delhi, India Le Thanh Trung University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, Vietnam S. S. Tyagi Department of Computer Science Engineering, Faculty of Engineering, IIMT, Greater Noida, India Shiva Tyagi Ajay Kumar Garg Engineering College, Ghaziabad, India Pranav Unnikrishnan Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India Shelly Vadhera National Institute of Technology Kurukshetra, Kurukshetra, India Pankaj Vaidya Shoolini University, Solan, HP, India Ravula Vaishnavi Lakireddy Balireddy College of Engineering, Mylavaram, India R. Vaisshale Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Vengal, Chennai, Tamilnadu, India K. Suvarna Vani Department Vijayawada, VR, India

of

CSE,

Siddhartha

Engineering

College,

V. Vanjipriya Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamil Nadu, India Aruna Varanasi SNIST, Hyderabad, Telangana, India Pratyush Vats Symbiosis Institute of Technology, Pune, India Vivek Veeraiah Department of R & D Computer Science, Adichunchanagiri University, Mandya, India

Editors and Contributors

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Smit Vekaria Pandit Deendayal Energy University, Gandhinagar, Gujarat, India Srikanth Vemuru Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India Devansh Verma Bharati Vidyapeeth’s College of Engineering, Delhi, India Rasika Verma Department of Electronics and Communication, SRM Institute of Science and Technology, Ghaziabad, India V. Vidya Chellam Department of Management Studies, Directorate of Distance Education, Madurai Kamaraj University, Madurai, India Xuan Nam Vu TNU–University of Information and Communication Technology, Thai Nguyen City, Vietnam Ankit Yadav Department of Computer Science and Engineering, Sharda University, Greater Noida, India Ashok Kumar Yadav Department of Computer Science and Engineering, ASET, Amity University, Noida, India Patibandla Yugala Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India

High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types Rabei Raad Ali, Lahib Nidhal Dawd, Salama A. Mostafa, Eko Hari Rachmawanto, and Mohammed Ahmed Jubair

Abstract Accurate file recovery of the corrupted or fragmented file is a very important task when missing the file system, since file recovery procedure involves methods, techniques, and tools that analyze and classify the contents of each data cluster including JPG file or non-JPG file. For classification problems, convolutional neural networks and high-deep learning have been applied to be very efficient and effective in file recovery. This study aims at finding a systematic comparison between a support vector machine (SVM) classifier method and an extreme learning machine (ELM) classifier method for classification issues. The classification methods automatically classify the files in a continuous series of data clusters based on three deep features which are rate of change (RoC), byte frequency distribution (BFD), and entropy. The methods are automatically assigning a class label of JEG file or non-JPG file for fragmented data clusters. The RABEI-2017 dataset was used for evaluating the performance of this study. The results demonstrate that the SVM R. R. Ali (B) National University of Science and Technology, Thi-Qar, Nasiriyah, Iraq e-mail: [email protected] L. N. Dawd Computer Techniques Engineering Department, Dijlah University College, Baghdad, Iraq e-mail: [email protected] S. A. Mostafa Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia e-mail: [email protected] E. H. Rachmawanto Department of Informatics Engineering, Dian Nuswantoro University, 207 Imam Bonjol Street, Semarang 50131, Indonesia e-mail: [email protected] M. A. Jubair Department of Computer Technical Engineering, College of Information Technology, Imam Ja’afar Al-Sadiq University, 66002 Al-Muthanna, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_1

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R. R. Ali et al.

method outperforms the ELM method with a high accuracy rate to classify fragmented JPG files. The accuracy of classification of the SVM method is 90.15% and of the ELM method is 96.21%. Keywords JPG file · Classification · File recovery · Support vector machines · Extreme learning machines · Entropy · Byte frequency distribution · Rate of change

1 Introduction The recent increase in the use of digital devices such as smartphones, tablets, cameras, and computers or laptops has become ubiquitous and important for file recovery. Digital devices can handle several numbers of multimedia files such as word, JPG, PDF, ZIP, and HTML[1–4]. The JPG file is the most popular among various multimedia files used in daily life, since JPG files can be fragmented (intertwined with another file type) due to many reasons such as human error, deliberate destruction, or criminal activities [2]. The disk drive of digital devices is divided into the smallest allocation units named sectors that hold information on a particular file. These sectors are grouped into fix size sections named blocks or clusters that consist of (512) bytes [3]. Figure 1 shows the architecture of a digital drive. Usually, the cluster of JPG files contains data that includes information about a particular JPG file [4]. In another word, the JEG file has groups of clusters that may contain the daily life clusters of concerning files or some clusters of another file type. Eventually, the JPG files might be exposed to damaged data clusters due to storing data at random places in storage media by operating system storage operations or human errors [5]. Applying file types identification techniques in the file recovery method is an important way to make up the file [6, 7]. The advanced file type identification methods analyze and filter the contents of the files [8]. The high-level deep features and convolutional neural networks are active research areas nowadays to recover files with damaged data clusters. Fig. 1 Disk drive architecture [4]

High-Level Deep Features and Convolutional Neural Network …

3

Karresand and Shahmehri [6] present an SVM method to identify different cluster data types of fragmented files when missing file system metadata. The SVM method tries to classify the number of file data clusters that might be effective in any file cluster classification. The classification method goal is to assign a class label of multiclass classification issue for each input [6]. For instance, Zhang et al. [8] propose a new classifier method by using deep convolutional activation feature representation. The proposed method used SVM and ELM methods for JPG file classification problems. Thus, efficient and effective classification methods always need to be improved the accuracy of the file clusters classification. The main aim of this study lies in discussing the deep feature of JPG files capability of convolutional neural networks by using the SVM method with polynomial function kernel and the ELM method with sigmoid function. The best high-level features which are entropy, BFD, and RoC use to find which classification method is better. The RABEI-2017 dataset is used to test and evaluate the performance of two classification methods.

2 Related Work Previous work has shown several classification methods to increase the accuracy of the file classification problems such as decision trees, neural networks, linear discriminant analysis, K-nearest neighbor, supervised learning, and Naive Bayes methods [7–11]. In [8], the authors propose a comparative investigation of the ELM method and SVM method for object recognition problems. The ten-category object JPG files are used based on deep convolutional features to train and compare the classification accuracy. The results show that the ELM method outperforms the SVM method in average accuracy. In [10], the authors present linear discriminant analysis (LDA) classifier method to classify multi-objects. In this method, 900 JPG files of apples were used. These are 400 JPG files used for testing JPG files and 500 JPG files used for training. The classification method divided data into several groups based on skin color to decrease dimensions. The results show that the LDA can accurately detect with 98%. In [12], the authors present face recognition across age progression based on the ELM method and SVM method. The classification methods used high-level deep features. This work compared the performances of the SVM method and ELM classifiers to prove the benefit of replacing which was considered. The results show that the ELM method works better than the SVM method by using connected layers. In [13], the authors propose an age function modeling (AFM) method based on the fusion of local features. The proposed method used the ELM method to calculate the output JPG files for the corresponding input JPG files. It can be simple for execution and normally attains the finer generalization performance. The result shows the accuracy of the AFM method is better when compared with the earlier methods.

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In [11], the authors present a power quality (PQ) classification based on the ELM method in combination with optimization techniques. The proposed method used transforms to the extraction of useful features to test the ELM method. The results show that the proposed approach can accurately detect and classify the PQ cases. However, the previous works do not work on the cases that the fragment JPG files case by JPG file intertwined with non-JPG files.

3 Methods and Materials This section presents the architecture of this study. Figure 2 shows the main components of the study architecture. As shown in Fig. 2, the first component is a feature extraction method that works based on entropy, BFD, and RoC features. The second component is a classification method that works based on the SVM method with the polynomial kernel function and the ELM method with the sigmoid function which is defined as a supervised learning model initially designed. The following subsections present each component in detail.

Fig. 2 Architecture of the study activities

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3.1 Features Extraction The most widely used feature extraction methods for the frequency distribution of the classifying JPG file contents are entropy, BFD, and RoC methods. • Entropy: It is found to be able to distinguish JPG files from other file types with a proper classifier [4]. The entropy method uses the decimals byte value from 0 to 255 and produces a feature value from 0–1. Equation (1) represents the method feature. E(i ) = N (i )/L ,

(1)

where E(i) presents the byte value repetitions, N(i) represents the repetitions number, and L presents the total size of the file fragment. Hence, the entropy of fragments can be represented by in Equation (2). Entr opy = −



E(i )log E(i) B , 0 < E ≤ 1(i ).

(2)

• Byte frequency distribution (BFD): It creates a histogram for file clusters to demonstrate mathematical features. The BFD includes analyzing the number of each byte value to find a centroid feature. It is usually used to generate a features vector that consists of 0 to 255 features [14]. Hence, it only considers their values and neglects the order of each byte. The BFD feature represents in Eq. (3). Fv(i ) = B(i ).

(3)

• Rate of change (RoC): It tracks the relevant bytes in their corresponding clusters from their orders. It measures the distinction between each two sequence byte values. Equation (4) represents the absolute value of the RoC. RoCi = |ci − ci+1 |,

(4)

where ci and ci+1 be two sequential bytes. Hence, the RoC feature has an ordering bytes mechanism to associate the rate of change and does not specify the direction of the byte stream change [4]. Equation (5) represents the RoC byte frequency. Fr (i ) = O(i)/L2.

(5)

3.2 Kernel Functions Kernel functions are a class of algorithms used to take data as input in classifier methods to transform the data into the required form of processing data. It is used

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due to a set of distance functions such as the polynomial kernel function or sigmoid function [15]. • Polynomial: It is a function typically used in learning of nonlinear classifier method. This function represents the training samples in feature space [16]. Equation (8) discusses the polynomial kernel [16].  d R(x, y) = x T y + c c ≥ 0,

(6)

where x and y represent vectors in the polynomial function. • Sigmoid: It is a hyperbolic tangent kernel. This function gets from the convolutional neural networks field [17]. Equation (7) represents the sigmoid kernel function [17]:   R(x, y) = tanh ∝ x T y + c c ≥ 0.

(7)

3.3 Extreme Learning Machine (ELM) Method ELM method is a single layer with a feedforward convolutional neural network. The weights of the ELM method are selected randomly, and the output layer is realized in one iteration. Figure 3 shows the architecture of the ELM method [18]. To consider the problem of binary classification of the ELM method [10], let us assume (xi , L i ) a group of N vectors xi ∈ R D with alike class labels li ∈ {1, . . . , n} that can be used for training any network consisting of D input, O output, and L

Fig. 3 Architecture of the ELM method [18]

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7

 T hidden layers, where xi = x1i , x2i . . . that can be used to train a sample of an nk  dimensional vector quantity and li = l1i , l2i . . . lni . A training vector (x i ) is a group to (t ik ) = 1 for vectors belonging to class k. Hence, the hidden layer bias values areb ∈ R L and the input weights areWinput ∈ R D×L and, but the output weights areWoutput ∈ R D×C as calculated by Eq. (10). Oki =

m 

 j  Wk φ c j , b j , xi , k = 1, . . . n,

(10)

j=1

where φ (·) is a linear activation function for the output layer, O i =  i i T O1 , O2 , . . . .Oci which is the network corresponding to xi , b j , c j , the W j column is the Winput , and Wk is the k column of Woutput . Equation (11) represents the network for all the training O ∈ R C×N . T O = Woutput φ,

(11)

where O = T, T = [t 1 ,..., t N ] is a matrix of the network target vectors and W i = t i,i matrix notation. Then, the W output can be calculated by Eq. (12): −1  Woutput = φφ T φ TT = φ†TT .

(12)

3.4 SVM (Support Vector Machine) Method The SVM method is a supervised learning model initially designed for binary classification problems and has a high-quality generalization ability for binary classification tasks [14]. It is assumed to be one of the most commonly used convolutional neural networks. Figure 4 shows the SVM method architecture. It has three or more layers of artificial neurons which are the input layer, hidden layers 1, 2… N, and output layer. To consider the problem of binary classification, let us assume (x 1 , y1 ), (x 2 , y2 ), … (x l , yl ) as the training dataset, where xi is a sample data and xi ∈ R D when yi is its label and yi ∈ {−1, 1} for i = 1, 2…, l [15]. Equation (13) defines a linear decision surface, where w · x ∈ R N and b represents the boundary between + and −. w · x + b = 0.

(13)

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Fig. 4 The architecture of the SVM method [19]

3.5 Datasets Description In this study, the classification method was trained by RABEI-2017 on a subset of eight documents from the DFRWS-2006 dataset. It contains only four JPG files and four word files that are closely related to the scope of this study. To create the testing dataset, the dataset is divided into 70% for testing and 30% for training. The data clusters of JPG files are obtained from the first data cluster that exists after the JPG file information data cluster. The last data cluster of a JPG file is usually detected after the last incomplete data cluster and disregards the partial data cluster, since the clusters are split such that four different JPG files occupy 200 and word files occupy 200 clusters which are content 512 bytes.

3.6 Evaluation Metrics There are four evaluation metrics used to check the performance of classification methods which are accuracy, precision, recall, and F-measure [4]. The following subsections are presented with a detailed description of the metrics of this study. • Accuracy: It is a measure of the percentage of the total number of samples that were exact. Equation (16) shows the accuracy calculation. Accuracy =



   T P + T N / T P + T N + FP + FN ,

(16)

where T P = true positives, T N =true negatives, F P = false positives, and F N = false negatives.

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• Precision: It is a percentage of the proportion of positive cases that were correctly classified. The precision value is calculated by Eq. (17).   Precision = T P / T P + F P ,

(17)

where T P = true positives and F P = false positives. • Recall: It is the metric of the ability of the model to present the number of true positive predictions.   Recall = T P / T P + F N ,

(18)

where TP = true positives and FN = false negatives. • F-measure: It provides a single score which is determined as the harmonic mean of recall and precision in one value.   F-measure = 2T P / 2T P + F P + F N ,

(18)

where TP = true positives and FN = false negatives.

4 Testing and Results The classification methods are applied to the RABEI-2017 dataset by using MATLAB 2017a Software. The RABEI-2017 dataset is divided into testing and training data. For training the dataset, we used the two with third of the data while the remaining data is used to test. The tenfold cross-validation learning approach is applied in the classification methods to evaluate the performance of this study. Table 1 shows the classification setting. Table 3 summarized the confusion matrix results of the SVM and ELM methods (Table 2). Table 3 shows the overall evaluation of the performance and average of classification. The results show that the highest accuracy (96.21%) is found in SVM, while the lowest accuracy (90.15%) is found in ELM where the data is in the dataset. Table 1 Setting of classification

Measures

Value

Cluster size

400 × 513

Kemal function

RBF, polynomial and sigmoid

Features

Entropy, BFD, and RoC

Cluster size

400 × 513

Hidden neurons

691

Kemal function

RBF, polynomial, and sigmoid

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Table 2 Confusion matrix of ELM and SVM methods SVM

ELM JPG file

Non-JPG file

JPG file

Non-JPG file

0.8788

0.1212

0.9545

0.0455

0.0758

0.9242

0.0303

0.9697

Table 3 Classification performance of SVM and ELM SVM measures JPG file

Measures

Non-JPG file

Overall

Accuracy

0.9621

0.9621

0.9621

Precision

0.9692

0.9552

0.9621

Recall

0.9545

0.9697

0.9621

F-measure

0.9618

0.9624

0.9621

JPG file

Non-JPG file

Overall

ELM measures Measures Accuracy

0.9015

0.9015

0.9015

Precision

0.9206

0.8841

0.9015

Recall

0.8788

0.9242

0.9015

F-measure

0.8992

0.9037

0.9015

Table 4 Classification results Measures

SVM

ELM

JPG files

Non-JPG files

JPG files

Non-JPG files

Actual

66

66

66

66

Predicted

63

2

61

8

Error

3

64

5

58

Total

132

132

Accuracy (%)

96.21

93.46

Subsequently, the comparisons find that SVM method outperforms ELM method to classify files of fragmented clusters (Table 4).

5 Conclusion This study adopts a systematic comparison among ELM and SVM methods to automatically assign a class label of a JPG file or non-JPG file for a continuous series of data clusters. The classification methods classify the files by evaluation measures of

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three high-level deep features which are Roc, BFD, and entropy. The methods were trained by RABEI-2017 on a subset of eight documents from the DFRWS-2006 dataset. The classification results demonstrate that the SVM method outperforms the ELM method in the tenfold cross-validation learning approach. In a particular method, the experimental results obtained by the SVM method outperform 3% more than the ELM method of average accuracy. Furthermore, the classification methods work in case of fragmented data clusters of two file types (JPG file and word) in a continuous series of data clusters. Thus, the SVM and ELM methods are invulnerable to classifying multi-fragmented file types such as the DFRWS-2007 dataset prepares more than two file types. This study will be employed in the file recovery process of fragmented JPG files.

References 1. Ali RR, Mostafa SA, Mahdin H, Mustapha A, Gunasekaran SS (2020, Jan) Incorporating the Markov chain model in WBSN for improving patients’ remote monitoring systems. In: International conference on soft computing and data mining. Springer, Cham, pp 35–46 2. Abdullah NA, Ibrahim R, Mohamad KM (2012, June) Cluster size determination using JPEG files. In: International conference on computational science and its applications. Springer, Berlin, Heidelberg, pp 353–363 3. Ali RR, Mohamad KM (2021) RX_myKarve carving framework for reassembling complex fragmentations of JPEG images. J King Saud Univ-Comput Inf Sci 33(1):21–32 4. Ali RR, Mohamad KM, Jamel S, Khalid SKA (2018, Feb) Classification of JPEG files by using an extreme learning machine. In: International conference on soft computing and data mining. Springer, Cham, pp 33–42 5. Data Cluster, http://en.wikipedia.org/wiki/Data_cluster 6. Karresand M, Shahmehri N (2006) Oscar-file type identification of binary data in disk clusters and ram pages. Security and privacy in dynamic environments. Springer, US, pp 413–424 7. Sari CA, Sari IP, Rachmawanto EH, Proborini E, Ali RR, Rizqa I (2020, Sept) Papaya fruit type classification using LBP features extraction and Naive Bayes classifier. In: 2020 International seminar on application for technology of information and communication. IEEE, pp 28–33 8. Zhang L, Zhang D, Tian F (2016) SVM and ELM: who wins? Object recognition with deep convolutional features from ImageNet. In: Proceedings Springer international publishing of ELM-2015, vol 1, pp 249–263 9. Xia J, Zhang J, Wang Y, Han L, Yan H (2022) WC-KNNG-PC: watershed clustering based on k-nearest-neighbor graph and Pauta criterion. Pattern Recogn 121:108177 10. Sucipto A, Zyen AK, Wahono BB, Tamrin T, Mulyo H, Ali RR (2021, Sept) Linear discriminant analysis for apples fruit variety based on color feature extraction. In: 2021 international seminar on application for technology of information and communication. IEEE, pp 184–189 11. Subudhi U, Dash S (2021) Detection and classification of power quality disturbances using GWO ELM. J Ind Inf Integr 22:100204 12. Boussaad L, Boucetta A (2022) Extreme learning machine-based age-invariant face recognition with deep convolutional descriptors. Int J Appl Metaheuristic Comput (IJAMC) 13(1):1–18 13. Agrawal S, Kumar S, Kumar S, Thomas A (2019) A novel robust feature extraction with GSOoptimized extreme learning for age-invariant face recognition. Imaging Sci J 67(6):319–329 14. Fan J, Lee J, Lee Y (2021) A transfer learning architecture based on a support vector machine for histopathology image classification. Appl Sci 11(14):6380 15. Iosifidis A, Tefas A, Pitas I (2015) On the kernel extreme learning machine classifier. Pattern Recogn Lett 54:11–17

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16. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst, Man, Cybern: Part B 42(2):513–529 17. Qiu W, Zhu R, Guo J, Tang X, Liu B, Huang Z (2014) A new approach to multimedia files carving. In: Bioinformatics and bioengineering, international conference on, pp 105–110 18. Kaloop MR, El-Badawy SM, Ahn J, Sim HB, Hu JW, Abd El-Hakim RT (2020) A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of international roughness index of rigid pavements. Int J Pavement Eng 1–15 19. Cho BH, Yu H, Lee J, Chee YJ, Kim IY, Kim SI (2008) Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels. IEEE Trans Inf Technol Biomed 12(2):247–256

Securing the Networks Against DDoS Attacks Using Blockchain Technology Noor A. Hussein

Abstract The Internet is vulnerable to major attacks due to its widespread use in all industries, which have interrupted tens of thousands of websites, web services, and social networks. The DDoS attack is one of these attacks; since it has been seen that this kind of attack has grown in recent years, it has become necessary to find answers and employ strategies to lessen it. In this paper, we present a solution to the DDoS attack using blockchain, one of the most cutting-edge and promising technologies. Smart contracts share the attack information in a fully distributed and automated manner, and this architecture effectively advertises IP addresses that have been blacklisted. Additionally, the use of this infrastructure adds a security mechanism to the DDoS defense systems already in place. Since Ethereum and smart contracts are accessible throughout the network, this also renders any attempt to change the blacklisted IP addresses impossible. The work is accomplished by pointing to white or blacklisted IP addresses across multiple domains to achieve the goal that every node on the blockchain network can access the distributed ledger and retrieve the blacklisted IP addresses. Keywords Network · Blockchain · Ethereum · Smart contract · DDoS attack

1 Introduction The Internet is arguably the most significant aspect of modern life. It is used for a variety of purposes, including online learning, chatting with friends and coworkers, conducting business, and more [1]. The network components that make up the Internet, such as switches, routers, and various middlebox kinds, are intended to be numerous. It indicates that devices that govern traffic for purposes other than packet forwarding, such a firewall, have numerous sophisticated protocols installed on them. Because networking enables user collaboration across a large range, it has N. A. Hussein (B) College of Information Technology, University of Babylon, Hilla, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_2

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several benefits in terms of security, efficacy, manageability, and cost-effectiveness. In its simplest form, a network is made up of physical elements including computers, hubs, switches, routers, and other devices. These are the items that are crucial for the various methods of moving data from one location to another [2]. As a result of the potential for this networking to become extensively used in the near future, it has become the major objective of attackers to use flaws to get access to the system [3]. DoS/DDoS attacks are attempts by hostile attackers to consume the bandwidth or resources of lawful users. When such attacks are launched from numerous compromised nodes, they are referred to as DDoS attacks. The most typical DoS attack comprises massive traffic flooding to eat up bandwidth, network resources, target CPU time, etc. DoS attacks include SYN floods, DNS floods, Ping floods, UDP floods, ICMP broadcasts, and others [4]. DDoS attacks are difficult to defend against because of how easily they can be launched, the variety of ways they can be carried out, and the amount of damage one attack can produce [5–7]. Fortunately, there are many countermeasures and techniques available to deal with these attacks in various environments. One of the newest and most promising defenses against DDoS attacks is blockchain. Due to its robust, decentralized, and secure architecture, blockchain technology is quickly being used in a wide range of applications, from gaming to finance [8–10]. We cover some earlier research in this area. Several researchers offered various strategies and ways to reduce DDoS attack on various networks. We propose a decentralized system that makes use of blockchain and smart contracts to mitigate a DDoS attack. The main advantages of using blockchain are removing any central authority and distributing the mitigation workload among various nodes and their peers in a decentralized architecture, making the decentralized ledger, storing the blacklisted IPs, immutable, and eliminating the possibility of a single point of failure of the ledger. Contrary to a multi-server distributed solution, blockchain’s decentralization prevents attackers from gaining illegal access to one of the nodes and modifying the list. When the incoming TCP requests to a device exceed a certain threshold value, the server under attack redirects them to various other nodes in the blockchain network. These individual nodes inspect the incoming requests to determine whether a DDoS attack is taking place. If the node does indeed detect an attack, the respective IP address will be added to the blockchain. Since all the devices on the network can access the blockchain, the system being attacked can block this particular IP address. As the network must be linked to a server, the server should be connected to smart contracts, by using web 3.0, and then linked with the blockchain network, which is a group of nodes connected to each other.

2 Related Work Javaid et al. [11] suggest integrating IoT devices with the blockchain to address DDoS security vulnerabilities in the Internet of things.

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Shafi et al.’s [12] effort with the use of distributed blockchain and software-defined networking (SDN) effort seeks to create an Internet of things (IoT) botnet avoidance solution (DBC). With the help of SDN and blockchain, we model and examine this. Manikumar et al. [13] seek to employ blockchain technology to store the blacklist and machine learning algorithms to assess whether an incoming packet is harmful or not. Reference [14] The project’s goal is to develop a distributed technique for monitoring DDoS attack traffic using a multi-layered convolutional neural network model in the blockchain network layer. The cooperative Blockchain Signaling System (BloSS) defines an effective and alternative solution for security management, especially cooperative defenses, by exploiting Blockchains (BC) and Software-Defined Networks (SDN) for sharing attack information, an exchange of incentives, and tracking of reputation in a fully distributed and automated fashion (Rodrigues et al. [15]).

3 Proposed System A. Environment of Proposed System The suggested system’s fundamental concept is a decentralized system that uses blockchain technology and smart contracts to reduce the impact of a DDoS attack on a network. As shown in Fig. 1, the environment of proposed system consists of tools: (1) Scapy: Used to cause the DDoS attack. (2) web3.js: Ethereum compatible JavaScript API which allows interaction with the Ethereum blockchain using JavaScript code. (3) ganache-cli: Ethereum test blockchain primarily used for testing purposes. (4) Truffle: It is a world-class development environment. It is most popular for developing blockchain applications. (5) MetaMask: It works as an Ethereum wallet, can be added to the browser so that users can handle any Ethereum address, and also allow Ether to be stored. (6) Node.js: It is an open-source server environment that allows a developer to run JavaScript on the server. The common task of a web server is to open a file on the server and return the content to the client. B. The mechanism of the proposed system In the first case, when the IP is in the normal state, it goes to the smart contract to check it. If it is within the IP table, in this case it will not be located within the IP table, the IP will continue to enter the network normally.

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Fig. 1 Environment of the proposed mechanism

The second case when the IP is spoofed is a DDoS attack. There are steps to making, as shown in Fig. 2. Step 1: Starting with a DDoS attack with the help of Scapy, which simulates a SYN flood using spoof IPs, the aim of a distributed denial of service (DDoS) assault is to overwhelm online service with traffic from several sources in an effort to render it unavailable. Step 2: Forwarding packets, distributing the massive load on the server among peers of a decentralized network, and forwarding packets from the system under attack to all other nodes in the blockchain, incoming packets are forwarded, sending packets to one node for five seconds. Step 3: Detecting malicious IP addresses and filtering redirected traffic to flag malicious IP addresses, once packets are forwarded to the other nodes in the blockchain, each of those nodes has to check the IP addresses of the packets to determine if they come from a valid source. This is done using Nmap, a network sniffing tool. Nmap uses a TCP scan to check if an IP address is spoofed. This includes sending requests to an IP address and waiting for an acknowledgment. In the case of a spoofed IP, the response will show that the IP address is either “closed” or “disabled”. Step 4: Adding to blockchain adding blacklisted IP addresses to the blockchain of each decentralized node once blacklisted IP addresses are stored in a file, they are extracted that triggers smart contract execution using web3.js. The smart contract interacts with the Ethereum blockchain and adds IP addresses that facilitate IP blocking.

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Fig. 2 Flowchart for proposed model

Step 5: Extracting blacklisted IP addresses from the blockchain to fetch the IP address from the blockchain, it runs a smart contract function using web3.js which fetches the data from the blockchain and returns it.

4 Implementation and Result 1_Software requirements: Operating system Any Linux-based operating system. 2_TCP Flood DDoS Attack: Scapy.

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3_Network Analysis: Blockchain: Ethereum, web3.js, ganache-cli, other packages: Nmap, IP tables. Solution steps. The blockchain is configured through (Ganache), which is a suitable work environment and uses smart contracts, as well as the availability of the electronic currency Ethereum. Create an account in web3 which is a means of transfer between smart contract from calculator represented by certain applications or application to generate IP which is Scapy. Create a wallet inside web3 and configure the private key taken from the server to Ganache. The attack occurs after running Scapy, which will flood the server with a set of SYN requests and the server will respond to SYN + Ask, and because the IP address has been spoofed, the Ask request will not be returned to the server, so the server will keep waiting for the result of the request. Threshold: It is possible to use a time limit of 10–20 s to distinguish between an attack request and a normal request. Result: Adding blacklisted IP addresses to the blockchain once the blacklisted IP addresses are stored in a file, they are extracted by a solidity code which triggers the execution of the smart contract using web3.js. The smart contract interacts with our test Ethereum blockchain and adds the IPs along with the timestamp which facilitates time-based blocking instead of permanently blocking an IP, as shown in Fig. 3.

Fig. 3 Solidity smart contract functions adding

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Fig. 4 Solidity smart contract functions extract IP

Extracting blacklisted IPs from blockchain to fetch the IP address from the blockchain triggers a smart contract function using web3.js which fetches the data from the blockchain and returns it, as shown in Fig. 4.

5 Evaluation Cost: In this section, the cost is calculated. The amount of gas used is recorded in each procedure that requires sending a transaction to the blockchain through Ganache. Solidity code is executed deterministically, and gas usages are calculated as the total gas used by the operating codes in the EVM being applied (Table 1). Immutability: Table 1 Cost calculation

No. IP

Transaction

Cost in gas

1

20

252,774

2

17

56,172

3

35

168,516

4

21

62,152

5

27

224,688

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Because transactions cannot be changed or deleted after being successfully authenticated and added to the blockchain, immutability, also known as irreversibility, is a key aspect of the technology. Each reading was shared among several peers. A block was represented by each transaction. A transaction was represented by each reading. Immutability for all system = 1 − (2/100)4 ∗ 100. = 0.999984 =1 Time: Estimated time is the measure of time that an attacker needs to hack into a block in the blockchain. If the attacker cannot hack the block on time, instead of the specified honest miner, it will retry from the last created block. E(time) = T ∗ (number of miner/number of pair)2z. E(time) = 10 ∗ (1/2)2 ∗ 1 E(time) = T ∗ (n/2)4z E(time) = 10 ∗ (100/2)4 ∗ 100 = 200, 000 Comparison: Comparison of the researches that preceded us in the field of network protection from DDoS attack closest to our work with our current proposal is given in Table 2.

6 Conclusion This article suggests using blockchain and smart contracts to reduce DDoS across several domains. The suggested remedy might be viewed as an extra security measure for the current DDoS defense systems. Benefits of our approach transparency—all banned IP addresses are accessible to any machine on the network. Cost effective— because it does not require a third party to provide DDoS protection, our method is far less expensive than the solutions offered by other businesses. Immutability— because the blockchain is a distributed ledger, no hacker will be able to attempt to change the IP addresses once they have been placed there. Making the detection process of blacklisting IPs a quicker and more efficient procedure in the future by using packages other than Nmap is suggested as a way to improve the project from its current state.

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Table 2 Previous research with the proposed solution Ref. No.

Year

Security threats

Comments

Environment

Javaid [11]

2018

DDoS attack

The suggested system is able to distinguish between trusted and untrusted devices and assigns each one a static resource limit over which it is unable to function

IoT device

Shafi [12]

2019

DDoS attack

The suggested solution stops IoT IoT devices from joining botnets to perform DDoS assaults on other network

Rodrigues [15]

2020

DDoS attack

Based on a permissioned Network PoA Ethereum, it offers a flexible and effective DDoS mitigation solution across numerous domains in which only preselected operators take part in the cooperative defense

Manikumar [13]

2020

DDoS attack

Utilizing such infrastructure IoT has the benefit of an additional security measure above the DDoS mitigation solutions already in use. By using machine learning techniques, the maliciousness of each packet is classified, and each network node runs the classifier model

Dai [14]

2022

DDoS attack

The model can successfully The network layer address the issues with the current detection techniques. The suggested model performed significantly better than other approaches at detecting DDoS attack traffic on the blockchain network layer

Our proposed

2022

DDoS attack

Provide a plan for resolving Network the DDoS assault utilizing blockchain, one of the most cutting-edge and promising technologies, and smart contracts, which disseminate and automate the sharing of attack information

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References 1. Alkhafaji R, Al-Turaihi FS (2021) Multi-layer network slicing and resource allocation scheme for traffic-aware QoS ensured SDN/NFV-5G network. In: 2021 1st Babylon international conference on information technology and science (BICITS), pp 327–331. https://doi.org/10. 1109/BICITS51482.2021.9509901 2. Forouzan BA, Fegan SC (2002) TCP/IP protocol suite, 2nd ed. McGraw-Hill Higher Education 3. Oleiwi WK, Abdullah AA (2021) A survey of the blockchain concept and mitigation challenges in different networks. J Hunan Univ (Nat Sci) 48(10):890–905 4. Krushang S, Upadhyay H (2014) A survey: DDOS attack on internet of things. Int J Eng Res Dev 10(11):58–63 5. Varma SA, Reddy KG (2021) A review of DDoS attacks and its countermeasures in cloud computing. In: 2021 5th international conference on information systems and computer networks (ISCON). IEEE 6. Lee Y, Chae H, Lee K (2021) Countermeasures against large-scale reflection DDoS attacks using exploit IoT devices. Automatika: cˇ asopis za automatiku, mjerenje, elektroniku, raˇcunarstvo i komunikacije 62(1):127–136 7. Bjerre SA, Blomsterberg MWK, Birger A (2022) 5G attacks and countermeasures. In: 25th international symposium on wireless personal multimedia communications. IEEE 8. Jawdhari HA, Abdullah AA (2021) A novel blockchain architecture based on network functions virtualization (NFV) with auto smart contracts. Periodicals Eng Nat Sci (PEN) 9(4):834–844 9. Mohammed MK, Abdullah AA, Abod ZA (2022) Securing medical records based on interplanetary file system and blockchain. Periodicals Eng Nat Sci (PEN) 10(2):346–357 10. Jawdhari HA, Abdullah AA (2021) A new environment of blockchain based multi encryption data transferring. Webology 18(2):1379–1391. https://doi.org/10.14704/WEB/V18I2/WEB 18396 11. Javaid U, Siang AK, Aman MN, Sikdar B (2018, June) Mitigating IoT device based DDoS attacks using blockchain. In: CRYBLOCK 2018—proceedings of the 1st workshop on cryptocurrencies and blockchains for distributed systems, part of MobiSys 2018, pp 71–76. https:/ /doi.org/10.1145/3211933.3211946 12. Zafar-uz-Zaman M. National centre for physics, centres of excellence in science & applied technologies, institute of electrical and electronics engineers. Islamabad section, and institute of electrical and electronics engineers. In: Proceedings of 2019 16th international Bhurban conference on applied sciences and technology (IBCAST): 8th–12th January, 2019 13. Manikumar DVVS, Maheswari U (2020) Blockchain based DDoS mitigation using machine learning techniques 14. Dai QY, Zhang B, Dong SQ (2022) A DDoS-attack detection method oriented to the blockchain network layer. Secur Commun Netw 2022. https://doi.org/10.1155/2022/5692820 15. Rodrigues B, Scheid E, Killer C, Franco M, Stiller B (2020) Blockchain signaling system (BloSS): cooperative signaling of distributed denial-of-service attacks, 28(4). Springer US. https://doi.org/10.1007/s10922-020-09559-4

A Machine Vision-Based Approach for Tuberculosis Identification in Chest X-Rays Images of Patients V. Vidya Chellam, Vivek Veeraiah, Ashish Khanna, Tariq Hussain Sheikh, Sabyasachi Pramanik, and Dharmesh Dhabliya

Abstract Tuberculosis is an infectious disease that affects a significant number of people all over the globe. The possibility of therapy does not exclude the need for an accurate diagnosis, however. Even while there is generally available X-ray equipment, particularly in underdeveloped nations, there is sometimes a lack of radiological expertise, which makes it difficult to accurately evaluate the images. The ability to diagnose and, ultimately, cure the illness might be vastly improved by the use of an automated vision-dependent approach which can carry out the task in a quick and cost-effective manner. In this study, we offer an image analysisbased framework for effectively identifying TB utilizing various machine learning techniques like SVM, random forest, KNN, and neural network. The accuracy of the suggested technique, which makes use of neural networks, comes in at 80.45%. It was able to categorize tuberculosis cases more accurately than other classifiers could.

V. Vidya Chellam Department of Management Studies, Directorate of Distance Education, Madurai Kamaraj University, Madurai, India V. Veeraiah Department of R & D Computer Science, Adichunchanagiri University, Mandya, India e-mail: [email protected] A. Khanna Maharaja Agrasen Institute of Technology, Delhi, India e-mail: [email protected] T. H. Sheikh Shri Krishan Chander Government Degree College, Poonch, India S. Pramanik (B) Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, India e-mail: [email protected] D. Dhabliya Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_3

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Keywords Machine learning · Neural network · Chest X-ray imaging · Tuberculosis

1 Introduction The infectious disease known as tuberculosis (TB) [1] is responsible for the deaths of millions of people all over the globe every year. The etiological agent of tuberculosis is mycobacterium tuberculosis, and WHO projected that it was 100 lakh cases of TB in 2018. This estimate included 58 lakh occurrences of TB in men and 10 lakh instances of TB in infants. Nearly 17 lakh individuals lost their lives as a direct result of tuberculosis, including 5 lakh populations who were infected with HIV virus. After the HIV, tuberculosis (TB) is now the infectious agent that is responsible for the second highest number of deaths globally. The diagnosis and treatment of TB provide a challenge that a significant number of researchers have been attempting to surmount for some decades now. A radiographic investigation, adequate microbiological issues, and clinical awareness are all necessary components in the diagnostic process for lung infection. X-rays of the chest are a very low-cost diagnostic tool that may identify abnormalities in the lungs in a short span of time. During the process of diagnosing the patient with the pulmonary infection, the radiologist is presented with a difficult scenario due to the fact that many different illnesses have similar signs. The knowledge of image processing techniques can be beneficial in identifying any issues that may have been missed by the radiologist during the X-ray examination. The identification, detection, and segmentation of features all make advantage of drastically improved analytical achievements of image processing software and algorithms. When combined with language and picture learning tasks, deep learning [2] is beneficial for knowledge-guided transfer learning. In this paper, the authors demonstrate how software-assisted image processing techniques may be used to quickly detect and differentiate chest X-ray pictures of people with TB from those of healthy individuals. The following is the outline for the further paper: Sect. 2 covers the relevant literary material for our project. In Sect. 3, the framework that we have developed for the diagnosis of TB is discussed. In Sect. 4, we provide the findings and describe how they relate to our intended study. Moreover, in Sect. 5 we have completed our task with regard to the work’s prospective elements.

2 Literature Review Exploration of lung infections is a distinct problem for many years, and sufferers who were given an incorrect diagnosis have often endured an ineffective treatment plan as a result. Two case studies by Abbas and Gaber [3] along with a case report

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on 210 patients carried out by Dr. Ferguson at the Johns Hopkins University School of Medicine in the USA have provided evidence to support the hypothesis that the medical and radiography characteristics of lung infections is not always easy to differentiate from one another. Hui et al. conducted a case report on a woman who was 28 years old. They observed that there may be infrequent scenarios in which pulmonary TB with breathing problem might be deceived as pneumonia, and the doctors would treat these cases as if they are cases of pneumonia. A case report with a female patient aged 16 that was quite similar to this one was provided by Singh et al. The young lady complained of a dry cough, severe weight loss, joint problems, nighttime temperature, and exhaustion in addition to her other symptoms. The physician was responsible for the main treatment of the cough and fever, and an HIV test was used to differentiate between pneumocystis pneumonia, viral/fungal pneumonia, and military TB as part of the differential diagnostic process. After the patient passed away, the illness was determined to be tuberculosis based on the consequence of a nucleic acid amplification test. However, sadly, an incorrect diagnosis resulted in the death of the tiny angel. In order to differentiate between the various lung infections, a diagnostic that is appropriate, accurate, and cost-effective is required. For the most part, the radiologist and the doctors will depend on the data from the chest X-ray to diagnose lung infections. A strategy to identifying pulmonary TB that is based on image processing has been described by Rohmah and colleagues. They made an effort to cut down on the amount of time required to wait for the diagnostic outcome. They found that the minimal distance classifier was the most effective classification approach for spotting TB in chest X-ray images. Using registration-based segmentation approaches, Poornimadevi et al. developed an automated system for identifying TB. In addition, Fatima and colleagues have devised a method for the automated detection of TB. For the identification of pneumonia infection, researchers Parveen and Sathik utilized an unsupervised fuzzy c-means clustering algorithm [4]. Sharma et al. have developed an innovative method for identifying pneumonia in lung X-ray images. Stephen et al. have implemented a number of data augmentation strategies, which has led to an improvement in the accuracy of the RNN [5] approach for diagnosing pneumonia. They presented a case study on the use of a CNN [6] model for the diagnosis of pneumonia. Since the first ML algorithms were developed, the number of applications for these techniques has expanded at an exponential rate. Applications of machine learning may be found practically everywhere in the modern world, including the creation of smart cities, the analysis of medical data, business, education, and other fields. For the purpose of illness diagnosis, a number of different algorithms, such as SVMs, random forests, and much iteration of these, are presented within the medical industry. Fatima and colleagues have published a detailed assessment on the use of ML approaches in the diagnosis and scrutiny of a variety of disorders, including those affecting the heart, liver, and other organs. The authors of [7] have provided a comparison and study of the KNN and SVM algorithms. In the movie provided in reference [8], CNN have been used effectively for the purpose of activity detection. The primary motive of this research is in developing a reliable technique for detecting tuberculosis (TB) in chest X-ray pictures that have been provided. It is still a difficult process to detect

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illness from the X-ray pictures that are provided, as shown by the literature review that was just presented. In addition, the results of our study imply that comparison analysis have to be used in order to illustrate the efficacy of any certain algorithm for TB analysis. This is necessary in order to choose an appropriate machine learning algorithm.

3 Methodology Application areas for machine learning are quite diverse, ranging from the diagnosis [9] of cancer to the identification of helmets. We presented a system that utilizes neural networks in order to diagnose cases of TB of the chest. Figure 1 illustrates our overall structure. As may be seen in Fig. 1, images are loaded into the framework, and after that, features are retrieved from the images. After that, training and testing are carried out. In order to obtain images for training and testing, we initially split the image database in the proportion of 8:2 and utilized 80% of the data for training. During training, we enforced lower dimension representation on the image database which is also called image embedding on two types of images: with TB and without TB. The images with TB type depict persons who are suffering from tuberculosis. The without TB class depicts people who do not have tuberculosis. Image embedding is the method that is used in the process of computing a feature vector in every image. It computes insights by interpreting images, which are later either uploaded to a server

Fig. 1 A suggested structure in the diagnosis of TB using chest X-ray images

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or evaluated locally utilizing a variety of machine learning [10] techniques. Next, the authors trained the CNN model that would be utilized for estimation with the help of the characteristics that were retrieved from the data. When comparing our outcomes, we have used several algorithmic approaches. The image embedding process was carried out with the assistance of GoogLeNet with Inception V3 modules pre-trained on the image database. Convolutional neural network is utilized in the feature extraction technique. It then sends the pictures it receives as input to the first layer of the convolution process. After that, a dot product is performed on the pictures using the feature descriptor, and this produces a feature map. The feature map is then given to the pooling layer of CNN, and for this particular instance, max-pooling is utilized. This results in a reduction of the feature map, which is followed by the formation of a convolution. The procedure is continued until the whole convolution layer has been processed, and after that, the 1-D vector representation of the resulting feature map is created. The categorization is thus carried out once the output is directed to artificial neural network (my cite). When it comes to support vector machine [11], the classification process is carried out by the means of the help of hyperplanes. Its objective is for acquiring ideal decision boundaries while simultaneously maximizing the separation or edge that exists between the aiding hyperplanes. The KNN classification method is the third kind of algorithm that is used. In this method, the classification is determined by locating the “K” closest neighbors. The approach of the type of the K neighbors is determined to be the needed type after the calculation of these K neighbors. The random forest [12] algorithm is the one that is employed most recently. This technique may be thought of as a collection of several decision trees which produce output by computing the information gain or entropies of parent and inherited classes.

4 Result and Discussion The dataset of the lung X-ray image is obtained from [13] and is divided into two distinct types. In order to attain outcomes and deploy ML approaches, the simulations were run on a system whose configuration consists of Windows machine having 8 GB RAM. The authors divided the image dataset in a ratio of 8:2, according to the benchmark settings that were used for the analysis. 80% of the images were utilized for training, and 20% of the images were utilized in testing. The validation amount is retained at 20 percent, and the authors have utilized a batch size of ten. The methodology was developed utilizing three convolution layers, each of which had a filter size of 3, and the relative numbers of filters utilized were 8, 16, and 32. The prototype was trained with 2500 iterations, while the computed epoch is 30. Extracting features for classification require converting the multi-dimensional array into the one-dimensional vector. Figure 2 displays some representative examples of the images included in our dataset. Figures 3 and 4 present comparative ROC curves [14] for the two classes, respectively. The ROC curves in each of these figures compare several machine learning

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Fig. 2 Examples of chest X-rays with and without TB

techniques. The performance of each method seems to be relatively steady, as seen by these curves. When neural network classification was used, the results are shown in Table 1 along with proportionate inspection in various statistical forms including precision, recall, and F1 score for the two classes. We are able to find that the recall value of people without TB is higher than that of people infected with TB, which indicates that it is able to detect normal people with a higher level of accuracy than it can detect abnormal people; however, the precision of finding people infected with TB is higher than the precision of finding people without TB, and it is a positive sign. The addition of additional pictures during training and testing may lead to further improvements in outcomes. The framework is not able to categorize all photographs of tuberculosis effectively since there are a limited number of them, and particularly because TB images have a very unique structure. Despite this, the framework is still producing effective results. In order to demonstrate the usefulness of NNs for the evaluation of TB and nonTB photographs, analysis was made having different categorization methods. The various machine learning methods are subjected to proportionate inspection, which is outlined in Table 2. As can be shown, neural networks provide the most effective outcomes when contrasted with other methods. In spite of the fact that the outcomes of all methods are quite similar to one another, it is abundantly obvious from the table that neural networks perform better than other methods in every parameter. The framework is efficient because the average of all techniques demonstrates that the standard deviations of the values of the various parameters are steady in nature.

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Fig. 3 ROC curve for class TB of several methods

Fig. 4 ROC curves for various approaches regarding the class without TB Table 1 Analyses of how various classes compare in the respect of recall, precision, and F1-score

Class

F1-score

Precision

Recall

Having TB

77.3

75.6

71.4

Without having TB

81.1

74.8

87.9

Average

79.36

80.57

81.32

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The framework is shown to be stable in Figs. 5 and 6, which also illustrate the efficiency of the neural network. It cannot be denied that neural networks perform much better than alternative methods. Table 2 Examination of each model in proportion to the others AUC

Approach

CA

F1

Precision

Recall

NNs

0.872

0.823

0.834

0.834

0.834

Support vector machines

0.861

0.781

0.786

0.784

0.796

K-nearest neighborhood

0.848

0.784

0.781

0.839

0.792

RF

0.892

0.776

0.783

0.727

0.782

Average

0.853

0.782

0.781

0.782

0.787

1 0.9 0.8 0.7 0.6 0.5

Precision

0.4 0.3 0.2 NN

SVM

KNN

RF

Fig. 5 Precision values for a variety of machine learning techniques applied to the TB dataset

1 0.9 0.8 0.7 0.6 0.5

Recall

0.4 0.3 0.2 0.1 NN

SVM

KNN

RF

Fig. 6 Recall values for different ML techniques using the TB dataset

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Table 3 Statistics illustrating the TP, TN, FP, and FN findings, in addition to the accuracy and the average accuracy Class

TP

TN

FP

FN

Accuracy (%)

Having TB

79

70

25

14

89.6

Without TB

71

85

13

26

74.4

Average accuracy

82

Table 3 is offered here to provide more insight into the results. The data for both classes are shown in Table 3, separated into the categories of true positive (TP) [15], true negative (TN) [16], false negative (FN), and false positive (FP). When it comes to recognizing normal persons, the overall accuracy is 89.6%, whereas when it comes to detecting aberrant people, the accuracy is just 74.4%. As seen in the row 1 of table (having TB), the FP score is 25. It indicates that the model incorrectly identified x-rays of persons with tuberculosis as that was taken from people who were not infected with TB. Similarly, row 2 provides useful fact about the usefulness and constraints of the approach. Increasing the number of datasets is one way to overcome this restriction of efficiency.

5 Conclusion Within the scope of the current investigation, the authors have produced a unique classification framework in separating lung X-ray image data into two distinct groups, namely “having TB” and “without TB.” A neural network was shown to be superior to other classification algorithms when it came to identifying tuberculosis (TB) from an X-ray picture. This was determined by comparing these methods. The model is efficient in accurately classifying each and every class that has been researched. In the future, the accuracy may be improved by adding additional images to the class having TB class and deleting undesired things from the lung X-ray images. Both of these steps are included in the process. This research is the first of its type where the inquiry between TB is undertaken, that is separating the X-rays images conforming to the availability and unavailability of tuberculosis. This distinction was made possible by the findings of this study. It is a difficult effort to differentiate between pulmonary illnesses such as pneumonia, TB, and lung cancer; nonetheless, our work may serve as a foundation for making these distinctions.

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Prediction of Patients’ Incurable Diseases Utilizing Deep Learning Approach S. Praveenkumar, Vivek Veeraiah, Sabyasachi Pramanik, Shaik Mahaboob Basha, Aloísio Vieira Lira Neto, Victor Hugo C. De Albuquerque, and Ankur Gupta

Abstract An accurate inquiry may aid timely infection diagnosis, patients’ communal security, and community amenities in today’s world, where data is quickly developing in fields such as bioscience and health protection. In the field of medicine, prediction is an important but sometimes overlooked component. In this paper, the authors construct deep learning and ML approaches in the estimation of chronic illnesses in patients. Conduct several tests using the revised model of prediction based on the standard dataset at your disposal. The purpose of this study is to predict the occurrence of chronic diseases in patients by employing the machine learning technique, KNN, decision tree, and DL employing (ReLU or rectified linear activation function and sigmoid activation function), with Adam serving as an optimizer. When compared with a number of standard algorithms, the suggested system’s accuracy improves significantly. When compared to different approaches, the DL approaches will produce a superior accuracy, which is around 97.9%. Approaches like this are S. Praveenkumar Madurai Kamaraj University, Madurai, India V. Veeraiah Adichunchanagiri University, Mandya, India e-mail: [email protected] S. Pramanik (B) Haldia Institute of Technology, Haldia, India e-mail: [email protected] S. M. Basha N.B.K.R. Institute of Science and Technology, Vidyanagar, India A. V. L. Neto Federal Institute of Ceará, Fortaleza, Brazil e-mail: [email protected] V. H. C. De Albuquerque Federal University of Ceará, Fortaleza, Brazil e-mail: [email protected] A. Gupta Vaish College of Engineering, Rohtak, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_4

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used in making predictions about chronic illnesses including diabetes, heart disease, and breast cancer. Keywords Medical care · Deep learning · ReLU activation · Chronic disease

1 Introduction AI is the analysis of providing computers to acquire knowledge by learning and act like humans and boosts their learning by experience over a period of time in selfsufficient design and thereafter by furnishing that information in terms of monitoring and real-world linkage. In machine learning, we have our dataset that needs to be trained by the ML approach which will be used, regardless of whatever method is used. Deep learning was developed as a means of overcoming the shortcomings of machine learning, which include the fact that ML is ineffective when the number of inputs is high and that it is unable to tackle fundamental AI issues such as natural language processing, picture recognition, and others. Deep learning is the most recent subfield of machine learning, and it is seeing a meteoric rise in popularity as a direct result of the exponential growth in the volume of available data. Chronic illnesses are a major contributor to mortality and incapacity rates all over the globe. In India, it is expected that chronic diseases would account for 53% of all illnesses taken together. The absolute number of deaths in India in 2005 was 10,362,000, while the total predicted number of deaths due to chronic infections in India in 2005 was 5,466,000. According to the World Health Organization (WHO), in India there would be over 60 million deaths caused by chronic diseases over the course of the next 10 years. The number of people who pass away as a result of incurable infections, maternal and perinatal confusions, and nutritional inadequacies blended will reduce by 15%. The death toll from chronic diseases will rise by 18%, with diabetes-related mortality rising by 35% as the single most notable contributor. In 1990, cardiac diseases were responsible for 15% of deaths in India; now, that number has risen to 28%. More than 2.1 million deaths in India occurred in 2015 across all age groups due to cardiac diseases [1], which accounts for more than a quarter of all deaths overall. Between the ages of 30 and 69, cardiovascular disease is responsible for 1.3 million fatalities per year. According to the findings of the study, adults who came into the world after the 1970s are substantially more vulnerable to this kind of mortality than adults who came into the world earlier in human history. Breast cancer illness is the major reason of loss of life among Indian females, as reported by the Union Ministry of Health, having a prevalence rate of 24.7 occurrences per 200,000 women and a death rate of 13.5 occurrences per 200,000 women. According to current projections, at least 18, 97,901 Indian females might be diagnosed with breast cancer by the year 2020. A raised weight record, whether it is due to obesity or underweight, is a key factor in the development of chronic illness. It is anticipated that the percentage of men and women who are obese will continue to rise in India in the next ten years. The expected prevalence of overweight among adults aged 30 years or more in India in

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2005 and 2015 was first estimated in 2005. This included both men and females. In 1995, just 22% of males were considered overweight, but by 2015, that number had increased by 31%. Similarly, in 2005, 21% of women were considered obese, but by 2015, that number had increased to 29%. Effect on the economy and chronic illnesses has tremendous unfavorable financial repercussions for families, communities, and countries, which are often ignored. It is estimated that India would suffer a loss of 10 billion dollars in national pay due to unforeseen casualties in 2006 alone as a result of cardiovascular disease, and it is projected that these calamities will continue to increase overall, and it is estimated that India stands to lose a total of 237 billion dollars over the course of the next ten years due to unforeseen losses brought on by heat disease, stroke, and diabetes. At least, 83% of premature heart disease and type-2 diabetes [2], and 50% of cancer might be minimized by maintaining a robust eating pattern, regularly engaging in physical activity, and staying away from products containing tobacco. Intercessions that are financially smart exist, and they have been successful in a number of nations: The most successful methods have incorporated a variety of population-wide mediations in conjunction with individual mediations for individuals. According to the WHO, a reduction of only 2% per year in the death rates attributable to chronic diseases at the national level in India would result in a financial gain of 15 billion dollars for the country over the course of the next ten years. There are a variety of approaches that have been familiarized with the purpose of preventing chronic illness. However, it is extremely hard for persons to alter their behaviors in order to prevent chronic diseases. This is due to the fact that various persons are ignorant of the chronic diseases to which they may be susceptible based on their current physical health and past medical record. It was discovered that drinking, smoking and having high blood sugar amount all contribute to ongoing health problems. With the development of new technologies for analyzing large amounts of data, there has been a greater emphasis placed on disease prediction. In none of the prior studies did anybody employ ReLU [3] or the corrected activation function, despite the fact that it has been proven to operate wonderfully in a neural network and has gained an incredible amount of notoriety over the course of the last year. It’s as simple as R(x) max (0, x), which means that if x > = 0, then R(x) = 0, and if x is equal to or less than zero, then R(x) = x. Adam is utilized as an optimizer, and the purpose of the survey is for correctly forecasting disease having a higher level of precision by employing machine learning, DL, random forest [4], and K-nearest neighborhood [5] approaches and applying the approaches to a quality dataset [6]. The study that was proposed began with Sect. 1, which provided a short introduction to chronic infections, ML [7], and DL, namely ReLU. In Sect. 2, the literature review describes the previous research that has been done by other researchers that are linked to this publication. In the third section, a detailed explanation of the dataset that was utilized in this investigation is provided. The architecture of the suggested method for predicting chronic diseases is stated in Sect. 4. The assessment of the approach is stated in Sect. 5. In Sect. 6, the authors go through the results analysis in further depth. The conclusion of the research study is found in Sect. 7.

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2 Literature Review Lamsal presented [6] a further CNN-dependent multimodal illness probability prediction computation that makes use of both structured and unstructured data regarding the hospital. Wang envisioned a disease prediction system for the different districts. They evaluated the likelihood of disease in patients with three different infections, including diabetes, cerebral infarction, and cardiovascular sickness. The anticipation of the disease is based on the organization of the information. Utilizing a variety of AI computations like NB, random forest, and KNN allows in the prediction of cardiovascular disease, diabetes, and cerebral infarction. Calculating the aftereffect using a decision tree yields better results than both the KNN method and the NB method. Also, they predict whether a patient would have a greater risk of intellectual localized necrosis. Both of these risks may occur simultaneously. The authors in [7] applied a CNN-based multimodal disease prediction on the content information for the purpose of hazard expectation of cerebral infraction. With a faster computation speed than the CNN-dependent unimodal sickness hazard detection, the accuracy of the illness prediction may reach up to 94.7%. The CNN-dependent multimodal infection probability expectation computation stages are equivalent to one another. In the study, an attempt is made to make sense of both structured and unstructured information, two different types of datasets. The creator tried their hand at working with unstructured data. Although previous work is only based on structured [6] information, none of the developers have attempted to deal with unstructured or just partially organized material. Regardless, this piece of writing makes use of both structured and unstructured data in its construction. Chen tried to develop an enhanced prototype of the information-gathering architecture associated with the IoT [9]. Currently, there is a sensor-based material being developed. The physician was able to determine the patient’s physiological status by using this cloth. In addition, with the assistance of the physiological information, the subsequent investigation will be carried out. It was with the use of this material that they were able to capture the biological condition of the patient. In addition to that, the information is used in the context of the examination to investigate the issues that are now being faced negative mental effects, opposition to distant for body area organization, and support for vast amounts of physiological data collection, etc. The many operations carried out on documents, such as conducting an inquiry on the material, keeping an eye out, and making predictions. The most recent record of 14,749 patients whose ages ranged anywhere from 18 to 90 years was used, coupled with the patients’ research center data, which included information such as blood pressure, age, body mass index, and gender.

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3 Description of the Dataset and the Model The patient’s information was included in the dataset that was utilized for this research. The data center of the patients remembers 1875 data of all patients and 136 items, like insulin, blood pressure, pregnancy, glucose, and body mass index. The majority of the information that makes up the inpatient division is composed of both unstructured and structured data. The dataset includes laboratory information as well as the patient’s important records, like the patient’s disease age, lifestyle, and sexual orientation. The dataset includes information pertaining to cancer, diabetes, and the heart. We are attempting to make a prediction about these chronic illnesses by using these datasets. At this time, diabetes data records were gathered from many origins, including a device for electronically recording records that had been amended and a record on a paper. Paper records have what might be thought of as hypothetical consistent account times, but electronic records contain what can be thought of as more realistic time stamps. A dataset has the properties of being multivariant and having a time series; the data has twenty attributes; the attributes have the properties of being categorical and integer; and the data comprises the attributes. Data includes no missing value. Each record has four different fields that make up its attributes. The names of files and their formats are in the format MM–DD–YYYY, and the timestamps are in the format XX: YY. However, the studies relate to employing just 14 subsets totaling 76 characteristics, even though the dataset on heart disease has 77 attributes. In the heart disease data collection, the columns indicate things like age, chest ache category (cp), cholesterol, evaluating food slugger. Table 1 displays these things and more. Table 1 Dataset of detailed descriptions Object

Specification

Characteristics of the patient

Sex, age group, etc.

Lifestyle practices

Record of patients smoking and drinking practices. A genetic biography

Examining elements and outcomes

Contains 64 elements, including things like blood pressure, sugar, pregnancies, BMI, and insulin

Illness

Heart illness, diabetes, and Minnesota prostate cancer are examples of the sufferers’ illnesses

Medical records

Record of physician cross-examinations

Total attributes

1726

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4 Framework for the Suggested Work Procedures to Carry Out the Suggested Work (Fig. 1): Step 1: Load the disease dataset; the authors begin by obtaining sickness datasets from the UC Irvine [10] machine learning repository. Each dataset consists of a set of diseases and the signs associated with them. Step 2: It involves loading the essential libraries and packages. Step 3: It involves doing preprocessing in order to clean. That includes getting rid of commas, accentuations, and other things like that. Step 4: After the data has been preprocessed, it is then split up into the testing set and the training set. In addition to that, that is used for the purpose of producing datasets. After that, the component split off and made their choice. Step 5: After that, a model is used to classify the information by employing a classification model, as well as utilizing ML approaches (K-nearest neighborhood and decision tree) [11] and deep learning (ReLU). Now the model is constructed by accepting two parameters into account, namely optimizer and loss. The optimizer is responsible for controlling the learning process. Adam is utilized as an optimizer to control the learning rate, and the learning rate is what detects how quickly the optimal weight for the methodology is computed. However, the amount of time to calculate the weight increases if standard datasets are used to estimate the chance of patients having or not having disease. Step 6: When the forecast of the sufferer having or not having the long-term illness isn’t a correct one, then testing the approach once again and repeating the procedure until the approach produces a correct estimation of chronic illness is the next phase. (A) Algorithms used Machine Training data

Data -set

Data Preprocessing

DT KNN

Illness estimation

DL

Test data

Approach

Fig. 1 Framework that will be used in the work that we intend to do in order to accurately anticipate long-term illness

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5 The KNN Step 1: Step 2:

First step is to get the data and then load it. In the second step, you will set the K value to the quantity of neighbors that you have chosen. Step 3: Repeat the process for every occurrence in the data. Step 4: Determine the distance of the query instance with current instance by using the data. Step 5: Add the traveled distance and the location of the instance’s file to the set that has been sorted. Step 6: Using the distance, sort the ordered collection of distances and records in ascending order. Step 7: Select the main items for the letter K from the collection that has been organized. Step 8: Collect the marks for the K-entries that were chosen. Step 9: When regression is successful, it yields the mean of K-labels. Step 10: When classification technique is selected, it returns the technique that was used to determine the K value.

6 Decision Tree Step 1: Step 2: Step 3: Step 4:

Choose the attribute that has a better quality. Supply appropriate queries as input. Act in the proper manner according to the prompt. If you still don’t have the correct answer, return to the first step.

7 Deep Learning Step 1: The data taken as input was transformed into a vector structure. Step 2: The second step is for the term “installation” to take on the attributes of “0” in order to fulfill the information. Step 3: Pooling layer will take the output from the convolutional layer [12] as its input and will then conduct max pooling on that output. Step 4: The data are transformed into a form called max pooling [13], which is a fixed-length vector representation. This layer has a comprehensive connection to the neural network. Step 5: The whole association layer that is connected to the classifier, which is a softmax classifier, is the fifth step. The complete connection layer is the layer that is linked to the classifier when using a softmax classifier [14].

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Fig. 2 Measurements for accuracy, precision, recall, F1-measure

8 Evaluation of the Method TP stands for “true positive,” which finds the quantity of cases estimated correctly as required; TN stands for “true negative [15],” which indicates that the total cases predicted correctly as not required; FP stands for “false positive [16],” which indicates the total number of cases that can’t be estimated correctly; and FN stands for “false negative [17],” which shows that the total cases cannot be estimated correctly as not required. These four values are initially given to find the performance of the calculation here. Now, let’s see if we can get some calculations by using the formulae that are described in Fig. 2: Whereas the F1-score [18] combines the precision and recall of a classifier into a single metric by taking their harmonic mean. Recall [19] refers to the ratio between the numbers of positive samples correctly classified as positive to the total positive samples.

9 The Discussion and Analysis of the Results Here, we are providing the result in a more generic sense. Python is the programming language that is utilized to calculate all the investigational scenarios, and Anaconda is the software that is used (Spyder). The fighting classification strategy, in conjunction with a variety of component extraction methods, and being operated in an environment with a 8 GB RAM. Following the presentation of the decision tree, KNN, and deep learning approaches, we will discuss the illness prediction. The accuracy is shown individually for each illness as well as for each algorithm. Figure 3 illustrates the potential of a patient having heart disease as well as a patient not having heart disease. Although a score ‘0’ denotes that the patient does not have a heart problem and a score ‘1’ denotes that the patient has a heart problem, the percentage of patients who do not have heart problems is 44.36%, while the number of patients who have heart problems is 55.64%. Now we are going to divide the collected data into test and training sets in order to acquire an accurate estimation of heart disease. The accuracy level for predicting heart

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Fig. 3 Difference between patients who have and do not have cardiac disease

disorders with K-nearest neighborhood is 68.34%, whereas the accuracy level for predicting heart chronic disease with decision tree is 82.38%. With neural networks, an accuracy score of 94.26% may be achieved for predicting cardiac disease. When compared to other techniques, the accuracy achieved by employing a neural network is much higher. Deep learning has a 98.2% success rate in accurately predicting cases of cancer. With the help of decision tree, we were able to attain an accuracy score of 92.98% for cancer. In addition, the KNN method has an accuracy score of 98.25% while assessing malignancy. The deep network has a prediction accuracy of 99.2% when it comes to diabetes. The accuracy score that may be reached while utilizing decision tree to diagnose diabetes is 71.356%. In addition, the amount of accuracy score that can be accomplished for diabetes by utilizing KNN is 79.17%. Figure 4 depicts the contrast between a patient with diabetes disease and one who does not have the condition. While the number “0” indicates that the person does not have a diabetic issue, the number “1” indicates that the person does have a diabetic condition. The dataset is being divided into the test and the train halves, and it demonstrates how accurate the data are after being trained and tested. As can be seen in Fig. 5, the accuracy level improves as a result of the training on the data. In Fig. 6, the amount of accuracy achieved by using decision trees, KNN, and deep learning, researchers were able to predict heart, breast, and diabetes illness (Table 2).

Fig. 4 Potential of a patient having or not having the chronic condition diabetes

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Fig. 5 A comparison of the accuracy of the training dataset and the test dataset

Accurecy level of Chronic Disease 120.00% 100.00% 80.00% 60.00% 40.00% 20.00% KNN

Decision tree

Heart disease

Diabetics

Deep Learning Breast Cancer

Fig. 6 Amount of accuracy for the heart disease, breast cancer, and diabetes illness

Table 2 A comparison of the diagnostic accuracy of deep learning, K-nearest neighborhood, and decision tree models for chronic diseases

Chronic diseases

KNN technique Decision tree (%) approach (%)

DL model (%)

Heart failure

68.3

82.85

95.63

Diabetics mellitus

80.25

72.63

98.94

Breast cancer

99.18

91.26

99.61

10 Conclusion A system for predicting chronic diseases, which is based on an ML algorithm, has been created as part of this planned body of work. In order to define the patient’s information, both the decision tree and deep learning models are applied. Here, KNN, decision tree, and deep learning algorithms, such as ReLU, as well as Adam as an

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optimizer, are used with datasets from UCI repository for predicting the likelihood of patients having or not having a chronic disease. To our knowledge, no published work was there previously which makes use of these methodologies. The accuracy score of the suggested framework is increased when compared to the accuracy level of many standard prediction algorithms. The accuracy on the training set is at 98.4%, whereas the accuracy on the test set is just 72.13%.

References 1. Chilazi M, Duffy EY, Thakkar A et al (2021) COVID and cardiovascular disease: what we know in 2021. Curr Atheroscler Rep 23:37. https://doi.org/10.1007/s11883-021-00935-2 2. Wu Z, Tang Y, Cheng Q (2021) Diabetes increases the mortality of patients with COVID-19: a meta-analysis. Acta Diabetol 58:139–144. https://doi.org/10.1007/s00592-020-01546-0 3. Laakmann F, Petersen P (2021) Efficient approximation of solutions of parametric linear transport equations by ReLU DNNs. Adv Comput Math 47:11. https://doi.org/10.1007/s10444-02009834-7 4. Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P (2021) Thyroid disorder analysis using Random forest classifier. In: Mishra D, Buyya R, Mohapatra P, Patnaik S (eds) Intelligent and cloud computing. Smart innovation, systems and technologies, vol 153. Springer, Singapore. https:/ /doi.org/10.1007/978-981-15-6202-0_39 5. Sun L, Qin X, Ding W et al (2021) Density peaks clustering based on k-nearest neighbors and self-recommendation. Int. J. Mach. Learn. Cyber. 12:1913–1938. https://doi.org/10.1007/s13 042-021-01284-x 6. Lamsal R (2021) Design and analysis of a large-scale COVID-19 tweets dataset. Appl Intell 51:2790–2804. https://doi.org/10.1007/s10489-020-02029-z 7. Bhattacharya A, Ghosal A, Obaid AJ, Krit S, Shukla VK, Mandal K, Pramanik S (2021) Unsupervised summarization approach with computational statistics of microblog data. In: Samanta D, Althar RR, Pramanik S, Dutta S (eds) Methodologies and applications of computational statistics for machine learning. IGI Global, pp 23–37. https://doi.org/10.4018/978-17998-7701-1.ch002 8. Lv F, Li Y, Lu F (2021) Attention guided low-light image enhancement with a large scale low-light simulation dataset. Int J Comput Vis 129:2175–2193. https://doi.org/10.1007/s11 263-021-01466-8 9. Dushyant K, Muskan G, Gupta A, Pramanik S (2022) Utilizing machine learning and deep learning in cyber security: an innovative approach. In: Ghonge MM, Pramanik S, Mangrulkar R, Le DN (eds) Cyber security and digital forensics. Wiley. https://doi.org/10.1002/978111979 5667.ch12 10. Pramanik S (2022) An effective secured privacy-protecting data aggregation method in IoT. In: Odhiambo MO, Mwashita W (eds) Achieving full realization and mitigating the challenges of the internet of things. IGI Global. https://doi.org/10.4018/978-1-7998-9312-7.ch008 11. Rani P, Kumar R, Ahmed NMOS et al (2021) A decision support system for heart disease prediction based upon machine learning. J Reliable Intell Environ 7:263–275. https://doi.org/ 10.1007/s40860-021-00133-6 12. Mandal A, Dutta S, Pramanik S (2021) Machine intelligence of Pi from geometrical figures with variable parameters using SCILab. In: Samanta D, Althar RR, Pramanik S, Dutta S (eds) Methodologies and applications of computational statistics for machine learning. IGI Global, pp 38–63. https://doi.org/10.4018/978-1-7998-7701-1.ch003

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13. Pramanik S (2022) Carpooling solutions using machine learning tools. In: Sarma KK, Saikia N, Sharma M (eds) Handbook of research on evolving designs and innovation in ICT and intelligent systems for real-world applications. IGI Global. https://doi.org/10.4018/978-1-7998-9795-8. ch002 14. Pramanik S, Sagayam KM, Jena OP (2021) Machine learning frameworks in Cancer detection, ICCSRE 2021. Morocco 15. Samanta D, Dutta S, Galety MG, Pramanik S (2021) A novel approach for web mining taxonomy for high-performance computing. In: The 4th international conference of computer science and renewable energies (ICCSRE’ 2021). https://doi.org/10.1051/e3sconf/202129 701073 16. Dutta S, Pramanik S, Bandyopadhyay SK (2021) Prediction of weight gainduring COVID-19 for avoiding complication in health. Int J Med Sci Curr Res 4(3):1042–1052 17. Zindler T, Frieling H, Neyazi A et al (2020) Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies. BMC Bioinf 21:271. https://doi.org/10.1186/s12859-020-03559-6 18. Kaushik D, Garg M, Gupta A, Pramanik S (2021) Application of machine learning and deep learning in cyber security: an innovative approach. In: Ghonge M, Pramanik S, Mangrulkar R, Le DN (eds) Cybersecurity and digital forensics: challenges and future trends. Wiley 19. Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:6. https:// doi.org/10.1186/s12864-019-6413-7

Real-Time Control of Humanoid Robotic Arm Motion Using IT2FLC Based on Kinect Sensor Saif F. Abulhail and Mohammed Z. Al-Faiz

Abstract The control of 5-degree of freedom humanoid robotic arm (HRA) in real time is proposed based on Kinect sensor. HRA model is designed based on Denavit– Hartenberg method. Kinect camera is employed to extract and pass the desired position from user to the inverse kinematic (IK). Next, IK is performed to find the desired angles for HRA model. These angles are converted to pulse width modulation signals and fed as a desired angle to each servo motor. Then, the interval Type2 fuzzy logic controller (IT2FLC) is work in order to minimize the root mean square error (RMSE) between the desired and actual position. Finally, the simulation of graphical user interface (GUI) is designed to show the controlled motion of HRA model. Implementation and calculation results show the efficiency of the IT2FLC implementation after it is compared with Type1FLC (T1FLC). The simulation results showed that the IT2FLC is more accurate than T1FLC. The RMSE calculated by IT2FLC is less than the T1FLC. Keywords Humanoid robotic arms · Interval type2 fuzzy logic controller · Kinetic camera · Forward kinematics and inverse kinematics

1 Introduction In recent years, there has been an interest in the human arm [1]. The robotic arm is used in several fields, including industry, health, etc. [2]. The main advantages of HRA are that it is more flexible, efficient, and it can be used for many purposes [3, 4]. After that, the HRA was used in a real-time environment and real human [5, 6]. Consequently, the control and optimization algorithms were used to make the HRA model working more accurate [7]. Many researchers proposed an adaptive control for a Tele-surgery that handles soft and hard tissues [8]. Adaptive sliding mode control for HRA with the aid of backstepping method was presented in [9]. The controller was designed base on S. F. Abulhail (B) · M. Z. Al-Faiz College of Information Engineering, Al-Nahrain University, Baghdad, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_5

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S. F. Abulhail and M. Z. Al-Faiz

nonlinear disturbance observer in order to record the disturbance and then overcome it. The researcher in [10] proposed two modern type controls for articulated humanoid robotic arm. H∞ controller was applied to provide optimal and robustness features to overcome the nonlinearities and uncertainties in the system model. Predictive controllers and additive fuzzy logic control for interactive human robot were proposed in [11]. FLC was presented for dual HRA in [12]. In addition, backstepping method was applied in order to isolate the unwanted terms. The researcher in [13] designed a FLC for a rehabilitation robotic that was controlled from far. Consequently, the gestures of physiotherapist were made base on Kinect camera and transmitted to the robot by a software. A combination of FLC and fast terminal sliding mode controllers was applied to compensate robotic arm assistant that was used to elbow joints [14]. A robust H∞ controller was proposed in [15] for medical HRA. The H∞ loop shaping was used to compensate for the swing of lower limb arm system. In this paper, the design, simulation, implementation, and control of the HRA model are proposed. The desired positions are extracted and transferred using Kinect camera to the MATLAB IK to obtain the required angles. Also, The IT2FLC is designed in order to minimize RMSE between the actual point and the desired one. Furthermore, in order to increase the accuracy of each servo, the transfer function model is identified for five identical servos using system identification MATLAB toolbox. The rest organization of the paper is prepared as follows: In Sect. 2, HRAs model, Kinect camera, IT2FLC are explained. The calculation result of the HRA model is presented in Sect. 3. Moreover, the GUI simulation and implementation results are described in this section. In Sect. 4, a discussion and conclusion of the results are discussed in details.

2 Components and Methodology In this section, the methodology of the work is presented. It includes HRA modeling, Kinect camera, and IT2FLC design.

2.1 HRA Model The modeling of HRA including forward kinematic and IK is described as in [16]. The IK is derived after Kinect sensor transferred the required positions into MATLAB m-file to obtain the required angles. The prototype of the HRA model is shown in Fig. 1.

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47

Fig. 1 5-D humanoid robotic arm [16]

2.2 Kinect Camera It is a line of motion sensing input devices produced by Microsoft. The device generally contains RGB cameras as shown in Fig. 2, and infrared projectors and detectors that map depth through either structured light or time of flight calculations, which can in turn be used to perform real-time gesture recognition and body skeletal detection, among other capabilities [17]. The Kinect for windows provides the tracking of up to 20 positions. Each position is defined by its names such as shoulders, arms, head, spine, ankles, elbows, wrists, knees, hips, and so on. Figure 3 represents a complete skeleton opposite the Kinect camera with 20 positions can be achieved by the Kinect sensor. In this paper, the Kinect sensors of the camera is employed to assign the coordinates for the desired point. The desired point is extracted from the right arm joint number 8. Consequently, the 3-D coordinates of the desired point are transferred to the model in order to be tracked by HRA.

48

Fig. 2 Kinect camera [17]

Fig. 3 Skeleton joints tracked by Kinect camera

S. F. Abulhail and M. Z. Al-Faiz

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2.3 The Controller Design A. Model of Servo Motor A servo motor takes the pulse width modulation as a control action in order to move to a certain angle. However, the controller embedded inside the servo motor represents a problem in several applications that are need robust controllers. Consequently, in order to improve the performance of each servo, the transfer function model is identified for five identical servos using system identification MATLAB toolbox. The estimated transfer function is denoted by the following equation: G (s) =

S2

1.1123S + 1.0231 + 1.1821S + 1.0581

(1)

Arduino board represents the interface between servo motor and host computer as shown in Fig. 4. Signals receive and transmit between servomotor and computer using USB cable. The choice and design of a certain controller for any system are depending on the behavior and time response of that system. In this paper, IT2FLC is designed for five identical servo motor. B. IT2FLC Design The membership function of IT2FLC contains footprint of uncertainty that is the area between lower membership (LMF) and upper membership (UMF), as shown in Fig. 5. Footprint of uncertainty is solving the problem of uncertainties [18]. An IT2FLC has at least one fuzzy set. The structure of IT2FLC is shown in Fig. 6 [19]. The IT2FLC has the following procedure [20]: ) ( (1) For an input vector X ' = i x1' , x2' , . . . , i x I' , calculate the membership function [ ] ( ) ( ) of xi' μ X˜ in xi' , μ X˜ in xi' , ni = 1, 2, 3 . . . , N , i = 1, 2, 3, . . . , I . (2) Compute the firing F n of the nth rule as in the following equation: [ ( ) ( )] ( ) ( ) F n = μ X˜ 1n .x1' × . . . × μ X˜ nI .x I' , μ X˜ 1n x1' . . . × μ X˜ nI x I' [ ] = f n , f n , n = 1, 2, 3, . . . , N

Fig. 4 Procedure of servo model identification

(2)

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S. F. Abulhail and M. Z. Al-Faiz

Fig. 5 IT2 membership function structure [18]

Fig. 6 Schematic diagram of an IT2 FLC

(3) The next step is the type reducer. In this paper, the type reduction is center of sets type reduction as follows: ∑N Ycos = ∑ n=1 N n=1

min yl = k ∈ [1, N − 1] max yr = k ∈ [1, N − 1]

Yn Fn

= [yl , yr ]

∑k

n n=1 y ∑k n=1

∑k

n n=1 y ∑k n=1

n

∑N

n

∑N

f + f + fn + fn +

(3)

n=k+1

yn f n

n=k+1

fn

∑N n=k+1 ∑N n=k+1

yn f f

n

(4)

n

,

(5)

where K is a possible switch point in the search method of all k in [1i, N − 1] need to be considered till the correct switch point is known [20]. (4) Calculate the crisp value by defuzzification output as [20]: y=

yl + yr 2

(6)

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Fig. 7 Membership functions design

Table 1 Rule base of the proposed IT2FLC e/Δe

NB

NM

NS

Z

PS

PM

PB

NB

NB

NB

NB

NB

NM

NS

Z

NM

NB

NB

NB

NM

NS

Z

PS

NS

NB

NB

NM

NS

Z

PS

PM

Z

NB

NM

NS

Z

PS

PM

PB

PS

NM

NS

Z

PS

PM

PB

PB

PM

NS

Z

PS

PM

PB

PB

PB

PB

Z

PS

PM

PB

PB

PB

PB

In this paper, the input to the controller is the error and the rate of change of error between the desired position and actual position. The rate of change of error is calculated as follows: Δe(k) =

e(k) − e(k−1) , T

(7)

where the error signal is represented by e(k) and is a the previous error is represented by e(k−1) . Δe(k) is the change of error, the sampling time is represented by T , and k represents the present sampling time. Seven triangular membership functions are used to fuzzify the parameters of IT2FLC as shown in Fig. 7. For each input and output, the universe of discourse is selected from (−1 to 1). The rules’ table for the proposed controller is illustrated in Table 1.

3 Results In this section, the calculation, simulation, and implementation results are presented. The desired points that are passed by Kinect camera in order to feed them to the HRA model are selected in the first quadrant. The HRA model located at the origin of axes and move on the first quadrant. Four desired points are located on the specific

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Fig. 8 Specific area with 3-D coordinates of the desired points

area to test the HRA model. Figure 8 describes these points on the specific area to be reached by HRA model.

3.1 Calculation Results In this subsection, the process is implemented on the HRA model using PID, T1FLC, and IT2FLC. Table 2 shows the calculation results of the HRA model with RMSE values for each position. The RMSE is calculated as follows: ⎤| ⎤ |⎡ | Dx − Ax | ex | | ⎣ ey ⎦ = |⎣ Dy − Ay ⎦| | | | Dz − Az | ez √ RMSE = (ex)2 + (ey)2 + (ez)2 , ⎡

(8)

(9)

Table 2 Results of HRA model Positions

Actual position using T1FLC

RMSE using T1FLC

Actual position using IT2FLC

RMSE using IT2FLC

PA

28.2, 29.1, −28.5

2.510

29.5, 30, 30

0.500

PB

0.5, 20, −29.5

0.707

0, 20, −29.8

0.200

PC

19.3, 18.9, −29.8

1.319

20, 19.6, −29.9

0.412

PD

14.6, 29.3, −29.5

0.948

14.8, 29.6, −29.9

0.458

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where the ex, ey, and ez are the position error of the HRA model between the desired position and actual position. The Dx, Dy, and Dz are coordinates of the desired position. The coordinates of the actual position are represented by Ax, Ay, and Az. The root mean square error between the desired and actual position is denoted as RMSE.

3.2 Simulation and Implementation Results Simulation of GUI is designed to show the characteristic motion of HRA model. GUI shows the effectiveness of IT2FLC. Figure 9 shows the GUI simulation for each position using IT2FLC. Moreover, the practical implementation of two positions (PB and PD) is shown in Fig. 10 using IT2FLC.

Fig. 9 GUI simulation of the desired positions using IT2FLC

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S. F. Abulhail and M. Z. Al-Faiz

Fig. 10 Hardware implementation of HRA model using IT2FLC

4 Discussion and Conclusion The control motion, simulation, and implementation of HRA model have been achieved successively using IT2FLC based on Kinect camera. The desired position has been passed through Kinect camera instead of manual selection. In addition, the calculation results listed in Table 2 show the IT2FLC is more accurate than T1FLC. The largest RMSE is 2.51 calculated by using T1FLC. However, the smallest RMSE is 0.2 calculated by using IT2FLC. The sample figures taken from different views in GUI simulation and practical implementation show the ability of the HRA to achieve the desired positions either by using T1FLC and IT2FLC which give the HRA the effectiveness of the controller to accomplish these motions. It can be concluded that the controlled motion of HRA model has been achieved effectively using T1FLC or IT2FLC. The desired positions have been sent using Kinect sensor with the aid of MATLAB m-file. The calculation, simulation, and practical implementation results show the HRA model accomplishing the desired positions with acceptable accuracy. Furthermore, table results show that IT2FLC has the edge over T1FLC and proved numerically by the criteria of RMSE and the actual positions for HRA model. Finally, this work can be extended by improved the accuracy of the controller by using an optimization method design to optimize the rule base.

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References 1. Mick S et al (2019) Reachy, a 3D-printed human-like robotic arm as a testbed for human-robot control strategies. Front Neurorobotics 13 2. Zanchettin A, Rocco P, Bascetta L, Symeonidis I, Peldschus S (2011) Kinematic analysis and synthesis of the human arm motion during a manipulation task. 2011 IEEE international conference on robotics and automation 3. Gulletta G, Erlhagen W, Bicho E (2020) Human-like arm motion generation: a review. Robotics 9(4):102 4. Potkonjak V (2020) Is artificial man still far away: anthropomimetic robots versus Robomimetic humans. Robotics 9(3):57 5. Schaal S (2007) The new robotics—towards human-centered machines. HFSP J 1(2):115–126 6. Wiese E, Metta G, Wykowska A (2017) Robots as intentional agents: using neuroscientific methods to make robots appear more social. Front Psychol 8 7. Erez T, Todorov E (2012) Trajectory optimization for domains with contacts using inverse dynamics. 2012 IEEE/RSJ international conference on intelligent robots and systems 8. Kolbari H, Sadeghnejad S, Bahrami M, Ali K (2018) Adaptive control of a robot-assisted tele-surgery in interaction with hybrid tissues. J Dynam Syst, Measur, Control, 140(12) 9. Bai K, Gong X, Chen S, Wang Y, Liu Z (2018) Sliding mode nonlinear disturbance observerbased adaptive back-stepping control of a humanoid robotic dual manipulator. Robotica 36(11):1728–1742 10. Iqbal J (2019) Modern control laws for an articulated robotic arm: modeling and simulation. Eng, Technol Appl Sci Res 9(2):4057–4061 11. Ardila L, Coronado E, Hendra H, Phan J, Zainalkefli Z, Venture G (2019) Adaptive fuzzy and predictive controllers for expressive robot arm movement during human and environment interaction. Int J Mech Eng Robot Res 207–219 12. Bai K, Jiang G, Jiang G, Liu Z (2019) Based on fuzzy-approximation adaptive backstepping control method for dual-arm of humanoid robot with trajectory tracking. Int J Adv Rob Syst 16(3):172988141983190 13. Bouteraa Y, Ben Abdallah I, ElMogy A, Ibrahim A, Tariq U, Ahmad T (2020) A fuzzy logic architecture for rehabilitation robotic systems. Int J Comput Commun Control 15(4) 14. Nguyen H, Trinh V, Le T (2020) An adaptive fast terminal sliding mode controller of exerciseassisted robotic arm for elbow joint rehabilitation featuring pneumatic artificial muscle actuator. Actuators 9(4):118 15. Ali S, Raafat S, Khazraji A (2020) Improving the performance of medical robotic system using loop shaping robust controller. Int J Model Ident Control 34(1):3 16. Abulhail S, Al-Faiz M (2021) Social spider optimization for solving inverse kinematics for both humanoid robotic arms. 2021 IEEE international conference on automatic control and intelligent systems (I2CACIS) 17. Al-Faiz M, Shanta A (2015) Kinect-based humanoid robotic manipulator for human upper limbs movements tracking. Intell Control Autom 06(01):29–37 18. Karnik NN, Mendel JM, Liang Q (1999) Type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 7:643–658 19. Karnik NN, Mendel JM (2001) Centroid of a type-2 fuzzy set. Inf Sci 132:195–220 20. Wu D, Tan WW (2006) Genetic learning and performance evaluation of type-2 fuzzy logic controllers. Eng Appl Artif Intell 19(8):829–841

Blockchain-Based Access Control with Decentralized Architecture for Data Storage and Transfer V. Vanjipriya and A. Suresh

Abstract When it comes to the security of data in the cloud storage model, access control is the most crucial factor. However, traditional methods of data sharing and access control present significant difficulties in the field of study due to issues like privacy data leakage and key abuse. The blockchain model also offers security because of the various encryption methods used for user authentication. Since this research focuses on the blockchain-based cloud, two major contributions are developed: one for effective access control and the other for privacy-based data sharing and retrieval. The first contribution implements a blockchain-based model for cloud-based access control and data sharing. This issue of a single point of failure in cloud architecture is effectively addressed by the developed blockchainbased access control and data sharing process. According to the credentials, the data user (DU) generates a registration request that is sent to the data owner (DO). In order to ensure safe transmission, an EHR application implements a data protection procedure. The cloud infrastructure incorporates components such as data consumers, data producers, blockchain transactions, smart contracts, and the Interplanetary File System (IPFS). To top it all off, DO includes a data protection algorithm for securing HER, wherein encrypted EHR is converted to IPFS before being shared with the data consumer. Keywords Blockchain · IPFS · Decentralized applications · Cloud computing · Hyperledger fabric framework · Access control

V. Vanjipriya · A. Suresh (B) Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamil Nadu 603203, India e-mail: [email protected]; [email protected] V. Vanjipriya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_6

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1 Introduction The phrase “cloud computing” is used to describe the delivery of computing services via the Internet when users begin employing VPN services to facilitate communication, and the term “cloud” emerges from the sphere of communications. The end user is not required to know the specifics of the physical connection and location of the entire network when using the cloud computing services typically associated with storage, software, installation, and information access. The cloud is a popular IT method that moves data and processing from individual computers to massive data centers in the “cloud” [1]. According to NIST, “cloud computing” is “a model for enabling convenient, on-demand network access to a shared pool of configurable, logically isolated computing resources that are managed centrally and made available by a service provider with minimal user interaction or intervention.” Since high-speed Internet is rapidly spreading across the globe, it is likely that applications will be distributed as services across the Internet, reducing the system’s overall cost [2].

1.1 Controlled Entry via Blockchain A model is presented of a decentralized privacy solution for protecting the confidentiality of data that is collected and managed by a third party. Pointers and an offblockchain Distributed Hash table (DHT) that must be authorized using blockchain in order to preserve the encrypted information are at the Lung of this technique, which relies solely on a blockchain to act as an access controller and guarantee privacy. The most recent composite identity is formed and added to the pool when a user logs in. Encryption and decryption of data are carried out with the help of an identifier key that is part of the compound identity that also includes login key pairs for the service and the user. In addition to verifying the user’s identity, the blockchain can also check if the service in question is authorized to access the data. It gets the hash code ready to use for retrieving the data from the off-chain repository [3].

1.2 Model for Safeguarding Data User, interface, platform, and management layers are the four tiers that make up the Data Protection Mechanisms. When it comes to data security and organization, the interface layer takes the reins, while this platform primarily handles the nuts and bolts. The user layer verifies the user’s identity and provides crucial context. The control layer, on the other hand, employs managing strategies to ensure the overall

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process is functioning effectively. This diagram shows how each layer can be used for its intended purpose: • • • •

User layer Interaction layer The platform layer Management layer.

1.3 Internet-Hosted Blockchain with Built-In Security Access Controls Integrating blockchain technologies into the cloud storage system is a promising development because it increases security while decreasing processing time. The system model can then be implemented. The various connected devices can achieve trust via blockchain because it is publicly verifiable, credible, and decentralized. Permission to own and control data was developed as part of the blockchain model’s data management strategy [4]. Additionally, the storage system can provide higher levels of confidentiality for user information. The blockchain system model was developed to do away with issues like data privacy [5]. Further, the access control mechanism is implemented using the attribute-based encryption (ABE) model [6]. With the intention of bolstering safety, the blockchain-enabled access control strategy was created [7]. Protection of sensitive information is greatly aided by access control systems. Since all users cannot create the Access Control List (ACL) in an IoT system due to the large number of anonymous identities, none of the currently available access control models—including Discretionary Access Control (DAC) and Identity-Based Access Control (IBAC)—are well suited for establishing the access control in an IoT system [8–10]. While the access control mechanism makes great use of the delegation unit, which temporarily assigns the user’s access rights [11], delegation is not permanent.

1.4 Significant Obstacles In this section, we look at the difficulties of using privacy-preserving methods that rely on the exchange of such information in a cloud-based infrastructure. • Although the ARBAC model successfully prevented unauthorized users from gaining access and ensured privacy, availability, and integrity, it lacked the trust necessary for protecting certificates. • The main issue was that the blockchain-based access control enforcement protocol, which encourages private data sharing between federated institutions, was not widely adopted.

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• Access control systems are complicated by the combination of the internet and mobile networks, the fusion of data from multiple sources, and the limitations of terminal storage space. • Cloud computing precludes the use of privacy-preserving techniques and standard encryption methods. • Cloud computing’s primary limitation is its inability to provide PaaS interoperability; this is because PaaS encompasses the entire software establishment lifecycle in the cloud, where it is very difficult to achieve uniformity. • It is still difficult to understand user privacy security in the context of a wide range of cloud services and to implement traditional privacy mechanisms in cloud computing.

2 Survey of the Related Research Internet of Things (IoT) privacy-protecting access control method developed. This paper introduces a novel decentralized pseudonymous and privacy system model of authorization management that ensures the reliability of blockchain-based access and security for remotely managed devices. In addition, the emergency cryptocurrency solution of authorization tokens was chosen as the model of access control. In this case, blockchain technology was deployed to ensure the integrity of the access system in decentralized architectures and the consistency of policies through the participation of all relevant parties [12]. To better the electronic health system, developed a model for medical data sharing and protection based on the blockchain. Openness, decentralization, and tamper resistance were among the initial security properties to be met. In addition, a trustworthy model for collecting medical data, retrieving historical data, and keeping individual privacy intact was developed. After that, we gave each patient access to a symptommatching model that not only enables mutual authentication but also generates a session key. Also, by improving on traditional delegated proof of stake, a more trustworthy, secure, and effective enhanced consensus model was incorporated [13]. A blockchain-based attribute enabled sign encryption model for secure cloudbased data sharing was developed. This method meets many of the cloud computing security requirements, such as unforgeability and confidentiality. As an added bonus, this method for addressing cloud storage concerns also introduced the use of smart contracts. In this case, this algorithm combined the privacy-enhancing features of encryption and digital signatures. The enforcement of user access policy across operations like encryption and decryption is central to this algorithm and relies heavily on the access structure tree [14]. Models for the distributed storage and sharing of individual medical records using blockchain technology have been developed. A conceptual framework for the private, immutable, and open exchange of individuals’ continuously updated health records via the blockchain. Furthermore, a machine learning-based data quality inspection

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strategy was used to maintain quality. A protocol based on the General Data Protection Regulation (GDPR) was created to ensure the safe collection, storage, and dissemination of patient medical records. High-quality individual health data was also collected for the data sharing process. Multiple strategies were implemented to facilitate the exchange of pointers to storage locations for continuously updated, massive datasets with high dimensions. The limitations of continuously updated health data were subsequently addressed by utilizing cloud computing and blockchain technology. A mountain of sensitive health information was stored safely in the cloud. After that, we’ll make sure our data is accurate. This model took into account the use of machine learning models in software and hardware to regulate data quality [15]. This model is developed in a location-aware and password-protected method of cloud data sharing. A new position verification model, dubbed Ears, was developed to counteract distance inflation and contraction fraud. It can also defend itself from an increasingly far-off assault. In addition, the pre-existing Distance-Bounding Protocol in wireless communication was expanded to incorporate Ears at a higher network layer (DBP). By taking this route, we can sidestep the need for precise synchronization of time [16]. The identity-based authorized encrypted diagnostic data sharing model in the cloud system was presented. This method facilitates user authorization and assistance in managing a convoluted procedure for sharing encrypted diagnostic data. In addition, an identity-enabled encryption model coupled with a keyword search process was used to accomplish the data sharing procedure. On the other hand, patients can access their own medical records by entering a keyword search. Additionally, security measures such as ciphertext in distinguishability, authorization unforgeability, and trapdoor privacy were thought about for the data sharing process [17]. In order to facilitate collaboration on cloud-based data, modeled a novel highly decentralized information accountability structure. In this case, an object-centered model was developed, which makes it possible to complete the logging model using data from actual users. It is guaranteed that no unauthorized parties will gain access to any user data, and the JAR file included here can generate moving and dynamic objects. Additionally, a distributed auditing model was implemented to better facilitate user management. There was no need for a centralized database or special hardware to run this model. More confidence in the record’s authenticity and integrity is now provided by a revised log record structure [18].

3 Methodology By storing and sharing information in the cloud, businesses and their customers can reap a number of benefits. When multiple users from different companies contribute data to the cloud, it saves everyone involved time and money compared to having to physically transfer the information between servers. In this section, we will discuss

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the precautions that must be taken to keep data safe when it is being shared via a cloud computing system [19]. • Privacy of Information • Privacy • Precise authorization. Electronic health records (EHR) hosted in the cloud have made it possible for various healthcare apps to share patient information. The blockchain guarantees trustworthiness by using rigorous cryptographic methods to verify users. Although cloud integration allows for cutting-edge management, it does raise some privacy concerns for individual patients. The rapid development of cloud computing has greatly aided the sharing of private data through a wide variety of users in different fields, and it is highly desirable to ensure that the sharing file is not exposed to cloud providers or illegal users. Since the security of users could be compromised by the unwarranted disclosure of these data to individuals or unscrupulous organizations, controlling who has access to what data is one of the most important safety requirements of data sharing in cloud computing. Therefore, it is highly desirable to safeguard the process of information exchange. As a central unit for solving the cloud infrastructure’s data origin problem, blockchain-based technologies provide a workable and unalterable public ledger for recording transactions of varying types. In this setup, cloud-based EHR applications are protected via a data protection model that incorporates a shared data approach. The eight steps involved in this model are as follows: preparation; user registration; encryption; token generation; control configuration; testing; validation; and decryption. This model takes into account four distinct parties: the data owner (DO), the data user (DU), the Smart Agreement, the Interplanetary File System (IPFS), and the transactional blockchain. Whoever or whatever “does” the sharing is the entity with the files. Clients of DO with access to the DU’s files are portrayed here as a DU. In this context, each component performs its specific function in the context of data storage and retrieval. The data protection model for encrypting electronic health record data is owned by the DO. First uploaded to the IPFS, then transferred to the DU. Last but not least, data security is maintained by ensuring each user’s privacy. The system model of the cloud-based blockchain-assisted data retrieval model is depicted in Fig. 1.

3.1 Methodology Typically Used When Exchanging Sensitive Information Only authorized users should be able to access cloud data in order to carry out the data sharing process in the cloud platform. When the data’s owner wants to share it with a select group, she or he sends the group’s members the completed key for data encryption. As an added bonus, the encrypted data can be sent to any member

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Fig. 1 Cloud system model

of the group from the cloud, who can then use the key to decrypt the data locally. As a result, the group member can proceed without the involvement of the data owner. Here, healthcare data is converted to privacy-protected data before being transmitted securely. The method was also able to manage sensitive information by incorporating privacy and utility parameters. Requesting access to encrypted data stored in the cloud, a CSP must first determine whether or not the information can be retrieved from the cloud’s storage or if it already exists elsewhere and matches the requested index terms. Data retrieval involves obtaining the unaltered data from encrypted files.

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4 Results and Analysis This part explains how to conduct a comparative analysis and talks about the different ways that have been devised to control who can see what information and how it is retrieved while maintaining users’ privacy using blockchain technology. Additionally, the experimental design, analysis, and discussion of the similarities and differences between the two methods are all laid bare here. Tools for an Experiment: Python tool running on Windows 10 with 4 GB RAM and an Intel i3 processor is used to carry out the implementation of the developed blockchain-based access control and privacy-based data sharing and retrieval model. A Comparative Analysis: Table 1 shows a comparison of the blockchain sizes 100, 200, 300, 400, and 500 in terms of the rate at which they detect genuine users and their responsiveness. The developed method has a 95%, BSeIn, and EACMS only achieve 83%, 83%, and 61%, respectively, for a blockchain of size 400. The developed technique takes 91 s to respond, while ABAC takes 93 s, BSeIn 97 s, EACMS 103 s, and the industry standard is 103 s. In Table 2, we compare the true user detection rate, privacy, information loss, and responsiveness of the CNN-based LSTMs developed using the lung disease Table 1 Summarized comparison table Blockchain size

Metrics

100

Genuine user detection rate (%)

65

65

58

95

Responsiveness (sec)

25

27

27

25

Genuine user detection rate (%)

83

70

38

95

138

141

151

136

Genuine user detection rate (%)

75

69

68

95

Responsiveness (sec)

80

81

86

78

Genuine user detection rate (%)

83

83

61

95

Responsiveness (sec)

93

97

103

91

Genuine user detection rate (%)

61

57

49

95

138

141

151

136

200

Responsiveness (sec) 300

400

500

Responsiveness (sec)

ABAC

BSeIn

EACMS

Developed blockchain-based access control and data sharing

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Table 2 Discussion of comparisons Dataset Metrics

Proposed BWO RDA PSO CAViaR-based BSA

Lung Responsiveness 102.54 disease (sec) Genuine user detection (%)

Skin cancer

36.66

CS

BS

Without optimization

100

101

102.69 107.52 206.43 310.09

34.3

31.7

30

30

30

30

Privacy (%)

82.87

80.4

77.8

74.02

72.52

71.38

68.56

Information loss (%)

17.12

19.6

22.2

25.97

27.47

28.61

31.43

Responsiveness 251.33 (sec)

417

455

270.26 276.52 508.66 784.85

Genuine user detection (%)

32.45

30.9

30.5

30

30

30

30

Privacy (%)

96.5

96.2

96

95.58

93.84

92.84

91.48

Information loss (%)

3.5

8.52

8.52

4.41

6.15

7.15

8.51

Table 3 Computation time for blockchain-based access control and data sharing Metrics

ABAC

BSeIn

EACMS

Developed blockchain-based access control and data sharing

Computational time (sec)

8.97

8.06

7.36

5.84

and skin cancer databases. The developed CNN-based LSTM has a high degree of responsiveness. 102.54 s, while BWO is at 100 s, RDA at 101 s, PSO at 102.69 s, CS at 107.52 s, and BSis at 102.54 s. Average time in 90% of training data is 206.43 s; without optimization, the time is 310.09 s. When using a 90% training data sample, the true user detection rates for BWO, RDA, PSO, CS, BS, and the CNN-based LSTM that was developed are 34.3%, 31.7%, 30%, 30%, 30%, and 36.66%, respectively. When compared to a standard 90% training set, the privacy achieved by BWO is 82.87%, by RDA it is 77.8%, by PSO it is 74.02%, by CS it is 72.52%, by BS it is 71.38%, and by not optimizing it at all it is 68.56%. By comparison, the information loss for BWO, RDA, PSO, CS, BS, and no optimization is 19.6%, 22.2%, 25.97%, 27.47%, 28.63%, and 31.43%, respectively. The developed CNN-based LSTM is only 17.12%. The developed blockchain-based access control and data sharing method has a relatively low computation time of 5.84 s, as given in Tables 3 and 4 display the results of an analysis of the computation time required by the CNN and LSTM based on the lung disease and skin cancer database with regard to genuine user detection.

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Table 4 Computation time for proposed CAViaR-based BSA without Optimization Metrics

Proposed CAViaR-based BSA

BWO

RDA

PSO

CS

BS

Without optimization

Computational time (sec)

5.28

6.74

7.12

7.63

8.42

9.01

7.98

5 Conclusion When it comes to the safety of data stored in the cloud, access control is one of the most crucial factors. Although this is the most important challenge in the research area, the traditional data sharing and access control approach has privacy data leakage and key abuse. When a user enters their ID and password into the DU, a registration request is created. The DO data is encrypted with a master key and then added to the blockchain. Additionally, a data protection algorithm has been implemented in the EHR application to ensure the secure transmission of sensitive data. In addition, there are a few other players: the IPFS, the data owner, the data user, the transactional blockchain, and the data owner. The bulk of the document is taken up by a data protection strategy for securing HER, in which encrypted EHRs are converted to an IPFS. When it comes to data security, the Tracy-Singh product and the CNN-based LSTM that we developed by combining LSTM and CNN are what we rely on to get the job done. This means the newly developed blockchain-based access control and data sharing method has a higher detection rate for genuine users and provides better overall performance. 95% in 25 s of response time. In addition, the responsiveness is 251.33 s, the information loss is 3.5%, the genuine user detection rate is 36.6%, and the privacy is 82.8% thanks to the privacy and utility-assisted data protection plan for safe data sharing and retrieval in the cloud model.

References 1. Indumathi J et al (2020) Block chain based internet of medical things for uninterrupted, ubiquitous, user-friendly, unflappable, unblemished, unlimited health care services. IEEE Access 8:216856–216872 2. Samanta D et al (2021) Cipher block chaining support vector machine for secured decentralized cloud enabled intelligent IoT architecture. IEEE Access 9:98013–98025 3. Carvalho GHS, Woungang I, Anpalagan A (2022) Cloud firewall under bursty and correlated data traffic: a theoretical analysis. IEEE Trans Cloud Comput 10(3):1620–1633 4. Zhang Y, Xu C, Cheng N, Li H, Yang H, Shen X (2020) An accurate blockchain-based timestamping scheme for cloud storage. IEEE Trans Serv Comput 13(2):216–229 5. Pyoung CK, Baek SJ (2020) Blockchain of finite-lifetime blocks with applications to edgebased IoT. IEEE Internet Things J 7(3):2102–2116 6. Zhao D, Ren J, Lin R, Xu S, Chang V (2019) On orchestrating service function chains in 5G mobile network. IEEE Access 7:39402–39416

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7. Biswas S, Sharif K, Li F, Nour B, Wang Y (2020) A scalable blockchain framework for secure transactions in IoT. IEEE Internet Things J 6(3):4650–4659 8. Liu Z, Feng Y, Ren L, Zheng W (2022) Data integrity audit scheme based on blockchain expansion technology. IEEE Access 10:55900–55907 9. Lei K, Du M, Huang J, Jin T (2020) Towards a scalable public blockchain in fog computing of IoT services computing. IEEE Trans Serv Comput 13(2):252–262 10. Almakdi S, Panda B, Alshehri MS, Alazeb A (2021) An efficient secure system for fetching data from the outsourced encrypted databases. IEEE Access 9:78474–78494 11. Lu Y et al (2022) A storage-elastic blockchain for latency-sensitive vehicular edge computing. IEEE Trans Intell Transp Syst 23(8):11862–11876 12. Li B, Lu W, Zhu Z (2020) Leveraging deep reinforcement learning to achieve adaptive vNF service chaining in DCI-EONs. J Opt Commun Network 12(1):A18–A27 13. Suresh A, Kishorekumar R, Kumar MS et al (2022) Assessing transmission excellence and flow detection based on machine learning. Opt Quant Electron 54:500. https://doi.org/10.1007/s11 082-022-03867-6 14. Li S, Qin T, Min G (2019) Blockchain-based digital forensics investigation framework in the internet of things and social systems. IEEE Trans Comput Soc Syst 6(6):1433–1441 15. Selvarani P, Suresh A, Malarvizhi N (2018) Secure and optimal authentication framework for cloud management using HGAPSO algorithm. Cluster Comput 22:4007–4016. https://doi.org/ 10.1007/s10586-018-2609-x 16. Qi J, Yu J, Jin S (2020) Nash equilibrium and social optimization of transactions in blockchain system based on discrete-time queue. IEEE Access 8:73614–73622 17. Huang B et al (2020) Toward high-performance permissioned blockchain in RDMA-enabled network. IEEE Trans Serv Comput 13(2):301–313 18. Banerjee S, Singh GK (2022) A robust bio-signal steganography with lost-data recovery architecture using deep learning. IEEE Trans Instrum Meas 71:1–10 19. Wu Y, Dai H-N, Wang H (2021) Convergence of blockchain and edge computing for secure and scalable IIoT critical infrastructures in industry 4.0. IEEE Internet Things J 8(4):2300–2317

Effective Strategies for Resource Allocation and Scheduling in Cloud Computing M. Jananee and A. Suresh

Abstract Cloud computing, the newest paradigm in distributed systems, and opens up vast possibilities for efficiently meeting demands without investing in costly infrastructure. In particular, IaaS clouds offer an accessible, flexible, and scalable infrastructure for the deployment of these scientific applications by giving users access to a virtually endless pool of resources that can be obtained, configured, and used on demand and charged on a pay-as-you-go basis. The main issue with cloud computing is the lack of resources. For cloud providers, managing resources is a challenging issue. To help they manage their resources efficiently and affordably, researchers offer their insightful recommendations. This paper proposes a number of methods for cutting down on scheduling-related expenses and lag time, as well as for making the most of available resources, meeting strict deadlines, and minimizing energy consumption—all without breaching service-level agreements. Comparisons of proposed and established methods have been found to yield positive outcomes in experiments. The utilization rate has been identified in existing system is 88, but the proposed system rate is 88.82. Keywords Cloud computing · SPAN · Energy consumption · Resource utilization

1 Introduction Historically, scheduling was a crucial tool for businesses to use in order to manage and improve the distribution of workload across numerous tasks and processes. Resource allocation, such as the assignment of internal and external machines, M. Jananee · A. Suresh (B) Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamil Nadu 603203, India e-mail: [email protected]; [email protected] M. Jananee e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_7

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hardware, and software assets, as well as the allocation of time spent on developing procedures, acquiring materials, designing, has traditionally been facilitated by scheduling. Historically, administrators were responsible for delegating tasks and balancing workloads among workers [1]. The many components that make up a computer include the screen, the central processing unit (CPU), the network, the primary and secondary storage devices, the printer, the scanner, the track pad, and much more. A scheduler is needed for an operating system so that resources can be set up ahead of time in case a situation or procedure calls for it. Providing a vast array of useful services and resources to users, cloud computing platforms are poised for rapid economic growth. The effectiveness of letting the consumer decide whether or not a service is necessary for him has also been greatly aided by intelligently built recommendation systems. In today’s era of cutting-edge technology, scheduling is one of the most popular methods for matching the requirements set by the user with the available resources at a given moment. Requests can be represented computationally virtually, with components like a process or thread being executed on physical resources like a computer’s RAM, IO cards, and CPU [2]. Since the cloud provides an unlimited supply of resources, scheduling methods are essential for reaping the most benefits from them. To efficiently carry out the requests, it is necessary to intelligently automate a large number of resources [3]. In the context of purchasing automation, an algorithm is the critical component responsible for coordinating the execution of tasks across several resources while maintaining data confidentiality. Cloud computing aims to take advantage of the rollout of a number of services by integrating a number of different, often incompatible, technologies. Delivering software as a service (SaaS) allows on-demand use of software that is made available online, whereas platform as a service gives the user access to a development environment [4] without worrying about the underlying hardware. The cloud computing model known as “Infrastructure as a Service” (IaaS) makes it possible to quickly and easily set up an adaptable, scalable, elastic, and highly available platform on which to run applications and provide a wide range of services. Excellent scheduling practices can benefit IaaS [5] in a variety of ways, including increased server utilization. In addition, IaaS vendors provide clients with the chance to deploy extra services cheaply, while they themselves need not worry about coordinating the use of various resources to ensure service delivery. The services offered by a Cloud Service Provider (CSP) like iCloud, Amazon Web Services, the Internal Business Machines (IBM) Corporation cloud, etc., are dynamically delivered by utilizing a network in which virtualized and scalable resources communicate with one another [6]. Cloud services are defined as the transfer of resources across several data centres, which are organized as computing clusters. There is a wide variety of unique liveware that can be used to access CSP services. In addition, these assets should be managed effectively so that they are utilized to the fullest extent possible while still meeting the barest minimum of necessary conditions. An effective and efficient scheduling technique must be implemented for the requests to be managed effectively.

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2 Literature Survey In order to prevent data loss, theft, or manipulation, the authors of [7] suggest a new method of safe cloud storage that is fog-centric. The suggested approach uses a novel technique called Xor (combination). $Xor-Combination to conceal data and prevent unauthorized access. Furthermore, Block-Management $BlockManagement decentralizes $Xor-results combinations to prevent malicious retrieval and improve data recovery in the event of data loss. At the same time, we suggest a hash algorithm-based method for improved probability in change detection. Through a thorough security study, we prove that the suggested system is secure. The experimental results confirm the proposed scheme’s superior performance in comparison to state-of-the-art solutions in terms of data processing time. Using the widely used private key cryptography methods of message authentication code (MAC) and hash-based message authentication code (HMAC), we create a new, more flexible auditing system in [8]. MAC and HMAC are actual examples of our auditing system, which we implement. We conclude that our suggested system is more cost-effective in both communication and computation, as evidenced by both theoretical analysis and experimental findings. This [9] provides a thorough comparison and methodical analysis of the most prominent methods for ensuring the safety of data when sharing and storing it in the cloud. Functioning for data protection, potential and innovative solutions in the domain, the core and suitable information comprising workflow, successes, scope, gaps, future directions, etc., are all discussed for each specialized technique. In addition, a thorough comparison of the various methods is provided. After that, we talk about how we may tailor our discussions of these strategies to meet specific needs before reporting on the gaps in our knowledge and potential future developments in this area. The authors hope that the work presented in this article will serve as a springboard for further investigation in the field. In an effort to reduce the overall complexity of the system, the authors of [10] suggest a novel identity-based RDIC scheme that employs a homomorphic, verifiable tag. By adding random integers to the proof’s original data, we can ensure that the verifier cannot learn anything about the data’s origins while verifying their integrity. Under the computational Diffie–Hellman problem assumption, the security of our approach is demonstrated. The experimental findings demonstrate the high efficiency and practicality of our method. We show the adaptability of the [11] method by implementing it for two different parameter sets in FrodoKEM and the Lattice Authenticated Cryptography (LAC) cryptosystem, both of which have a tiny modulus. The proposed high-throughput implementation on GPU is highly helpful in safeguarding IoT communication since IoT gateway devices and cloud servers need to manage huge connections from sensor nodes. In order to easily audit the integrity of group-shared material while protecting the uploader’s anonymity, [12] introduces a new identity-based PDP protocol. Instead of dealing with the hassle of managing certificates, our PDP approach is able to do

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away with it altogether thanks to the underlying security of an identity-based crypto process. Our technique is distinct from others in that the data uploader relationship is guaranteed during the proof-generation stage rather than the integrity-audition stage. Because of this, the auditor of the data does not have the same level of knowledge of the relationship as the person who uploaded the challenged data as an extract. At the same time, the computing cost of the data auditor can be considerably reduced by establishing the association through a cloud server in the evidence-generating stage. Additionally, the proof is strengthened by randomizing the relationship between the data uploader and the disputed data. Our programme takes these precautions to ensure the data uploader’s privacy as effectively as possible.

2.1 Existing System Load balancing is just one of the many obstacles present in a cloud computing setting. As a result, the performance suffers significantly. Effective load balancing can raise both resource utilization and user satisfaction. This proposed algorithm uses LBDA to solve the load balancing problem by mapping tasks to the most suitable virtual machines (VMs) based on their capacities and the estimated times at which they can be completed. To test the efficacy of the proposed algorithm, we ran five experiments. The comparison of this algorithm is done with Round Robin, Max– Min, and SJF algorithms under same configuration environment, by decreasing the average makespan, Mean of Average Response Time, and Total Execution Time across all VMs.

3 Methodology for the Study Research is one of the driving forces behind the development and refinement of any field. To be successful, researchers need a strong work ethic and an in-depth understanding of their subject matter. Several methods for beginning research have been covered in this part to aid computer scientists in getting their studies underway. Despite the fact that this approach can be used in any area of study. In conducting this study, we followed these procedures: • Make a choice of topics. The first step is for the researcher to pick the area of study on his or her own, without being swayed by the opinions of others. Therefore, we decided to focus on one aspect of cloud computing: scheduling. • Get in touch with the right database and search engine. When looking for specific information, “Google” is the most popular choice of search engine. Google isn’t the only search engine people use; encyclopaedias like Wikipedia and Yahoo! are extremely popular as well. The reliability of a search engine is important to keep in mind while making your selection. Scholarly search engines

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such as Microsoft Academic Search, Google Scholar, the ACM Digital Library, DataBase systems and Logic Programming (DBLP), ScienceDirect, Scopus, and IEEE are recommended for researchers to do keyword searches. Keeping these things in mind, a literature search was conducted using several different databases. • Organize academic papers and books. One can divide research papers into analytical and argumentative types. Analytical research involves an author’s logical analysis of a topic and presentation of his or her own viewpoint on the matter, whereas argumentative research involves the presentation of arguments based on evidence. Researchers can quickly determine if an article is relevant to their work by reading its abstract. After that, he will be able to tell which academic publications are worth reading and which aren’t. During this study, we read through the abstracts of around 200 research publications to choose the ones that were most relevant to our inquiry. • Reading papers in a detailed fashion. A researcher’s ability to make reasonable arguments depends on his having read the work in question. The publications were carefully reviewed, including an analysis of the methods that were employed. • Innovation. The research process is particularly significant at this stage. A methodology is proposed and the study into the following questions is completed at this stage. – – – – –

A challenge that can be overcome. How do we get to the answer? Challenges and assumptions in the research process. Next steps/projects. Conclusion.

3.1 Studying with CloudSim Grid was created in the past to provide a mechanism for delivering high-performance services for data-critical and computational applications. It was suggested that multiple simulators be used to test grid elements, its middleware, and alternative regulations in order to help researchers and developers come up with new concepts. Because of this need, the event-driven simulation tool GridSim was introduced. GridSim can simulate the entire network, including all of the devices and traffic, in great detail. The grid and simulated virtual resources are the focus of another simulator, GangSim. Although these simulators could replicate the actions of grid application managers, they were unable to distinguish between the various services provided by the cloud. Services for applications “To test if their designs and methodologies will work in a cloud setting, developers and researchers can use this simulator without needing an in-depth expertise of cloud architecture”. During the early stages of development of CloudSim, the SimJava engine was used to mimic individual events. It made simple tasks like creating cloud system

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Fig. 1 CloudSim’s layered architecture

entities (virtual machines, services, data centres, hosts, and brokers) as well as facilitating communication between components, queuing and processing of events, and a simulated clock much easier. Figure 1 depicts CloudSim’s layered design.

3.2 Proposed Cloud Model In order to reduce the overall workload and maximize CPU utilization, a hybrid load balancing algorithm is proposed in this part. Tasks in the cloud require efficient management of scarce resources. Following the layout of the suggested system model in Fig. 2, we will describe its four components. In this scenario, requests are sent to a remote server in the cloud. – Quadratic probing is used in the server’s task allocation. – With this method, the workload of overburdened servers is spread to less taxed machines. – The approach optimizes CPU use while load balancing. • Requests from users are the first thing that happens in Million Instructions Per Seconds (MIPS).

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Fig. 2 Model of the suggested system

Table 1 Several methods for allocating workers are compared

No. of tasks Sequential Linear hashing Quadratic hashing 10

0.72

1.06

0.71

50

1.46

2.43

1.23

100

2.41

3.92

2.12

200

3.5

4.25

2.91

400

5.8

6.79

4.5

• Second, using the data in Table 1, we compared the various approaches to assigning jobs. Based on the results given in the table above, the hashing with quadratic probing technique was selected for task allocation. • It is at this stage that the recommended methodology is drafted. You may see the acronyms employed in the proposed system in Table 2 and the results of the computations in Table 3 of the same document. Pseudocode for proposed approach Construct matrixes ETC(ti , cj ), ETC’(ti , cj ), CAT(cj ), and CANDIDATE(ti , cj , minimum). For each unexecuted task ti a job do receive dynamically tasks with arrival rate ⃣ and put them into queue For each task ti in the queue For each computer cj involved computers do compute ETC’(ti , cj ) = ETC(ti , cj ) + CAT(cj )

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Table 2 Abbreviations used in the suggested methodology

Table 3 Proposed method expressions

Notations

Description

Ti

ith task/current task

Vj

jth resource/virtual machine

W ij

Waiting Time of ith task on jth resource

E ij

Execution Time of ith task on jth resource

N

Number of tasks

M

Number of resources

VM

Virtual Machine

MSij

Makespan of jth resource after assigning ith task

RUi

Resource utilization in percentage of ith resource

UTj

Total used time of jth resource

AVMS

Average makespan of all resources

T_MS

Total makespan of all resources

Notation

Calculation

E ij

(T i .length)/V j .Size)

W ij

(T ij .finish_time) ~ (E ij )

MSij

W ij + E ij

T_MS

(MS00 + · · · + MSn−1 m−1 )

AVMS

T_MS/m

RUj

(UTi /MSij )*100

each task ti obtains minimum completion time by computer cmin in ETC’(ti , cj ) and set task ti , computer cmin and minimum completion time to CANDIDATE(ti , cj , minimum) do choose task tmax with maximum completion time from CANDIDATE(ti , cj , minimum) dispatch task tmax to computer cmin and update CAT(cmin ) Makespan = max {CAT(cj )}.

3.3 Indictor of Performance Makespan, average makespan, average mean response time, and average resource utilization are used to gauge the success of the proposed methodology in the current study. An efficient algorithm will have a short make time, a short average mean reaction time, and a high average use of available resources. Average resource utilization =

(( ) ) UT j /Makespan 100 /mm Avg RT j

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makespan = Wi j + E i j avg makespan = makespan/m Avg RT = ∑n RTi Time Required(RTi ) = Money Invested(FIi ) − Time Spent(STi ) where TTi is the due date for the ith assignment and FIi is the completion time. For maximum effectiveness, it is essential that no available resources go unused. They must be in the midst of calculating something.

4 Result and Discussion Algorithms have been simulated using CloudSim, and the results compared to those of other algorithm. The same parameters as those used by pre-existing algorithms are taken into account during the analysis. Overloaded, balanced, highly balanced, and under loaded virtual machines (VMs) were used to determine how work should be distributed in Load Balancing Decision Algorithm (LBDA). The projected duration of the project was calculated by adding the times at which individual tasks were completed and the workload of the virtual machines. After that, jobs that take less time to finish than expected were chosen and moved to other virtual machines (VMs) to cut down on makespan and boost VM utilization. Tables 4 and 5 present the results of an experiment that compare the proposed algorithm to LBDA in terms of mean response time and average makespan. To ensure consistency with LDBA, we used the same number of VMs and predefined configuration for our experiments in the simulator, and we also used the same task sizes. Table 4 shows that the suggested method achieves a lower average makespan than LBDA. Figure 3 provides a graphical depiction of the findings to aid with comprehension. Table 5 shows that the suggested algorithm has a faster mean response time than LBDA on average. Figure 4 also displays graphical representations of the results. Additionally, the suggested technique is contrasted with another load balancing algorithm (LBA). LBA’s primary goal was to shorten production times and maximize resource use. The workload of each virtual machine (VM) was calculated by assigning jobs in a QUEUE based manner. New virtual machines (VMs) were made if Table 4 Makespan comparison of proposed method with LBDA

Experiments (Tasks/VM)

LBDA (Existing system)

Proposed algorithm

100/6

360

161.96

150/8

427

205.02

200/10

430

277.34

250/12

433

302.67

300/14

458

319.59

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M. Jananee and A. Suresh

Experiments (Tasks/VM)

LBDA

100/6

277

Proposed algorithm 71.90

150/8

300

79.21

200/10

313

84.58

250/12

326

113.28

300/14

332

123.15

Fig. 3 Results are depicted graphically

Fig. 4 Mean response times: a comparison

Effective Strategies for Resource Allocation and Scheduling in Cloud … Table 6 Makespan comparison with n number of tasks

No. of tasks

LBA

Proposed algorithm

10

505

168.85

15

634

225.10

20

559

215.10

25

632

310.10

30

615

487.60

40

793

575.10

50

912

706.35

79

the anticipated workload was going to exceed the current VMs’ capacity. Makespan comparison between the proposed and the current algorithms is presented in Table 6. In Table 6, we can see the results of our analysis, which show that the suggested algorithm requires less time to complete a task than LBA. The proposed algorithms are inferred to be more efficient in terms of running time. Figure 5 is a visual representation of the data analysis found in Table 6. Table 7 displays the data in a comparative format. The outcomes show that the suggested method outperforms alternatives under typical loads on resources. For the convenience of the users, we have provided a graphical depiction of Table 7 in Fig. 6. Figures 3, 4 and 5 provide a visual comparison of the aforementioned tables with respect to makespan, average makespan, average mean reaction time, and average resource consumption rate. A closer look at the tables reveals that the proposed method achieves the shortest possible makespan, average makespan, mean average response time, and optimal typical use of available resources, in contrast to the LBDA and LBA algorithms.

Fig. 5 Makespan comparison

80 Table 7 Utilization rate comparison of common resources

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Experiments (Tasks)

LBA

Proposed algorithm

10

73

77.42

15

85

85.33

20

75

79.23

25

66

78.43

30

71

81.18

40

88

88.82

Fig. 6 Average resource utilization comparisons

5 Conclusion The suggested load balancing method in this part not only achieves greater resource efficiency than the state-of-the-art LBDA and LBA algorithms, but it also achieves better load balancing results. The results of two experiments show that the suggested algorithm requires less makespan and mean response time on average than LBDA. Makespan is reduced and average resource consumption is increased in the second experiment. The implementation for future enhancements can include additional factors like a time limit.

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References 1. Shen W, Qin J, Yu J, Hao R, Hu J, Ma J (2021) Data integrity auditing without private key storage for secure cloud storage. IEEE Trans Cloud Comput 9(4):1408–1421 2. Thangavel M, Varalakshmi P (2020) Enabling ternary hash tree based integrity verification for secure cloud data storage. IEEE Trans Knowl Data Eng 32(12):2351–2362 3. Tao Y, Xu P, Jin H (2020) Secure data sharing and search for cloud-edge-collaborative storage. IEEE Access 8:15963–15972 4. Lee K (2020) Comments on “secure data sharing in cloud computing using revocable-storage identity-based encryption.” IEEE Trans Cloud Comput 8(4):1299–1300 5. Yang Y, Zheng X, Rong C, Guo W (2020) Efficient regular language search for secure cloud storage. IEEE Trans Cloud Comput 8(3):805–818 6. Kumar S, Cengiz K, Vimal S, Suresh A (2021) Energy efficient resource migration based load balance mechanism for high traffic applications IoT. Wireless Pers Commun. https://doi.org/ 10.1007/s11277-021-08269-7 7. Yuan Y, Zhang J, Xu W (2020) Dynamic multiple-replica provable data possession in cloud storage system. IEEE Access 8:120778–120784 8. Mendes R, Oliveira T, Cogo V, Neves N, Bessani A (2021) Charon: a secure cloud-of-clouds system for storing and sharing big data. IEEE Trans Cloud Comput 9(4):1349–1361 9. Zhang Y, Yu J, Hao R, Wang C, Ren K (2020) Enabling efficient user revocation in identitybased cloud storage auditing for shared big data. IEEE Trans Dependable Secure Comput 17(3):608–619 10. Khashan OA (2020) Secure outsourcing and sharing of cloud data using a user-side encrypted file system. IEEE Access 8:210855–210867 11. Suresh A, Kishorekumar R, Kumar MS et al (2022) Assessing transmission excellence and flow detection based on machine learning. Opt Quant Electron 54:500. https://doi.org/10.1007/s11 082-022-03867-6 12. Zhang Z et al (2019) Achieving privacy-friendly storage and secure statistics for smart meter data on outsourced clouds. IEEE Trans Cloud Comput 7(3):638–649

A Brief Review Particle Swarm Optimization on Intrusion Detection System G. M. Nandana and Ashok Kumar Yadav

Abstract There has been an improvement in recent years towards increase in volume for remarkable data in the field of Internet and computer networks. This brings up significant security-related challenges. In the past, a number of systems that provide host or network-level security have emerged. Many conventional security measures, such as, firewalls, spyware, authentication methods and antivirus software provide some way towards ensuring security, but they are still vulnerable to cyber-attacks and other system vulnerabilities. Some noteworthy alternatives are discovered, namely Intrusion Detection & Prevention Systems are an intriguing development, but they also have significant drawbacks, such as the inability to identify new assaults and respond to them in real time. Overtime, many researchers have proposed to develop a robust as well as intuitive infrastructure intrusion detection systems (IDS) utilising a variety of neural network models, i.e. support vector machines (SVM), rough sets, Particle Swarm Optimization (PSO) and many other versions. In this paper, we discuss all proposed models have been implemented by researchers in improving IDS performance with PSO model. Also, considering different IDS implemented, we can derive various IDS datasets. Keywords PSO · IDS · Machine learning (ML) · Network security

1 Introduction Presently, any organisation must use computer networks and the Internet in order to sustain in the modern world. As businesses increasingly rely on computer networks for daily operations like online services, customers are now sharing and storing their G. M. Nandana (B) · A. K. Yadav Department of Computer Science and Engineering, ASET, Amity University, Noida, India e-mail: [email protected] A. K. Yadav e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_8

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personal information in these networks. Therefore, businesses need to be increasingly cautious about safeguarding themselves against unwanted hacks or strange behaviour aimed at compromising their networks. Therefore, the need for more reliable intelligent solutions is significant. Using a security information model, the intrusion detection system analyse over various network traffics for malicious behaviour that is suspicious, unexpected, or in violation of policy. It alerts the administrator to take suitable action against attacks. If a network is breached by hostile activity, it may result in the loss of potentially crucial information and prone to various cyberattacks. Further leading declining in user confidence in using system. Private resources must be protected from assaults coming from both inside and outside of organisation that take advantage of its deficiencies [1]. False alarm detection rate is common feature in IDS. It tracks and identify networks for potential harmful activity. Also, it will notify the system or network administrator if it finds any threats. Two most popular performance metrics are defined: Detection Rate (DR), outlined with respect to ratio of successfully identified attacks to all attacks, and False Alarm Rate (FAR), which may be outlined with respect to ratio in correctly identifying connections against all connections that are normal. It is difficult to construct an effective IDS, thus it must possess a larger intrusion DR and have lower FAR simultaneously [2]. Two different kinds of schemes are introduced based on data collecting techniques, namely Host intrusion detection systems (HIDS) and Network intrusion detection systems (NIDS). Firstly, depending on data network packets received only from clients or standalone hosts are monitored. If any suspicious or malicious behaviour is found, the administrator is notified. This is done by collecting copies of original device files and comparing them to earlier copies. Secondly, depending on the IDS device source, these systems which are deployed intelligently throughout channels using hardware or software focussed devices that may be linked using to network medium like Ethernet, LAN, WAN, etc. IDS may be categorised to Misuse Detection and Anomaly Detection depending on classification methods used. Misuse Detection is the process of looking for harmful behaviour in the explored data by scanning network traffic. This technique’s key benefit is that it is simple to design and comprehend and offers excellent detection results for certain, well-known threats. They are unable to identify new threats, though. Normally patterns are used by the anomaly intrusion detection system (AIDS) to spot the intrusion. The statistical measurements of the system properties are used to create the typical use patterns. While the system for identifying anomalous activity generates a typical traffic sketch. Then, it may be used in spotting any mismatched traffic patterns and intrusion attempts. The most recent ML advancement has significantly increased IDS’s effectiveness. The cutting-edge machine learning techniques also depend on a significant amount of labelled data, which takes a lot of effort and money [3]. PSO has recently drawn a lot of notice for its amazing results in the creation of IDS. It is one of these methods, which Eberhart and Kennedy first used in 1995 [4]. It is regarded as an artificial intelligence method that took its inspiration from group of birds, group of fish swimming, or an ant civilization and exceptional capacity to resolve difficult issues raised

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by current research problems [5]. Swarm intelligence (SI) in summary is a set of approaches, strategies, and algorithms that were motivated by research on group behaviours in decentralised systems [6]. This has been defined as population-based meta-heuristic optimization model which closely resembles interpersonal behaviour in all points defined by collection. Each point in a collection that may be thought of possible result moves around an n-dimensional search space while using particle swarm optimization. Following population initialization, each iteration’s particle changes its direction, which is made up of its velocity and location, in an effort to discover the best option. The expression for any jth point is:   X j = x j1 , x j2 , . . . , x jD

(1)

and with most advantageous location (with best fitness value) is:   P j = p j1 , p j2 , . . . , p jD

(2)

Pi is known as pbest. The notation g, i.e., Pg , will denoted as gbest, is the best index number location where most of the points of a cluster have been tracked. The velocity of particle j is denoted by:   V j = v j1 , v j2 , ..., v jD

(3)

Also, d-dimensional (1 ≤ d ≤ D) for each generation changes based on the equation given [7]:    v[ j] = w∗v[ j] + a1*rand()∗ pbest[ j] − present x j      + a2*rand ()∗ gbest x j − present x j

(4)

The point velocity is known as v(). present (solution) may be known as specific point at which the best solution may be given. As discussed earlier, pbest () and gbest () have been predefined. and rand () function may be known as an integer chosen from intervals 0 to 1. a1 and a2 are factors that have an impact on learning (acceleration). Primarily, a1 = a2 = 2. Also, w known as weight of inertia. The following are the main contributions: • Gathered all research reports having PSO or IDS as main tags which are published in the past few years. • Excluded those with lower citations or relevance, and then selected the remaining papers to be included in this review. • Key ML techniques known to us are included in this review as well as their hybrid models. • Concise table mapping commonly used IDS database with No. of attribute and Label Status.

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• Compares results from models which have been defined over performance metrics namely, DR and FAR matrix values. The remaining report has been organised as: Sect. 2, describes literature review from all suggested systems across different research papers. The literature, including datasets is covered in Sect. 3. The findings were discussed in Sect. 4. Section 5 came to a conclusion for the study. Future exploration of this topic is also discussed.

2 Literature Review Many scholars have viewed classification challenge with respect to intrusion detection mechanism. Various scholars have presented many approaches based on ML techniques such as SVM [8, 9], Decision Trees (DT) [10], K-nearest Neighbor (KNN) [10], and Artificial Neural Networks (ANN) [11, 12], for which we are able to see brief overview in Amudha and et al. [13]. The majority of these technologies apply conventional algorithms directly to publicly accessible intrusion detection datasets. When computer invasions are significantly infrequent than typical behaviour, standard classification systems do not function effectively. Much more recent research has been done under this subject which have concentrated on enhancing the performance of mining techniques through the application of swarm intelligence optimization approaches. There has been quite a lot of research on intrusion detection using PSO and other machine learning techniques as well as hybrid approaches. PSO-LightGBM is a hybrid model presented by Liu et al. [14]. In terms of accuracy and FAR, the model outperforms other IDS in the evaluation. Particularly notable is the considerable drop in FAR, which demonstrates strong durability and robustness. It outperformed simple ML algorithms with an accuracy of 80.85%. Bamakan et al. [8] suggested efficient IDS model that employs a novel optimization strategy that is adaptable, resilient, and exact method, timevarying chaos particle swarm optimization (TVCPSO), for immediately define constraints, choose attributes of multiple criteria linear programming (MCLP) and SVM. After all iterations, it was shown the model performed with an accuracy of 86.68% as compared against some basic ML process defined in the paper. The current paper, Dash et al. [11] describes a novel hybrid intrusion detection system which combines gravitational search (GS) with PSO. This approach has been successfully pre-owned in building an artificial neural network (ANN). Also, this resulting model (GSPSO-ANN) was successfully used for intrusion detection model building. The effectiveness of the suggested strategy has also been compared to other ML techniques, and it may perform well when evaluated with extremely unbalanced datasets. Also, it obtained a detection accuracy of up to 98.13%. In this current study, Einy et al. [12] proposed the MOPSO-FLN network IDS, which was created by combining a multiobjective particle swarm optimization algorithm- (MOPSO-) with a fast-learning network (FLN). The dataset served as the basis for selecting features, training the features, and testing the effective model.

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The experiment outcomes show that the technique as comparison to other earlier methods has significantly contributed in increasing performance of this IDS model with respect to assessment matrixes by striking a compromise between the quantity objectives of distinguishing characteristics and training mistakes based on intelligent design capability for MOPSO. Also, with an accuracy of 95.6% this model was able to detect intrusion over a network. The goal in this paper Seraphim et al. [15], was to enhance the efficacy for an IDS by employing suggested Stacked Autoencoder Hoeffding Tree technique (SAEHT) for feature selection and Darwinian Particle Swarm Optimization (DPSO). This experiment was carried towards NSL_KDD datasets, with the main characteristics generated defined by DPSO as well as the classification carried out by the suggested SAE-HT approach. After compared to all other ML procedures, the suggested technique obtains a greater accuracy of 97.7%. The suggested approach improves accuracy and DR as well as lowering FAR respectively. The goal of this research report was, Tama et al. [16], experimental results for network outlier detection employing PSO towards feature selection as well as a combination of tree-based classifiers (C4.5, Random Forest, and CART) towards this classification problem are presented in this research. When compared to existing ensemble approaches, the recommended model for detection yields encouraging results for higher DR and much lower FAR. The objective of the study was as follows: first, utilising PSO and correlation-based feature selection (PSO-CFS) in picking the most suitable features for IDS’s; second, to present fusion for tree-based classifiers in maximising classification accuracy. Combination of fifty PSO particles and also an average probability polling method yielded with a promising 99.8% accuracy. According to the authors Xu et al. [17], this study proposes a hybrid classifier built of KPCA, RBFNN, and PSO to improve the accuracy of these type of problems for intrusion detection. KPCA was applied in minimising some features in the hybrid classifier, RBF is the main classificatory parameter as well as PSO has been utilised in optimising features needed in EBFNN. This hybrid classifier employed KPCA in extracting the key non-linear properties of any raw data, and by incorporating PSO to help in seeking parameters, it was able to overcome RBFNN’s weaknesses such as readily limiting towards all local minimum points along with a lower recognition rate as well as poor generalisation. Along the simulation was performed in the matlab environment using the KDD-99 datasets. Finally, trials demonstrated that usefulness of this hybrid classifier after accuracy of this model was improved to 98.9547% as compared to other models discussed according to this research. According to the authors Sakr et al. [9], this research proposes anomaly-based network intrusion detection system (NIDS) that can observe and analyse traffic flows aimed at towards cloud infrastructure. A system administrator must be alerted of nature of all networks traffics so that any unwanted network connections may be dropped and blocked. As the network connection classifier SVM will be used. In identifying the best and important network feature, binary-based Particle Swarm Optimization (BPSO) was used, whereas standard-based Particle Swarm Optimization (SPSO) was utilised in tuning SVM related selected attributes. This suggested system was built and evaluated using the standard NSL_KDD as well as many

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Table 1 Comparative analysis Classification

Illustration

Algorithms

Statistical

Using a specified set of criteria, this technique compares distinct events statistically

DPSO PSO-MCLP PSO+KNN

Rule based

This method is based on predetermined rule sets that are submitted by a system. Every rule corresponds to a certain system operation

PSO+DT PSO-LightGBM PSO-CFS KPCA-PSO-RBF PSO-CFS

Neural Network

It is based on learning from examples. After learning, the system may recognize intrusion

GSPSO-ANN PSO-BPNN MOPSO-FLN

SVM based

Based on SVM as an input model. It can be easily TVCPSO–SVM combined with various hybrid models to get better analysis PSO-SVM BPSO+SVM

networks related data source. According, for acceptable assessment findings it was suggested that this model will detect unwanted network interactions with higher DR and better FAR with an accuracy of almost 99.10%. The goal of this research paper was, Bamakan et al. [18], was in order to improve the accuracy of intrusions detection, we introduce a novel approach in this study that combines multiple criterion linear programming with PSO. A classification technique based on mathematical programming called multiple criteria linear computing has demonstrated the potential to address practical data mining issues. Nevertheless, fine-tuning its settings is a crucial stage in the training process. The performance of the MCLP classifier has been enhanced using PSO, a reliable and straightforward optimization approach. The outcome showed that, when compared to two other benchmark classifiers, the suggested model performed similarly in terms of detection rate, false alarm rate, and running duration. In comparison to the others, the model that has a greater detection rate and a lesser false alarm rate performs better. The model improved its accuracy by 97.46%. Table 1 discuss different classification with algorithms.

3 Dataset for IDS In order to reduce security threats and network invasion, IDS systems are created to monitor and analyse overall network traffics. Table 2, discusses well-known IDS datasets with details including the title, number of attributes as well as labelling status included [1]. KDD_99 dataset consists 41 features. It is further divided into three classes: fundamental, traffic and content features. It is one of the most frequently used databases in evaluating detection bases models. It includes a specified set of auditable data

A Brief Review Particle Swarm Optimization on Intrusion Detection … Table 2 Common dataset used

89

S. No.

Dataset

No. of attribute

Label status

1

KDD-99

41

Yes

2

NSL_KDD

41

Yes

3

UNSW-NB15

49

Yes

4

CICIDS2017

80

Yes

5

CICDDoS2019

80

Yes

6

DEFCON

None

No

7

DARPA-98

Multiple Dataset

No

8

ISCX 2012

9

Yes

9

Kyoto 2006+

24

No

and variety of attacks. Tavallaee et al. [19], proposed a solution to the previously noted challenges in KDD namely, NSL_KDD data set. Furthermore, train and test sets have a decent number of records. This flexibility makes it possible to perform the tests on the entire set without having to choose a tiny section at random. As a result, the assessment outcomes of various research projects will be more uniform and accurate. Moustafa and Slay [20], KDD-99 and NSL_KDD are baseline data sets that was created around a decade ago. However, multiple recent studies have revealed that, in the present network security environment, traditional data sets do not comprehensively capture network traffic and new tiny footprint assaults. To address the issues of network basic datasets availability, research investigates have established UNSW-NB15 data set. This data collection is a combination of recent standard and contemporary synthetic network traffic counter attack activities. DEFCON was formed by gathering normal and irregular traffics results while participating in hacking and anti-hacking events in a confined area. Ring, Markus et al. [21], provided a survey of new data sets for researches to work with and compare their results. As per Yulianto et al. [22], DARPA-98 dataset was created for data security analysis and revealed vulnerabilities related to artificial infiltration of malicious and traffic over a network. This dataset does not represent real time network traffics and has anomalies, such as the lack of false–positive value. As per Jungsuk et al. [23], provide a new assessment dataset, Kyoto 2006+, which was created from three years of real traffic data (2006 to 2009) underneath the guidance of University of Kyoto. It comprises of 10 additional characteristics that may be utilised for further analysis and IDS assessment together with 14 statistical features that were extracted from the KDD_99 dataset. It offers an abundance of statistical data on the most recent cyberattacks seen in honeypots. As per Shiravi et al. [24], to meet shortcomings from fundamental datasets, a methodical strategy for creating datasets was needed namely ISCX 2012. The basic underlaying principle was built on the idea of profiles, which provide abstract distribution models for applications, protocols, or lower level network elements as well as

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precise descriptions of intrusions. For agents that generate actual traffic for IMAP, HTTP, SSH, POP3,FTP and SMTP genuine traces are evaluated to develop profiles. In this regard, a set of standards is created to define legitimate datasets, which serve as the foundation for creating profiles and supplying a fundamental dataset. As per Sharafaldin et al. [25], create a new IDS dataset called CICIDS2017 that includes updated threats including port scanning, DDoS, DoS, XSS, SQL injection, brute force, infiltration, and botnets that meets all eleven required criteria. Using CICFlowMeter software, more than 80 network traffic characteristics were extracted and computed for all harmless and intrusive flows from the fully labelled dataset. As per Iman Sharafaldin et al. [26], by using CICFlowMeter software, more than 80 network traffic characteristics were extracted to get labelled DDoS dataset called CICDDoS2019. It addresses and overcomes the flaws and restrictions of earlier datasets.

4 Results Some related works (Table 3) displays the most recent strategies utilized along with DR% and FPR% matrix values: Table 3 Comparative analysis of PSO algorithms for IDS References

Algorithm

Domain

DR

FPR

[10]

PSO+DT

KDD-99

89.60

1.10

[10]

PSO+KNN

KDD-99

96.20

0.40

[14]

PSO-LightGBM

UNSW-NB15

80.85

10.60

[8]

TVCPSO–SVM

KDD-99

95.49

3.29

[8]

PSO-SVM

KDD-99

95.48

2.36

[11]

GSPSO-ANN

NSL_KDD

95.26

NA

[12]

MOPSO-FLN

KDD-99

97.51

8.88

[15]

DPSO

NSL_KDD

97.10

1.25

[16]

PSO-CFS

NSL_KDD

93.76

0.60

[17]

KPCA-PSO-RBF

KDD-99

98.95

2.05

[17]

PSO-BPNN

KDD-99

97.65

2.35

[9]

BPSO+SVM

NSL_KDD

96.94

1.48

[18]

PSO-MCLP

KDD-99

99.13

1.94

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5 Discussion and Conclusion IDS in light of PSO is right now drawing in impressive interest from the exploration view-point [27]. In the paper, introduction to IDS and PSO is covered in beginning part. Followed by literature review, next part covers most common datasets available for testing. Also, it includes analysis of various ML along with validation parameters matrix values. It is having the option to fulfil the developing interest of dependable and Intelligent Intrusion Detection Systems (IIDS) [2]. A primary benefit of PSO that it may not be difficult towards execution on dataset. With couple of information parameters which will be required it can be handled. Also, results may be viable to a multiphase improvement application. Likewise, updating of speed and position in PSO depends on basic conditions so it tends to be effectively utilized on enormous informational indexes. It has drawn in numerous scientists according to numerical perspective [28]. However, according to application perspective, only a couple of specialists have fostered their endeavours.

6 Future Scope and Benefit Towards Society Future exploration of PSO may be summarised towards following directions: • Many algorithms improved which are being recommended, like QPSO algorithm based in cloud architecture, adaptive multi-objective PSO algorithm based on Gaussian mixed variance [29], ANN based PSO (PSO+ANN), Genetic based Particle Swarm Optimization (GPSO) and more. To carry out effectively these improvised algorithms further study is needed. • For PSO, input parameters are inertia weight and learning factors. Enhancements to parameter inertia weights may include linearly decreasing, fuzzy, random inertia weights, etc. Enhancements to learning factors should be implemented. In future research purpose, we may proceed to improve all associated input parameter definition. Additionally, consider whether the two or many influence one another. • At the point if there exists absent or inaccurate datasets then there may be trouble for ensuring PSO calculation’s exactness as well as execution not having preprocessing like depending on specific adaptation to non-critical failure. In light to society aspects, this study will help in many ways. Firstly, it has concluded that we need IDS to be a part of our networks that we use regularly to prevent security attacks. Secondly, we can suggest that researches and fellow authors can use any models captured to create their own systems which may be better compared to those previous generations systems. Thirdly, it has a brief introduction to PSO algorithm and how different models are using it to create IDS.

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References 1. Verma J, Bhandari A, Singh G (2020) Review of existing data sets for network intrusion detection system. Advan Math: Sci J 9(6):3849–3854 2. Satpute K, Agrawal S, Agrawal J, Sharma S (2013) A survey on anomaly detection in network intrusion detection system using particle swarm optimization based machine learning techniques. In: Proceedings of the international conference on frontiers of intelligent computing: theory and applications (FICTA). Springer, Berlin, Heidelberg, pp 441–452 3. Balyan AK, Ahuja S, Lilhore UK, Sharma SK, Manoharan P, Algarni AD, Elmannai H, Raahemifar K (2022) A hybrid intrusion detection model using EGA-PSO and improved random forest method. Sensors 22(16):5986 4. Kennedy J, Eberhart R (1995, Nov) Particle swarm optimization. In: Proceedings of ICNN’95international conference on neural networks, vol 4, pp 1942–1948 5. Kolias C, Kambourakis G, Maragoudakis M (2011) Swarm intelligence in intrusion detection: a survey. Comput Secur 30(8):625–642 6. Wu SX, Banzhaf W (2010) The use of computational intelligence in intrusion detection systems: a review. Appl Soft Comput 10(1):1–35 7. Hudaib AA, Hwaitat AK (2017) Movement particle swarm optimization algorithm. Mod Appl Sci 12(1):148 8. An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing 199:90–102 (2016n July 26) 9. Sakr MM, Tawfeeq MA, El-Sisi AB (2019) Network intrusion detection system based PSOSVM for cloud computing. Int J Comput Network Inf Secur 11(3):22 10. Ogundokun RO, Awotunde JB, Sadiku P, Adeniyi EA, Abiodun M, Dauda OI (2021, Jan 1) An enhanced intrusion detection system using particle swarm optimization feature extraction technique. Procedia Comput Sci 193:504–12 11. Dash T (2017) A study on intrusion detection using neural networks trained with evolutionary algorithms. Soft Comput 21(10):2687–2700 12. Einy S, Oz C, Navaei YD (2021) Network intrusion detection system based on the combination of multiobjective particle swarm algorithm-based feature selection and fast-learning network. Wirel Commun Mob Comput 16:2021 13. Amudha P, Karthik S, Sivakumari S (2013) Classification techniques for intrusion detection-an overview. Int J Comput Appl 76(16) 14. Liu J, Yang D, Lian M, Li M (2021) Research on intrusion detection based on particle swarm optimization in IoT. IEEE Access 2:938254–938268 15. Seraphim BI, Poovammal E, Ramana K, Kryvinska N, Penchalaiah N (2021) A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree. Math Biosci Eng 18(6):8024–8044 16. Tama BA, Rhee KH (2015) A combination of PSO-based feature selection and tree-based classifiers ensemble for intrusion detection systems. In: Advances in computer science and ubiquitous computing. Springer, Singapore, pp 489–495 17. Xu R, An R, Geng X (2011, July) Research intrusion detection based PSO-RBF classifier. In: 2011 IEEE 2nd international conference on software engineering and service science vol 15. IEEE, pp 104–107 18. Bamakan SMH, Amiri B, Mirzabagheri M, Shi Y (2015) A new intrusion detection approach using PSO based multiple criteria linear programming. Procedia Comput Sci 55:231–237 19. Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009, July) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications, pp 1–6 20. Moustafa N, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Mil Commun Inf Syst Conf (MilCIS) 2015:1–6 21. Ring M, Wunderlich S, Scheuring D, Landes D, Hotho A (2019) A survey of network-based intrusion detection data sets. Comput Secur 86:147–216

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22. Yulianto A, Sukarno P, Suwastika NA (2019) Improving adaboost-based intrusion detection system (IDS) performance on CIC IDS 2017 dataset. J Phys: Conf Ser 1192(1):012018. IOP Publishing 23. Song J, Takakura H, Okabe Y, Eto M, Inoue D, Nakao K (2011) Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation. In: Proceedings of the first workshop on building analysis datasets and gathering experience returns for security, pp 29–36 24. Shiravi A, Shiravi H, Tavallaee M, Ghorbani AA (2012) Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput Secur 31(3):357–374 25. Sharafaldin I, Lashkari AH, Ghorbani AA (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1:108–116 26. Sharafaldin I, Lashkari AH, Hakak S, Ghorbani AA (2019) Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. IEEE 53rd international carnahan conference on security technology, Chennai, India 27. Khraisat A, Gondal I, Vamplew P, Kamruzzaman J (2019) Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1):1–22 28. Jain NK, Nangia U, Jain J (2018 Aug) A review of particle swarm optimization. J Inst Eng (India): Ser B 99(4):407–11 29. Li G, Wang T, Chen Q, Shao P, Xiong N, Vasilakos A (2022, Sept 24) A survey on particle swarm optimization for association rule mining. Electronics 11(19):3044

CryptoDataMR: Enhancing the Data Protection Using Cryptographic Hash and Encryption/Decryption Through MapReduce Programming Model G. Siva Brindha and M. Gobi

Abstract Cloud computing has become the most significant technology in today’s world as it provides several services including physical resources and platforms for their users through distributed and parallel computing. Due to the usage of technology and its facilities, large data has been stored and retrieved by their users. Thus, processing a huge volume of data has become progressively essential to satisfy their business and other private users effectively. A tool that is most popularly used for processing this big data is MapReduce. However, providing security for the private data stored by the users is the most challenging task. Despite using several security measures such as authentication, providing complete security for private data is almost critical as there is a possibility of compromising the security policies. Thus, there is an exigent need to protect data from leakage and other security breaches. This paper presents a solution CryptoDataMR model to enhance data protection by implementing cryptographic hash and encryption/decryption algorithms in the MapReduce programming model. The proposed model uses a chain rolling double hash algorithm at the map layer and position-based shift hill cipher at the reduce layer for retaining data privacy. This model secures the data effectively through simple cryptographic algorithms with intricate results. Experimental analysis has been made for the proposed model to verify the performance in enhancing data security. The results show that the proposed model provides better performance with minimum execution time and ensures the privacy of the data. Keywords Cloud computing · Data privacy · MapReduce model · Encryption/ decryption · Hash algorithm

G. S. Brindha (B) · M. Gobi Coimbatore, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_9

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1 Introduction Due to the increase in the usage of the Internet, the data to be stored and retrieved from the server has become an inevitable process and thus it leads to an increase in the volume of data. Cloud computing and big data are the two main technologies that provide the solution to process this enormous volume of data. Specifically, cloud computing plays a significant role by providing shared virtual resources and storage with pay per use strategy over the Internet in a distributed manner. The main reason for the popularity of cloud computing is zero maintenance with scalability, complete accessibility of resources at anytime from anywhere for its users [1]. Cloud computing provides services in a distributed manner by using various nodes that are distributed worldwide [2]. To process the large data stored in the cloud, Hadoop like frameworks are employed. To implement distributed computing in a reliable manner, the MapReduce programming model that uses thousands of worker nodes is adapted widely for many applications [3, 4]. The main advantage of using MapReduce is scalability and speed as it enhances data processing by adding multiple worker nodes. The MapReduce model uses two main tasks such as a map and reduce in which several mapper nodes work parallel by mapping the key with the value which is then shuffled and sorted and then is given to the reducer to process the key-value pair [6]. However, as the data stored in the cloud is distributed, the privacy of data is a big concern and a challenging task [5]. Generally, security is a big issue in almost all fields particularly in the field of information technology. The core elements of security are confidentiality, availability, and integrity. In the case of storing user’s data in distributed computers, protecting their privacy data from unauthorized access is foremost important for cloud providers [7, 8]. Consequently, the trust delivered by cloud providers is not 100% effective [9]. Several mechanisms are popularly used to protect the data and preserve privacy. They are encryption/decryption, auditing, access control, and differential privacy [10]. Several cryptographic techniques like hash functions and encryption/decryption algorithms are utilized for protecting data; however, they require high power designs to protect data. Also, they take more time to compute the ciphers. Since the data is processed by distributed nodes, effective cryptographic algorithms must provide better security with low poser designs and less execution time [12]. The main focus of this paper is to provide privacy and security for cloud users’ data. The proposed framework termed ‘CryptoDataMR’ has been suggested to enhance data protection using cryptographic algorithms. The proposed model uses a chain rolling double hash algorithm at the map layer and position-based shift hill cipher encryption at the reduce layer for preserving data security. The rest of the paper is organized as follows. Section 2 discusses various works available in the literature related to the field of study. Section 3 describes the overall framework of the proposed model. Section 4 explains the chain rolling double hash algorithm at the map layer, and Sect. 5 explains the position-based shift hill cipher used at the reduce layer with algorithm pseudocode and illustration. Section 6

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discusses the result for the proposed work, and Sect. 7 concludes the work with suggestions for future work.

2 Related Works Several works have been suggested in the literature to enhance data protection in big data since privacy and security are challenging tasks. [7, 13, 14] suggested a layer for security and privacy between HDFS and MapReduce. The main advantage of this SMR model is to stimulate data sharing for information retrieval with minimum execution time. Derbeko et al. [15] analyzed the privacy-preserving techniques in big data including data anonymization and homomorphic encryption in which the main drawback of homomorphic encryption is its complexity. To enhance the speed while implementing encryption, a faster homomorphic encryption scheme was introduced. The scheme used fully homomorphic encryption with other methods for big data and cloud [16]. Several types of single homomorphic encryption algorithms and fully homomorphic encryption algorithms were analyzed and summarized by Geng and Zhao [17]. Other techniques such as attribute-based encryption (ABE) with reduced computational cost [18], anonymization technique in a distributed environment on cloud [19–21] was also suggested. The fast anonymization technique has been suggested to improve overall performance [22]. On the other hand, the multidimensional kanonymization Mondrian algorithm was established for the MapReduce framework [23]. The main drawback of this method is that decreased performance with an increasing number of iterations. An anonymous privacy-preserving scheme specifically big data over the cloud was introduced which helps to improve the encryption/decryption time of big data by utilizing the MapReduce framework. To provide anonymity and security, a model was suggested in which it uses a secure hash algorithm [10]. Nagendra and Sekhar [24] implemented the AES cryptography algorithm on a dual-core processor by utilizing the OpenMP API to decrease the execution time. To protect big data from malicious mapper and reducer, a differential privacy algorithm was recommended [3]. Order-preserving encryption (OPE) [25] using a general approximate common divisor (GACD) was utilized to secure the computation with MapReduce in a cloud environment [26]. The method included noise to secure the data. Similarly, for securing the data and to deliver indistinguishability notions, various attempts were made including Boldyreva et al.’s algorithm [27] with its variants utilizing beta distribution and uniform distribution and Popa et al. [28] original version of CryptDB. Boldyreva et al. introduce an order-preserving function by mapping integers in a large range dividing the range into subranges. CryptDB generally executes the SQL queries on encrypted ciphertexts by using chain encryption keys on user passwords. This scheme allows the decryption using the password in order to access the data by preserving privacy. Though several solutions exist in the literature for preserving the privacy of the data, the solutions are not

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providing complete satisfactory results in performance while protecting data. With this knowledge, the proposed research has been carried out to provide satisfactory results.

3 Overall Proposed Framework This paper presents a model called CryptoDataMR that employs cryptographic hash and encryption algorithms for the MapReduce model. The proposed model helps in enhancing data protection to preserve data confidentiality in the cloud environment using the Hadoop framework. As MapReduce is a programming model used for processing big data in a cloud environment using several worker nodes, the privacy or security of the data stored in the Hadoop distributed file system (HDFS) must be preserved for its users. Thus, for providing security, the proposed model uses encryption/decryption and hash algorithms as a data protection shield. To process the big data, several computer systems termed as worker nodes are used to implement parallel processing [3]. Generally, the framework contains four phases such as split phase, map phase, shuffle and sort phase, and reduce phase in which map and reduce are two significant phases that process the data in a key-value pair in which cryptographic algorithms are implemented. The overall framework of the proposed CryptoDataMR model is presented in Fig. 1. The input document is given as an input for the model in which the document is partitioned into several parts at the split phase for assigning each part of the document to each worker node (mapper) for the map task. The second phase is the map task in which each part of the document is assigned for each worker node termed as a mapper. The mapper is responsible for performing tokenization. Tokenization is the process of splitting the terms in the given document partition, the terms act as a key, and the count of each term becomes the value for the key. In addition to tokenization, it also provides privacy and security for the data

Data Partition1 Data Partition2 Original Data

. . . .

Mapper 1 Mapper 2

Data_fp1 Data_fp2

. . .

. . . .

Data 1, ν1 Data 2, ν2

. . .

Mapper n Secured

Data_fp m

Data k, νk Secured

Data Partition n

Secured Map Layer

Shuffle & Sort

Secured Reduce Layer

Metafile Split

Hashing

Encryption/ Decryption

Fig. 1 Overall framework of the proposed CryptoDataMR model

Secured Output Data

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by applying the proposed chain rolling double hash algorithm. Each key is given as an input for the proposed hash algorithm in which it produces the fingerprint for the terms in binary format as output. The original key and its fingerprint along with the position of the term in the partitioned data are stored securely in the HDFS as a metafile along with the position of the term for extracting the original key in the reverse operation. The hashed data is then passed to the next phase in which the data obtained from the previous phase is collected, shuffled, and sorted before passing it to the reduce phase. The shuffle and sort phase is required to minimize the task of the reduce phase. The sorted data termed as fingerprint obtained from the previous phase is given as an input for the reduce phase in which the count of each unique key is computed. The obtained results are then encrypted using and the proposed position-based shift hill cipher algorithm. The encrypted data is then stored in the file system for processing securely. To obtain the original data, the process is reversed. The encrypted data is decrypted using the proposed position-based shift hill decipher algorithm, and it results in the fingerprint of the key. Finally, with the help of information such as original data with its fingerprint mapping and the position of the term stored in the metafile, the original data will be identified and retrieved. A detailed explanation of the proposed encryption and hashing algorithms is presented in successive sections.

4 Secured Map Layer with Chain Rolling Double Hash Algorithm The map layer uses a proposed chain rolling double hash algorithm, and the steps for the proposed algorithm are depicted in Fig. 2. The input text given to the map layer is processed by each mapper. The initial step is the preprocessing in which tokenization is performed by splitting each term in the given input text and is called terms or tokens. Additionally, the alphabets and other characters are assigned with numeric values and it is termed as a lookup array. In the proposed model, the lookup starts with a, b, c, …, A, B, C, …. It contains 94 graphical ASCII characters. Then the unique values are assigned to the variables as 1, 2, 3, …, 94. Then each character in the token is processed based on their position and the base value. The base value is key for performing the hash function. The base value p is computed by finding the nearest prime number to the length of the token. The weights for the characters in the token are the position of the character in the particular token and the position always starts with the value 1. The hash value of each character c in the token is computed using the formula given in Eq. (1). ⎧ h i (c) =

p × c mod m i =1 . p × c × h i−1 (c) mod m i /= 1

(1)

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Input data for mapper and tokenization

Assigning values to letters (lookup array)

Splitting the characters of each term

Assigning Weights & Value for Base

Identifying the position of each character

Assigning the weights for the characters

Finding the length of the string and base

Computing Hash Value

Computing hash for 1st character using base

Computing hash for other characters

Computing binary hash values

Computing fingerprint of a Word

Applying weights for each digit in binary hash

Summing individual values of all characters

Computing fingerprint by binary conversion

Pre-Processing the Input Data

Fig. 2 Secured map layer with proposed chain rolling double hash algorithm

The proposed method uses modulo arithmetic in which m refers to the number of elements in the lookup array. The computed hash value of each character is converted to a binary number. The weights of the characters are assigned to each binary digit in such a way that 1 implies positive weight and 0 implies negative weights [29–31]. Then the sum of all the individual weighted values for all the characters of the term is computed. Finally, the fingerprint of the term is computed by assigning the value 1 for positive numbers and 0 for the negative numbers obtained from the summation. The final fingerprint is the value comprising 0s and 1s. The algorithm pseudocode for the proposed chain rolling double hash algorithm is presented in Fig. 3. An illustration of the proposed method is presented in Fig. 4. Here the plain text is ‘HASH’, and as the length of the string is 4, the nearest prime to the value 4 is computed and is assigned to the variable p as 5 and m as 94 (94 ASCII graphical characters). Here the values for the characters from the lookup array for each character in the input are identified. Then the hash function given in Eq. (1) is computed for which the binary conversion is carried out. Here the leading zeros are neglected. Then, the weights (position of each character in the particular string) are applied to each digit in such a way that 0 is replaced with negative weight, 1 with the positive weight, and the missing places (leading zeros) with 0. The weights for all the characters at each position are summed individually. Finally, the result of having a positive value is replaced with the binary value 1 and a negative value with 0. Thus, the fingerprint after applying the proposed hash algorithm is ‘1001100’.

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Algorithm: Chain Rolling Double (CRD) Hash Algorithm Input: Input data string str, character value for characters (lookup array) Output: Fingerprint for the input data Procedure CRD-Hash(input string str): Begin Initialize output variable hash as 0 Compute the length of the input string str Compute the nearest number p to the length of the string which is prime (base) For each character chr in the input string If it is the first character then Compute the product of p and chr and Compute hash by performing modulo with the number of elements in lookup Else Compute the product of p, chr, and previous hash value and Compute hash by performing modulo with the number of elements in lookup End If End For //Compute the binary value for the given hash value For each value in the hash While hash>0 Compute binary_hash as the remainder of hash/2 Reduce hash as hash/2 End While End For //Assigning position as weights for the binary hash For each character in the string (i) For each binary bit of the character hash If binary_hash is equal to 1 then Fingerprint of the binary hash is computed by multiplying bit by i Else Fingerprint of the binary hash is computed by subtracting i from a bit End If End For End For For each position in the fingerprint binary hash For each value in the fingerprint binary hash Compute sum as of fingerprint binary hash at a specific position End For If the sum is greater than 0 then fingerprint is appended with 1 then Else fingerprint is appended with 0 End If End For Return fingerprint End CRD-Hash Fig. 3 Algorithm pseudocode or proposed chain rolling double hash algorithm

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H

34

76

1001100

1 -1 -1 1 1 -1 -1

A

27

14

1110

0 0 0 2 2 2 -2

S

45

48

110000

0 3 3 -3 -3 -3 -3

H

34

76

1001100

4 -4 -4 4 4 -4 -4

Input String with 4 characters

values for the characters using lookup

Computing the hash value for the characters

Converting the hash to binary hash

Assigning the position of characters as weights

HASH - 1 0 0 1 1 0 0 Final Output

1001100

5 -2 -2 4 4 -6 -10

Fingerprint of the given string

Summing the column values

Fig. 4 Simple illustration for proposed chain rolling double hash algorithm

5 Secured Reduce Layer with Position-Based Shift Hill Cipher The reduce layer uses a proposed position-based shift hill cipher for performing encryption and decryption on the plain text. The detailed steps in performing encryption on the plaintext using the proposed method are shown in Fig. 5. The shuffled and sorted hashed fingerprint of data (fingerprint as a key) is given to the reducer for performing the reduce task. In this case, the count of the unique key is identified and by which the value is updated with a count. This highly reduces the number of entries and the size of the input. The hashed fingerprint of the data is given as input, and here it is referred to as plain text. Additionally, the alphabets and Input plain text (P)

Shift key S0 computation

Input ciphertext

Seed number Identification

Ciphertext (C2)

Position based shift cipher for encryption

Key matrix generation (K)

Matrix Multiplication (C1 x K) mod m

Ciphertext (C1)

Y Check for inverse N matrix existence

Add identity matrix with key matrix

Fig. 5 Secured reduce layer with position-based shift hill cipher encryption

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other characters are assigned with numeric values by creating a lookup array with 94 graphical ASCII characters as similar to the hash algorithm. The initial shift key S 0 is computed by dividing the length of the input string by 2. The shift key is not constant; however, it is computed dynamically. The successive shift key is computed by adding the previous shift key with the previous position value. As a first level, the position-based shift cipher is computed for the given input by using the formula given in Eq. (2) with the initial key S 0 and successive key S i as given in Eq. (3). ⎧ C1i =

Pi + (S0 + pos(Pi ))mod m i = 1 Pi + (Si + pos(Pi ))mod m i /= 1

Si = Si−1 + pos(Pi ) where i /= 1.

(2) (3)

After computing the shift cipher dynamically, the next step is to apply the hill cipher for which the key matrix has to be generated. The elements of the key matrix are computed using an initial seed value. Here, the seed value is the length of the string to be encrypted and it is the first element of the key matrix. The successive elements are computed by multiplying the previous value with the size of the key matrix and by applying modular arithmetic with the number of elements in the lookup array [32]. The elements of the key matrix can be generated using the formula given in Eq. (4). ⎧ K (ei ) =

s i =1 . K (ei−1 ) × n mod m i /= 1

(4)

The first element is the seed value (s), and for all the other elements, the values can be computed by multiplying the previous key element with the size of the key matrix and by performing modular arithmetic with the number of characters in the lookup array. Here the size of the key matrix will be n for n × n matrix and m is the size of the lookup array which will be 94 as specified in the hash method. Here, the key matrix must be reversible to decrypt the text. So the key matrix is verified to check whether it is reversible by computing the determinant of the matrix. If the determinant of the matrix is not 0, then the matrix is reversible, whereas if the determinant is 0, then the matrix is irreversible. Thus, to make the key matrix reversible, a unit matrix is added with the key matrix to make it reversible [33]. Then the hill cipher is applied for the given plain text with the computed key matrix. To implement hill cipher, the plain text needs to be broken into parts having the number of characters equal to the size of the key matrix (n). For an incomplete partition at the end, a filler must be added and then each partition is multiplied with the key matrix as given in Eq. (5). C2i = (K × Pi )mod m.

(5)

Here i denotes partitions. Finally, ciphertext characters obtained for each plain text partition are merged to obtain the entire ciphertext. The algorithm pseudocode

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for the proposed position-based shift hill cipher encryption is shown in Fig. 6. The reduced ciphertext which is obtained as an output from the reduce phase is again stored in the file system. However, the encrypted data can be retrieved and decrypted based on the demand. Thus, the procedure to decrypt the proposed position-based shift hill cipher is presented in Fig. 7. The procedure is just a reverse in which the ciphertext is an input and the seed value s can also be computed. Using seed value, the key matrix is generated as specified in Eq. (4). Then the inverseable property is verified for the key matrix and the diagonal value is modified by adding a unit matrix if necessary. Then Algorithm: Position based Shift Hill (PSH) Cipher Encryption Input: Input data string str, lookup array, number of elements in lookup m Output: Encrypted ciphertext Procedure PSH_Encryption(plaintext str): Begin Assign the plain text characters into an array p = str Initialize the ciphertext arrays c1, c2 and key matrix km to 0 Compute initial shift key S0 as the length of the plain text divided by 2 For each character c in the plain text p If the position is 1 then Compute shift value S1 as the sum of initial key S0 and position of c in p Compute c1 for the character by shifting the character to S1 positions towards right from c in the lookup array Else Compute shift value Si as the sum of previous shift value Si-1 and the position of c in p Compute c1 by shifting the character to Si positions towards right from c in the lookup End If End For Initialize seed value as the length of the plaintext Assign the seed value to the first element of the key matrix For each row in the key matrix km For each column in the key matrix km If the position is not the first element then Multiply previous element value with the number of rows in the km Compute the result by performing modulo with m End If End For End For If the determinant of the key matrix equal to 0 then Add the diagonal matrix with the key matrix End If Split the plaintext characters having elements equal to the number of rows in the km For each row in the key matrix For each column in the plaintext Compute c2 using matrix multiplication Apply modulo operation using the number of elements in the lookup array End For End For Return (Encrypted ciphertext) End PSH_Encryption

Fig. 6 Algorithm for position-based shift hill cipher encryption

CryptoDataMR: Enhancing the Data Protection Using Cryptographic … Input ciphertext

Key matrix generation (K)

Seed number computation

Y Check for inverse N matrix existence

Compute key inverse matrix (K-1)

Matrix Multiplication (C2 x K-1) mod m

105

Add identity matrix with key matrix

Position based shift cipher For decryption

Ciphertext (C1)

Plain text (P)

Fig. 7 Secured reduce layer with position-based shift hill cipher decryption

the inverse of the matrix is identified by finding the determinant d and the adjacency matrix adj(K) as in Eq. (6), and the decryption is carried out as in Eq. (7) where i refers to the partitions. K −1 = d × ad j(K ) mod m

(6)

) ( C1i = K −1 × C2i mod m.

(7)

The obtained output is again passed to the position-based shit cipher for decryption using the initial shift key computed by dividing the length of the input by 2. The formula to obtain the plain text is given in Eq. (8) and by computing the dynamic shift key using Eq. (3). ⎧ Pi =

C1i − (S0 + pos(Ci )) mod m i = 1 . C1i − (Si + pos(Ci )) mod m i /= 1

(8)

Thus, the result is the plain text after successfully decrypting the message. The algorithm pseudocode for the proposed decryption algorithm is given in Fig. 8. For illustration, consider the plain text ‘ATTACK’ on which the position-based shift hill cipher is applied. The values for the plain text characters from the lookup array are [27 46 46 27 29 37]. The ciphertext after position-based shift cipher can be computed by finding the initial shift key S 0 as half of the length of the plain text as 3. Plain text (Pi )

A

T

T

A

C

K

Pos (Pi )

1

2

3

4

5

6

Shift key (S i )

4

6

9

13

18

24

Value (Pi )

27

46

46

27

29

37

Ciphertext (C i )

31

52

55

40

47

61

106

G. S. Brindha and M. Gobi

Algorithm: Position based Shift Hill (PSH) Cipher Decryption Input: Input ciphertext, lookup array, number of elements in lookup m Output: Decrypted plain text p Procedure PSH_Decryption(ciphertext c2): Begin Initialize the ciphertext arrays c1, plain text p, and key matrix km to 0 Assign the numeric values to the alphabets and numbers as lookup array Compute key k as the length of the ciphertext divided by 2 Initialize seed value as the length of the ciphertext Assign the seed value to the first element of the key matrix For each row in the key matrix For each column in the key matrix If the position is not the first element then Multiply previous element value with the number of rows in the key matrix Compute the result by performing modulo with m End If End For End For If the determinant of the key matrix equal to 0 then Adjust the adjacent elements of the matrix End if Compute the inverse key matrix performing modulo with m Split the plaintext characters having elements equal to the number of rows in the key matrix For each row in the inverse key matrix For each column in the plaintext Compute c1 using matrix multiplication Apply modulo operation using the number of elements in the lookup array End For End For For each character c in the ciphertext c1 If the position is 1 then Compute shift value S1 by summing initial key and position of c in c1 Compute p for the character c by shifting the character to S1 positions towards left from c in the lookup array Else Compute shift value Si by summing previous shift value Si-1 and position of c in c1 Compute p by shifting the character to Si positions towards the left from c in the lookup End If End For Return (Decrypted plain text) End PSH_Decryption

Fig. 8 Algorithm for position-based shift hill cipher decryption

The key matrix for the proposed algorithm is generated using a seed value which is the length of the plain text as 6 for the element at the first position. The further elements are computed as in Eq. (4) in which n is 3 and m = 94. Thus, the gener⎛ ⎞ 6 18 54 ated key matrix is K = ⎝ 68 16 48 ⎠. Here as the determinant d is 0 and so the 50 56 74 key matrix is added with the identity matrix and so the new key matrix will be

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107

⎛

⎞ 7 18 54 K = ⎝ 68 17 48 ⎠. Here the new determinant will be d = 3. Thus, the cipher50 56 75 (text can be)(computed)by multiplying the key matrix with the broken plain text 31 52 55 40 47 61 as ⎛

⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ 7 18 54 31 4123 81 ⎝ 68 17 48 ⎠ × ⎝ 52 ⎠ ≡ ⎝ 5632 ⎠mod 94 ≡ ⎝ 86 ⎠ 50 56 75 55 8587 33 ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ 7 18 54 40 4420 02 ⎝ 68 17 48 ⎠ × ⎝ 47 ⎠ ≡ ⎝ 6447 ⎠mod 94 ≡ ⎝ 55 ⎠. 50 56 75 61 9207 89 Thus, the encrypted ciphertext after applying position-based shift hill cipher on ‘ATTACK’ is ‘16 Gb#9’. For decryption, the encrypted cipher C 2 along with the inverse matrix of the key is identified. To find the inverse of the key matrix, the inverse of the determinant value and adjugate of the key matrix are identified. K −1 = d −1 × ad j (K ) d × d −1 = 1mod m ⇒ 3 × d −1 = 1mod 94 ⇒ d −1 = 63 ⎛

⎞ ⎛ ⎞ ⎛ ⎞ 7 18 54 −1413 1674 −54 91 76 40 ad j ⎝ 68 17 48 ⎠ = ⎝ −2700 −2175 3336 ⎠mod 94 = ⎝ 26 81 46 ⎠ 50 56 75 2958 508 −1105 44 38 23 ⎛ ⎞ ⎛ ⎞ 91 76 40 93 88 76 K −1 = 63 × ⎝ 26 81 46 ⎠mod 94 = ⎝ 40 27 78 ⎠. 44 38 23 46 44 39 Thus, the ciphertext ( C 1 can be)(computed )by multiplying the key matrix with the broken ciphertext C 2 81 86 33 02 55 89 as ⎛

⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ 93 88 76 81 17609 31 ⎝ 40 27 78 ⎠ × ⎝ 86 ⎠ ≡ ⎝ 8136 ⎠mod 94 ≡ ⎝ 52 ⎠ 46 44 39 33 8797 55 ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ 93 88 76 02 11790 40 ⎝ 40 27 78 ⎠ × ⎝ 55 ⎠ ≡ ⎝ 8507 ⎠mod 94 ≡ ⎝ 47 ⎠. 46 44 39 89 5983 61

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This obtained ciphertext C 1 is again given as an input for positon-based shift cipher to obtain the plain text as in Eq. (8) with initial shift key S 0 as 3. Pos (C i )

1

2

3

4

5

6

Shift key (S i )

4

6

9

13

18

24

Value (C i )

31

52

55

40

47

61

Plain text (Pi )

27 (A)

46 (T)

46 (T)

27 (A)

29 (C)

37 (K)

Thus, the plain text after applying position-based shift hill cipher on ‘16 Gb#9’is ‘ATTACK’.

6 Experimental Analysis This section presents the experimental and performance analysis for the proposed model for preserving data security. Analysis 1: A simple analysis has been made for the proposed chain rolling double hash algorithm (CRD) and position-based shift hill cipher (PSH). The hardware used for this implementation is Intel (R) Core(TM) i3-4005U CPU at 1.70 Hz with 4 GB RAM in Windows 8, 64 bit operating system. The algorithms are implemented using Java programming. To compare the proposed hash algorithm with the existing algorithms such as MD5, SHA-1, SHA-256, and polynomial rolling hash, the program has been executed with a various number of input characters, and the execution time for all the methods is listed in Table. 1 and the values are represented as a graph in Fig. 9. From the experimental analysis, the polynomial rolling hash which is the base for the proposed CRD hash has a very low execution time when compared with other complex algorithms such as MD5, SHA-1, and SHA-256 algorithms. Though the proposed CRD hash is 21% slower than the polynomial rolling hash, the method provides additional security than the former algorithm. The proposed position-based shift hill cipher (PSH) is implemented and is analyzed with existing hill cipher, RSA, ElGamal, and Paillier [34]. For experimental analysis, the method is evaluated by providing a 50 MB file as an input Table 1 Comparison of hash algorithms Input characters

Hash algorithm comparison (ms) MD5

SHA-1

SHA-256

Polynomial rolling

Proposed (CRD)

36

635.23

614.15

752.47

156.25

211.12

49

772.12

748.53

866.12

178.85

223.58

64

820.14

79,519

897.54

212.12

241.98

72

857.69

814.56

945.32

236.47

265.78

CryptoDataMR: Enhancing the Data Protection Using Cryptographic …

49

236.47 265.78

857.69 814.56 945.32

Proposed (CRD)

212.12 241.98

178.85 223.58

156.25 211.12 36

Polynomial rolling 820.14 795.19 897.54

SHA-256 772.12 748.53 866.12

SHA-1

635.23 614.15 752.47

Time in milliseconds

MD5

109

64

72

Number of Input Characters

Fig. 9 Comparison of hash algorithms

and the execution time is evaluated [17]. The results are presented in Table 2. The graphical representation of the values obtained is shown in Fig. 10. Generally, the execution time for hill cipher is very low when compared with other complex algorithms, and also, it can be used specifically in low-security applications due to its low complexity nature. However, RSA, ElGamal, and Paillier take more time to compute the ciphertext and plaintext based on its level of complexity. On the other hand, the proposed method uses an enhanced model of Hill cipher, and it takes less time than other complex algorithms while improving the security of hill cipher. Analysis 2 The proposed algorithms are executed by establishing private cloud storage having five centers. The hardware used for this implementation is Intel (R) Core(TM) i34005U CPU at 1.70 Hz with 4 GB RAM. Hadoop framework is used in the proposed model. The proposed method has been analyzed by comparing the execution time for encryption, decryption, and MapReduce task of the proposed CryptoDataMR model (CDMR) with OPE algorithm (GACD) [26], Boldyreva et al.’s algorithm (BCLO) with other variants such as sampling ciphertext from the beta distribution Table 2 Comparison of cipher algorithms Methods

Algorithms (in seconds) Hill

RSA (512)

ElGamal (256)

Paillier (256)

Proposed (PSH)

Encryption

26

201

271

299

89

Decryption

25

384

161

350

94

110

G. S. Brindha and M. Gobi

450

Time in Seconds

400 350 300 250

Encryption

200

Decryption

150 100 50 0 Hill

RSA (512)

ElGamal (256)

Paillier (256) Proposed (PSH)

Various Algorithms Fig. 10 Comparison of cipher algorithms

(beta) [27] and sampling ciphertext from the uniform distribution (uniform) [28]. The experiment has been performed by varying the input bit length with the number of input as 10,000. The details are presented in Table 3. The average time to encrypt and decrypt the given text and the time taken to execute the model using MapReduce presented in Table 3 are presented as a graph in Fig. 11. As the proposed method uses hashed input, it is highly difficult for breaking the encrypted text using the proposed position-based shift hill cipher algorithm. On comparing the proposed model with other models, the execution time has been evaluated. The GACD method takes double the time for decrypting the text than encryption, whereas beta takes a similar time for encryption and decryption. However, the time to encrypt and decrypt the text is more when compared to the GACD method. Though the uniform model takes more time than GACD and beta, the time for encryption and decryption is similar. The method BCLO takes more time to complete the encryption and decryption on increased bit length. On comparing these existing methods with the proposed method, the method takes less time to encrypt and decrypt the plain text as well as to complete the MapReduce execution. Analysis 3: The proposed method has been analyzed by comparing the execution time for encryption, decryption, and MapReduce task of the proposed CryptoDataMR model (CDMR) with OPE algorithm (GACD) [26] and other variants with sampling ciphertext from the uniform distribution (uniform) [28]. The experiment has been performed by varying the input bit length with the number of input as 106272 K. The details are presented in Table 4.

CryptoDataMR: Enhancing the Data Protection Using Cryptographic …

111

Table 3 Comparison of existing algorithms with n = 10,000 inputs Algorithm

Bit length

Execution time Encryption (µs)

CDMR

MapReduce (s)

7

0.75

55.47

0.64

15

0.85

55.98

0.76

31

0.98

56.21

0.87

63

1.32

56.88

1.01

127

1.51

57.12

1.24

7

1.51

63.79

1.47

15

2.18

61.28

2.46

31

2.07

63.02

2.59

63

1.94

65.2

4.22

127

2.38

61.08

6.29

7

191.48

70.78

192.42

GACD

BCLO

15 Beta

74.391

65.47

79.255

7

57.87

64.77

58.27

15

124.79

63.7

121.53

31

221.92

63.64

221.83

63

477.23

66.74

466.03

7

42.61

64.64

42.92

15

83.4

66.29

82.53

31

179.92

63.89

180.52

63

409.13

63.91

Uniform

127

1237.3

70

400 CDMR GACD Beta Uniform

200 100

Time in Seconds

500

300

412.79

65.3

Average Encryption /DecryptionTime Time in Microseconds

Decryption (µs)

1232.2 MapReduce Execution Time

66 CDMR GACD Beta Uniform

62 58 54 50

0 7

15

31

63

7

(a) Average Encryption/Decryption Time

15

31

63

Bit Length

Bit Length

(b) Average MapReduce Time

Fig. 11 Comparison of average execution time with 10 k input

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Table 4 Comparison of existing algorithms with n = 106,272 k inputs Bit length

Algorithm

Execution time Encryption (µs)

CDMR

GACD

Uniform

MapReduce (s)

Decryption (µs)

15

2.64

58.74

1.75

31

3.14

61.47

2.31

63

3.89

65.23

2.88

127

4.32

69.25

3.23

15

6.97

59.97

4.32

31

7.95

63.02

4.58

63

8.74

71.76

7.28

127

9.93

92.09

10.31

15

307.71

56.53

280.22

31

498.78

54.89

506.35

63

1248.96

59.4

1324.25

127

4018.86

69.02

4360.11

Average Encryption/Decryption Time 1200 1000 800 CDMR GACD 600 Uniform 400 200 0

Time in Seconds

Time in Microseconds

The average time to encrypt and decrypt the given text and the time taken to execute the model using MapReduce presented in Table 3 are shown as a graph in Fig. 12. From the result presented in Table 4, GACD takes less time to encrypt and decrypt the messages; however, the proposed CDMR method takes less time than the GACD method. In the case of MapReduce execution time, the uniform method takes less time when compared with the proposed model. Thus, with GACD, the execution time is decreased by 12% with input length 15 and 31, and the execution time decreased by 19.75% and 37.5% with input length 63 and 127 for the proposed method. Similarly, the execution time for the proposed method is 164 times faster for the input length 15 than the uniform model and 176, 189, and 196 times faster with input length 31, 63, and 127 than the uniform

100

Execution Time

80

CDMR GACD Uniform

60 40 20

15

31

63

Bit Length

(a) Average Encryption/Decryption Time

15

31 63 Bit Length

(b) Average MapReduce Time

Fig. 12 Comparison of average execution time with 106,272 k input

CryptoDataMR: Enhancing the Data Protection Using Cryptographic …

113

model. From various analyses made for the proposed model, it is clear that the proposed model provides more security than traditional cryptographic algorithms with minimum execution time.

7 Conclusion As the usage of the Internet and cloud computing is increasing every day, the amount of private data to be processed is also increasing regularly, due to which processing the large volume of data and private data securely is a major issue in today’s’ world. This can be completely achieved by using the MapReduce programming model. It allows the processing of data in a parallel and distributed environment to increase the speed. This paper presents the CryptoDataMR model to enhance data protection using cryptographic methods such as hash and encryption/decryption algorithms through the MapReduce programming model. The proposed model employs a chain rolling double hash algorithm at map layer and position-based shift hill cipher, an enhanced hill cipher at the reduce layer for preserving the data security. To analyze the performance of the proposed model, experimental analysis has been made for the proposed method by varying the input size. Various results of the proposed model are also compared with the traditional and existing models. From the analysis, it is clear that the proposed model provides better performance with minimum execution time and also ensures the privacy of the data. The future work aims at cryptanalysis of the proposed algorithms as well as experimental analysis with real-time document corpus and to explore other security concerns in processing big data.

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5G Wireless Network-Based Cybersecurity Analysis Using Software Defined Phy_HetNets and Boltzmann Encoder Convolutional Basis Neural Network Manikandan Parasuraman, Ashok Kumar Munnangi, Sivaram Rajeyyagari, Ramesh Sekaran, and Manikandan Ramachandran

Abstract Machine learning [ML] is becoming more effective in the field of cybersecurity. The main objective of applying ML in cybersecurity is to make malware detection more actionable, scalable, and efficient than current methods, which rely on human engagement. Field of cybersecurity includes machine learning problems that call for effective theoretical and methodological handling. This research proposes novel technique in 5G wireless network-based cyber-attack detection utilizing deep learning [DL] methods. Here 5G wireless network cybersecurity is analysed using software defined Phy_HetNets. The cyber-attack detected using Boltzmann encoder convolutional basis neural network. the experimental analysis has been carried out based on security analysis of 5G wireless network in terms of packet delivery ratio, throughput, network security and detection of cyber-attack based on classification in terms of accuracy, precision, recall, and RMSE. Proposed technique attained accuracy of 94%, precision of 76%, recall of 66%, and RMSE of 55% in detection of cyber-attacks and packet delivery ratio of 94%, throughput of 96%, network security of 93% based on security analysis of 5G wireless network.

M. Parasuraman · R. Sekaran Department of Computer Science and Engineering, JAIN (Deemed-to-Be University), Bangalore, Karnataka 562112, India A. K. Munnangi Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh 520007, India S. Rajeyyagari Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Kingdom of Saudi Arabia e-mail: [email protected] M. Ramachandran (B) School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_10

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Keywords Wireless network · 5G networks · Cyber-attack detection · Deep learning · Security analysis

1 Introduction The exponential expansion in the importance of information technology over the past ten years has led to an increase in a range of security issues, such as unauthorized access, denial-of-service attacks, malware assaults, zero-day attacks, data breaches, social engineering or phishing, [1]. In 2010, the security industry had records for fewer than 50 million different malware executables. Over 100 million people, according to estimates, lived there in 2012. Over 900 million malicious executables were discovered in 2019 and this number is still growing, according to AV-TEST statistics. Cybercrime and network attacks may cause significant financial losses for both businesses and individuals [2]. Cybercrime damages the global economy $400 billion yearly, and the average data breach is expected to cost USD 8.19 million globally and USD 3.9 million in the United States. Security community predicts that the number of records broken will virtually treble over the next five years. By 2023, more than 10% of mobile connections worldwide are expected to be supported by 5G [3]. This increase in network traffic as well as Internet-connected devices has led to a rise in malicious network attacks, some of which can be challenging to identify. A network attack is a form of cyber-attack where attacker tries to enter a computer network or a device linked to the Internet without authorization for malevolent objectives or reconnaissance. Fastest-growing type of crime in United States, cyber-attacks severely harm companies. By 2025, it is anticipated that annual damages from cybercrime will amount to US $10.5 trillion globally. “An attack, via cyberspace, targeting an enterprise’s use of cyberspace for the purpose of disrupting, disabling, destroying, or maliciously controlling a computing environment/infrastructure; or destroying the integrity of the data; or stealing controlled information,” is how NIST defines a cyber-attack [4]. Any industrial setting can greatly benefit from predictive capabilities, especially when it comes to preventing cyber-attacks. Utilizing a strategy or technique based on the facts at hand, machine learning assists in task solving. Businesses can use machine learning to detect harmful activity faster and thwart assaults before they start, which is a common application in cybersecurity. Any asset should be protected with layers of cybersecurity. While ML alone will never be a panacea for cybersecurity, when used in conjunction with other measures, it can enhance intrusion detection. IoT is gaining popularity and is now present in homes, cars, and wearable technology [5]. These networking devices lack a user interface, a security protocol, computing power, and storage space to support firewalls as well as diagnostic tools, and they also are unable to establish a Wi-Fi direct connection to the Internet. These flaws create a temptation for those looking to spread DDoS attacks or other criminal intrusions, as well as for companies looking to collect data for intelligent management as well as digital evidence [6].

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Contribution of this research is as follows: 1. To propose novel method in 5G wireless network-based cyber-attack detection using DL methods. 2. Here 5G wireless network cybersecurity is analysed using software defined Phy_ HetNets. 3. The cyber-attack detected using Boltzmann encoder convolutional basis neural network. Organization of this paper is as follows: Sect. 2 gives related works, Sect. 3 explains proposed cyber-attack detection utilizing DL methods based on 5G networks, Sect. 4 discuss experimental analysis with discussion, and Sect. 5 gives conclusion and future scope.

2 Related Works The work that has been published on the subject of analysing CPSs’ susceptibility to malicious assaults has only looked at a few specific attacks against different CPSs. Integrity attacks on state estimation methods, for instance, were described in [7], where integrity assaults consciously weakened integrity of sensor measurements or control packets. When part of sensors or actuators are taken over by deception assaults, issue of state estimation as well as control for linear systems is taken into consideration in [8]. The impact of sparse sensor assaults, in which an adversary arbitrarily fabricated data from a subset of sensors, was taken into consideration in [9] in order to develop a state reconstruction of discrete-time linear CPSs. State evaluation frameworks for electrical power networks were developed in [10, 11], and took false data injection (FDI) attacks into consideration. In order to distinguish DDoS assaults from IoT devices, the authors of [12] use stateless features, packet size, inter-packet interval, protocol, bandwidth, and the number of different destination IP addresses. A framework for multi-level DDoS mitigation and a technique to thwart and recognize DDoS attacks at each tier are provided in the literature [13]. In order to make an early detection of ML-based DDoS attacks, authors in [14] blend new features with legacy features. A classification-based DDoS attack detection in IoT is suggested in study in [15]. To determine if a packet in an IoT network is anomalous or not, authors of [16] offer DL methods such MLP, CNN, and LSTM. Literature [17] focuses on protocols HTTP, TCP, and ICMP and seeks to assess botnet assaults with the SVM algorithm in IoT. A measure of variance between two probability distributions is called an data metric. The shortcomings of DDoS detection systems have been addressed by a number of information theory-based metrics [18]. The image below [19] provides a high-level overview of End-to-End 5G network danger landscape. The classification of the dangers that might occur on 5G networks was the inspiration for this article. In [20], a resistive framework is built to defend the system once the probability of a data integrity attack in the ideal power flow is first examined.

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3 System Model This section discusses novel method in 5G wireless network-based cyber-attack detection utilizing DL methods. Here 5G wireless network cybersecurity is analysed using software defined Phy_HetNets. The cyber-attack detected using Boltzmann encoder convolutional basis neural network. Proposed architecture is given in Fig. 1. 5G wireless network cybersecurity analysis using software defined Phy_ HetNets: Using the SDx paradigm, we define a broad framework for SD-IoT, as shown in Fig. 2. Proposed SD-IoT framework is thought of as both a specific sort of suggested SDN-based IoT architecture and an expanded version of SDN structure applied to IoT. Through APIs, IoT servers provide a range of applications and services. The control layer, which is made up of a controller pool, is made up of several SD-IoT controllers. Infrastructure layer is made up of many SD-IoT switches. Each SD-IoT switch combines the capabilities of an SDN switch and an IoT gateway. Consider independent and identically distributed Rayleigh fading on all sub-6 GHz channels, as well as a significant route loss with an exponent of αμ. The letter Eq. (1) stands for the channel that connects kth user in jth cell to ith MBS hi(k) j =

√

(k) X i(k) j ui j

(1)

∑ We begin by introducing the auxiliary variables k x j,k and defining y = {yj}j, then we loosen the restrictions and think of problem (P1' ) as an Eq. (2)

Fig. 1 Proposed architecture based on 5G network security analysis with deep learning training

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Fig. 2 SD-IoT architecture

( ') ∑∑ ( ) ∑ ( ) Pl : maxx,y x j,k log r j,k − y j log y j s.t. yj =

∑

k∈K j∈B

j∈B

x j,k , ∀ j

(2)

k

Lagrangian function is derived by Eq. (3, 4) ∑∑ k

=

x j,k logr j,k −

j

( ) ∑ ( ) ∑ y j log y j + μj yj − x j,k

j

∑∑ k

∑

j

[ ( ) ] ∑ [ ( )] ∑ x j,k log r j,k − μ j + y j μ j − log y j x j,k

j

(3)

k

j

(4)

k

where the nonnegative Lagrangian multipliers are μ = {μj}. As a result, Eq. (5–7) is used to denote the equivalent Lagrangian dual function. g(µ) =

∑

gk (µ) + g y (µ), gk (μ) = supx

k

∑

[ ( ) ] x j,k log r j,k − μ j

(5)

j

s.t. x j,k = {0, 1},

∑

x j,k = 1,

(6)

j

g y (μ) = sup y

∑ j

( ( )) y j μ j − log y j

(7)

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As a result, the dual problem in terms of μ can be expressed as Eq. (8) minμ

∑

gk (μ) + g y (μ).

(8)

k

By updating xj,k as Eq. (9), the dual problem’s solution can be obtained ) ( ( ) jk∗ = argmax log r j,k − μ j .

(9)

j

Then UE k will link up with BS j, meaning that xj,k will equal 1 if j = j and 0 otherwise. ( ) 2 (10) λu (r ) = λμ 1 − e−πλμ r where r stands for the mileage from the 0th MBS. Consider that Nk is a homogeneous PPP with density λμ outside of an exclusion ball with radius Req = p 1/(π λμ) and a centre at the 0th MBS. Furthermore, for k 6 = k 0 Nk and Nk 0 are independent. After comparing received signal with appropriate pilot sequence during uplink training stage, observed channel from ith MBS to kth user is stated as Eq. (11) yii(k) =

∑√

∑ √

Pp hi(k) j +

(l) Pe hie + ni

(11)

l∈Φ(k) e

j∈Φμ

where Pp and Pe stand for the user’s and Eve’s respective uplink transmission power levels. (k) e is collection of Eves that launch pilot attacks for user k, and (k, ni) is noise with CN (0, σ 2). Independent thinning of PPP indicates that significant density of Φ (k) e is λ μ e = λE since each Eve will arbitrarily select pilot sequence from K sequences by Eqs. (12) and (13) (k) hii

√ =

Pp X ii(k) ∑ii(k)

yii(k)

(12)

where ∑ii(k) =

∑

∑

Pp X i(k) j +

(l) Pe X ie + σ 2.

(13)

l∈Φ(k) c

j∈Φμ

Then channel error is given by Eq. (14) ( hˆ ii(k)

=

hii(k)

−

(k) hii

∼ CN 0,

( X ii(k)

1−

Pp X ii(k) ∑ii(k)

) ) I .

(14)

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RBs can then be distributed to the users when each SC’s broadcast antenna set has been chosen. In the light of coordinated beam forming, this is done jointly for all BSs; for the purpose of mitigating interference, cardinality Ks, b and Is, b. We employ an F-norm-based strategy, similar to the antenna selection. BS-user-RB index triplets are first sorted by Eq. (15) { argsort

2 h˜ s,n,bF

} (15)

Nssel

(s,n,b),∀s∈S,∀n∈K j ,∀b∈(1,...,B]

˜ Notation hs,n,b ∈ C1 × N sel and Nssel = NM are the relevant values for macro BS, which does not perform antenna selection. Boltzmann encoder convolutional basis neural network: Boltzmann machines that are general (unrestricted) are within the category of stochastic energy-based models. An energy is connected to each configuration (state) of the system under consideration in energy-based frameworks [13]. Equation (16) provides energy of a specific joint configuration between two layers E(v, h) = −

m ∑

ai vi −

i=1

n ∑

bjh j −

m ∑ n ∑

j=1

vi h j wi j

(16)

i=1 j=1

If wij is weight of link between visible unit I and hidden unit j, I v, and j h are binary states of respective units, I a and j b are biases. A probability of joint existence of visible and hidden layer is described as Eq. (17) based on formulation of energy function p(v, h) =

1 −E(v,h) e Z

(17)

∑ where Z = v,h e−E(v,l) is partition function. As a result, by adding up all potential hidden vectors as eq. (18) it is feasible to determine the likelihood that the visible vector will be assigned the value v. p(v) =

1 ∑ −E(v,h) e Z u

(18)

Given a visible vector v and the assumption that there are no direct connections between hidden units, conditional probability of unit j h’s binary state being set to 1 can be determined using the formula Eq. (19) (

)

⎛

p h j = 1|v = σ ⎝b j +

∑ j

⎞ vi w y ⎠

(19)

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When the sigmoid function σ() is present. Additionally, given a hidden vector h, it is possible to calculate the probability that the visible unit is 1 using the Eq. (20) ⎛ p(vi = 1|h ) = σ ⎝ai +

∑

⎞ h jwj⎠

(20)

j

Minimization of a log-likelihood could be taken into consideration when training a generative model using training samples. Log-likelihood is defined specifically as Eq. (21) L(Dtrain ) =

∑

log p(v, h)

(21)

where the training dataset Dtrain is used. / / \ \ ∂log(L(Dtrain ))) ∂log(Dtrain )) ∂log( p(v, h)) = − ∂wi j ∂wi j ∂wi j data model

(22)

Considering Eqns. (21), (22), it could be obtained that Eq. (23) ⟩ ⟩ ⟨ ∂log( p(v, h)) ⟨ = vi h j data − vi h j model ∂wi j

(23)

where “. > ” stands for the expectation under data or methods distribution. However, it is impossible to learn this model’s exact maximum likelihood. Exact computation of data-dependent expectation takes an exponentially long time no matter how many hidden units there are, and the model expectation takes an exponentially long time no matter how many hidden and visible units there are. In this context, symbol denotes a convolutional operation with operands I and W, where I Rcwinhin and c, I ∈ Rc × win × hin, respectively, stand for channels, width, and height. Each decoder corresponds to a pooling unit of encoder, which forms foundation of entire design. Consequently, CNN decoder has 13 de-convolution layers. A multi-class soft-max classifier receives computations from decoder and outputs class probabilities intended for each individual pixel. It is preferable to maintain the buried layer’s neurons “inactive” for the majority of the time. Let’s assume that means hidden unit j has been activated. For a certain input X in the forward propagation process, the hidden layer activation may be written as, where W stands for weights between input layer and hidden layer and b for biases. Then, Eq. (24) can be used to express the average activation of the hidden unit j: aρ j =

n ] 1 ∑[ a j (x(i )) n i=1

(24)

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Each hidden neuron should have an average activation close to zero, meaning that the hidden layer’s neurons are largely “inactive.” To do this, the objective function that penalizes if it deviates noticeably from is given a sparse term that is added. The sentence is written as Eq. (25): Ppenalty =

S2 ∑

( || ) K L ρ ||ρ j

(25)

j=1

where S2 is the hidden layer’s total number of neurons. The Kullback–Leibler divergence, denoted by KL(), is represented by the expression (26): ( || ) ρ 1−ρ K L ρ ||ρ j = ρlog + (1 − ρ)log ρj 1 − ρj

(26)

) ( The feature of this penalty function is that K L ρ||ρ j = 0 if ρ j = ρ. If not, it grows monotonically as it deviates from, serving as the sparsity constraint. Equation (27) is the definition of the neural network’s cost function. [ C(W, b) = +

)] n ( 1∑ 1 2 h w,b (x(i)) − y(i ) n i=1 2 m l−1 sl sl+1 ∑( ) γ ∑∑ Wi j (l) 2 l=1 i=1 j=1

(27)

Figure 3 illustrates the variational autoencoder (VAE), a crucial generation model made up of an encoder network Qϕ (Z|X) and a decoder network Pθ (X|Z). VAE is trainable using gradient descent approach and can pick up on approximative inference. The parameterized θ decoder network generates data that maps Z to reconstructed data X using the latent variable. Here, we build encoder and decoder with specifications θ and ϕ using deep neural networks. Fundamental principle of VAE is to pick data points that fit the probability distribution P(X), where X stands for a random variable of data. Objective of VAE is to maximize log-likelihood probability of P(X), which can be achieved by reconstructing

Fig. 3 Variational autoencoder (VAE) architecture

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the input data as much as possible, as shown in Eq. (28): [ ] logP(X ) = E logP(X |Z ) − D K L [Q(Z |X )|P(Z ) ] [ ] + D K L [Q(Z |X )||P(Z |X ) ≥ E logP(X |Z ) − D K L [Q(Z |X )||P(Z ) ]

(28)

Here variational lower bound objective is described as Eq. (29): [ ] L(θ, φ; X ) = E logP(X |Z ) − D K L [Q(Z |X )||P(Z ) ].

(29)

The VAE objective function, L, is described as variation lower bound. The reconstruction loss is represented by first element in Eq. (29). It motivates the decoder to develop the ability to recreate the input data. To maximisz variational lower bound L is the aim of training VAE, in other words. The Radial Basis Function-specific activation functions are present in this network’s hidden layer (RBF). In addition, output layer has linear neurons, while the hidden layer also includes radial kernel functions. RBF is referred to as a special class of linear functions with a distinguishing characteristic, namely, a response that monotonically reduces or grows with distance from a centre point. In order to predict the expected outcomes, the hidden layer must execute non-linear transformation of the input and output layers while performing linear regression. When compared to other networks, RBF has more hidden layers functioning simultaneously. The Gaussian and Multiquadric are widely utilized, despite the fact that there are numerous radial kernels that can be utilized for RBF. Refer to (30) and as the scalar input for Gaussian and Multiquadric (31). ) ( (γ − u)2 Gaussian φ = exp − v √ v2 + (x − u)2 Multiquadric φ = v

(30)

(31)

where v = radius and u = centre. The Ith radial basis hidden unit receives the planned k-dimensional input vector γ . The radial centre of the concealed layer is indicated by the units u1, u2,…, uh. Refer to for the output of the Ith hidden unit (32) ) ( ) ( γ − u i2 φi = φ γ − c j = exp − 2σi2 m √ δ = dmax / h

(32) (33)

where dmax is the greatest distance possible between the network’s centres and its output y (34).

5G Wireless Network-Based Cybersecurity Analysis Using Software …

y=

n ∑

127

wi · φ(||γ − u i ||)

(34)

i=1

where h is number of centres and wi is weight of i-th concealed unit. By employing the metaheuristic BFO technique during network training, the weight of the RBF will be determined.

4 Performance Analysis We first assess our suggested method’s effectiveness using the three previously mentioned datasets. Then, to demonstrate superiority and effectiveness of our proposed technique, we compare it to state-of-the-art system. Dataset description: Majority of ML studies have validated their findings using synthetic or actual network data. Some of the databases, including DARPA 98, KDD99, UNSW-NB15, and ISCX, are now publicly accessible, however the majority remain secret for security reasons. There have been many datasets produced, but few of them are true IoT and network traffic datasets that include new Botnet instances. Additionally, some databases don’t provide any new capabilities, while others don’t account for IoT-generated traffic. In some circumstances, the testbed used was not realistic, and in others, the assault scenarios weren’t diversified enough. Table 1 gives analysis based on security analysis of 5G networks. Here the security analysis has been carried out in terms of packet delivery ratio, throughput, network security. The dataset compared are DARPA 98, KDD99, UNSW-NB15, ISCX and compared with existing CNN and LSTM. Table 1 Analysis based on security analysis of 5G wireless network for various security dataset Dataset DARPA 98

KDD99

UNSW-NB15

ISCX

Techniques

PDR

Throughput

Network security

CNN

85

88

81

LSTM

88

91

83

5G_ Phy_HetNets_BECBNN

89

93

85

CNN

86

89

82

LSTM

88

92

86

5G_ Phy_HetNets_BECBNN

90

94

88

CNN

87

91

85

LSTM

89

93

88

5G_ Phy_HetNets_BECBNN

92

95

89

CNN

88

92

89

LSTM

92

94

92

5G_ Phy_HetNets_BECBNN

94

96

93

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The above Fig. 4a–c gives analysis based on security analysis for security dataset. Here proposed technique attained packet delivery ratio of 89%, throughput of 93%, network security of 85%, existing CNN attained packet delivery ratio of 85%, throughput of 88%, network security of 81%, and LSTM attained packet delivery ratio of 88%, throughput of 91%, network security of 83% for DARPA 98 dataset. For KDD99 dataset, the proposed technique attained packet delivery ratio of 90%, throughput of 94%, network security of 88%, existing CNN attained packet delivery ratio of 86%, throughput of 89%, network security of 82%, and LSTM attained packet delivery ratio of 88%, throughput of 92%, network security of 86%. The proposed technique attained packet delivery ratio of 92%, throughput of 95%, network security of 89%, existing CNN attained packet delivery ratio of 87%, throughput of 91%, network security of 85%, and LSTM attained packet delivery ratio of 89%, throughput of 93%, network security of 88% for UNSW-NB15 dataset. For ISCX dataset, the proposed technique attained packet delivery ratio of 94%, throughput of 96%, network security of 93%, existing CNN attained packet delivery ratio of 88%, throughput of 92%, network security of 89%, and LSTM attained PDR of 92%, throughput of 94%, network security of 92%.

(b) Throughput

(a) PDR

(c) network security Fig. 4 Analysis based on security analysis of 5G wireless network in terms of a PDR, b Throughput, c network security

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Table 2 gives analysis of detection of cyber-attack based on classification. Here the security analysis has been carried out in terms of accuracy, precision, recall, and RMSE. The dataset compared are DARPA 98, KDD99, UNSW-NB15, ISCX, and compared with existing CNN and LSTM. Above Fig. 5a–d gives analysis for detection of cyber-attack based on classification. Here proposed technique attained accuracy 83%, precision 71%, recall 58%, and RMSE 45%, existing CNN attained accuracy 79%, precision 65%, recall 54%, and RMSE 42%, and LSTM attained accuracy 81%, precision 68%, recall 56%, and RMSE 43% for DARPA 98 dataset. For KDD99 dataset, proposed technique attained accuracy 85%, precision 75%, recall 61%, and RMSE 51%, existing CNN attained accuracy 81%, precision 68%, recall 55%, and RMSE 44%, and LSTM attained accuracy 83%, precision 72%, recall 59%, and RMSE 49%. The proposed technique attained accuracy 86%, precision 75%, recall 62%, and RMSE 52%, existing CNN attained accuracy 82%, precision 71%, recall 56%, and RMSE 45%, and LSTM attained accuracy 84%, precision 73%, recall 58%, and RMSE 49% for UNSW-NB15 dataset. For ISCX dataset, proposed technique attained accuracy 94%, precision 76%, recall 66%, and RMSE 55%, existing CNN attained accuracy 89%, precision 72%, recall 59%, and RMSE 51%, and LSTM attained accuracy 92%, precision 74%, recall 63%, and RMSE 53%. The fundamental prerequisite of any remote organization isn’t just its spectral efficiency however its viability of energy boundaries also. The development of 5G organization will prompt a multi-overlap expansion in quantity of associated devices. This will prompt a critical ascent in power utilization by organization. In this manner, it is vital to carry out a manageable organization with sustainable power sources, ideal usage of force, and least power leakage. Authentication of network devices is essential in any organization and needs a many milliseconds postpone in current framework. This strategy offers a preventive structure that can keep away from event Table 2 Analysis for detection of cyber-attack based on classification Dataset

Techniques

Accuracy

Precision

Recall

RMSE

DARPA 98

CNN

79

65

54

42

LSTM

81

68

56

43

KDD99

UNSW-NB15

ISCX

5G_ Phy_HetNets_BECBNN

83

71

58

45

CNN

81

68

55

44

LSTM

83

72

59

49

5G_ Phy_HetNets_BECBNN

85

75

61

51

CNN

82

71

56

45

LSTM

84

73

58

49

5G_ Phy_HetNets_BECBNN

86

75

62

52

CNN

89

72

59

51

LSTM

92

74

63

53

5G_ Phy_HetNets_BECBNN

94

76

66

55

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(a) Accuracy

(b) Precision

(c) Recall

(d) RMSE

Fig. 5 Analysis based on classification in terms of a Accuracy, b Precision, c Recall, d RMSE

of such assaults or possibly easing their effect. A profound comprehension of potential danger situations is of foremost significance to have the option to recognize the related security weaknesses and in the long run execute proper preventive (rather than receptive and exorbitant) activities particularly in the signalling plane.

5 Conclusion Existing methods for network security are insufficient in the light of requirements for 5G network as well as new feature sets to serve constantly expanding and complicated business requirements. This research proposes novel technique in 5G wireless network-based cyber-attack detection using deep learning architectures. Proposed 5G wireless network cybersecurity is analysed using software defined Phy_HetNets and cyber-attack detected using Boltzmann encoder convolutional basis neural network. In comparison to conventional ML techniques, adoption of deep learning model in this method increases detection accuracy. Proposed technique attained accuracy 94%, precision 76%, recall 66%, and RMSE 55% in detection of cyber-attacks and packet delivery ratio of 94%, throughput of 96%, network security of 93% based on security analysis of 5G wireless network. On-device as well as traffic behaviour learning will also be a part of the future model, which will be trained in real-time

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utilizing reinforcement as well as recurrent learning. This will help us increase detection accuracy for a secured 5G environment. In order to strengthen the security of 5G frameworks and more, it may be necessary to develop the cyber protocol behaviour of 5G wireless networks to be incredibly distinctive and adaptable with real-time service awareness capabilities through ubiquitous intelligence. This will serve as the foundation for our upcoming works.

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17. Park C, Lee J, Kim Y, Park JG, Kim H, Hong D (2022) An enhanced AI-based network intrusion detection system using generative adversarial networks. IEEE Internet Things J 18. Mishra S (2022) Cyber-security threats and vulnerabilities in 4G/5G network enabled systems. Int J Comput Sci Eng 25(5):548–561 19. Tai Y, Gao B, Li Q, Yu Z, Zhu C, Chang V (2021) Trustworthy and intelligent covid-19 diagnostic iomt through xr and deep-learning-based clinic data access. IEEE Internet Things J 8(21):15965–15976 20. Tanveer J, Haider A, Ali R, Kim A (2022) An overview of reinforcement learning algorithms for handover management in 5G ultra-dense small cell networks. Appl Sci 12(1):426

Wearable Sensor Based Cloud Data Analytics Using Federated Learning Integrated with Classification by Deep Learning Technique Ashok Kumar Munnangi, Sivaram Rajeyyagari, Ramesh Sekaran, Nashreen Begum Jikkiriya, and Manikandan Ramachandran

Abstract Wearable sensors and person to person communication stages assume a key part in giving another strategy to gather patient information for productive medical care observing. In any case, persistent patient observing utilizing wearable sensors produces a lot of medical care information. Deep learning (DL) is a potential method for big data analytics since it can be utilized to quickly classify as well as analyse this plethora of data. This research proposes novel method in wearable sensor based cloud data analysis by federated learning as well as classification using DL. Input is collected by cloud architecture based wearable sensor and processed for noise removal as well as normalization. Cloud data is secured based on federated learning and classified using U-Net convolutional auto encoder radial neural networks. Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and NSE for different wearable sensor dataset. Proposed method attained accuracy 98%, precision 85%, recall 75%, F-1 score 66%, RMSE 55% and NSE 55%.

A. K. Munnangi Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh 520007, India S. Rajeyyagari Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Kingdom of Saudi Arabia e-mail: [email protected] R. Sekaran Department of Computer Science and Engineering, JAIN (Deemed-to-Be University), Bangalore, Karnataka 562112, India N. B. Jikkiriya Department of Artificial Intelligence and Machine Learning, Malla Reddy University, Hyderabad, Telangana 500100, India M. Ramachandran (B) School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_11

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Keywords Wearable sensor · Cloud data analysis · Federated learning · Classification · Deep learning

1 Introduction The goal of medical practise is to identify patients at the earliest possible clinical symptom, track the development of the disease, and quickly identify the best treatment options. To maximise the scientific validity of research, clinical scientists as well as drug developers aim to enrol large numbers of patients into trials with little expense as well as effort. Patients want to improve their quality of life while lowering the number of times they physically visit the clinic, and patient care aims to reduce this dependence and move treatment into patients’ homes [1]. A notion known as wearable DL emerged from a comprehensive analysis of the architecture and functionalities of the developing big data system as well as human nervous system (NS). Despite being a biological mechanism, the human nervous system (NS) essentially motivates convergence, cooperation, and coordination of 3 crucial components, such as wearable technology (WT), IoT and DL in development of big data systems for useful results and well-informed decision making [2]. One of the most hotly debated research areas among academic and commercial academics working to develop ubiquitous computing as well as human computer interaction is Human Activity Recognition (HAR). Live streaming of artificial intelligence (AI) and IoT is now possible [3]. The development of HAR has made it possible for practical applications to be made in a variety of real-world industries [4]. The identification of various human activities, such as running, sitting, sleeping, standing, and walking, is made possible by mathematical models that are based on data about human activity. The classic machine learning (ML) process has undergone a revolution in the last ten years thanks to deep learning (DL), which has increased performance in a variety of areas [5]. The performance and resilience of HAR have been enhanced by DL, accelerating its adoption and use in a variety of wearable sensor-based applications. DL works well for many applications for two main reasons. DNN with little domain knowledge can effectively learn representative features from raw inputs. Number of HAR-based applications has significantly increased thanks to this expressive capacity in DL [6]. Contribution of this research is as follows: (1). To propose novel method in wearable sensor based cloud data analysis by federated learning as well as classification utilizing DL. (2). The cloud data is secured based on federated learning as well as classified utilizing U-Net convolutional auto encoder radial neural networks.

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2 Related Works The majority of these methods use CNN [7], RNN [8], LSTM network, hybrid CNNLSTM network, and a more complex LSTMCNN-LSTM hierarchical network [9] to automatically extract features from collected raw sensor data. Impacts of noise or random changes have been reduced in [10] by using a variety of ways to represent time series. More options to examine the variances of characteristics from other spaces are provided by these manipulations on time series sensor data [11]. Other applications frequently employ “shallow” elements [12]. The data is subsequently taught to be classified using the provided features using techniques like decision trees and SVM [13]. In order to optimise the accuracy that may be gained from any classification approach, Work [14] presented a technique for HAR that combines numerous classification techniques, often known as an ensemble of classifiers. DL is the capacity to use back-propagation (BP) to learn the deep NN designs [15]. When training multi-layer perceptrons (MLP), the error backpropagation method [16] was first proposed in 1986. This method involves backpropagating error between predicted output as well as provided labels into network in order to fine-tune weights and minimise the loss/cost function on a non-convex surface. The visual cortex served as a loose inspiration for DL [17]. Similar to [18], but without a focus on ML. Reviews of accelerometer-based gait analysis as well as inertial sensor-based gait analysis are provided in the studies [19]. Reviews of gait-based recognition, which determines a person’s identity based on their walking style, are provided in [20]. Paper [21] focus on deep learning methods for gait analysis in security and healthcare. Similar to this, [22] presents a survey on gait analysis that is focused on fall detection and fall prevention. Jenkins [23] offers a thorough overview of human gait analysis, including methods, uses, and ML.

3 System Model This section discusses novel method in wearable sensor based cloud data analysis by federated learning and classification utilizing DL. Input has been collected by cloud architecture based wearable sensor and processed for noise removal and normalization. Cloud data is secured based on federated learning and classified using U-Net convolutional auto encoder radial neural networks. Proposed architecture is given in Fig. 1. Data Pre-Processing: The wearable sensors’ data is cleaned up and normalised during the data pre-processing stage to provide a dataset that is appropriate for developing an identification method. Following this procedure, all of the missing as well as anomalous data values are eliminated as follows: • Imputation using linear interpolation technique fixes missing values in sensor data;

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Fig. 1 Overall architecture

• Noises are removed. Using a median filter as well as third order low-pass Butterworth filter with a cutoff frequency of 20 Hz, noise in sensor data utilized in this study was minimised. This rate is suitable for recording human movements because 99% of its energy is held below 15 Hz; • Special characters are removed; • Each sensor’s data is transformed using mean as well as standard derivation using a normalisation procedure. The normalisation procedure in this work employs a Min–Max technique to make a linear modification of raw sensory data. Procedure states that data is divided to train classifier. Securing Cloud Data Based on Federated Learning In a decentralised scenario without a central PS to oversee training of all edge devices, we consider a federated learning method. Every n ∈ N device has a unique data set. Dn and all devices seek to cooperatively develop a shared ML method while addressing the distributed stochastic optimization issue by Eq. (1),

Wearable Sensor Based Cloud Data Analytics Using Federated Learning …

minimize F(θ ) Δ θ

∈Rd

1 ∑N f i (θ ) i =1 N

137

(1)

∑ f i (θ ) = |D1i | ξ ∈ Di f i, ξ (θ ) is local loss function at device I while F(θ ) is overall loss function. Loss for specification θ ∈ Rd on a data sample ξ at device I is specifically given as f i, ξ (θ ). Each device i∈N in distributed learning method has a local specification vector θ it that, at t-th iteration, roughly approximates solution to issue Eq. (1). Designed communication method contains following 2 parts: • Scheduling: It is advisable to arrange various devices to communicate in distinct transmission blocks in order to deal with wireless interference. • Transmission: The consensus phase over wireless networks is supported by the transmission techniques. (1) Scheduling: In this study, we investigate D2D communication to enhance communication performance. Therefore, by arranging enrolled receiving devices as active “PS” in separate transmission blocks, we concentrate on designing interference-free scheduling. There are no two enrolled receiving devices attached to same device in this interference-free schedule arrangement. You can use the graph colouring technique to discover a good scheduling strategy that reduces the number of transmission blocks. (2) Transmission: The signal received at the enrolled receiving device I at transmission block t can be expressed mathematically as Eq. (2) yit =

∑ j ∈ Ni

h it j x tj + z it

(2)

where the transmit signal x tj encodes the data from the local model h it j ∈ C, the additive noise vector z it ∈ Cd , and θ tj . Additionally, Eq. (3) places a peak power restriction on channel inputs [|| || ] t 2 E || x it ||2 ≤ P , ∀i ∈ N

(3)

Loss Function Basic objective is to identify the model parameter w ∈ Rd that, when combined with the loss function f (w, xi , yi ), best describes the output yi . For sake of simplicity, we rewrite f (w, xi , yi ) as f i (w). On the data collection Dk of UEk , the loss function can be defined as Eq. (4) Fk (w) Δ

1 ∑ f i (w). i ∈ Dk Dk

(4)

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The following distributed optimization issue, Eqs. (5, 6), is the focus of the FL algorithms, which seek to minimise the global loss value: minw ∈ Rd F(w) =

∑ k ∈ Ktot

pk Fk (w),

/ where pk Δ Dk D is the weighting for ∑ pk = 1. satisfying pk ≥ 0 and

(5)

(6)

k ∈ Kcot

The implementation of Deep Learning with Differential Privacy (DP) [5] as a useful learning technique at an acceptable complexity cost. Figure 2 depicts the federated learning architecture. We take into account a federated deep learning situation in which K individuals learn a multi-layered deep neural network model collectively without disclosing their own training data. In the sense that they do not send any messages that are erroneous but may conduct privacy attacks on the shared information from other participants, we consider that certain participants or the server may be semi-honest adversaries. It should be noted that while data x is only kept a secret from its owner, the mapping g(t) is presumed to be known by all participants in federated learning. U-Net Convolutional Auto Encoder Radial Neural Networks The data was initially pre-processed to reduce noise and resize the image. Following that, the image was segmented for edge normalisation and smoothing. In a 2D CNN, two-dimensional feature graph is subjected to convolution, and feature is only derived

Fig. 2 Federated learning architecture

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from spatial dimension by Eq. (7) ⎛ vi j = Φ⎝bi j + xy

i −1 i −1 Q ∑ P∑ ∑

m

p=0 q =0

⎞ (x + p)(y + q) ⎠

pq

wi jm × v(i − 1)m

(7)

In a 3D CNN, 3D image cubes like HSIs are subjected to 3D convolution kernels, and the image characteristics are concurrently derived from spatial dimension and spectral dimension. In formal terms, Eq. (8) provides value for position (x, y, z) on jth feature map in ith layer ⎛ x yz vi j

= Φ⎝bi j +

i −1 R i −1 Q i −1 ∑ P∑ ∑ ∑

m

p=0 q =0 r =0

⎞ pqr wi jm

×

(x + p)(y + q)(z + r ) ⎠ v(i − 1)m

(8)

The other specifications are same as in 2D convolutions, and Ri is depth of 3D pq convolutional kernel. wi jm is weight specification at location (p, q, r). However, amount of excessive weights in NN methods is the key distinction between regression as well as NN methods. With specialised variables for difficult-to-interpret effects, this feature gives neural networks more flexibility for modelling non-linear functions through many interactions. Prediction loss function is then minimised during training as well as validation stages to create the recognition models that are utilised in deep learning. Figure 3 depicts the CNN building’s layout. With so many layers, the Convolutional Neural Network resembles a lengthy shelf. Every layer contains a enough number of computational units or components to handle multiple datasets simultaneously. Same function is carried out by evaluating units or elements in the same layer when processing input data. Block B˙ mn = {b(x, y), x = 1, · · · , P, y = 1, . . . . . . , Q , which denotes m-th row as well as n-th column blocks, is subject of our current attention. The strided convolution’s (SC) output is controlled by Eqs. (9, 10) because it traverses the input activation map with strides that are equal to the block’s (P, Q) size

Fig. 3 Architecture of CNN

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⎧ ⎨ ⎩

L2P Bmn

=

AP Bmn =

√∑

P x =1

(9)

∑Q

2 y = 1 b (x, y) P×Q ∑P ∑Q 1 y =1 x =1 P×Q

(10) b(x, y)

The flaws of AP and MP are addressed by the SP. The average produced by the AP. However, MP exacerbates the overfitting issue while reserving the maximum value. Three steps make up the SP process. ⎧ ⎪ pm (x, y) = ⎪ ⎪ ⎨

b(x,y) Q P ∑ ∑ b(x,y) x=1 y=1

Q P ∑ ∑ ⎪ ⎪ ⎪ pm (x, y) = 1 ⎩ s.t.

(11)

x=1 y=1

where Pr is a probability symbol. The result of SP is thus found at pos(r0 ) = (|xr θ , ||||| yr 0 ), specifically Eq. (12), SP Bmn = b(xr 0 , yr 0 ).

(12)

The gradients of the cost should be determined during training using partial derivatives. There are two gradients: one for factor W and one for factor b. After that, we’ll take partial derivative for W and b. It could appear as ∂∂W J = ∂∂ Jz ∂∂Wz . Now, using Eqs. (13, 14), we should first find the partial derivative from cost J to intermediate variables “a” and “z”: ∂ J (W, b, x, y) = − (y − a) ∂a

(13)

∂ J ∂a ∂ J (W, b, x, y) = = δ (a) a(1 − a) ∂z ∂a ∂ z

(14)

δ (a) = δ (b) =

Then, using Eqs. (15, 16), derive factor W and b gradients in accordance with the chain rule: ∇W J (W, b, x, y) =

∂ J ∂z ∂ J = = δ (z) x T ∂W ∂z ∂W

(15)

∂ J ∂z ∂ J = = δ (z) ∂b ∂z ∂b

(16)

∇b J (W, b, x, y) =

In the aforementioned procedures, we start with ∂∂zJ , figure ∂∂zJ out, and then arrive at and ∂∂bJ . As a result, we can conclude that this method, known as back propagation, propagates cost function increment from past to present. ∂J ∂W

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141

4 Performance Analysis The proposed scenarios on version 3.1 were examined using three open-source broker software solutions that were deployed using VMs. Oracle hosted 3 virtual machines (VMs) in 2018 on a Windows 10 machine with 64 GB RAM, an Intel Core i7-5820 K processor, six real CPUs, and 12 virtual CPUs running at 3.30 GHz. We first assess our suggested method’s effectiveness using the three previously mentioned datasets. Then, to demonstrate superiority and effectiveness of our proposed technique, we compare it to state-of-the-art system. Datasets: Following are two open human activity datasets that were used in the data gathering and are often utilized in human activity research fields: • UCI HAR is first dataset created utilizing waist of 30 participants engaged in 6 daily activities and embedded tri-axial accelerometer as well as gyroscope in a smartphone; • USC HAD is second dataset captured utilizing the magnetometer, accelerometer, and gyroscope triaxial sensors built into the Motion Node gadget. 100 Hz was used as the research sample rate. The dataset contains activity data from 14 people, aged between 21 and 49, comprising seven males and seven female subjects, who participated in 12 different activities. Table 1 gives analysis between proposed and existing technique based on various wearable sensor dataset. Here dataset analysed are UCI HAR and USC HAD. Existing technique compared are CNN_LSTM and SVM in terms of accuracy, precision, recall, F-1 score, RMSE and NSE. Figure 4a–f gives analysis between proposed and existing technique for UCI HAR dataset. Here the proposed technique attained accuracy 92%, precision 79%, recall 71%, F-1 score 63%, RMSE 51% and NSE 47%. Figure 5a–f gives analysis between proposed and existing technique for UCI HAD dataset. Here the proposed technique attained accuracy 98%, precision 85%, recall 75%, F-1 score 66%, RMSE 55% and NSE 55%. Table 1 Comparative analysis between proposed and existing technique based on various wearable sensor dataset Dataset

Techniques

Accuracy

Precision

Recall

F1_Score

RMSE

UCI HAR

CNN_LSTM

88

75

67

59

45

41

SVM

89

77

69

61

48

45

WS_CDA_FL_ DLT

92

79

71

63

51

47

USC HAD

NSE

CNN_LSTM

94

81

72

62

48

51

SVM

96

83

73

64

52

52

WS_CDA_FL_ DLT

98

85

75

66

55

55

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(a) Accuracy

(b) Precision

(c) Recall

(d) F-1 Score

(e) RMSE

(f) NSE

Fig. 4 Comparative analysis between proposed and existing technique for UCI HAR dataset in terms of a Accuracy, b Precision, c Recall, d F-1 Score, e RMSE, f NSE

The proposed huge information investigation motor consequently identifies significant data, extricates valuable highlights from medical services information, decreases the dimensionality of information, arranges patient ailments, and predicts drug secondary effects. This system warns diabetes and high blood pressure patients before risks to their health become obvious. Additionally, this framework supports physicians in providing patients with genuine medications by deftly assessing their

Wearable Sensor Based Cloud Data Analytics Using Federated Learning …

(a) Accuracy

(b) Precision

(c) Recall

(d) F-1 Score

(e) RMSE

(f) NSE

143

Fig. 5 Comparative analysis between proposed and existing technique for UCI HAD dataset in terms of a Accuracy, b Precision, c Recall, d F-1 Score, e RMSE, f NSE

illnesses. For the purpose of enhancing performance of health condition classification, this technique can store and deconstruct vast amounts of data related to medical services, separate significant highlights, and offer the semantic meanings of features.

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5 Conclusion This study suggests a novel method for classifying wearable sensor-based cloud data utilising federated learning and deep learning. To accurately store and analyse healthcare data and increase classification accuracy. A novel healthcare monitoring system built on a big data analytics engine and cloud environment is suggested. U-Net convolutional auto encoder radial neural networks were used to classify the cloud data and protect it via federated learning. The proposed method achieved 98% accuracy, 85% precision, 75% recall, an F-1 score of 66%, 55% RMSE, and 45% NSE. The findings demonstrate that the suggested model accurately manages heterogeneous data, enhances the classification of medical conditions, and improves the predictability of pharmacological side effects. In order to reduce the consumers’ stress, our study’s ultimate goal is to create a high-accuracy method based on real-time data by resolving outstanding issues. Later on future studies, we will go further to concentrate on more proficient profound gaining methods for significant example location from the pitifully named information. More assessments in various circumstances will be directed to work on the calculation with better precision and proficiency.

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Comparison of Decision Tree and Random Forest for Default Risk Prediction Usha Devi and Neera Batra

Abstract The growth of the country is significantly influenced by the bank. The overall economic and financial health of any country directly affects a few of the services that the bank provides. Any bank’s income comes from loans, so they work incredibly hard to ensure that they only lend money to people who will be able to make their monthly payments on time. They give this problem a lot of consideration and employ a variety of strategies to identify and anticipate the typical customer behaviours. Machine learning offers options for resolving the present issue and can perform admirably in handling credit risk using readily available data about customers. The purpose of this analysis is to predict the inability to repay the bank debt. This research implements decision tree and random forest on LendingClub data; machine learning algorithms were tested for their accuracy in classifying borrowers into various credit categories. Further, two data mining techniques are compared for their ability to forecast the probability of default. Keywords Loan prediction · Predictor · Classifiers · Python · Decision tree (DT) · Credit risk prediction models (CRPM) · Artificial intelligence (AI) · Probability of default (PD) · Machine learning techniques

1 Introduction One of the nation’s most risky and unstable sectors is banking. One of the most recent services to be offered in the last 20 years is bank loans. Bank loans play a significant role in the financial industry. Banks’ main function is to provide loans, including short-term loans, assets, loans, and other types. This appears to be a significant factor in the millions of dollars in credit defaults, which prevented the customers’ money from being reimbursed. Credit quality has become an important criterion for banks’ lending decisions in recent decades [1]. From a business theory standpoint, the U. Devi · N. Batra (B) Maharishi Markandeshwar (Deemed To Be University), Mullana-Ambala, Haryana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_12

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current study emphasizes on discovering why organizations lending arrangements are effective in minimizing defaults or addressing the issues [2]. Credit scoring is the process of determining whether an individual’s application should be approved or refused based on their creditworthiness [3]. It assesses whether an application should be allowed or denied based on the degree of credit risk. Decision models are used by financial lending businesses and strategies to analyse credit risk. These organizations determine the level of risk they must take in order to achieve their financial responsibilities [4]. In a microlending setting, credit scoring is critical, but the absence of a credit history and, in some instances, even a bank account necessitates the use of creative techniques to determine a person’s reliability. On the other hand, there are regional differences in the legal structure for access to credit reporting data. As a consequence, the borrower’s consent to send information to the bureau and obtain a credit report may be required based on the jurisdiction (IFC 2006). On the other hand, unbanked consumers typically don’t have access to such a comprehensive database of prior credit history. Mariel F. Musso et al., Bhumika Bhatt et al., and Professor Sulin Pang have all described uses of machine learning in various domains with astounding outcomes [5–7]. When using machine learning for credit scoring specifically, Linlin Zha showed promising estimation outcomes [8]. We looked at two machine learning models to evaluate how accurate they were in predicting credit scores for individuals in this study. The following is a breakdown of the research: Sect. 2 gives a summary of previous research on each of the machine learning techniques we use to analyse credit scoring. Despite the fact that these strategies are common, we present an overview here for the sake of completeness. The dataset and feature summary statistics are introduced in Sect. 3, and the classification reports and analytic results of the machine learning approaches are presented in Sect. 4. The research conclusions are presented in Sect. 5.

2 Techniques of Machine Learning for Credit Risk Models built on machine learning are becoming more popular in a variety of academic disciplines. Classification is an important part of data mining because it helps to develop models while characterizing important data classes [9, 10]. As a result, such models estimate categorical class labels, allowing users to gain a better comprehension of the data as a whole. Significant improvements in classification accuracy were achieved. In our study of microlending data for credit risk, which was driven by existing literature, we analysed two machine learning methods.

2.1 Decision Tree Classifier Many different disciplines have made extensive use of decision tree classifiers. Since the tree flow display uses a tree framework to group cases according to their feature

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149

values, it can be compared with a progress diagram [11]. As a result, a tree could be a leaf belonging to one of the classes. The most important feature of decision tree classifiers is the ability to gain thorough decision-making experience using the data set [12]. A training set of objects S is applied to generate such a decision tree, each of which is connected with one of the classes C1, C2,…, Ck. As a result, (a) There is a leaf for this class in the decision tree for S if all the objects in S fall under it, such as Ci. (b) Choose T to stand in for any test with the outcomes O1, O2,…, On. Since each object in S has a result for T, the test subdivides S into the groups S1, S2,…, and Sn. We consequently produce a secondary decision tree for each finding. Oi by asking for a similar procedure to be repeated on the set Si. Oi is the decision’s tree root.

2.2 Random Forest Classifier The random forest classifier is a decision tree ensemble method where each tree is based on independently trained samples that are randomly chosen, distributed similarly across all trees in the forest, and that are trained individually. In order to decrease over fitting and increase total accuracy, it combines a number of classifiers with a tree-structure [13]. As a consequence, the effectiveness of each tree classifier and their connections influences the accuracy of random forest. N

1 r N (X, β) = i = N

yi1 x j ∈ A N (X, β)

i =1

with L N =

N  i =1

1x j ∈ A N (X, β)

1L N

yi1 x j ∈ A N (X, β) = 0. Approximation of r N can be obtained

with respect to the β parameter by using the prediction of r N . In this study, we compared two machine learning classifiers to see how accurate they were at estimating credit scores for individuals. In our tests, random forest performed better in terms of classification accuracy, with an accuracy of around 85%, compared to 73% for the popular decision tree classifier.

3 Methodology 3.1 Data Collection The data taken in this study came from the publicly accessible LendingClub.com. Lending Club helps match up those in need of money with those who already have it (financial institutions). As an investor, you want to invest in individuals who have a

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good chance of paying you back. We will categorize and predict if the borrower fully repay the loan using lending data from 2007 to 2010. Data can be downloaded from decision trees and random forest | Kaggle. A total of 9578 customers were included in the survey. Customer information is contained in the records, such as credit policy, purpose of loan, interest rate, monthly instalments, and FICO score.

3.2 Summary Statistics of Features This research explores the failure risk of loans supported by public credit guarantee policies. Banks, funding agencies, and other organizations which grants loan set a policy for customers requesting for funds [14]. This policy contains rules and conditions for eligibility of customer for loan/credit [15]. A comparison of records fulfilling policy conditions (blue colour) and not fulfilling the given policy conditions (violet colour) has been done and represented in Fig. 1. Customers may default on the loan and considered to be not paid records. Those who returns the debt are considered as paid records [16]. A comparison of paid (in red colour) and not paid (in blue colour) records from the dataset taken for experiment is represented in Fig. 2. The Python programming language was used to conduct all of the analyses described in this study.

Fig. 1 Policy versus credit score

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Fig. 2 Paid versus not paid records

4 Results and Discussion The classification reports and analytic results of the decision tree classifier and random forest classifier are discussed in this section.

4.1 Classification Report In this part, we look at the F1-score, precision, recall, and prediction accuracy of each classifier. As a result, Table 1 [17] shows the random forest classifier’s classification report. Precision is the proportion of a risk class’s expected values that, in contrast to all other values, truly fall into that class. Among all possible positive forecasts, recall (true positive rate) measures the percentage of correct positive projections [18]. The F1-score is a statistic that evaluates precision and recall concurrently, giving you a full picture of both [19]. Recall and precision are typically negatively related [20]. It is the harmonic mean of the two periods [21]. Support is the number of instances of a particular risk class in the data collection [7, 22]. The accuracy measure serves as a representation of the classifier’s total prediction power. The percentage of the sample data that the categorization model correctly classified can be used to describe it [23]. The arithmetic mean of each precision value across all classes is used to compute the macro-average precision score [24]. Be aware that the classifier computes the accuracy, recall, and F1-score globally using these two measures (macro-average and weighted average). Global support is

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Table 1 Report on classification using decision trees [17] Precision

Recall

F1-score

Support

0

0.85

0.82

0.84

2431

1

0.19

0.23

Accuracy

0.21

443

0.73

2874

Macro avg

0.52

0.53

0.52

2874

Weighted avg

0.75

0.73

0.74

2874

F1-score

Support

Table 2 Report on classification using random forest [17] Precision

Recall

0

0.85

1.00

0.92

2431

1

0.53

0.02

0.04

443

0.85

2874

Macro avg

0.69

0.51

0.48

2874

Weighted avg

0.80

0.85

0.78

2874

Accuracy

the sum of all separate supports for each risk class. As a result of the fact that the random forest classifier can be subjected to the analysis presented for the decision tree classifier, we present the same metrics in Table 2.

4.2 Discussion The efficacy of machine learning models in predicting default in a microcredit context was examined in this research. Microlending organizations have a great deal of difficulty deciding whether or not to grant microloans to certain individuals [25]. To address this flaw, this research shows how machine learning algorithms can aid in the assessment of microcredit defaults [26, 27]. The validation/test set provided the basis for all performance measures used in this study. The data set was fitted with a number of machine learning models, but two models with an overall accuracy of 70% or above on the validation set are presented in this study. Random forest performed better with an accuracy of 85% between the two models DT and RF analysed and implemented in this paper. Other performance indicators used in this study demonstrated that the RF classifier had a high predictive value for predicting microcredit defaults.

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5 Conclusion Using machine learning approaches, this study assessed individuals’ credit risk performance in a microfinance environment. We examined two machine learning models to see how accurate they were at estimating credit scores for individuals. In our tests, the random forest model performed better in terms of classification accuracy, with an accuracy of around 85%, compared to 73 per cent for the popular decision tree model. To get the optimal decision boundaries of the classifiers’ organizational microlending evaluation judgements, we adjusted the hyperparameters of the classifier. We changed the classifier’s hyperparameters to obtain the best decision boundaries for its institutional microlending assessment judgements. Other model diagnostic measures, such as the F1-score, precision, and recall, demonstrated that the random forest classifier outperformed the decision tree model using our data set in addition to predict the validation set’s precision. We conducted a thorough experimental analysis using data from LendingClub to evaluate the two machine learning algorithms’ accuracy while categorizing loan requests into three categories: excellent, average, and bad. This research, like many others, had its limitations. Although the focus of our research was on data from a LendingClub, future research will use a larger data set for experimental analysis. While our findings can be used to draw some general qualitative conclusions about the value of particular qualities, they cannot be utilized to infer specific quantitative conclusions and the use of random forest and decision tree classifiers in microlending scenarios, the specific features chosen, for example, it’s possible that it won’t work in all countries and institutions. The usage of a large data collection may help the model perform better and allow it to make more accurate predictions [28]. Similarly, we might be able to better manage the amount of outliers if we understand the limitations of machine learning techniques [29, 30]. Another possible direction for future research is to include the temporal components of credit risk.

References 1. Chavan P, Gambacorta L (2019) Bank lending and loan quality: an emerging economy perspective. Empirical Economics 57:1–29 2. Pucci R, Skærbæk P (2020) The co-performation of financial economics in accounting standardsetting: a study of the translation of the expected credit loss model in IFRS 9. Account Organ Soc 81:101076 3. López J, Maldonado S (2019) Profit-based credit scoring based on robust optimization and feature selection. Inf Sci 500:190–202 4. Gonçalves EB, Gouvêa MA (2021) Credit risk analysis applying logistic regression, neural networks and genetic algorithms models. IJAERS 8(9):198–209

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5. Musso MF, Hernández CFR, Cascallar EC (2020) Predicting key educational outcomes in academic trajectories: a machine-learning approach. High Educ 80:875–894 6. Bhatt B, Patel PJ, Gaudani H (2014) A review paper on machine learning based recommendation system. IJEDR 2(4):3955–3961 7. Pang S, Hou X, Xia L (2021) Borrowers’ credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine. Technol Forecast Soc Change 165:120462 8. Zha L, Ma K, Li G, Fang Q, Hu X (2022) A robust double-parallel extreme learning machine based on an improved M-estimation algorithm. Adv Eng Inform 52:101606 9. Hou W-H, Wang X-K, Zhang H-Y, Wang J-Q, Li L (2020) A novel dynamic ensemble selection classifier for an imbalanced data set: an application for credit risk assessment. Knowl-Based Syst 208:106462 10. Feng X, Xiao Z, Zhong B, Qiu J, Dong Y (2018) Dynamic ensemble classification for credit scoring using soft probability. Appl Soft Comput 65:139–151 11. Yufei X, Chuanzhe L, Ying LY, Nana L (2017) A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst Appl 78:225–241 12. Czajkowski M, Kretowski M (2019) Decision tree underfitting in mining of gene expression data. An evolutionary multi-test tree approach. Expert Syst Appl 137:392–404 13. Zhu L, Qiu D, Ergu D, Ying C, Liu K (2019) A study on predicting loan default based on the random forest algorithm. Procedia Comput Sci 162:503–513 14. Hill J (2018) Chapter 6—Bank lending. Fintech and the remaking of financial institutions. Academic Press, Elsevier, pp 139–156 15. Caselli S, Corbetta G, Cucinelli D, Rossolini M (2021) A survival analysis of public guaranteed loans: does financial intermediary matter? J Financ Stab 54:100880 16. Jackson A (2001) An evaluation of evaluation: problems with performance measurement in small business loan and grant schemes. Prog Plan 55(1):1–64 17. Jozefek P (2020) Python for decision trees and random forests. Retrieved from https://rstudiopubs-static.s3.amazonaws.com 18. Nigmonov A, Shams S, Alam K (2022) Macroeconomic determinants of loan defaults: evidence from the U.S. peer-to-peer lending market. Res Int Bus Finan 59:101516 19. Giglioni V, García-Macías E, Venanzi I, Ierimonti L, Ubertini F (2021) The use of receiver operating characteristic curves and precision-versus-recall curves as performance metrics in unsupervised structural damage classification under changing environment. Eng Struct 246:113029 20. Lee JW, Lee WK, Sohn SY (2021) Graph convolutional network-based credit default prediction utilizing three types of virtual distances among borrowers. Expert Syst Appl 168:114411 21. Lim S-J, Thiel C, Sehm B, Deserno L, Lepsien J, Obleser J (2022) Distributed networks for auditory memory differentially contribute to recall precision. NeuroImage 256:119227 22. Fontem B, Smith J (2019) Analysis of a chance-constrained new product risk model with multiple customer classes. Eur J Oper Res 272(3):999–1016 23. Bianco S, Mazzini D, Napoletano P, Schettin R (2019) Multitask painting categorization by deep multibranch neural network. Expert Syst Appl 135:90–101 24. Wang L, Chen Y, Jiang H, Yao J (2020) Imbalanced credit risk evaluation based on multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble. Appl Soft Comput 91:106262 25. Vuttipittayamongkol P, Elyan E, Petrovski A (2021) On the class overlap problem in imbalanced data classification. Knowl-Based Syst 212:106631 26. Papouskova M, Hajek P (2019) Two stage consumer credit risk modelling using heterogeneous ensemble learning. Decis Support Syst 118:33–45 27. Ashofteh A, Bravo JM (2021) A conservative approach for online credit scoring. Expert Syst Appl 176:114835 28. Zhang G, Davoodi S, Band SS, Ghorbani H, Mosavi A, Moslehpour M (2022) A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques. Energy Rep 8:2233–2247

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29. Cha SH, Steemers K, Kim TW (2018) Modeling space preferences for accurate occupancy prediction during the design phase. Autom Constr 93:135–147 30. Gunduz V (2020) Chapter 7—Risk management in banking sector. In: Management and Strategy. Artikel Akademi, pp 121–135

Trusted Cloud Service Framework for Cloud Computing Security Sasmitha and A. Suresh

Abstract Cloud computing is the pooling of several adaptive computing resources including servers, networks, storages, and services to provide users with convenient and timely access. IAM is a virtual server which manages user and resource rights and authorization. To control who has entry to what and how, IAM rules are collections of permissions that may be applied to either users or cloud resources. Cloud computing is an easily managed technology in which the apps we use are issued by a mysterious node that will never claim ownership but actually has full control over the application. The proliferation of personal computers and mobile devices across all industries has made secure data storage an urgent priority. There has been a recent explosion in the number of large and small firms investing in data infrastructure. Plus, they’re shelling out a lot of cash to keep the data secure. Using this approach to cloud computing significantly reduces the user’s dependence on hardware and software combinational models. Keywords Identity and access management · Central authority · Very lightweight proxy re-encryption · Attribute-based secure data sharing

1 Introduction The phrase “cloud computing” is used to describe the delivery of computing services via the Internet. Most people today utilize cloud computing services for their own individual purposes. To keep up with the ever-increasing needs of businesses, today’s software organizations need a reliable and scalable information technology infrastructure. The difficulty, however, is in the fact that this set-up must occur in the individuals’ own spaces. A considerable budget is spent on IT infrastructure, personnel, Sasmitha · A. Suresh (B) Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Tamil Nadu 603203, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_13

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and expertise to keep an eye on things. As a result, they are shifting their focus from developing their main business to managing risks. Managing resources in the cloud is difficult because clouds are complex, large-scale, and represent heterogeneous dispersed systems. They view this as a complicated procedure; therefore, they cling to an automated and combined clever plan for putting those resources to use in a way that’s trustworthy, secure, and cost-effective. This demonstrates the importance of software platforms, which stand in for the clouds’ underlying computer infrastructure [1].

1.1 Essential Features of Cloud Computing Systems The cloud’s resources must be available at all times, and the client company must be able to access its cloud resources without the company’s interaction, but only if the user has enabled on-demand self-service. • Extensive network accessibility: because cloud computing is network-based, it can be used from any location and on any standardized platform (such as smartphones, desktop PCs). This means that all of the services and their associated capabilities are housed in the cloud, and users only need network access to gain access to and make use of the virtual servers that host them. • Resource pooling: in the cloud, resources are pooled so that multiple users can share the same pool of computing power. The company uses a pay-as-you-go model to offer its processing power to a large number of customers, thereby avoiding a large upfront investment. • Since the cloud is responsive and can store a large amount of data in its memory, it exhibits rapid elasticity. A situation’s response time may shift automatically between rapid scaling out and rapid scaling in. • Measuring the amount of service supplied and making adjustments as necessary is the responsibility of the cloud service provider (both in terms of updating and software and hardware billing the client as appropriate). Both the components and the current operation of cloud computing may be traced back to its architecture. The front-end and the back-end are the two main parts of a cloud computing system. The front-end of a cloud-based system is where the client component is located. Interfaces and apps necessary for working with a cloud computing environment are included [2, 3]. When we talk about the cloud’s “back-end”, we’re referring to the cloud’s own infrastructure, which houses the effective resources necessary to run cloud-based services. The provider is responsible for its infrastructure, which includes servers, virtual machines, data storage, security systems, etc. (see Fig. 1). Cloud computing delivers a file system that may be shared across multiple servers and hard discs. The information is not kept in a single place, and if one server goes down, the other will take over automatically. The user’s disc space is allocated within the distributed system file, and the algorithm used for resource allocation is another key factor. The

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Fig. 1 Cloud computing system architecture

modern algorithm that underpins the cloud is fundamentally robust and is generally regarded as a stable distributed system [4]. Computing in the cloud “implies to the applications that are supplied as services over the Internet, and the system hardware and software models in the data centres serve to supply such services”, as one definition puts it. Armbrust et al. described “a utility-oriented distributed computing system that incorporates a collection of inter-connected and virtualized PCs that are dynamically deployed”. One or more combined computer resources are offered to customers based on the service level agreements negotiated between the service provider and the customers. According to [5], “distributed systems” refer to the “original flavour” of the current evolution. When it comes to software development and deployment, private distributed systems use the resources and data centres of third parties and provide Internet connectivity for its users. Infrastructure, platform, and software are all available through cloud computing as subscription-based, pay-as-you-go services (applications). IaaS stands for “Infrastructure as a Service”, PaaS for “Platform as a Service”, and SaaS for “Software as a Service” in today’s business world. Service providers such as Amazon, HP, and IBM have toiled away at cloud data centres to support end-user applications on a worldwide scale. The spectrum of these programmes extends from the ubiquitous word processor to virtual medical clinics applications. Once cloud apps are presented on cloud platforms, users can access the cloud from any device, anywhere in the world [6]. The cloud’s back-end technology undermines computers’ ability to perform complex computations. It noticeably improves the performance of the application, which is designed to bill the user for the services they use. However, it becomes a difficult task to manage their elastic cloud and infrastructure in a way that provides a safe, dependable, and cost-effective service. It displays characteristics of autonomy

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at several levels, requiring joint improvement of infrastructure, platform, and application. Cloud computing is the delivery of computing resources (network, central processing unit, random access memory, operating system software storage, speed) over a network (often the Internet) as a service, rather than the provisioning and maintenance of on-premises hardware. Cloud services like Azure, Amazon Web Services (AWS), and Google Cloud are just a few examples.

2 Survey of the Related Research In this part, we’ll talk about some of the technical difficulties you can have when trying to put into practise cloud computing, as well as some of the security concerns you might have with this approach, especially with regard to data security, integrity, and authentication. Cloud environments are particularly vulnerable to security threats like worms, viruses, DoS attacks, cracked passwords, malware code insertion, and scanning. Companies risk losing both reputation and money if these attacks go undetected. Access control and authentication are already major issues for cloud computing, and this part presents a taxonomy and assessment of these issues. An authorization framework for a cloud services ecosystem was developed in [7]. Traditional methods of access control are insufficient for a cloud infrastructure. In addition, a novel attribute-based credit computational model is proposed by the author. In this work, we use the credit component module to implement attributebased access control. When determining a user’s credit worth, we looked to the idea of audience appraisal and sign. In order to keep vast amounts of data safe in a multi-authority cloud, [8] proposes a paradigm of flexible access control at the granularity level. The Very Lightweight Proxy Re- Encryption (VL-PRE) system was developed to facilitate the rollout of policy updates to cloud servers. VL-PRE, which addressed file re-encryption and reencryption key generation, optimized communication cost and computation at data owners and a proxy in the cloud. The need for transparency, accuracy, and safety in policy updates led to the creation of a powerful new algorithm. Therefore, our VLPRE-embedded access control deployment will one day provide a useful security function for the cyber-enabled big data cloud. T-ABAC is an advanced access control model for IoT based on the ABAC paradigm, and it was developed by [9]. T-ABAC, a hybrid of ABAC and TBAC, is used to implement a protected evaluation model in an Internet of Things (IoT) infrastructure that meets stricter privacy standards. In addition, attribute consistency across domains allowed us to circumvent the complexity of traditional cross-domain

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mapping, recasting the cross-domain problem as one of distributed identity authentication. Furthermore, an integrated weighting approach has been developed by the authors to ensure the objectivity and accuracy of the trust evaluation and lessen the impact of the subjective element. It has been suggested [10] that access control and encryption be used to guarantee the right control, integrity, and confidentiality of access to sensitive data. This model was proposed by them in order to ensure the safety of data stored in the cloud. This concept incorporates a role-based access control model, a markup language for extensible access control (XACML), and a modified RSA encryption method to enhance security and ease of access to data. On this study, we propose using cryptography to store data in the cloud, and we show how this, in conjunction with an access control mechanism, may ensure the data’s security while also imposing reasonable constraints on its storage duration and encryption/decryption overhead. Attribute-based secure data sharing (ASDS) is a technique introduced in [11] for cloud computing that ensures data privacy, permits versatile user withdrawal, authenticates data, and controls access. The proposed method also prevented attacks such as replay and collusion from succeeding. Attribute-based safe data sharing (ASDS) has been proven to function for cloud users based on evaluations of system security and performance comparisons with another data sharing framework.

3 Methodology When compared to traditional approaches, this technique is secure and trustworthy, and it may be used in real-time settings. The proposed encryption scheme takes into account both multi-authority security and collusion resistance, in addition to finegrained access control. The suggested procedure involves two stages—the system stage and the algorithm stage. The AES algorithm is used to define the operations at the system level during this phase of the process. During a conflict, the system level describes the most important processes, such as cancelling a user account, setting up the system, adding a new user, making a new file, accessing the file, and deleting it. Processing at the Level of Algorithms Encryption with AES both the hardware and software efficiency of AES are quite high because it is based on the substitution– permutation network. Unlike its predecessor, DES, AES, this method does not rely on a Feistel network. AES is a variant of Rijndael with a fixed block size of 128 bits and a key size of 128, 192, or 256 bits (see Fig. 2). The size of the key used in an AES cypher is what determines the number of iterations that are required to convert plaintext into cyphertext. The following is an explanation of the amount of times that this pattern will be repeated: a 256-bit key requires 14 cycles, but a 192-bit key only requires 12 cycles.

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Fig. 2 Flow chart of the AES encryption process

Only ten cycles are necessary for 128-bit keys. There are four separate stages of processing that are connected to one another throughout the entire procedure. In order to turn the encrypted text back into plaintext, it must first go through two rounds of decryption while utilizing the same encryption key. The following four types of transformations are used by AES to assure its users’ safety: • The process known as “permutation” involves switching the order of the rows by a certain number of stages. • The next stage is called “substitution”, and it includes changing each byte to a different value depending on a lookup table. • The mixing phase is responsible for blending the four bytes that are present in each column of the state columns. At this point, the state combines the partial key, and a sub-key is generated from the master key using Rijndael’s key schedule. The size of the sub-key is the same as the size of the state’s key. The state bytes and the sub-key are concatenated together with the help of the bitwise XOR operator. In this section, we will discuss the algorithm that is utilized in the encryption and decryption processes using AES.

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The AES Encryption Algorithm

Decrypting with the AES Algorithm

3.1 Process at the Systemic Level The challenger is responsible for running the algorithm for global set-up and obtaining the global public parameters as the first step in the process of setting up a system. During the phase set-up, the data bearer chooses a security parameter that ultimately results in the generation of the secret key SK by sending a request message to the interface of the algorithm. After being encrypted by the data holder, each encryption key component is then sent to the central authority (CA). This

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Fig. 3 Detailed illustration of the owner-side encryption procedure

happens after the encrypted module has been encrypted. The holder’s signature will, however, still be subject to examination by the CA. If everything is in order, the certification authority will utilize the public key and master key of the system to generate the secret and public keys for the newly registered users. The weighted attribute analysis (WAA) determines the relative value of each characteristic with regard to the operation of the business. As part of the second phase in the process of producing a key, CA will provide a one-of-a-kind user ID for the user while they are connected to the system. On the other hand, the WAA is sent an attribute set that has been encrypted by the customer. The authority on attributes checks the consumer’s signature to ensure its authenticity. In the event that this is the situation, WAA will provide the secret keys for the identical attributes as well as the weight for the new client. After that, the WAA and the CA, one at a time, give the attribute secret key to the new consumer. They also give the new consumer the system secret key that the previous consumer used. The challenger prevails in the competition to build the central authority and the related keys algorithm, and the hacker’s public keys are subsequently issued. Encryption: prior to uploading the file, the data holder encrypts the data using a one-of-a-kind login ID and a symmetric data file key. The “weighted threshold access structure” (W), it is established by the data owner for each data file and user, and the consumption of W data is encrypted (see Fig. 3). Four, once the customer has downloaded the data from the cloud, they must then use the decryption method to make sense of the encrypted data. Assuming that the associated secret key of the data consumer has been verified, the system will assign varied degrees of priority to each user (see Fig. 4).

4 Results and Analysis The first step in the process of setting up a system is for the challenger to be in charge of executing the algorithm for the global set-up and getting the global public parameters. The data bearer selects a security parameter to be used during the phase set-up,

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Fig. 4 User-side decryption method

which ultimately leads to the generation of the secret key SK. This is accomplished by the data bearer transmitting a request message to the interface of the algorithm. Each encryption key component, once it has been encrypted by the data bearer, is subsequently delivered to the central authority (CA). This occurrence takes place after the encryption of the module has been completed. Despite this, the CA will nevertheless scrutinize the holder’s signature in order to ensure its authenticity. If everything is in order, the certification authority will use the public key and master key of the system to generate the secret and public keys for the newly registered users. These keys will be used to verify the newly registered users’ identities. The weighted attribute analysis (WAA) is a method that analyses the relative importance of each quality with reference to the functioning of the company. CA will issue a one-of-a-kind user ID for the user while they are connected to the system as part of the second phase of the process of obtaining a key. This step takes place while the user is connected to the system. On the other hand, the customer is responsible for encrypting the attribute set before it is submitted to the WAA. The authority on attributes verifies the signature of the customer to determine whether or not it is genuine. In the event that this is the case, WAA will supply the new client with the secret keys for the similar attributes as well as the weight. After that, the WAA and the CA both hand the attribute secret key over to the new customer in turn, one after the other. Additionally, they provide the new consumer with the system secret key that was utilized by the prior consumer. The challenger emerges victorious in the competition to develop the central authority and the related keys algorithm, and as a result, the hacker receives their public keys. Encryption: the data holder encrypts the data using a one-of-a-kind login ID and a symmetric data file key before uploading the file. This must be done before the file can be uploaded. The “weighted threshold access structure” (W), which can be defined by the data owner for each data file and user, and the consumption of W data is encrypted. The figure clearly illustrates

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this point. Fourth, once the consumer has downloaded the data from the cloud, they are required to next apply the decryption procedure in order to make sense of the encrypted data. Assuming that the associated secret key of the data consumer has been validated, the system will assign varying degrees of priority to each user in accordance with their specific needs. The time of encryption is specified as the amount of time needed to encrypt the data. To verify the system’s speed and evaluate an encryption method’s throughput, this tool is employed. How long it takes to transform plaintext into cyphertext is the encryption time. The cypher time is depicted (Table 1). Finally, it’s time to decrypt your data, which is the inverse operation of encryption. Time to decrypt means how long it takes to convert a cyphertext into plaintext. The difference between the suggested and traditional decryption times is shown in Fig. 5. A comparison of the suggested method’s decryption time to that of the state-ofthe-art alternatives. Throughput: the term “throughput” refers to the ratio of encrypted data to total time spent encrypting a file. The high throughput attained by the proposed method (see Fig. 6). Breakdown of the prices involved in user secret key production (1, time 2 storage analysis), and storage (2, cost). The price of deciphering a message (see Fig. 7) Table 1 Transmission rates, encryption/decryption success rates, and runtimes from experiments using AES-WABE Input data Size of the File (kb) Encryption Time (s) Decryption Time (s) Throughput (bps) I1

1

120

115

0.00850

I2

2

235

226

0.00870

I3

3

344

600

0.00876

Fig. 5 Diagram of the proposed plan

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Fig. 6 CPABE, HABE, and AES-WABE throughput comparison

Fig. 7 Breakdown of the prices involved in user secret key production

depicts the increase in computational and storage costs associated with encrypting data. On the X-axis is the total amount of attributes with weights, and on the Y-axis is the time and space required to encrypt the data (see Fig. 8).

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Fig. 8 Monetary investment required to decipher a text message

5 Conclusion Cloud systems pose unique challenges in terms of user authentication and data security. As a result of this research, a scalable and efficient access control method has been developed. During this investigation, the confidentiality of the data is protected by using an AES hybridized weight attribute-based encryption strategy. During the data transmission process, the Advanced Encryption Standard (AES) is used for both encrypting and decrypting the data. When an authorized user makes a request, the encrypted data is delivered to the customer with the most weight. The data can be decrypted by the information consumer using the key generated by the AES algorithm. The results of the experiments show that the strategy under consideration is both effective and dependable. Encryption and re-encryption based on security and quality assurance requirements can be added to this work in future to expand it. These are the areas in which a variety of methods can be used to facilitate knowledge sharing, and they are described below.

References 1. Yang Y, Chen Y, Chen F, Chen J (2022) Identity-based cloud storage auditing for data sharing with access control of sensitive information. IEEE Internet Things J 9(13):10434–10445 2. Hu Q, Duan M, Yang Z, Yu S, Xiao B (2021) Efficient parallel secure outsourcing of modular exponentiation to cloud for IoT applications. IEEE Internet Things J 8(16):12782–12791 3. Hu X, Li J, Wei C, Li W, Zeng X, Yu P, Guan H (2021)STYX: A hierarchical key management system for elastic content delivery networks on public clouds. IEEE Trans Dependable Secure Comput 18(2):843–857 4. Naseri AM, Lucia W, Youssef A (2022) Encrypted cloud-based set-theoretic model predictive control. IEEE Control Syst Lett 6:3032–3037 5. Meng X, Zhang L, Kang B (2022) Fast secure and anonymous key agreement against bad randomness for cloud computing. IEEE Trans Cloud Comput 10(3):1819–1830

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6. Suresh A, Kishorekumar R, Kumar MS et al (2022) Assessing transmission excellence and flow detection based on Machine Learning. Opt Quant Electron 54(500):1–12. https://doi.org/ 10.1007/s11082-022-03867-6 7. Li H, Kong F, Yu J (2022) Secure outsourcing for normalized cuts of large-scale dense graph in internet of things. IEEE Internet Things J 9(14):12711–12722 8. Chen Y, Li W, Gao F, Wen Q, Zhang H, Wang H (2022) Practical attribute-based multi-keyword ranked search scheme in cloud computing. IEEE Trans Serv Comput 15(2):724–735 9. Almakdi S, Panda B, Alshehri MS, Alazeb A (2021) An efficient secure system for fetching data from the outsourced encrypted databases. IEEE Access 9:78474–78494 10. Yang Y, Miao Y, Ying Z, Ning J, Meng X, Choo K-KR (2022) Privacy-preserving threshold spatial keyword search in cloud-assisted IIoT. IEEE Internet Things J 9(18):16990–17001 11. Lyu L, Chau SC-K, Wang N, Zheng Y (2022) Cloud-based privacy-preserving collaborative consumption for sharing economy. IEEE Trans Cloud Comput 10(3):1647–1660

HITR-ECG: Human Identification and Classification Simulation System Using Multichannel ECG Signals: Biometric Systems Era Alaa Sabree Awad, Ekram H. Hasan, and Mustafa Amer Obaid

Abstract Electrocardiogram (ECG) signals were utilized as biometrics in this research to simulate a system for identifying persons. As part of this study, ten people sent their ECG signals to the ECG lead equipment to collect data. Each of the ten patients (starting from 100 to 109) provided 65 raw ECG waveforms for analysis. Ensemble empirical mode decomposition (EEMD) and entropy sample (SampEn) are utilized for features extracting of the signals from the unprocessed signal. Based on the ECG signal registration, the XGBoost classifier method was utilized to verify the identity of subjects. As a consequence of the study, it was determined that the EEMD had the greatest accuracy value of 95.66% percent. As a replacement for SampEn, this technique offers better performance and less functionality than SampEn. Keywords Electrocardiogram (ECG Signals) · Ensemble empirical mode decomposition (EEMD) · Sample entropy (SampEn) · XGBoost algorithm

1 Introduction It may be characterized as a unique characteristic measurement based on a person’s physical traits. It is possible to tell one person from another based on their behavioral or physiological features. There are several applications of the automatic biometric system, including personal identity and controlling the access, area of inspection, and criminal treatment. This is especially true for the multimodal biometric system A. S. Awad (B) · E. H. Hasan College of Basic Education, University of Anbar, Haditha, Iraq e-mail: [email protected] E. H. Hasan e-mail: [email protected] M. A. Obaid College of Medicine, University of Anbar, Ramadi, Iraq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_14

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that employs more than one biometric modality to increase safety and accuracy [1]. There are two types of biometrics exist: physiological and behavioral biometrics. It is connected to the physical features of the body or the organs of the human, like the pattern of the face, fingerprints, iris, and hand geometry. This is due to the fact that certain biometric features are easy to falsify and can be gained by force or bodily injury. An alternative biometric system with a unique, difficult-to-falsify feature is thus needed [2]. The biopotential or biosignal is the biometric modality with the specified requirements. According to the study, electroencephalogram (EEG) and electrocardiogram (ECG) as biometric modalities based on biological signals have been widely investigated. The biological signal might be the biometric future, as it is impossible to fake or assault. ECG has various advantages, having a continuous signal is similar to being linear (regular rhythm), minimal complexity, and being comparatively easier to read than EEG data [3]. ECG signals were utilized as biometric modalities in this study because of this rationale. Utilizing cardiac signal biometrics has the benefit that it’s almost hard to replicate the activity of the heart electrical of the human. Additionally, biometrics’ inherent properties have enhanced security compared to other traditional biometric systems [4].

2 Literature of Related Works According to research of “Belgacem et al.” [5], the ECG signal of 20 observation participants was analyzed utilizing discrete wavelet transform (DWT). An ECG wave’s characteristic coefficient was determined by utilizing DWT in the study. For feature-based authentication, the random forest method was employed. When Anita suggested the biometric ECG for human recognition utilizing hair wavelets in her study, she reported classification accuracy of 98.96 percent in the QT database as well as 98.48 percent in the PTB database as well as an MIT-BIH arrhythmia database. The author “Wei-Quan et al.” [6] also utilized the wavelet transformation approach in the biometric ECG by deducing the WT in detail and continuing the precision test with a MATLAB simulation. However, the “Wei-Quan et al.” inquiry did not provide any information on the classification or authentication process utilized. The research of the author “Yeen and Ramali” [7] employed WTs as a biometric foundation for ECG, with the goal of examining the impacts of numerous functions utilized to increase the authentication’s performance or correctness. The author “Yeen and Ramali” wanted to find a prominent trait that would provide the greatest authentication results. For feature-based authentication, the Naive Bayes classifier was utilized. The Fourier fast transformation method (FFT) combined with the neighbor’s classifier El closer had a useful performance for biometric ECG, according to the study [7]. The author “Belgacem et al.” [8] utilized the FFT technique for authentication and used the optimum-path forest classifier in simultaneous biometric investigations based on ECG waves and electromyogram (EMG). The research mentioned above are examples of ideas for biometric ECG systems that [9, 10].

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3 Methodology 3.1 Biometric In essence, a biometric system is a method of identifying people based on variations in their behavioral and psychological traits. It is conceivable that these qualities differ from one person to the next. Furthermore, as compared to passwords/tokens and knowledge authentication, biometric-based authentication is seen to be more trustworthy. The most difficult aspect of developing a functional biometric system is determining who needs to be verified [11]. The biometric system’s process is divided into many steps, the first of which is enrolment. A biometric sensor will scan the input and convert it to digital format. At this point, the next, is the stage of the matching, when the input is matched with the dataset that has been stored [12]. The physiological biometrics are concerned with the physical features of the human body, as stated in the preceding section. Sounds, movement, signatures, and speech patterns are all behavioral biometrics that might be utilized. Behavioral biometrics, on the other hand, are notoriously easy to debunk [13].

3.2 Sample Entropy (SampEn) SampEn is a kind of approximation entropy (ApEn) that is utilized to analyze the complexity of physiological time series data and diagnose illness. SampEn offers two benefits over ApEn: It doesn’t care about the length of the data, and it’s easy to set up. There is also a little computational difference: The comparison between the template vector (shown below) and the rest of the vectors in ApEn also includes a comparison with the template vector [14]. The signals are regarded as more regular than they are because template comparisons with themselves reduce ApEn levels. SampEn does not contain these self-matches. However, because SampEn uses the correlation integrals directly, it is an estimate rather than a true measure of information [15]: Sampler(m, r ) = lim − ln N →∞

X m (r ) . Y m (r )

(1)

3.3 XGBoost The gradient boosting framework is utilized by the decision-tree-based ensemble machine learning algorithm known as XGBoost. ANN s often outperform all other

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algorithms or frameworks for prediction issues involving unstructured data (image, text, etc.).

4 Designing of the System The two types of biometric systems: • The verification system compares a person’s biometrics to a reference biometric in the database that the individual claims to have. Only one input is entered into a single database in the verification system. • A biometric is compared to all of the biometrics in the database by the identification system. The identification system has a search component that requires matching one entry with numerous samples from the database. The biometric system suggested in this paper is an identification system in which the operation was implemented by storing the template database of ECG signal and then comparing the data when an authentication input request was received. Figure 1 depicts the biometric technique employed in this investigation, which is detailed in the next part. The suggested approach is depicted in the diagram (Fig. 1).

4.1 ECG Signal Acquisition ECG stands for electrocardiogram, and it is an instrument that measures the activity of the heart electricity. It is commonly utilize to monitor cardiovascular illness. Each human’s ECG has a unique rhythm, shape, and amplitude, making it a good candidate

Fig. 1 Schematic of the proposed biometric system

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for biometrics. ECG signal collection was done with a one-lead ECG device in this suggested biometric system. On ten individuals, data was collected for about 60 s at a sample frequency of 100 Hz. The ECG signal acquisition scenarios were carried out in a quiet/relaxing environment with no distractions. The feature extraction procedure heavily relies on this raw data.

4.2 Extracting the Features To decrease the processing cost of the feature extraction approach, the raw ECG data for every participant was preprocessed at this stage by setting the amplitude of the signal in range between − 1 to + 1. The preprocessing formulae are shown: xDC (n) = x(n) −

1 N x(n). n=1 N

(2)

Equation (2) is utilize to eliminate the DC signal components. xn (n) =

x(n) . max|x|

(3)

The amplitude of the signal at level 1 is increased to + 1 utilizing Eq. (3). To calculate the coefficient value of feature extraction, the next step is to perform feature extraction. The signal characteristics were obtained utilizing the EEMD and Sample Entropy techniques in this investigation. From each subject’s ECG data, this technique would extract the signal complexity parameters. Fig. 2 presented both normal and abnormal ECG signal. The characteristics database of each individual would be obtained as a result of this procedure and then compared to the test results. The signal characteristics for every subject are presented in the tables form of and graphs. As shown in Tables 1 and 2, the values of the signal characteristics average in each individual varied from one another, even within a limited values range. The little variation in value was due to the fact that the ECG signals of one person and the other were of equal magnitude, frequency, and QRS complex shape. However, the differences in signal qualities for each individual could still be seen visually. Furthermore, the similarity of values occurred only in a few characteristics. As a result, the classifier will have an easier time matching the people to one another.

4.3 Validating and Classification Utilizing XGBoost as a classifier, the method’s accuracy in authenticating people was assessed. The goal of employing XGBoost was to get the highest level of accuracy possible. The tenfold cross-validation (NFCV) technique was utilized to validate the

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Fig. 2 Normal and abnormal ECG signal for both #Rec. 122 and #Rec. 114

Table 1 Mean EEMD measuring

Measuring of mean Item

The values of the features (1)

The values of the features (2)

The values of the features (10)

Patient_Rec# (100)

0.0489

0.0581

0.0973

Patient_Rec# (101)

0.1297

0.1141

0.23

Patient_Rec# (102)

0.0575

0.0671

0.0871

Patient_Rec# (103)

0.0756

0.0851

0.1742

Patient_Rec# (104)

0.0573

0.0661

0.1133

data, which divided it into N datasets, one for test data and the other data for training. In this manuscript, the iteration method was performed ten times, and the average accuracy of each step was utilized to calculate accuracy (Tables 3, 4).

HITR-ECG: Human Identification and Classification Simulation … Table 2 Standard deviation EEMD measuring

Table 3 Mean sample entropy measuring

Table 4 Standard deviation entropy measuring

177

Standard deviation measuring Item

The values of the features (1)

The values of the features (2)

The values of the features (5)

Patient_Rec# (100)

0.015

0.0199

0.0411

Patient_Rec# (101)

0.0402

0.0407

0.0571

Patient_Rec# (102)

0.0183

0.015

0.0341

Patient_Rec# (103)

0.0203

0.0235

0.0631

Patient_Rec# (104)

0.0158

0.0149

0.0251

Item

Mean Activity

Mobility

Complexity

Patient_Rec# (100)

0.2721

1.298

1.4351

Patient_Rec# (101)

0.4501

0.26

1.5091

Patient_Rec# (102)

0.2701

1.423

1.3101

Patient_Rec# (103)

0.7551

1.364

1.3631

Patient_Rec# (104)

0.4601

0.0639

1.2281

Item

Mean Complexity

Activity

Mobility

Patient_Rec#( (100)

0.2851

0.1721

1.3691

Patient_Rec#( (101)

0.4001

0.1023

1.3761

Patient_Rec#( (102)

0.4751

0.1073

1.5031

Patient_Rec#( (103)

0.2341

0.2463

1.2631

Patient_Rec#( (104)

0.2151

0.1689

1.3861

5 Findings and Discussion The suggested technique is put to the test in this section utilizing the Arrhythmia database at MIT–BIH. The suggested approach is assessed utilizing the machine learning classifier XGBoost algorithm and a dataset from Physio net. The Hospital Arrhythmia Laboratory was the source of the MIT–BIH Arrhythmia ECGs. This dataset contains forty eight half-hour samples of two-channel, twenty four hour ECG recording divided into two portions: The first is a set of twenty three files

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Table 5 XGBoost classification findings in terms of confusion matrix Classifiers

Output/target

Class of normal ECG signal

Class of abnormal ECG signal

Accuracy of classifier (%)

XGBoost

Class of normal ECG signal

12

1

94

2

6

77

14

7

95.66

Class of abnormal ECG signal Total

Table 6 Performance measure of XGBoost classifier Classifier

TP

FP

Pr

Se (%)

Sp (%)

PPV

NPV

XGBoost

83

0

96

93

97

96

84

(numbered 100 to 124 inclusive, with some numbers missing) chosen at random from the collection, and the second is a set of twenty five files (numbered 100 to 124 inclusive, with some numbers missing) (numbered from 200 to 234 inclusive, again with some numbers are absent). Each of the forty eight tracks lasts around 30 min. There are forty eight recordings in all, twenty four of which are normal and twenty one of which are aberrant, as well as recordings with timed beats and fifteen records that are considered for the task. There are ten normal records and five aberrant records. Table 5 shows the XGBoost classification results in terms of a confusion matrix. Table 5 shows that all normal and abnormal classes are accurately categorized, as well as some misclassifications. With 95.66 percent accuracy, the performance of the classification was gotten. Accuracy is generally the most important parameter for measuring performance of the entire system. The following is the definition of the classifier’s total accuracy (Table 6):

5.1 False Ratio (FR) The false classification ratio obtained for a given number of test samples is represented by the FR. It’s calculated as follows: The number of true-negative classifications is TN, whereas the number of false positives is FP. Table 7 compares the accuracy of the classifier with values of the false ratio value (FR) between the existing suggested approach and the literature-based method: False ratio =

TN + FP . Total samples

(4)

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Table 7 Comparison in term of prediction accuracy and false ratio values Classifier type

Artificial neural network

Support vector network

Adaptive Boosting (https://en.wikipedia.org/ wiki/Boosting_(meta-algorithm) (AdaBoost)

XGBoost

Prediction accuracy (%)

93

86.5

92

95.66

False Ratio Values (%)

5

13.5

6

0.27

5.2 The Accuracy of Prediction The prediction accuracy is a metric that reflects the percentage of correctly classified samples for a given number of samples. TP: the no. of the classification of the truepositive; FP: the no. of the classification of the false-negative. The performance of several approaches in terms of prediction accuracy is tested and compared. Prediction Accuracy =

TP + FN . Total samples

(5)

As noted in previous studies, the comparison is made utilizing some of the MIT–BIH arrhythmia database entries. For every signal, existing models are trained utilizing twenty distinct waveforms from the MIT-BIH arrhythmia database. To categorize ECG signals, the proposed approach utilized SampEn SampEn as a feature extraction technique in combination with the EEMD method. Table 7 compares the proposed performance versus other ANN and BpNN implementations in previous work, utilizing precision as a performance metric in terms of prediction accuracy and false ratio values. Figure 3 presented a comparison of comparable literature methods and the proposed method in terms of classifier accuracy with error values. Fig. 3 Comparison of comparable literature methods and the proposed method in terms of classifier accuracy with error values

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6 Conclusion Human authentication has been successfully simulated in this study employing the biometric features of ECG signals as novel biometric modalities. The EEMD and sample entropy techniques were utilized to calculate the signals characteristics in this investigation. For authentication reasons, certain SVN techniques were also utilized to categorize the signals. The 10-cross-validation method was utilized in the validation procedure. With the SVN, the maximum accuracy value of 95.66% was attained in the EEMD. When compared to sample entropy, this technique appears to be a viable candidate for implementation due to its high performance and lack of features. The EEMD descriptor, on the other hand, is sensitive to noise that modifies the activity’s value or variance. As a result, at the signal preprocessing step, denoising must be performed to reduce noise without losing a little information about the ECG signals, notably the “PQRST” waves. By utilizing feature selection that has a substantial influence, sample entropy still has a lot of room to improve accuracy.

References 1. Celin S, Vasanth K (2018) ECG Signal classification using various machine learning techniques. J Med Syst 42(241):1–11 2. I¸sik S, ¸ Özkan K, Ergin S (2019) Biometric person authentication framework using polynomial curve fitting-based ECG feature extraction curve fitting-based ECG feature extraction. Turk J Electr Eng Comput Sci 27(5):3682–3698 3. Jiang W, Kong SG (2007) Block-based neural networks for personalized ECG signal classification. IEEE Trans Neural Networks 18(6):1750–1761 4. Abdulbaqi AS, Abdulhameed AA, Obaid AJ (2021) Cardiopathy symptoms diagnosis based on a secure real-time ECG signal transmission. Webology 18(5):183–194 5. Belgacem N, Nait-Ali A, Fournier R, Bereksi-Reguig F (2012) ECG Based human authentication using wavelets and random forests. Int J Crypt Inf Secur (IJCIS) 2(2):1–11 6. Wei-Quan W, Pan L, Jia-Lun L, Jin Z (2016) ECG Identification based on wavelet transform. In: Proceedings of the 2016 joint international information technology, mechanical and electronic engineering (JIMEC 2016), pp 497–501. Atlantis Press 7. Yeen CC, Ramli DA (2018) Development of heartbeat based biometric system using wavelet transform. J Eng Sci 14:15–33 8. Belgacem N, Bereksi-Reguig F, Nait-Ali A, Fournier R (2012) Person identification system based on electrocardiogram signal using LabVIEW. Int J Comput Sci Eng 4(6):974–981 9. Hadiyoso S, Rizal A (2017) Electrocardiogram signal classification using higher-order complexity of hjorth descriptor. Adv Sci Lett 23(5):3972–3974 10. Najumnissa D, Alagumariappan P, Bakiya A, Ali MS (2019) Analysis on the effect of ECG signals while listening to different genres of music. In: 2019 Second international conference on advanced computational and communication paradigms (ICACCP). IEEE, pp 1–5 11. Smíšek R (2016) ECG signal classification based on SVM. Biomed Eng (NY) issue 1, pp 365–369 12. Rizal A, Hadiyoso S (2015) ECG signal classification using hjorth descriptor. In: 2015 International conference on automation, cognitive science, optics, micro electro-mechanical system, and information technology (ICACOMIT). IEEE, pp 87–90

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13. Rizal A, Hadiyoso S (2018) Sample entropy on multidistance signal level difference for epileptic EEG classification. Sci World J 2018(8463256):1–6 14. Abdulbaqi AS, Al-din M Najim S, Al-barizinji SM, Panessai IY (2021) A secured system for tele cardiovascular disease monitoring. In: Computational vision and bio-inspired computing. Advances in intelligent systems and computing, vol 1318. Springer, Singapore, pp 209–222 15. Nasiri JA, Naghibzadeh M, Yazdi HS, Naghibzadeh B (2009) ECG arrhythmia classification with support vector machines and genetic algorithm. In: 2009 Third UKSim european symposium on computer modeling and simulation. IEEE, pp 187–192

Brain Disorder Classification Using Deep Belief Networks Rehana Begum, Ravula Vaishnavi, Kalyan Rayapureddy, and Gelli Sai Sudheshna

Abstract Brain disorder classification is an important task in evaluating disorders and making treatment decisions. There are numerous imaging techniques used to detect brain disorders. In that MRI (Magnetic Reasoning Images) is commonly used for segmentation and detection of brain disorders, it plays an important role in medical treatment. In this chapter, the brain disorder can be done in four stages: preprocessing and segmentation, feature extraction, feature reduction, and classification. Using a discrete wavelet transformation, the features on the preprocessed image were extracted in the second stage (DWT). Deep Learning (DL) is a subfield of machine learning that has demonstrated incredible performance, particularly in classification and segmentation problems. We must use Deep Belief Network in this chapter (DBN) for classification of different brain disorder types. Keywords Brain disorder · Deep belief network · Feature extraction · MRI · Classification · AdaBoost

1 Introduction The brain is our body’s most sensitive organ. It regulates many of the human body’s regulatory functions, including memory, vision, emotion, and touch [1]. Certain disorders that develop in our brain can have serious consequences. A brain disorder occurs as a result of abnormal cell growth in the brain. As a result, early detection of a brain disorder is used to reduce the risk of severe defect in an infected patient [2]. Disorder in the human body can be caused primarily by uncontrolled and unlimited tissue growth. There are many different types of brain disorders, such as cancerous (Maligant) brain disorders and non-cancerous brain disorders (Benign). Tumors can develop anywhere in the body. Benign does not contain cancer cells and can grow slowly, occurring only in one part of the brain. Treatment is not required R. Begum (B) · R. Vaishnavi · K. Rayapureddy · G. S. Sudheshna Lakireddy Balireddy College of Engineering, Mylavaram, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_15

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for a non-cancerous disorder. Chordomas, gangliocytomas, meningiomas, and other benign brain tumor are common [1]. If not treated, these non-cancerous disorders can progress to cancer (Precancerous). Maligant has the ability to spread to nearby glands, tissues, and other parts of our body. Gliomas account for approximately 78% of malignant primary brain tumor. Astrocytoma, Ependymomas, Glioblastoma, and other malignant tumor are examples. Speech difficulties, fatigue, headaches, vomiting, difficulty making decisions, and poor concentration are common symptoms of brain disorders [2]. According to the World Health Organization, there are 780,000 patients in the United States suffering from brain diseases. There will be approximately 831,730 patients diagnosed with brain tumor by 2021 [3]. The majority of research in developed countries shows that the majority of people with brain tumors died as a result of inaccurate detection. Because brain tumors are the most lethal and aggressive type of cancer, identifying them correctly and early will be difficult. Depending on the type and size of tumor, there are numerous treatment options for brain disorders [4]. Brain tumor of various sizes are difficult to detect. In the medical field, imaging techniques have had greater success and can be used to diagnose complex human diseases such as skin cancer, brain disorders, and cancer. Magnetic resonance imaging (MRI) scans are thought to be the best diagnostic system for brain disorders [5]. The magnetic resonance imaging (MRI) has become a valuable diagnostic tool for the diagnosis and identification of brain disorders in various imaging techniques such as Computed Tomography (CT), Magnetic Resonance Spectroscopy (MRS), Diffusion-Weighted Imaging (DWI), and Positron Emission Tomography (PET). MRI also provides ultrasound CT scan and X-ray images of various parts of the brain [6]. Detecting a brain disorder entails recognizing the infected portion of the brain by detecting its shape, size, position, and boundary. Early detection of a brain disorder can improve patient survival. However, early detection of brain disorders requires the involvement of experts throughout the patient’s evaluation. The importance of Computer-Aided Diagnosis (CAD) systems in detecting brain tumors at an early stage cannot be overstated [6]. During the early stages of treatment, the BT classification is critical in determining the type of tumor that is currently present. Several cuttingedge computer-aided diagnostic tools are available in the field of biomedical image processing to assist radiologists with patient guidance and improve BT classification accuracy [7]. Brain tumors are a dangerous condition that significantly reduces a patient’s life expectancy when high-grade tumors are present. More specifically, BT diagnosis is critical for treatments that have failed to improve the patient’s quality of life. The proposed CAD system is designed to work under the supervision of a radiologist, and accurate tumor segmentation is required for more accurate cancer identification [8]. For the segmentation and detection of medical images, many artificial intelligence techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning methods such as Convolutional Neural Network (CNN) are used. CNN has been widely used in the classification and grading of images [3]. In general, classification can be divided into two categories: classifying MRI images

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into normal and abnormal types of brain cancer. The goal of this chapter is to perform brain disorder classification using Deep Belief Network (DBN) [6]. The segmentation process is one technique used in the medical field to extract information from complex medical images. The primary goal of the segmentation process is to divide an image into multiple regions based on the properties of the pixels in the image. Manual, automatic, or semi-automatic segmentation is possible. The manual method is time-consuming, and its accuracy is highly dependent on the operator’s domain knowledge. Specifically, various approaches to dealing with the task of segmenting brain tumors in MR images have been proposed. The accuracy of the spatial probabilistic information collected by domain experts usually determines the performance of these approaches [9]. In previous work, we proposed an automatic segmentation algorithm based on the concept of fuzzy connectedness.

2 Literature Review In 2020, Haris and Baskar [10] proposed a new technique for brain tumor classification using an effective hybrid transfer learning model, (GN-AlexNet) model of BT tri-classification. This model combines Google Net architecture with the AlexNet model by removing Google Net’s five layers and adding the AlexNet model’s ten layers, which extracts and classifies features automatically. The proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL on the same CE-MRI dataset. In terms of accuracy and sensitivity, the proposed model outperformed the current methods (accuracy of 99.51% and sensitivity of 98.90%). In 2021, Cheng et al. [11] improved the performance of the brain tumor classification process by using ROI augmentation and fine ring-form partitioning. They applied these improvements to three different feature extraction methods: intensity histogram, GLCM, and Bag-of-Words (BoW), where the feature vectors are fed into a classifier. The experimental results revealed that the accuracy of the intensity histogram, GLCM, and BoW increased from 71.39 to 78.18%, 83.54 to 87.54%, and 89.72 to 91.28%, respectively. In 2018, Mohsen et al. [12] proposed a system that combines Discrete Wavelet Transform (DWT) features and deep learning (DL) techniques. They used the fuzzy c-mean method to segment the brain tumor, and for each detected lesion, the DWT was used to extract the features, which were then fed into the Principal Component Analysis (PCA) for feature dimension reduction, and finally the selected features were fed to Deep Neural Networks (DNN). The results show that they achieve an accuracy rate of 96.97% and a sensitivity rate of 97.0%. In 2018, Shree and Kumar [13] published a system for classifying MRI brain tumors from images of the human brain into normal, benign, and malignant tumor. Preprocessing and segmentation, feature extraction and feature reduction, and classification were the four stages of the system. The threshold function was used to develop preprocessing and segmentation in the first step. In the second stage, the

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features were extracted using a discrete wavelet transformation (DWT) tailored to MR images. In the third stage, principal component analysis (KPCA) was used to reduce the magnetic resonance imaging features to their most important components. The final stage was the Classification stage, in which a classifier called KSVM was used to categorize the infecting region of the brain tumor. The findings demonstrated good experimental accuracy in distinguishing between normal and abnormal tissues in brain MR images. When compared to many existing frameworks, the proposed method was effective at detecting tumors. In 2020, Seetha and Raja proposed a deep CNN-based system for automated brain tumor detection and grading in their paper [14]. The system is based on Fuzzy CMeans (FCM) for brain segmentation, and texture and shape features were extracted from these segmented regions before being fed into SVM and DNN classifiers. The system achieved a rate of 97.5% accuracy, according to the results. In 2022, Kesav and Jibukumar [15] proposed a novel architecture for brain tumor classification and tumor type object recognition using the RCNN technique using two publicly available datasets from Figshare and Kaggle. It was intended to use a simple framework to shorten the execution time of a traditional RCNN architecture and to suggest a system for brain tumor analysis. To distinguish between glioma and healthy tumor MRI samples, researchers first used a Two Channel CNN, a simple architecture that was completed successfully with an accuracy of 98.21%. This was accomplished with a 98.21% success rate. Later, the tumor regions of the Glioma MRI sample that had been previously classified were detected using the same architecture as the feature extractor of an RCNN, and the tumor region was constrained using bounding boxes. When compared to other existing systems, the approach achieved relatively short execution times with an average confidence level of 98.83%. In 2020, Rammurthy and Mahesh [16], published their findings. The author proposed an optimization-driven method called Whale Harris Hawks optimization to detect brain tumors using MR images (WHHO). Rough set theory and cellular automata were used to segment the data in this case. Furthermore, tumor size, Local Optical Oriented Pattern (LOOP), Mean, Variance, and Kurtosis are retrieved from the segments. Deep convolutional neural networks (Deep CNN) are used to identify brain tumors, with proposed WHHO as training data. Combining the Whale Optimization Algorithm (WOA) and the Harris Hawks Algorithm resulted in the proposed WHHO (HHO). With maximum accuracy of 0.816, maximum specificity of 0.791, and maximum sensitivity of 0.974, the proposed WHHO-based Deep CNN outperformed alternative approaches. In 2021, Kumar and Mankame [17], presented the Dolphin-SCA algorithm for the detection and classification of BT, which is a novel optimized DL method. A novel optimized DL method is Dolphin-SCA. A deep convolutional neural network is used by the mechanism. The researcher used a fuzzy deformable fusion model in conjunction with a sine cosine algorithm based on dolphin echolocation to segment the data (Dolphin-SCA). A deep neural network was built on Dolphin-SCA and based on power statistical features, with LDP used to exploit the retrieved features. LDP was used to create the deep neural network. The accuracy of the categorization was found to be 96.3% when using the proposed method.

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In 2019, Özyurt et al. [18] combined CNN with neutrosophic expert maximum fuzzy (NS-CNN)-sure entropy for brain tumor classification. Faith et al. used the neutrosophic set–expert maximum fuzzy-sure method for brain tumor segmentation, after which the images were fed to CNN to extract features, which were then fed to SVM classifiers to determine whether the tumor was benign or malignant. They had a success rate of 95.62% on average. In 2016, Sahoo et al. [19] utilized a support vector machine algorithm to assess data and identify objects by generating a hyperplane. SVM may also be used to predict outcomes. SVMs are popular in machine learning. They are sometimes referred to as support vector networks. The accompanying algorithms evaluate the data and identify patterns for study. If a collection of training examples is provided, with each sample labeled as belonging to one of two categories, an SVM training method builds a model that assigns subsequent instances to one of the two categories, resulting in a non-probabilistic binary linear classifier.

3 Methodology Early detection of brain tumors is critical for clinical diagnosis and effective treatment. Manual brain tumor detection is a difficult task that requires expertise in detecting brain tumors. In this chapter, we have to use deep learning methods to classify brain disorders with minimal doctor intervention [20]. Figure 1 depicts the workflow of our proposed brain tumor classification method. The proposed framework model is divided into four stages. First the input Dataset contain MRI images is preprocessed. In the second step segmentation, images can be divided into different segments. Third step is mainly of feature extraction, and the final step is the disorder classification. (A). Data Set We ran a series of experiments on one publicly available brain MRI data sets. The MRI dataset can be taken from Kaggle website [21]. The MRI dataset consists of 10,000 of images. In Fig. 2, the MRI images can display different types of disorders like Alzheimer, Glioma, Acute stroke. (B). Preprocessing The most important aspect of image analysis is data preprocessing. Almost all of the images in the brain’s MRI dataset have extraneous space and areas, noise, and missing values. This may impair the classier’s performance. As a result, unwanted areas and noise in the MRI image must be removed [6]. The MRI images are too large by preprocessing, we can improve the image quality, brightness, contrast, and so on. The first step is to reduce the original

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Data set

Pre-processing

Segmentation

Feature Extraction

Classification

DWT

(PCA)Principal component Analysis

DBN+Adaboost

Normal

Tumor

Fig. 1 Workflow of our proposed system

Fig. 2 MR images from Kaggle website. The first represents different tumor images, where second represents normal images

image from 512 × 512 × 1 pixels to 128 × 128 × 1 pixels in order to reduce dimensionality, computations, and help the network perform better in less time and with simpler calculations. Because the MR images in the dataset are of varying sizes, it is recommended that they be adjusted to the same height and width for the best results [10]. The data is separated into three sections: training,

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validation, and test sets, each with its own target label (68% for training and 32% for system test and validation). (C). Segmentation The task of object segmentation is critical in computer vision and object recognition. In this chapter, we present an image segmentation technique that extracts edge information from wavelet coefficients and segments the image using mathematical morphology [22]. We threshold the image to get its binary version, and then use the inverse DWT of its high frequency sub bands from the wavelet domain to get a high-pass image. (i) Discrete Wavelet Transform The Discrete Wavelet Transform is a method for converting image pixels into wavelets, which are then used for wavelet-based compression and coding. The Wavelet Transform (WT) is widely used in signal processing and image compression. The majority of image compression techniques rely on DWT (Discrete Wavelet Transform) transformations for compression. DWT is used for image decomposition, and an N × N image is decomposed into hierarchical blocks using DWT. The decomposition is repeated until the sub block is 8 × 8. The DWT is defined as [23] 1  Wϕ ( j0 , k) = √ f (x)ϕ j0 , k(x) k M 1  Wψ ( j, k) = √ f (x)ψ j, k(x) k M

(1) (2)

where f (x), ϕ j0 , k(x), and ψ j, k(x) are discrete variable functions x = 0,1,2,…,M − 1. Normally, we leave j0 = 0 and choose M to be a power of two (i.e., M = 2 J ) such that the sums in Eqs. (1), and (2) are done over x = 0,1,2,…,M − 1, j = 0,1,2,…,J − 1, and k = 0,1,2,…,2j − 1. Equations (1) and (2) define the coefficients (2). (D). Feature Extraction The properties that describe the entire image are referred to as features. To reduce processing time and complexity in image analysis, a feature extraction method is required. This is done to extract the image’s most important features. These features were extracted by using Principal Component Analysis (PCA) [24]. (i) Principal Component Analysis PCA is a dimensionality reduction technique that identifies important relationships in our data, transforms existing data based on these relationships, and then quantifies the importance of these relationships so that we can

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Fig. 3 Directions of maximum variance

keep the most important relationships while discarding the others. It seeks the maximum variance directions in high-dimensional data and projects the data onto a new subspace with equal or fewer dimensions than the original one [24]. Take note of the maximum variance of data directions in the diagram (Fig. 3). This is represented by PCA1 (first maximum variance) and PCA2 (second maximum variance) (2nd maximum variance). The steps for doing PCA are as follows: • Use one-time encoding to convert a categorical data set to a numerical data set. • Divide the dataset into training and test segments. • Make the training and test data sets uniform. • Generate a covariance matrix for the training data set. • Create the covariance matrix’s Eigen decomposition. • Using explained variance, choose the most significant attributes. • Make a project matrix. The projection matrix is built in the code below using the five eigenvectors that correspond to the top five eigenvalues (biggest), capturing approximately 75% of the variation in this dataset. • Modify the training data set to create a new feature subspace. The formula for variance of PCA1 and PCA2 is   λ1 + λ2 p i=1 λi

(3)

(E). Classification Our proposed model’s main goal is to automatically distinguish people with brain tumors while reducing the time required for classification and improving accuracy [6]. We have to use two classification techniques in our proposed system.

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(i) DBN Model Deep Belief Network (DBN) classification of MRI images is one of the most effective deep learning algorithms, both practically and theoretically. DBN will explore the data model and train large datasets. When trained on a set of unsupervised examples, a DBN can learn to probabilistically reconstruct its inputs. On the inputs, the layers then act as feature detectors. A DBN can be further trained in a supervised manner after this learning step to perform classification. DBN is a hybrid generative graphical model. The first and second levels are undirected. Lower levels have directed links to higher layers [25]. All lower layer connections are directed, with arrows pointing toward the layer nearest to the data. Lower Layers include directed acyclic connections that translate associative memory into observable variables. The input data is received by the lowest layer or visible units. Data input might be binary or real. A symmetrical weights matrix W connects two layers [25] (Fig. 4). (ii) AdaBoost Grading Technique Because classifying complex tumor texture across patients is ineffective, this chapter considers an ensemble boosting method. By combining many moderately accurate component classifiers, this boosting method produces a highly accurate classifier. In this method, each component classifier is added to the ensemble one at a time and trained on a subset of the training Fig. 4 Architecture of DBN

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data that is ‘most difficult’ given the current set of component classifiers [26]. Adaptive boosting, such as AdaBoost, is the most common type of boosting method. AdaBoost was the first truly successful boosting algorithm designed for binary classification. This algorithm employs the approach to improve on its predecessor. The preceding model’s underfit training occurrences are given higher weight. As a result, with each new predictor, the emphasis is more on the complex instances than the rest. Each Weak Learner is also given a weighting by the AdaBoost algorithm. As a result, not every weak learner has the same impact on the ensemble model’s prediction. This method of calculating the overall model’s prediction is known as soft voting [26]. Finally all component classifiers are linearly combined by AdaBoost into a single final hypothesis. Component classifiers with lesser training mistakes are given more weight. AdaBoost’s crucial theoretical characteristic is that the training error of the final hypothesis exponentially decreases to zero when the accuracy of the component classifiers is only slightly greater than 50%. Therefore, the component classifiers simply need to marginally outperform random performance. AdaBoost is a process of learning mistakes of weak classifiers and convert into strong classifiers (Fig. 5). The formula for weak classifiers is h 1 (x) ∈ {−1, +1} h 2 (x) ∈ {−1, +1} .. . h T (x) ∈ {−1, +1}

Fig. 5 Overview of AdaBoost

(4)

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The formula for Strong classifier is HT (x) = sign

 T 

 ∝t h t (x)

(5)

t=1

Pseudocode for Proposed AdaBoost Technique Step 1: Let wt (i ) = N1 where N denotes the number of training samples, and T be the chosen number of iterations. Step 2: For t in T: (a) Pick h t the weak classifier that minimizes ∈t ∈t =

m 

wt (i )(yi = h(xi )

(6)

i=1

(b) Compute the weight of the classifier chosen: ∝t =

1 1− ∈t ln 2 ∈t

(7)

(c) Update the weights of training examples wit+1 and go back to step (a).  Step 3: H (x) = sign ∝1 h 1 (x)+ ∝2 h 2 (x) + · · · + ∝T h T (x) . If you would like to understand the intuition behind wit+1 , here the formula: wt+1 (i ) =

wt (i ) −∝t h t (x)y(x) e z

(8)

4 Experiment and Results We utilized the Python 3.7 programming language with few packages like numpy, scilpy, TensorFlow 2.0 libraries to develop and test the suggested models. For visualization, the matplotlib and seaborn libraries were utilized. Intel(R) Core (TM) i5 @ 2.50 GHz, 12 GB RAM, NVIDIA Tesla K80 GPU are the system specifications. For each validation set, several sets of training testing samples are considered. The mean and standard deviation of the accuracy, precision, recall, and F1-score after testing all validation sets are shown in Table 1. This table makes it very evident that DBN with AdaBoost outperforms all other approaches in terms of performance.

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Table 1 Analysis of classifiers CNN

SVM

RNN

Gaussian NB

DBN with AdaBoost

Accuracy

0.748378

0.895973

0.77552

0.80276

0.85–0.99

Specificity

0.698765

0.8659

0.68549

0.80261

0.86–0.96

Sensitivity

0.834678

0.91775

0.8291

0.90713

0.9–1.00

F-Measure

0.829474

0.91968

0.82136

0.90418

0.87–0.95

NPV

0.7299

0.88567

0.7081

0.8021

0.87–0.9

Precision

0.821

0.92087

0.81

0.81

0.95–1.0 s

5 Conclusion In our Proposed System feature extraction and unsupervised classification techniques have been used for improved brain disorder detection in our system, we have to use DWT segmentation are to be developed. Our proposed System works with 4 stages, the enhanced MRI images are collected from Kaggle datasets, they can be preprocessed. The preprocessed images can be segmented by using DWT to get high-pass image, the features can be extracted by using Principal component analysis (PCA). Lastly to classify the disorder, we have to use unsupervised learning technique DBN, followed by an optimization technique AdaBoost has been used as a classifier. Therefore, the Proposed DBN model has achieved efficient results when compared to other systems in terms of accuracy, precision, sensitivity, and F-measure.

References 1. Retrieved from https://www.mayoclinic.org/diseases-conditions/brain-tumor/symptoms-cau ses/syc-20350084 2. Retrieved from https://www.hopkinsmedicine.org/health/conditions-and-diseases/brain-tumor 3. Alqudah AM, Alquraan H, Qasmieh IA, Alqudah A, Al-Sharu W (2019) Brain tumor classification using deep learning technique–a comparison of cropped, uncropped, and segmented lesion images of different sizes. Int J Adv Trends Comput Sci Eng 8(6):3684–3691 4. Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62(6):1609–1618. https://doi.org/10.1002/mrm.22147 5. Gunasekara SR, Kaldera HNTK, Dissananyake MB (2021) A systematic approach for MRI brain tumor localization and segmentation using Deep Learning and Active Contouring. J Healthc Eng 2021(6695108):1–13. https://doi.org/10.1155/2021/6695108 6. Jemimma TM, Raj YJV (2018) Brain tumor segmentation and classification using deep belief network. In: 2018 Second international conference on intelligent computing and control systems (ICICCS). IEEE, pp 1390–1394 7. Sohaib A, Wenhui Y, Qurrat UA, Jin H, Tao Y, Jinhai S (2022) Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from MR images. IEEE Access 10:34716–34730

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8. Dandil E, Çakıro˘glu M, Ek¸si Z (2015) Computer-aided diagnosis of malign and benign brain tumors on MR images. In: International conference on ICT innovations, pp 157–166 9. Hua L, Gu Y, Gu X, Xue J, Ni T (2021) A novel brain MRI image segmentation method using an improved multi-view Fuzzy c-means clustering algorithm. Front Neurosci 15(662674):1–12 10. Haris P, Baskar S (2020) Brain tumor classification by using a hybrid transfer learning model (GN-Alex Net) model of BT tri-classification 11. Cheng J et al (2021) Brain tumor classification using deep learning technique 12. Mohsen H et al (2018) Classification using deep learning neural networks for brain tumors. Future Comput Inf J 3(1):68–71 13. Shree NV, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inf 5:23–30 14. Seetha J, Raja SS (2018) Brain tumor classification using Convolutional Neural Networks. Biomed Pharmacol J 11(3):1457–1461. https://dx.doi.org/10.13005/bpj/1511 15. Kesav N, Jibukumar MG (2022) Efficient and low complex architecture for detection and classification of brain tumor using RCNN with two channel CNN. J King Saud Univ Comput Inf Sci 34(8, Part B):6229–6242 16. Rammurthy D, Mahesh PK (2022) Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. J King Saud Univ Comput Inf Sci 34(6):3259–3272 17. Kumar S, Mankame DP (2020) Optimization driven deep convolutional neural network for brain tumor classification. Biocybern Biomed Eng 40(3):1190–1204. https://doi.org/10.1016/ j.bbe.2020.05.009 18. Özyurt F, Sert E, Avci E, Dogantekin E (2019) Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy. Measurement 147:106830. https://doi.org/10.1016/j.measurement.2019.07.058. 19. Sahoo L, Yadav PS, Ali SM, Panda AS, Mahapatra S (2016) Alternate machine validation of early brain tumor detection. In: 2016 International conference on information communication and embedded systems (ICICES). IEEE, pp 1–4 20. Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using Deep Neural Network. IEEE Access 7:69215–69225. https://doi.org/10.1109/ACCESS.2019. 2919122 21. Retrieved from https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumordetection 22. Ul Haq N, Hayat K, Sherazi SH, Puech W (2011) Segmentation through DWT and Adaptive Morphological Closing. In: 2011 19th European signal processing conference. IEEE, pp 31–35 23. Retrieved from https://doi.org/10.1007/978-0-387-78414-4_305 24. Zhao K (2019) Feature extraction using principal component analysis–a simplified visual demo, understand the transformation between features and principal components 25. Khandelwal R. Retrieved from https://medium.datadriveninvestor.com/deep-learning-deep-bel ief-network-dbn-ab715b5b8afc 26. Athira VS, Dhas AJ, Sreejamole SS (2015) Brain tumor detection and segmentation in MR images using GLCM and AdaBoost Classifier. IJSRSET 1(3):142–146. Print ISSN: 2395-1990 | Online ISSN: 2394-4099

Diabetic Retinopathy Detection Using Deep CNN Architecture and Medical Prescription Rajasekhar Kommaraju, Nallamotu Haritha, Patibandla Yugala, Mukkera Pushpa, and Sanikommu Yaswanth Reddy

Abstract One of the complications that occurs to the diabetic patients is diabetic retinopathy. Retinopathy is the abnormal condition of retina which results in impairment or vision loss. The development of DR significantly depends on how long a person had diabetes. There is no cure for the DR, so we must detect DR in early stage so that we can prevent loss of vision. The manual process by ophthalmologists is a time-consuming process. Here in this paper, we provide a framework for the classification of diabetic retinopathy into different levels namely L0, L1, L2, L3, L4 by using deep learning methods and providing the medical prescription for different levels of diabetic retinopathy by collecting the data from the ophthalmologist. The dataset is taken from the Kaggle, and we compare the deep learning techniques such as convolutional neural network, DenseNet, EfficientNet, and Inception for early detection of diabetic retinopathy. In the end, the EfficientNet gives the best accuracy. By using the data collected from the ophthalmologist, the framework will also provide the medical prescription. Keywords Diabetic retinopathy · Medical prescription · Convolutional neural network (CNN) · DenseNet · EfficientNet · Inception

1 Introduction Diabetics retinopathy is a common disorder that happens to the diabetic patients. Diabetics affects the retina’s blood vessels that is called as diabetic retinopathy. The ignorance of control of diabetics is a risk factor. If anyone have diabetics, it is better to have a dilated eye check once a year. Diabetic retinopathy is detected by the R. Kommaraju (B) Department of Information Technology, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India e-mail: [email protected] N. Haritha · P. Yugala · M. Pushpa · S. Y. Reddy Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_16

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presence of wounds on the retina. These wounds are hemorrhages, microaneurysms, soft exudates, and the hard exudates. • Microaneurysms are the early viewable essence of diabetic retinopathy. They can be viewed as small red spots scattered in retina and adjoined by yellow ring. The microaneurysms size is less than 125 micrometers with the sharp borders. • Hemorrhages are the broad spots scattered in retina; the size of hemorrhages is greater than 125 micrometers with an ununiformed margin at outer layers of retina. • Hard exudates are the broad yellow spots on the retina because of discharge of plasma. These are having sharper margins at outer layers of retina. • Soft exudates are the white spots in the retina because of inflammation of the neuron. The soft exudates shape is circular. The diabetic retinopathy has two types. The first type is premature diabetic retinopathy also known as non-proliferative diabetic retinopathy (NPDR). If the new blood vessels are not growing in retina, then it can be treated as NPDR. The second type is advanced diabetic retinopathy also known as proliferative diabetic retinopathy (PDR). If there is a growth of new, abnormal blood vessels in the retina, then it can be treated as PDR. Early DR symptoms are typically absent. Some people become aware of their fuzzy vision as soon as it starts. The retina’s blood vessels will leak into the vitreous in the subsequent stages. The classification of diabetic retinopathy is done by with its associative wounds [1]. We can control our diabetics by treating the diabetic retinopathy. There are two stages of diabetic retinopathy: an early stage and an advanced one. We have categorised diabetic retinopathy into L0, L1, L2, L3, and L4 substages based on the phases. According to the stages, we prescribe the medications. For providing better understanding and better prescription, we provide a description to the substages like L0 means no diabetic retinopathy; if there no wounds or lesions on the fundus images, it can be treated as no DR. L1 means mild nonproliferative DR; if there is only presences of microaneurysms, then it can be treated as the mild non-proliferative DR. L2 means moderate non-proliferative DR; If there are more than microaneurysms, then it can be treated as the moderate nonproliferative DR; it is less than severe DR. L3 means severe non-proliferative DR. if there are more than 20 intraretinal haemorrhages in each of the four quadrants of the fundus image. L4 means proliferative DR, if there are one or more vitreous or pre-retinal hemorrhages, neovascularization in fundus image. These are the description that we are provided for the L0, L1, L2, L3, L4. L1 and L2 stages are considered as an early stage. Early stage patients with L1 (mild non-proliferative DR) need to examine by the doctor for every 12 months, and the patient must follow the diet preferred by the doctor to control the sugar levels in the blood. The patients who are in L2 (moderate non-proliferative DR) stage need to examine by the doctor for every 6 to 8 months. These are the early stages in diabetic retinopathy. L3 and L4 are considered as an advanced stage. In advanced stages, we face many complications so we have used some treatments like injecting medicines

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to the eye, photocoagulation, pan retinal photocoagulation, and vitrectomy. These are treatments that we are preferred to the patients in advance stage (L3 and L4). And, we prescribe the medicines for diabetic retinopathy based on the L0, L1, L2, L3, and L4 stages.

2 Literature Review In 2020, Mishra et al. [2] proposed the architecture for the detection of diabetic retinopathy. Here, they used two deep learning techniques, namely VGG16 and DenseNet21.They applied these two methods on the APTOS dataset which is accessible on Kaggle. In this paper, they compared the above two techniques and proved that the DenseNet21 give the more accuracy. The fundus retinal images of diabetic patients are used as input. The DenseNet21 model will extract the features of Input Images, and at end, activation function will predict the result. Finally, the proposed VGG16 gives 0.7326 accuracy, and DenseNet21 gives the 0.9611 accuracy. In 2021, Saranya et al. [3]. Evaluated a convolutional neural networks mechanism to detect the stage of the diabetic retinopathy in short span of time. The dataset is taken from the Kaggle. The implemented convolution layer consists of two 32 filters having size 3 × 3 and max pooling layer is used with 2 × 2 pool size and again convolution layer having 64 filers of 3 × 3 size followed by global average pooling, and this is again followed by two dense layers. The proposed model got the specificity of 89.99884. In 2020, Chaudhary and Ramya [4] compared two classifiers, namely fuzzy classifier and CNN classifier. In this paper, STARE, DIARETDB1, and DIAREDB2 datasets are used. The retinal nerves and optic disk are being segmented. For feature extraction, gray level co-occurrence metrics are used. Here, it just classifies whether it is PDR or NPDR. The fuzzy classifier gives 85% accuracy, whereas CNN classifier gives accuracy of 90%. In 2019, Maya and Adarsh [5] proposed an automatic and efficient method to detect and classify the diabetic retinopathy. In this paper, hard exudates, microaneurysms, and hemorrhages are the features extracted. These extracted features are fed to the CNN for the classification. The accuracy of this model is around 98%. In 2018, Chetoui et al. [6] defined the texture features like local energy-based histogram and ternary pattern for the DR detection. For the classification of resultant histogram, support vector machines are used. SVM gives best performance of 0.931. In 2021, Kalyani et al. [7], for the detection and the classification of diabetic retinopathy, used a capsule network. The CapsNet architecture has convolution layer for input image’s feature extraction, primary capsule layer to process the result of previous layer, class capsule for the purpose of classification, and a SoftMax layer to convert previous result to probabilities. The accuracy is 97.98%. In 2018, Wan et al. [8] define an automatic way to classify the fundus images. In this paper, they used CNN architecture for the detection of DR which includes classification, segmentation, and detection. The CNN relates to transfer learning and

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hyper-parameter tuning. The better accuracy is 95.68% is given by the combination of the CNN and transfer learning. In 2016, Gao et al. [9] used deep neural networks for the automatic diagnosis of diabetic retinopathy. They deployed their models on cloud computing environments and facilitated this assistance to various health centers. The model achieved an accuracy of 88.72%, illustrating the novelty of their proposed work. In 2018, Kumar and Kumar [10], by determining the quantity of microaneurysms and the precise area, took a novel technique to the identification of diabetic retinopathy. The following methods are utilized to detect microaneurysms: Principal component analysis, contrast limited adaptive histogram equalization, average filtering, and morphological process. Support vector machines are employed for categorization. This method’s specificity and sensitivity are 92 and 96%, respectively.

3 Dataset The data here we are using is the image data. This data is taken from the dataset which is freely available in Kaggle. The images in this dataset are high resolutional retina images under different imaging circumstances. The count of the images in this dataset is 2750. This number of images is distributed among those five classes (Fig. 1). In the dataset, it has 2750 rows which were the number of images in the dataset and had three columns, namely the disease that means healthy or mild or moderate

Fig. 1 Number of pictures in each category

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Fig. 2 Dataset description

or severe or proliferative DR, path that means the path in which image is in our local system, and the last one is the class label either 0 or 1 or 2 or 3 or 4 (Fig. 2).

4 Methodology In this paper, we are constructing the model using different models namely CNN, Inception V3, DenseNet, EfficientNet. This model will help us to early detect the diabetic retinopathy. We will consider the method which gives more performance among the models we constructed. The considered method will be taken for detection of DR in our frame work.

4.1 Using Convolutional Neural Network CNN has greater capability of automatic feature extraction from the images in the dataset. It follows the feed-forward technique which is very helpful to assess the images. CNN is helpful for the detection and categorization of images. In this, every image can be defined as an arrangement of pixels. Convolutional layer, pooling layer, fully connected layer, and dropout layers are some layers which are present in the deep convolutional neural network.

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Fig. 3 CNN architecture

If the input size is H × H × B and spatial size is X with stride Z and with padding amount P, then the volume of the output of convolutional layer can be obtained by below formula. V = ((H − X + 2 P) ÷ Z) + 1 If size of activation map is H × H × B, spatial size is X with stride Z, then the volume of the output of pooling layer can be obtained by below formula (Fig. 3). V = ((H − X) ÷ Z) + 1

4.2 Using DenseNet DenseNet model also uses the feed-forward mechanism which is very helpful to assess the images. In DenseNet, training is done effectively by taking the shorter connections between the layers. DenseNet will allow us to get the strong gradient flow. One of the advantages of DenseNet are giving the best performance even if there is sufficient data for training. The ith layer gets the features from its previous layer. The formula is shown below. V i = K i ([ p1, p2, p2, . . . , pi]) Firstly, we need to import all the required libraries like matplotlib, CV2, Keras, and other libraries the model need. Next, we need to create the class label for each image present in dataset. And then we create the data frame with pixel and labels. And the next step is to select the features and target. Thereafter, we need to split

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the data into train data and test data. Here, we consider 60% data as train data and 40% data as train data. Next, we do data augmentation by using the image data generator. Data Augmentation aims to produce the new training images from the original images by applying random functions. By applying translations, shearing, rotations, and some other geometric transforms, we can create the new data from the original data. After that, we can initialize DenseNet model by creating the DenseNet layers. And we need to fit the model by using optimizers and finally save the model and check the loss and accuracy of model. Thereafter, we evaluate the model based on the performance measures like precision, recall, f1-score, support, and accuracy.

4.3 Using Inception-V3 Inception V3 uses CNN for the classification of image. It also has convolution layers and a pooling layer. It makes the smaller convolutions by factorization. It has low error rate when it is compared with the preceding models. The Inception V3 model has more layers than Inception V1 and V2. Totally, there are 42 layers which help us to get the better result. The data is now divided into train and test data. The train data is about 90%, and test data is 10%. And then, we apply Inception v3 algorithm. After that, save and evaluate the model. This will give the efficient performance as it is the combination of CNN.

4.4 Using EfficientNet A technique is developed based on the concept of Compound Coefficient is called EfficientNet. It is helpful expand the models in a simple and understandable way but gives the result very effectively. Balancing the proportions of depth, width, and image resolution to a common ratio is known as Compound Coefficient. We have implemented DR detection using EfficientNet as firstly we have imported all the libraries such as matplotlib, NumPy, pandas, seaborn, CV2, and other modules which we need to our project. And then, we need to prepare our data such as resizing the data; afterward, we need that resized data into NumPy arrays. And then ,we shuffle the data if we need or else, we directly going to divide the data into training data and testing data. Here, we are considering the train data as 90% of our whole data and 10% of our data as test data. And then, we create the model by calling EfficientNetB2 model. And, we can modify the model by adding the dropout layers and kernel regularizer ‘L2’ and writing the callback functions to help with model debugging, and we can reduce the learning rate when model reached plateau. Thereafter, we can save the model and evaluate the model based on the performance measures like precision, recall, f1-score, support, and accuracy.

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5 Results and Analysis Our experimentation evaluated the four models, namely the models are CNN, DenseNet, Inception-V3, EfficientNet. And the results are as follows (Fig. 4). As we observe that among the four models, the efficient gives the best accuracy of 98.38%, followed by CNN with the accuracy of 88.89% and the DenseNet with 71.70%, and the least accuracy is got with the inceptiom-v3 with 63%. The CNN model’s accuracy during training and validation as well as its loss during training and validation is shown in Fig. 5. Figure 6 displays the DenseNet model’s accuracy during training and validation as well as its loss. Figure 7 displays the Inception-V3 model’s training and validation accuracy, as well as its training and validation loss. Figure 8 displays the EfficientNet model’s training and validation loss and accuracy.

Fig. 4 Accuracy of four models

Fig. 5 Accuracy and loss of train and validation data in CNN model

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Fig. 6 Accuracy and loss of train and validation data in DenseNet model

Fig. 7 Accuracy and loss of train and validation data in Inception-V3 model

Fig. 8 Accuracy and loss of train and validation data in EfficientNet model

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6 Conclusion Diabetic retinopathy detection is primarily helpful for the diabetic people, and the detection of DR by ophthalmologists took a long time. So here we are investigating four different models in order to detect the DR in early stage. CNN, DenseNet, Inception-V3, and EfficientNet give the accuracy of 0.8889, 0.7170, 0.6301, and 0. 9838. So by the experimentation, our paper proves that the EfficientNet gives the best accuracy. So, we use EfficientNet model to detect the DR. After the detection of DR based on the data given by the ophthalmologists, our frame work will also provide the medical prescription for the patient.

References 1. Wilkinson CP, Ferris FL, Klein RE, Lee PP, Agardh CD, Davis M, Dills D, Kampik A, Pararajasegaram R, Verdaguer JT, Global Diabetic Retinopathy Project Group (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9):1677–1682 2. Mishra S, Hanchate S, Saquib Z (2020) Diabetic retinopathy detection using deep learning. In: 2020 International conference on smart technologies in computing, electrical and electronics (ICSTCEE). IEEE, pp 515–520 3. Saranya P, Umamaheswari KM, Sivaram M, Jain C, Bagchi D (2021) Classification of different stages of diabetic retinopathy using convolutional neural networks. In: 2021 2nd International conference on computation, automation and knowledge management (ICCAKM). IEEE, pp 59–64 4. Chaudhary S, Ramya HR (2020) Detection of diabetic retinopathy using machine learning algorithm. In: 2020 IEEE International conference for innovation in technology (INOCON). IEEE, pp 1–5 5. Maya KV, Adarsh KS (2019) Detection of retinal lesions based on deep learning for diabetic retinopathy. In: 2019 Fifth international conference on electrical energy systems (ICEES). IEEE, pp 1–5 6. Chetoui M, Akhloufi MA, Kardouchi M (2018) Diabetic retinopathy detection using machine learning and texture features. In: 2018 IEEE Canadian conference on electrical & computer engineering (CCECE). IEEE, pp 1–4 7. Kalyani G, Janakiramaiah B, Karuna A, Prasad LVN (2021) Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst 8. Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274–282 9. Gao Z, Li J, Guo J, Chen Y, Yi Z, Zhong J (2019) Diagnosis of diabetic retinopathy using deep neural networks. IEEE Access 7:3360–3370 10. Kumar S, Kumar B (2018) Diabetic retinopathy detection by extracting area and number of microaneurysm from colour fundus image. In: 2018 5th International conference on signal processing and integrated networks (SPIN). IEEE, pp 359–364

An Innovative Software Engineering Approach to Machine Learning for Increasing the Effectiveness of Health Systems Ananapareddy V. N. Reddy, Mamidipaka Ramya Satyasri Prasanna, Arja Greeshma, and Kommu Sujith Kumar

Abstract By increasing diagnostic precision, lowering healthcare costs, and enabling tailored treatment regimens, there is potential for machine learning to revolutionize the healthcare sector. Realizing the full potential of artificial intelligence in healthcare requires finally adoption of cutting-edge software engineering methodologies. This review of the literature looks at the present status of software engineering and healthcare practices in the area of machine learning. It also covers cutting-edge software engineering methodologies for machine learning and their successful application through case studies and real-world examples. The poll also outlines potential adoption hurdles and future initiatives. The conclusion underlines the importance of continued research and development in this area in order to fully realize machine learning’s promise to increase the efficiency of healthcare systems and boost patient outcomes. Keywords Medical artificial intelligence · Diagnostic precision · Individualized care · Software development · Model creation and deployment · Ideal government

1 Introduction Global healthcare systems are struggling with a number of issues, including rising prices, a staffing deficit, and an aging population [1]. It is critical to develop fresh, creative approaches to these problems in order to raise the general efficiency of the healthcare system [2, 3]. A possible response to these problems has been discovered A. V. N. Reddy (B) Department of Information Technology, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India e-mail: [email protected] M. R. S. Prasanna · A. Greeshma · K. S. Kumar Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_17

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as artificial intelligence’s field of machine learning. Healthcare practitioners may increase diagnostic precision, lower costs of care, and enable more individualized treatment regimens by utilizing machine learning algorithms. However, integrating machine learning into the healthcare system is more difficult than it first appears. Adopting cutting-edge software engineering techniques is essential if machine learning is to be used in healthcare to its full potential [4]. These methods make sure that machine learning models are developed, deployed, and governed in a way that is both ethical and effective. The goal of this literature review is to give readers a thorough overview of the status of software engineering and healthcare as it pertains to machine learning. The survey will look at cutting-edge software engineering methods for machine learning, as well as case studies and actual instances of successful implementation, future directions, and potential adoption impediments [5, 6]. The significance of ethical issues in the creation and application of machine learning models in healthcare will also be examined by the study. The survey’s findings will highlight the significance of ongoing research and development in this area in order to fully realize machine learning’s promise to boost the efficiency of healthcare systems and improve patient outcomes [7].

2 Need for ML in Healthcare Healthcare services are getting better all the time, and we are getting better at treating complicated conditions. The dosage and duration of medicines depending on patient characteristics or for patient in an often chaotic and unpredictable work environment. Hospitals and health systems have used ML to overcome specific difficulties. One of the most intriguing areas of AI is machine learning, and many businesses are working to take advantage of it. ML is growing more and more well-liked [8, 9]. It uses algorithms to enable data-driven learning and is applicable in a variety of settings, including business and healthcare. Because new technologies and ideas are continuously being developed, healthcare is continually changing. In some of these novel situations, ML could help medical experts [10, 11]. With the help of modern technology, unstructured text that was once difficult to produce and use extensively can now yield valuable insights. Physicians and administrators may make timely, educated decisions about patient care and operational programs that impact millions of lives with the help of this new richness of ML-derived intelligence [12, 13].

3 Literature Survey An evaluation of the previous research and studies that have attempted to use software engineering practices and techniques to improve the development and deployment of machine learning models in healthcare would probably be included in “an innovative

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software engineering approach to machine learning for increasing the effectiveness of health systems”. This could cover matters like the governance of machine learning models in the in the medical field and the model deployment, repeatability, and explain ability [12, 13]. The study might also look at how these methods affect efficiency, accuracy, and patient outcomes in healthcare. The poll may also take into account the privacy and ethical ramifications of using machine learning to healthcare, as well as how software engineering techniques might assist allay these worries [14, 15]. The present situation of the healthcare sector and the difficulties it encounters, such as rising prices, access to treatment, and data security, might also be discussed in “an innovative software engineering approach to machine learning for increasing the effectiveness of health systems”. The poll might look at how machine learning could be able to help with these issues, such as by enhancing diagnostic accuracy, lowering healthcare costs, and enabling more individualized treatment options. The study may also look at how software engineering approaches, such as the usage of agile development methodologies, DevOps techniques, and continuous integration and deployment, are currently applied in the machine learning and healthcare industries. The survey might also go through the best procedures for developing, validating, and deploying models, as well as case studies and actual instances of these procedures being used successfully. The survey could also take into account the field’s future development and potential research and development areas, such as the integration of machine learning with electronic health records, using federated learning to share data among healthcare organizations while maintaining privacy, and creating standards and guidelines for the use of machine learning models in healthcare. The study might also look at the factors that prevent widespread use of software engineering principles in the fields of healthcare and machine learning, such as a lack of funding, a shortage of skilled workers, and regulatory and legal constraints [16]. Through the application of machine learning and cutting-edge software engineering techniques, the survey may offer methods for removing these obstacles and enabling a more effective and efficient healthcare system [17–19].

4 Methodology The creation of A MLA-based feature engineering model and performance evaluation is discussed in order to collect datasets. Figure 1 depicts the methodology’s workflow for this research project. Clinical data on diabetics were used to produce the data analysis file. The procedures required to create a realistic framework utilizing machine learning approaches based on ensemble learning are described.

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Fig. 1 Methodology

5 Regarding Dataset We accumulated the scientific facts use a snow sample approach in collaboration which has a diagnosed case of diabetes specialist. There are 11 features present in each of the 403 examples in the collected dataset. To safeguard the participants’ privacy, the dataset does not include any of their personally identifying information, such as names or personal identification numbers. But the numbers appear to be imbalanced. By eliminating balance factors, accuracy may be improved overall in a number of different ways. The experimental trial’s dataset has become produced the use of medical information according to the endocrinologist’s instructions (diabetes specialists). Determine 2 illustrates the selected trends. The dataset has become obtained by calling a collection of clinical citizens and starting a short conversation with the patients. The records collection approach discern 3 indicates a precis of the facts set (Fig. 2). Additionally, Fig. 3 illustrates end goal, which is diabetes versus nondiabetic. Here, 0 denotes a non-diabetic, and 1 denotes a diabetic.

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Fig. 2 Each attribute’s histogram Fig. 3 Positive and negative correlation, i.e., diabetic versus nondiabetic

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5.1 Data Reprocessing The proposed strategy was assessed and tested using the clinical dataset. There are numerous distinct sorts of illnesses in the clinical dataset. For cleaning and feature extraction, the uncooked statistics are converted into a record layout for statistics analysis. The definition of diabetic medication is provided on this page. The patient differs from a healthy patient in their ideal state.

5.1.1

Data Cleaning

Data were used to gather unprocessed information. As a result, a variety of methods have been utilized to clean the data, including eliminating duplicates and extraneous information.

5.1.2

Data Balancing

Forecast modeling is a greater challenging when there are uneven distributions of categories balanced. For categorization, MLA typically begins with an equal number of samples for each class that is being studied. In this step, erroneous data are managed and eliminated in order to produce more exact and accurate conclusions. For example, missing variables include those for patient-number-age, diabetes pedigree characteristics, insulin, pores and skin thickness, gender glucose, and result. Because those parameters cannot contain null values, clean values are assigned to them. We balanced all values by scaling the dataset. This study has significantly changed resampling methods. For instance, the majority of the classifier may be merged, and undersampling can be carried out by eliminating entries from each group. The results of using the under-sampling and oversampling approaches are shown in Figs. 4 and 5, respectively. The following records balancing methods have been applied in this look at: (a) oversampling at random (learn): Random-under sampler is a method for repairing inconsistent datasets. Information validation is made quick and easy with this strategy. Data are randomly picked from each goal institution. Every target class’s statistics is randomly chosen. Choose random samples with or without alteration

Fig. 4 Tomek links

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Fig. 5 Diabetes

to assess the number class. (b) Using a random oversample (learn): Create a new minority sample size to address the partiality issue. The easiest approach is to manually choose fresh samples in order to discard the old ones. (c) Under-sampling (Tomek links): There are significant parallels between opposed grouping pairs and tomek connections. The categorization is improved by enlarging the zone separating the two classes and deleting instances of the higher class from each pair. Tomek’s connection is important since the two samples are near to one another. (d)SMOTE oversampling: The methodology results in erroneous facts about the minority. Additionally, all locals are taken into account. The synthetic points in Fig. 6 are inserted between the selected point and its neighbors. IQR technique is used to remove outliers, while a boxplot’s statistics exceeds a certain scope. The difference various maximum and lowest quartiles may be calculated by the usage of the range of interquartile (IQR). Additionally, the top and backside quartiles are separated via the interquartile range (IQR). Utilizing statistical strategies together with interquartile, z-rating, and dataset smoothing, outliers in the research’s information had been detected. The primary and 1/3 quartiles of a statistics series, or the twenty-fifth and seventy-fifth deciles, are blended to assemble the IQR; this is produced by means of deducting Q1 from Q3, as illustrated in Figs. 6 and 9.

5.2 Feature Engineering This approach entailed fostering skills that MLA may use by using data from a particular area. It involves the extraction of raw data and its translation into MLA representations. To determine the relationships between various variables, the study employs a correlation matrix.

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Fig. 6 Insulin

5.2.1

Matrix of Correlation

The concept of correlation describes how regularly and in what path two quantitative variables be a part of in an immediately line. The correlation additionally determines the linear connection’s strength. R stands for the variety of values between one and one. The terrible affiliation in Fig. 7 shows that neither affected person-number nor age have any impact on any of those variables.

5.2.2

Verification Across

Cross-validation in gadget getting to know refers back to the practice of assessing methods with a restricted facts pattern. The simplest alternative that governs the procedure and determines what number of organizations of data have to be generated from a particular series is okay. Every other call for this technique is “ok-fold passvalidation”.

5.2.3

K-Fold Cross-Validation

The entire dataset should be split into ok-folds, depending on how much information is there. Test the version using the final fold ok after becoming it to folds K1. (Ok less 1).

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Fig. 7 Correlation matrix of clinical datasets

5.3 Classification of Algorithms In this part, we provide an MLA application that is being investigated in order to detect diabetes at an early stage. Using the clinical dataset, six efficient categorizers are used to anticipate diabetes. Seven classifiers, including classification, the DT, GBC, MLP, and LR algorithms, are employed. A composite of these six most accurate classifications is used to assess the voting classifier.

5.3.1

Linear Regression (LR)

The kind of numerical variable is predicted by this approach. Relying on the number of records, dividing the whole dataset into ok-folds the usage of numerous among five and 10. Statistical strategies are used by LR to forecast binary outcomes (y = zero or 1). To create LR predictions, the chance of an occasion happening is taken into account. Each data factor is mapped via the sigmoid characteristic inside the LR set of rules. The S-shaped curve is the output of the common logistic function. The following equation shows the sigmoid function.

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Decision Tree (DT)

The choice tree (DT) supervised MLA becomes advanced for categorization. It is far organized like a tree, with the branches denoting the effects of the check and the interior (non-leaf) nodes denoting features, elegance predictions, and leaf nodes. A shape similar to a tree is created through the prediction version’s recurrent information department based totally on a parameter that maximizes the statistics department. The most usually used check is information gain. In step with the data advantage, each split represents the best entropy misplaced due to the cut-up. The ratio of values within the y elegance to all of the capabilities inside the leaf node that holds the facts item x is referred to as estimated.

5.3.3

Gradient Boost Classifier (GBC)

Group-based algorithms, or GBCs, combine numerous constrained learning models to provide a precise prediction. It is usual practice to increase the gradient using DT. GBC is an MLA that may be used to solve issues with classification methods since it builds a prediction model derived from a number of inadequate models. In contrast to RF, GB uses weak learner decision trees and typically outperforms it. As opposed to using the model sequentially, alternative strategies alone would reduce an infinitely differentiable loss function.

5.3.4

Support Vector Machine (SVM)

The hyperplane is built using a variety of classes or objects in SVM. A hyperplane is created by calculating the size of the issue capacity. In order to balance data dimensions, SVM also permits data reduction. Utilizing class corner points and support vectors, calculating the minimal distance between the classes using the hyperplane’s center. Kernels, C coefficients, and intercepts are a few of the variables utilized in SVM. The kernel is the most important part of the SVC. These kernels have been modified to take into account the type of data they receive.

5.3.5

Random Forest (RF)

The best tree estimate and training data are used to generate decision trees in the RF ensemble learning approach for classification and additional duties. Prior to categorization, nearly all vote approach is used to get findings that are significant in identifying the kind of diabetes illnesses. A healthy results from patients might either be classified as healthy or sick.

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5.3.6

217

Multilayer Perceptron (MLP)

Hidden, input, and output layers make up a neural network’s layers. The enter layer accepts the facts, whereas the output layer provides the consequences. Between the input layer and the output layer, there is a hidden layer. The neural network was inspired by the neural network of the body. Network neurons behave probabilistically, just like human neurons. Neural networks require a substantially longer processing time. It is also referred to as a multilayer perceptron in Weka.

5.3.7

Ensemble Learning

The system’s categorization precision may be increased by including several distinct classifiers. When tackling two or more machine Learning methods for the same problem collaborate to improve classification precision.

5.3.8

Performance Analysis

Some of the performance metrics used to validate the approaches include memory and accuracy, the F-measure, the MCC, and the ROC area.

5.3.9

Comparative Analysis of Existing Work

Utilizing a range of pertinent approaches, literature, and dataset analysis, comparisons of the efficacy of our proposed framework have been made. It was shown that our well-considered methodology produced favorable outcomes for a number of assessment metrics, including accuracy for predicting the probability of developing diabetes. To manage missing values and substitute outliers in boxplot techniques, a variety of techniques are utilized, including data imputation, standardization, and balancing. Techniques for data transformation have been used to achieve better outcomes. Results in comparison with related works. Additionally, the ensemble technique and K-fold validation technique were used to create the suggested framework and produce results that were more reliable than those of comparable research.

6 Model Accuracy By showing the accuracy numbers in Fig. 8, which compares many, we can better grasp the changes. MLA style depending on their consistency. The comparison shows that RF is more accurate than the alternative models. The following bar graph illustrates how accurate various algorithms are.

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Fig. 8 Accuracy of methods

Fig. 9 Depiction of scatterplot feature of data

7 Result and Discussion There are six different algorithms: RF, MLP, SVC, DT, GBC, and LR algorithms, were used. Random forest was the algorithm that performed the best. From each component, we chose the best-performing algorithms and carefully examined the results. The results of MLA splitting can be tested, accurate, and in some situations, they may even surpass learnable ability. The correlations between the characteristics that were used to build the dataset are shown in the scatterplot in Fig. 9. The X and Y axes display all dot position values that were utilized to quantify each data point. The machine learning model assesses the performance of the algorithms using a confusion matrix. Through the use of statistical criteria including accuracy,

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remember, particularity, F-degree, MCC, and ROC area, the matrices of misunderstanding have been used to evaluate different MLT. The rows indicate the actual values, whereas the columns represent the expected values, in a tabular format. These classifiers’ confusion matrices are shown in Fig. 10. To get more accurately the medical diabetes dataset included in this observation is shown in Fig. 1. It may be utilized to identify and forecast early diabetes using MLA in Fig. 11. Numerous more statistical measures are also calculated, as seen in Fig. 12. These variables are used to validate the machine learning models. Figure 11 compares the clinical dataset utilized in this work with the PIMA diabetes dataset so that it will be extra as it should have become aware of the early

Fig. 10 L R algorithm

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Fig. 11 Evaluation of ML approaches

prediction of diabetes based totally on a machine studying the set of rules. The clinical dataset for diabetes has better accuracy when compared to the PIMA diabetes dataset.

8 Results In the palms of any physician, scientist, or researcher, ML has the ability to be a mighty device. There seems to be a step forward in gadget studying every day. With each innovation, a sparkling gadget studying (ML) application seems which could address an actual healthcare problem. The scientific zone is closely monitoring this trend as ML era continues to progress. Doctors and surgeons are the usage of ML thoughts to help save lives, identify ailments and other troubles even earlier than they show up, better manage patients, include sufferers extra absolutely within the restoration method, and do lots greater. Utilizing AI-pushed technologies and device studying models, global corporations decorate healthcare delivery. This era allows agencies and pharmaceutical groups create therapies for severe illnesses extra fast and correctly. Via employing digital clinical trials, sequencing, and sample recognition, businesses may additionally now quicken their checking out and statement processes. Extra vast determinants of well-known fitness include fitness behaviors and socioeconomic factors together with cash, social aid networks, and schooling. Health organizations remember the fact that they must target the entire individual, including life- style and surroundings, for you to enhance general health. ML fashion scan identify people who are more likely to get continual, treatable ailments like diabetes, coronary heart disease, and many others.

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Fig. 12 Result

9 Conclusion and Future Scope A conclusion to a literature review on “an innovative software engineering approach to machine learning for increasing the effectiveness of health systems” might highlight the potential effects of innovative software engineering approaches on the healthcare sector while summarizing the major findings from the survey. The issues that still need to be solved, such as the requirement for funding, the need for competent employees, and statutory and regulatory limitations, might be highlighted in the conclusion. Future research and development in the field may focus on issues like how to integrate machine learning with electronic health records, use federated learning to share data among healthcare organizations while protecting patient privacy, and create standards and best practices for the application of machine learning models in healthcare. Additionally, future research can concentrate on removing obstacles

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including a lack of funding, a shortage of skilled employees, and regulatory and legal constraints that prevent the broad use of software engineering approaches in the fields of machine learning and healthcare. Overall, the conclusion can underline the significance of ongoing research and development in this area to fully realize the promise of machine learning and cuttingedge software engineering techniques to boost patient outcomes and the efficacy of healthcare system.

References 1. Abdelaziz A, Elhoseny M, Salama AS, Riad AM (2018) A machine learning model for improving healthcare services on cloud computing environment. Measurement 119:117–128 2. Char DS, Abràmoff MD, Feudtner C (2020) Identifying ethical considerations for machine learning healthcare applications. Am J Bioeth 20(11):7–17 3. Ahmad MA, Eckert C, Teredesai A (2018) Interpretable machine learning in healthcare. In: Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics, pp 559–560 4. Kaur P, Sharma M, Mittal M (2018) Big data and machine learning based secure healthcare framework. Procedia Comput Sci 132:1049–1059 5. Sarwar MA, Kamal N, Hamid W, Shah MA (2018) Prediction of diabetes using machine learning algorithms in healthcare. In: 2018 24th International conference on automation and computing (ICAC). IEEE, pp 1–6 6. Sendak MP, D’Arcy J, Kashyap S, Gao M, Nichols M, Corey K, Ratliff W, Balu S (2020) A path for translation of machine learning products into healthcare delivery. EMJ Innov 4(1) 7. Gupta A, Katarya R (2020) Social media based surveillance systems for healthcare using machine learning: a systematic review. J Biomed Inf 108(103500):1–13 8. Chen IY, Joshi S, Ghassemi M, Ranganath R (2021) Probabilistic machine learning for healthcare. Ann Rev Biomed Data Sci 4:393–415 9. Siddique S, Chow JCL (2021) Machine learning in healthcare communication. Encyclopedia 2021(1):220–239 10. Waring J, Lindvall C, Umeton R (2020) Automated machine learning: review of the state-ofthe-art and opportunities for healthcare. Artif Intell Med 104(101822):1–12 11. Ahmad MA, Patel A, Eckert C, Kumar V, Teredesai A (2020) Fairness in machine learning for healthcare. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3529–3530 12. Manogaran G, Lopez D (2017) A survey of big data architectures and machine learning algorithms in healthcare. Int J Biomed Eng Technol 25(2–4):182–211 13. Jones LD, Golan D, Hanna SA, Ramachandran M (2018) Artificial intelligence, machine learning and the evolution of healthcare: a bright future or cause for concern? Bone Joint Res 7(3):223–225 14. Ahmad F, Farid F (2018) Applying internet of things and machine-learning for personalized healthcare: issues and challenges. In: 2018 International conference on machine learning and data engineering (iCMLDE). IEEE, pp 19–21 15. Prosperi M, Guo Y, Sperrin M, Koopman JS, Min JS, He X, rish S, Wang M, Buchan IE, Bian J (2020) Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nat Mach Intell 2(7):369–375 16. van der Schaar M, Alaa AM, Floto A, Gimson A, Scholtes S, Wood A, McKinney E, Jarrett D, Lio P, Ercole A (2021) How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Mach Learn 110:1–14

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17. Han T, Stone-Weiss N, Huang J, Goel A, Kumar A (2020) Machine learning as a tool to design glasses with controlled dissolution for healthcare applications. Acta Biomater 107:286–298 18. Rajendran S, Mathivanan SK, Jayagopal P, Janaki KP, Bernard BAMM, Pandy S, Somanathan MS (2021) Emphasizing privacy and security of edge intelligence with machine learning for healthcare. Int J Intell Comput Cybern 15(1):92–109 19. Seneviratne MG, Shah NH, Chu L (2019) Bridging the implementation gap of machine learning in healthcare. BMJ Innov 6:45–47

Region of Interest and Feature-based Analysis to Detect Breast Cancer from a Mammogram Image D. Saranyaraj, R. Vaisshale, and R. NandhaKishore

Abstract This paper discusses the categorisation of breast cancer based on mammography images—MLO perspective. This research presents a method for finding malignant tumours in mammography images utilising the Gabor cut algorithm for region of interest from the morphologically upgraded image for cancer detection with better detection rate and accuracy. To eliminate spurious matching points, the features were examined using the enhanced feature extraction technique. The improved ORB method with RANSAC implementation is utilised in this work to extract the necessary features by masking the background data. The methods offered provide characteristics with exact information about cancerous tissues. This automated extraction has a 98.006% accuracy. Keywords Breast cancer · Feature extraction · Gabor cut algorithm · ORB algorithm

1 Introduction In the realm of medicine, medical image analysis is critical. With a huge quantity of patient data, various clinical applications demand large data to be processed. This study focuses on one of these diagnoses, cancer. It has been estimated that 21.7 million new cancer cases would be discovered by 2030. Computer-based approaches D. Saranyaraj (B) · R. Vaisshale Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Vengal, Chennai, Tamilnadu, India e-mail: [email protected] R. Vaisshale e-mail: [email protected] R. NandhaKishore Technical Lead, Ascendas IT Park, Trane Technologies, CSIR Road, Phase II, Tharamani, Chennai, Tamilnadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_18

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have been developed to progress and improve the diagnosis of pictures acquired from medical imagery. Image processing techniques have created an excellent analysis for diagnosing different types of cancer. Expert technicians are needed to diagnose the test results as well as the mutation of cancer cells, which can be reduced with digital image processing in which the data of the image constructed does not change even if it is reconstructed several times while retaining the image’s originality [1, 2]. Breast cancer is the deadliest and most frequent disease in women [1, 3]. At the moment, mammography is the most effective method for detecting cancer early, enhancing patients’ chances of survival. Several computer-aided detection and diagnostic methods have been developed in recent years to aid radiologists in the interpretation of mammography [4, 5]. Recent research on CADe and CADx systems using DDSM data relies on segmentation. This is true for mass detection [6–13], false positive reduction [14–18] and mass segmentation [12, 19]. This is also true for MC detection [8, 20], as well as segmentation [21, 22]. Most CADx systems rely on manual segmentations, whether they are mass classification systems [7, 23–33]. Three others from [34] and [35] measure the overall risk rating assigned to photographs [36] or examination records. These systems, which rely on the content-based pattern classification framework [37], function as bounding box, unable to determine which parts of the images prompted the automatic diagnostic. Masses and microcalcifications are two key cancer signs that are universally employed in estimating mammography. The bulk is the microcalcification’s expansion [38, 39]. For an impermeable tissue with an anomaly, many approaches are required. A CAD study will enable a radiologist to do a visual examination of these locations to discover lumps [40, 41]. A lump and microcalcification have been identified in several instances in women over the age of 35 [42]. While mammography is currently regarded as a method of identification that is susceptible to radiation, it is not as much as X-ray [43–46]. The texture is the most essential visual signal in distinguishing different types of homogenous areas because it provides information about surface properties [47– 50], depth and direction [51–55]. Expert coders create neural network algorithms, which require a significant amount of effort and time through arduous trial-and-error procedures. The most challenging step is to teach the algorithm to detect objects automatically. This work focuses on extracting features from textural data using the ORB and RANSAC algorithms, as well as a unique area of interest approach termed the Gabor cut algorithm, and boosting segmentation accuracy in breast cancer diagnosis, consequently minimising misclassification.

2 Breast Cancer Detection Using Feature Extraction To analyse Breast health the mammogram is used which is the higher version of an X-ray that has very less radiation. The images captured by the mammogram are generally analysed with greyscale. Masses will have high gradients of grey, whereas microcalcifications are low gradients of grey. Mammograms typically reveal only

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MLO view of the Breast Mammogram

Image Preprocessing Bilateral Filter

Noise reduction

Edge Segmentation Gabor Cut

Region of Interest

Canny Edge Detection

Feature Extraction FVM Building Textural Feature Extraction

Feature Vector Modeling ORB & RANSAC

Breast Mass Localization

Fig. 1 Representation of the proposed breast cancer detection

85% of the breast content, which is evident that the scan couldn’t analyse fifteen per cent of breast cancers. Mammograms are captured from two to four angles from which the mediolateral view is considered in this image which has the advantage of the lateral side where the changes in the breast tissue occur (Fig. 1).

3 Image Preprocessıng 3.1 Image Denoising As an image is acquired or transmitted, noise is injected into it. Noise in an image can be caused by a variety of sources. The noise is nothing but the variation in the pixels of the image. These images will decide the noise characteristic. The noise can be obtained if there are some changes in the environment while capturing images, the sensor is not suitable for improper lighting, the transmission of electrons in

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inappropriate channels, etc., in this article, we replaced the median filter with a bilateral filter [56].

3.1.1

Bilateral Filter for Edge Conserving

For normalising, the bilateral filter considers the variance in the differences of the neighbour pixels. It is important to train pixel of surrounding pixels; it should not only occupy a nearby position but also have a similar worth. This subject has been formalised in the literature [57–60]. The bilateral filter, EC(L), is as follows: ( / )∑ EC(L)h = 1 Rh

q ∈s

|) (| G σ s (||h − q||)G σ r | L h − L q | L q ,

(1)

where generalisation factor Rh ensures pixel weights sum to 1.0: Rh =

∑ q ∈s

|) (| G σ s (||h − q||)G σ r | L h − L q |

(2)

The amount of denoising for an image L is decided by parameters σ s and σ r as space and range, where G σ s may be a spatial Gaussian weighting, G σ r is an array Gaussian which weighs the pixel q in Eq. (1), when divergence of intensity I p takes place. Figure 2b illustrates the output of the bilateral filter, and Fig. 3 exemplifies weights computed for every pixel in the edge. Evaluate the image restoration and assign to Eq. (3). From Eq. (3), N (h) denotes the shortest distance pixel h, as weighting function. The first term is a sustaining term which preventing the drifting of the noisy input values. The second term is a regularisation term that has the function. This approach is very resilient since it preserves substantial intensity variations such as edges. This equation is a weighted average of the data, ( ) G σ s (||q − h||)G σ r L qt − L th L qt ( ) ∑ t t q G σ s (||q − h||)G σ r L q − L h

∑ L (th + 1)

=

q

(3)

and, Eq. (4) are two ways to solve the minimisation approach. The difference in cost is the weight of the centre pixel L ht . Fig. 2 MLO view of the breast a the original image, b denoised image using bilateral filtering

a.

b.

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Fig. 3 Bilateral filter levelling an input image. The figure is duplicated from [61]

Table 1 MSE and SSIM of both the median and bilateral filters

70 60 50 40 30 20 10 0

S. No

Filter

MSE

SSIM

1

Median

62.44

0.84

2

Bilateral

27.20

0.87

62.44 27.2

1 Bilateral Filter 0.87 0.84

MSE

2 Median Filter

SSIM

Fig. 4 Bar graph between bilateral and median filter

The median filter was utilised by the author in [56] for denoising, which only analyses the space surrounding the pixel. The advantage of the bilateral filter is considering entire entity on space and range around the pixel, yielding more accurate results. The mean squared error and structural similarity index ratios are shown in Table 1 to compare the performance of the median and bilateral filters (Fig. 4).

3.1.2

Image Enhancement

To improve image details, the contrast enhancement approach is utilised [24]. The details in most natural images are obscured due to the close distribution of greyscale. In this research, we employed Finite impulse response to improve such images. Wiener filter applied to discrete series. Given that we have, h(m, n) = p(m, n) ∗ q(m, n) + r (m, n),

(4)

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where h(m, n) is the noisy image, p(m, n) is the concealed image, r (m, n) is random noise and q(m, n) is the blur kernel. Equation (4) in frequency domain is: H (u, v) = P(u, v) ∗ Q(u, v) + R(u, v).

(5)

The mean inverse is applied to Q(u, v) representing squared error on Eq. (6) by using the Wiener filter. e2 = E

[(

))] ( p(m, n) − mean p(m, n)2 .

(6)

The frequency domain equivalent of Eq. (5) is given by T (u, v) =

N ∗ (u, v) / . |N (u, v)|2 + Sn (u, v) S f (u, v)

(7)

N ∗ (u, v) . |N (u, v)|2 + L

(8)

Equation (7) is denoted as, T (u, v) = Thus, the Wiener filter is obtained. Algorithm 1: Image Enhancement (1). (2). (3). (4). (5). (6). (7). (8). (9). (10).

The original image is dumped. Transform to a greyscale image. Employ a bilateral filter to reduce noise. Examine the image/DFT of the loaded image/inverse DFT to examine Blur kernel DFT and inverse DFT DFT of loaded picture product and complex conjugate of blur kernel Estimation of the blur kernel modulus The Wiener constant is incorporated in the modulus of the blur kernel. Subtract Numerator/ A2 + B 2 . Apply inverse DFT to the resulting filtered picture.

See Fig. 5.

4 Segmentation of the Edge In image processing, edge segmentation is a crucial yet difficult approach. In this study, we used the Gabor cut technique, which is an intelligent area of interest picker that automatically chooses the region of interest when we identify the place of interest.

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b

231

c

Fig. 5 Original and filtered image a computed image with a tumour, b pre-processed image of a, c filtered image and d preprocessing of c using the Wiener filter

4.1 Gabor Cut Algorithm Grab cut was developed as a technique for foreground mining with minimal user interactions. The rectangular border is entered by the user. Pixels outside of this limit might be considered certain background. The number of pixels within the rectangle is infinite. Similar to this, all user input establishing foreground and background is correctly thought out as painstaking labelling, meaning they cannot be changed at different points in the development. The PC initially classifies the supplied data. It shows the pixels in the background and foreground. A Bayesian GMM is used to sculpt the foreground and background. In a multivariate distribution, one might use a Gaussian mixture model to describe a vector of parameters before allocating on the vector of ballpark figure given by p(θ ) =

∑K i =1

( ∑ ) , ∅i N μi i

(9)

where∑the ith vector portion is defined by the probabilities ∅i, means μi and ( )covariance i n. The foregoing is compounded by the recognised distribution p θx based ( / ) on limit θ is detected. The subsequent distribution, p x y , is also a mixed Gaussian using this method. p(θ ) =

∑K i =1

( ∑ ) n ∅i N μi i

(10)

Unidentified pixels are classified as plausible foreground or likely background based on the data. Every source node is associated with a foreground pixel, whereas every sink node is connected with a background pixel. The edge information or pixel similarity provides the pixel weights. Finally, using the least amount cost function, a mincut algorithm divides the network into two sections supply nodes and sink nodes. The procedure is advanced till the categorisation is complete.

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4.2 Morphological Operation Upon the completion of the preceding stages, all of the questionable edge pixels are acquired. They, however, are not tumour region edge pixels [56]. Moreover, all of the suspicious edge pixels are scattered in the shape of stripes, making it hard to build a closing region. As a result, corrosion and closure procedures are required to enclose the zone. Corrosion of suspicious edge pixels is defined as a growth of an area containing suspicious edge pixels that link to other suspicious edge pixels to create a figure. Moreover, the closing operation can fill the space between figures to make an enclosed area.

4.3 Canny Edge Detection A popular edge detection approach is called canny edge detection. The outcome of the canny detection is calculated using the distance of the Gaussian segment of an image. The Gaussian width expansion reduces detection at the expense of losing some of the image’s greater detail. The issue is that when the Gaussian width increases, so do the edges. To create the sharp edges that divide the raising the lower margins too high will produce noticeable outcomes. The top edge might be adjusted excessively low, which can result in a lot of fictitious and undesirable edge sections. The essential canny detection is a problem when it comes to Y-intersections, which occur when three edges meet in the image’s diagonal end and utilising a Gaussian element with a 2.0 standard deviation, top and lower boundaries of 255 and 1 and a standard deviation of 1. The real edges are known, and many key locations have been carefully chosen. But, there may be too much attention to detail for the preparation that will follow. The lower left corner of the photograph has the “Y-Junction impact” that was previously stated. Algorithm 2: Edge Detection (1). We will go through each level of the multi-organisational computation in step one. (2). Removing noise from the image by employing a 5 × 5 Gaussian channel. The Im-Intensity: age’s Determining Gradient: To obtain the initial order derivatives in the horizontal (Gx) and vertical directions, the smoothed picture is then filtered with a Sobel fragment across the horizontal as well as vertical directions (Gy). (3). Calculate each pixel’s edge angle and course using the formula Edge_ Gradient(G) = (G2x + G2y) − (− Angle()) = tan− 1 (GyGx). (4). Non-maximum suppression: A pixel is checked to determine if it is the gradient’s closest most extreme. (5). Addition of the threshold values minVal and maxVal for hysteresis.

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Fig. 6 ROI selection using the filter

Every level of the multi-organisational calculation will be felt by us (Figs. 6, 7, 8). Fig. 7 Morphological result with the tumour found is shown in a and b

a

Fig. 8 Improved canny edge detection was used to obtain the dominant edges

b

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5 Feature Extraction 5.1 Feature Vector Model Building The attributes and qualities of a picture are revealed through its features. As a result, the characteristics must be distinguishable, dependable and self-contained. ci = [ci1 + ci2]

(11)

If ci1 and ci2 are both multidimensional vectors, ci1 represents the geometrical properties of a tumour and ci2 represents the textural features of a tumour. The d1 and d2 forms are shown as ci1 = [ f 1, f 2, f 3, f 4, f 5]

(12)

ci2 = [h1, h2, h3, h4, h5],

(13)

where the five geometric characteristics of a tumour are roundness, the entropy of the standard radius, the variation of the standard radius and the ratio of area to roughness (abbreviated as f 1, f 2, f 3, f 4 and f 5), where the five textural characteristics of the greylevel co-occurrence matrix (h1, h2, h3, h4, h5) are reverse gap, correlation coefficient, entropy, energy, correlation coefficient and contrast.

5.2 Geometric Feature Extraction The form and location of breast tumours vary. Roughness, size, form, edge and density are geometrical traits that are crucial for tumour diagnosis due to variance. Based on the segmentation of tumour, its geometrical features: roundness, the entropy of standardised radius, the variance of standardised radius, the ratio of area and roughness are retrieved and examined. One of the most significant geometrical properties is roundness. The smoother the margin, the smaller the roundness and the higher the likelihood of a benign tumour. In contrast, the larger the roundness, the more likely it is a malignant tumour. The term “roundness” is defined as / f 1 = p 2 A.

(14)

Entropy of standardised radius is obtained by first calculating the normalised radius l z of each edge pixel q using the following equation: lz =

Sz − Smin , Smax − Smin

(15)

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where Smax (Smin ) is the maximum (minimum) separation between an edge pixel and the central C of the tumour area. Sz is a symbol for the separation between z and C. It is obvious that all of the normalised radii fall within the range of 120; 1. The interval was divided into 100 subintervals, and we tallied how many edge pixels were included inside each subinterval. Next, as demonstrated in Eq., calculate pk , which is the proportion of edge pixels in the kth subinterval to all edge pixels (19). The probability that an edge pixel’s standardised radius falls within the kth subarray is another way to define pk. pk = P(l z ∈ (0, 1(k − 1), 0, 1k)), k ∈ 1, 2, 3, . . . , 10

(16)

Then, entropy of standardised radius is defined as, g2 = −

∑100 k =1

pk (log( pk ))

(17)

The variance of standardised radius: It describes the range of variance of the standardised radius. It is defined as √ )2 1 ∑N ( l(t) − lavg , (18) g3 = i =1 N −1 where N is the number of edge points, l(i) is the ith standardised radius of edge points. The ratio of the area: It describes the circularness of a tumour. If the value is smaller, the shape of the image is similar to a circle. The ratio of area is defined as g4 =

1

∑N

lavg N

i =1

( ) l(i ) − lavg ,

(19)

where l(i ) − lavg = 0 davg is the average standardised radius of edge points. Roughness: The more irregular the shape is, the bigger the roughness. It is defined as g5 =

1 ∑N [l(i ) − l(i + 1)] i −1 N

(20)

5.3 Textural Feature Extraction Textural feature extraction oriented FAST and rotated BRIEF (ORB) is proposed which is a combination of FAST and BRIEF algorithm. The bilateral filter is used for the image preprocessing, i.e. for more exact focus on the pixel. Enhancing the speed by utilising the ORB and RANSAC the textural features can be extracted with

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more nominal speed rate and to discard the false matching. The outcome illustrates the enhanced ORB which gives fast detection and more accurate prediction. ORB calculation is advanced in view of FAST and BRIEF calculation, not just the speed has enormously increased; additionally, it has the rotational invariance contrasted and the one BRIEF calculation. Initially, this paper utilises a bilateral filtration to eliminate the noises in the image reducing the hindrance, after which the improved ORB protocol to plot descriptors. Then to eliminate the false matching pixels RANSAC algorithm is implemented. The process increases the speed of matching and obtains correct points. To increase the speed and loc of the images, this paper concentrates on the ORB protocol. This includes 1. Using the bilateral filter to eliminate the clatter. 2. Usage of the Oriented FAST and BRIEF to extract the descriptors. 3. Implementing RANSAC algorithm and homography matrix to eliminate the incorrect matching pixels. 4. The angle transformation matrix can be used to fix the images and, in the end, reaching images correlation.

5.3.1

Extracting the Descriptors

To extract the feature points ORB is implemented which is a binary series, hamming distance is used for matching feature pixels. When the pixel of a small area is identical and matching, hamming distance is 0 or is 1, then < 1 is an additional match of the descriptors. The threshold is set as T < 0.8 for exact extraction of features. This algorithm simplifies the computation.

5.3.2

Reducing the False Matching Pixels

This paper focuses on discarding the false matches by RANSAC algorithm along with the homography matrix. RANSAC algorithm relies on a group in place that contains excessive data, determining the mathematical vogue of the info parameter, getting effective look at. RANSAC algorithm uses less indicate to calculate the model. It is the parameter estimation technique with defect, lowering the impact of unusual knowledge then. Finding homography matrix between 2 images that is a five ∗ five matrix. We tend to arrange homography as a two-dimensional matrix M, and therefore picture1 multiple M is a picture of a pair. The feature pixel of the source image and the prospect matching factors from image 2 will be linked by homography providing the threshold for filtering out some points. The better matching points are the correct matrix given by the RANSAC, and therefore, the bilateral filtering method can increase the accuracy. Given that, (1) Model adapting to the assumption of a preferred subset, and others calculated from the hypothesis of the sub-subset points. (2) The rest of the data is done as step (1). (3) If there are enough pixels grouped, the estimated algorithm is a success.

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5.3.3

237

˙ The Correction and Matching of Images

We tend to tend to use perspective transformation to correct footage and ultimately to complete two images matching. The standpoint transformation is that the venturing transformation of the protuberance centre and used in image correction. This increases the computation time with point-of-view transformation, matching time covers a very little adjustment; thus, this paper implements image correction. Then the coordinates of both images are matched. Figure 9 is that the flowchart of the improved ORB algorithm. Figure 10 shows the feature matching with 242 features from image 1 which was the result of the improved Gabor cut algorithm and 2844 features from image 2 which is obtained after the bilateral filtering. 143 inliners were obtained using the modified ORB algorithm and RANSAC algorithm. Though using RANSAC algorithm, we obtained 145 matches with 143 inliners which gave 2 matching error (marked in RED). When the basic ORB was used, the accuracy was 80.3% with the error 19.7% (Table 2, Fig. 11).

Fig. 9 Improved ORB flowchart

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Fig. 10 Feature descriptor using improved ORB algorithm

Table 2 Comparison between basic ORB and Improved ORB algorithm S.No

Inliners

Matched

Accuracy

Error

Basic ORB

143

178

80.3

19.7

Improved ORB

143

145

98.006

200 150

1.994

178 143 143

145 98.006 80.3

100 50

Basic ORB 19.7 1.994

Improved ORB

0 Inliners

Matched

Accuracy%

Error%

Fig. 11 Graphical representation between Basic ORB and Improved ORB with accuracy and error

6 Conclusion The identification of breast cancers in digital mammography was investigated using an improved Gabor cut algorithm and an improved ORB. The image preprocessing was done before the segmentation of the tumours in the pictures using enhanced bilateral filter. To automatically choose the area of interest, the modified Gabor cut method was applied. The discovered tumour area was subsequently subjected to feature analysis. The modified ORB provided exact characteristics of the malignant tissue with a 98.006% accuracy. The accuracy and speed were enhanced by using the upgraded ORB and Gabor cut algorithms. This study discovered that digital mammography’s CAD-based diagnosis of breast tumour detection provides radiologists with confirmation when seeing dubious regions in images, improving accuracy and efficiency.

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Vehicular Ad Hoc Network: A Review Monika and Poonam Rani

Abstract Every industry, including academia and business, has seen a major change as a result of technological advancement. In the last two decades, smart transportation has gained a lot of popularity and is now seen as a crucial component of the automotive industry. Driverless cars, autonomous vehicles, and electric vehicles are all becoming a reality these days. When it comes to road transportation, the term “smart transportation” refers to vehicular ad hoc network (VANET). A lot of the work is still only in research articles and has not yet been applied practically. When discussing VANET, the primary issue is for road user safety. Mobile vehicle intercommunication and intelligent infrastructure are used to transmit emergency information among network nodes for quick and effective decision-making. This article provides an overview of VANET, its characteristics, and application areas. Afterward paper discusses various challenges in VANET and analyzes the future scope of VANETs. In end, paper discusses some of the very popular simulators used for VANETs simulation. This survey article that covers issues, challenges, and applications of VANET can benefit society by promoting the development and deployment of VANETs, which can improve road safety, enhance transportation services, and provide economic benefits. Keywords Vehicular ad hoc network (VANET) · Onboard unit (OBU) · Roadside unit (RSU) · Wireless access in vehicular environment (WAVE) · Dedicated short-range communication (DSRC) · V2I · V2V · V2X

Monika (B) · P. Rani Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India e-mail: [email protected] P. Rani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_19

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1 Introduction The development of technology has made smart transportation a reality today. All modes of transportation, such as air, water, and land travel, are included in smart transportation. As a part of ITS, VANETs are wireless ad hoc communication networks that enable cars to communicate with one another as well as with all other network participants, such as intelligent roadside infrastructure, pedestrians, and other vehicles. Intelligent transportation system (ITS) has received a lot of interest from the auto industry, academia, and the government due to features that reduce traffic congestion, increase traffic efficiency, and improve road safety. Road transportation networks are those in which participants include automobiles on the road, infrastructure along the side, and pedestrians. Comparatively speaking, VANETs are more mobile than mobile ad hoc networks (MANETs). The vehicle nodes in VANET are movable and travel in very predictable routes. When we talk about VANETS, the safety of the traveling public is our first priority because it will help to reduce traffic accidents [1]. Vehicle networking is crucial for the transmission of safety information. This is being done to guarantee driver comfort and safety as well as to give emergency vehicles the quickest paths. VANETs now use the WAVE, DSRC, and LTE-V2X standards for communication. IEEE 802.11p, which counts radio frequencies, is the foundation of DSRC. Using two-way vehicle to vehicle (V2V) or vehicle to infrastructure (V2I) connection, it recognizes high-speed mobile objects in a specified area and provides real-time data transmission. Low interference and low latency are two major benefits of DSRC. Only short-range communications can be done with DSRC. The LTE-V2X standard can support both short-distance and wide-area communications and is dependent on existing infrastructure. Long-distance coverage, high capacity, great reliability, and low delay are only a few of its benefits [2]. Vehicles can process, transmit, receive, and communicate with other entities using DSRC. They can also share beacon messages and forecast emergency warnings, among other things. Known more commonly as Wi-Fi, wireless access in vehicle environments (WAVEs) are a modified version of IEEE 802.11. Communication between onboard and roadside units is made possible via WAVE. Let us now talk about the key elements of the VANET system architecture. The parts of the VANET are as follows (Fig. 1). • Onboard Units (OBUs): These are automobiles that have smart gadgets like cameras, GPS, etc., built into them. Smartphones, smartwatches, and other similar gadgets that gather information about the car and the driver are included in this. • Roadside units (RSUs): This unit consists of roadside infrastructure that is a part of VANET. • Communication models: In a network, a communication model describes how information flows and how communication occurs. All of these communication models—vehicle to vehicle, RSUs to vehicles, and RSUs to RSUs—are chosen in accordance with network data flow.

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Fig. 1 Vehicular ad hoc network

The main contributions of this paper are as follows: • This survey article on VANET provides readers with a comprehensive overview of the technology, including its key features, potential applications, and challenges. • By surveying the existing literature, the article identifies key research gaps and areas where further investigation is needed. This can help guide future research efforts and inform the development of new technologies. • VANETs present several technical challenges, such as maintaining connectivity in highly dynamic environments, ensuring network security, and managing the large amounts of data generated by connected vehicles. This article identifies these challenges and discuss potential solutions. The rest of the survey article is organized as follows. Section 2 reviews various research articles in this field, Sect. 3 discusses features of VANET, Sect. 4 summarizes the application areas of VANET which are categorized in basically four parts. Section 5 discusses the current challenges facing by the VANETs, and Sect. 6

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discusses the future scope ideas for VANET. Section 7 gives some of details of available simulators for VANET. Section 8 concludes this survey covering brief summary of the paper.

2 Reviewed Work There has been a lot of study on wireless ad hoc networks and the Internet of things over the past 20 years. Over the next two decades, researchers and inventors would create a wide range of unique systems and technologies, ranging from speed cameras to driverless vehicles. Gradually, these innovations would combine into more expansive, cooperative IoT-style systems [1]. The present study [2] examines location privacy research in VANETs from the perspectives of functionality and security. The paper provides a comprehensive study of numerous threats, identity thefts, and manipulation, as well as solutions for location privacy. [3] This study examined several security vulnerabilities that occur with VANETs. The paper discusses the many aspects of VANET and its layers, as well as possible assaults. There is also discussion of other protocols. Existing solutions are analyzed for potential security flaws [4]. This research examines various communication models such as V2V, V2I, and V2E. Following that, the paper discusses several communication technologies utilized in VANETs such as the IEEE 802.11p standard, cellular networks such as 4G and 5G, and how these technologies are integrated cooperatively to get advantage of these technologies. The use case is also carried out in the 5G test network. Xia et al. [1] this paper discusses the current state of VANET development in general. The paper discusses the different issues that VANET faces, including key technology, resource management, applications, and communication protocols. Finally, the study discusses upcoming technologies, and how they might be integrated with VANETs [5]. This article discusses various VANET topics and challenges. The paper focuses on cloud-based techniques such as cloud computing, fog computing, edge computing, mobile edge computing, and their application in VANET. Two scenarios for prompt safety message dissemination are described. This paper discusses [6] a layered architecture of VANET by extending seven tiers of the OSI model, as well as different issues that arise as a result of high mobility. Furthermore, each layer and its protocol suite are discusses, as well as difficulties and challenges, including security concerns. This paper proposes [7] a solution to the issue of multi criteria multi-hop routing in vehicular ad hoc networks. (VANETs). A heuristic protocol for urban environments with two major components is suggested. The simulation findings revealed that HERO performed well in terms of delivery success ratio, latency, and communication overhead. A routing [8] protocol based on historical traffic flows via Q-learning and monitoring real-time network status is suggested in this paper. This technique reduces communication overhead and latency while ensuring reliable packet transmission. Three reference algorithms are used to evaluate the proposed technique in depth. The suggested work [9] aims to guarantee

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Table 1 Literature review Paper year

Summary

2021 (A comprehensive survey of the key technologies and challenges surrounding vehicular ad hoc networks) [1]

Paper discusses: 1. Key technologies used for data collection and data dissemination in VANET 2. Various routing protocols 3. Communication models 4. VANET’s applications and related issues 5. Future scope having emerging technology in mind

2020 (A survey on security in VANETs) [10] Paper discusses: 1. Various security issues 2. Possible security attacks and their nature 2021 (Security in vehicular ad hoc networks: Paper discusses: challenges and countermeasures) [3] 1. Security issues in VANET 2. VANET layered architecture and issues with every layer 3. Routing protocols and their related security issues 4. Possible solutions for various issues

efficient and reliable message delivery in V2V with the longest compatibility time and trust using a fog node-based architecture. Survey table is shown in Table 1.

3 Features of the VANET There has been a lot of study on wireless ad hoc networks and the Internet of things over the past 20 years. Over the next two decades, researchers and inventors would create a variety of unique systems and technologies ranging from driverless automobiles to speed cameras, and these developments would gradually combine into larger, more cooperative IoT-style systems [2]. A heterogeneous road network called VANET incorporates both infrastructure–based and infrastructure-less networks. As opposed to the roadside devices, which are stationary, the network’s vehicle nodes are mobile. While connections between other infrastructures can be either wired or wireless, connections between automobiles and everything else are wireless. Here, some crucial VANET features are discussed. A few of the features are as follows: • Constantly shifting topology: Vehicles are mobile and move in various directions. Due of its great mobility, predicting a vehicle’s position is exceedingly unpredictable. Therefore, a specific network topology cannot be used there. • Extremely frequent network disconnections: A mobile vehicle may occasionally leave the range of the network. Therefore, network disconnections occur frequently in VANET.

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• Adequate power supply: In a wireless sensor network (WSN), where sensors are randomly placed, power is supplied by batteries incorporated in sensor nodes, but in a VANET, where smart devices are installed either on the vehicle itself or on stationary roadside units. Since we can routinely replenish batteries, there is never a power deficit. One of VANET’s key features is this. • Smart infrastructure: All roadside structures, roadways, and other features feature smart technology. • Greater mobility [5]: Due to greater mobility, vehicles can speak with one another without experiencing additional communication delays. Rapid transmission of urgent messages across the network is possible. • Network density is highly erratic: Because vehicles are so mobile, it is difficult to forecast network congestion. Compared to running roads, the intersection has a higher vehicle density [5]. • Restricted transmission power [5]: The transmission power of communication protocols utilized by VANET, such WAVE, is constrained. Transmission power varies with the size of the service area. • Comfort and safety for drivers: The use of VANETs may result in greater driver safety, increased traveler’s comfort, and safe and effective traffic flow. The main advantage of VANETs is that mobile nodes may communicate with each entity in the network directly, allowing for effective emergency message transmission [8]. • Scalability: Because VANETs may involve roads, rural areas, and metropolitan areas, the network is extremely scalable in these systems.

4 Application Areas of VANET Researchers have explored a lot of applications of VANETs. VANETs have various safety applications, information and entertainment applications, and environment friendly applications. Some of the applications are following (Fig. 2): • Applications for safety: When discussing road transportation, safety always comes first. Real-time traffic condition sharing, emergency message forecasting, and other crucial application areas are covered under these applications. • Infotainment applications: These applications look after the entertainment needs of travelers. Examples of such uses include video streaming and advertising. • Applications pertaining to driver convenience: These applications address the comfort of the driver while driving, such as smart parking and map access. • Applications that are environmentally friendly: Environmental contamination is a result of transportation. These applications protect the environment by promoting energy efficiency, electrical vehicles, and other practices that contribute to lower greenhouse gas emission.

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Fig. 2 Application areas of VANET

5 Challenges in VANET The automobile industry has been extensively studied by researchers over the past 20 years, but there are still numerous unresolved issues that demand their attention. The following list of open challenges is discussed: • Security: Because VANETs are open networks, they are vulnerable to a number of security assaults. These networks inherit all security flaws present in other traditional wireless networks because of their open nature. • Interaction intermittent connectivity: A major difficulty is controlling and managing the network connections between infrastructure and cars. It is necessary to prevent intermittent connections caused by high packet loss or excessive vehicle mobility [7]. • Latency: Low latency is a crucial requirement in future VANETs for real-time applications. Future VANETs ought to offer real-time applications, such safety messages, with very low latency [7]. • Reliable network topology: A network architecture that includes mobile vehicles and wireless connections between them creates an unstable and unreliable network topology. Network connectivity is weakened by frequent dynamic topology. Due to the great mobility of the vehicles passing by on the same side, communication with them is not always possible, leading to frequent network failures. The resilience of networks is impacted by communication channels’ often relatively brief lifetimes [2]. • Limited transmission power: Wireless access to vehicle environment (WAVE) has a limited transmission power that changes depending on the region covered by the coverage range. Transmission power will be greater for longer distances than for shorter ones.

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• Restrictions on uniform standards: [2] Urban traffic may be made efficient, safe, and environmentally friendly by using VANETs. VANETs must adhere to the same standards as the Internet in order to be widely used. • Lack of intelligence: Although researchers have been studying VANETs for years, only a small portion of their work is properly implemented and becomes widely known. The development of VANET has been significantly hampered by issues with network reliability and connectivity, which prevents decision-makers from having access to the fundamental information about vehicles they need to be informed. • Safety security: Transmission delays are a constant problem for safety-focused applications. In such applications, the sooner a warning is received, the more time one has to deal with data and react, and thus the better the likelihood of avoiding danger. Existing standards, however, do not yet provide the performance required for transmitting emergency communications. As 6G evolves, with its extremely low latency and support for long-distance communications, combining new technologies with VANETs may change the situation [2].

6 Future Scope • Security improvement: VANETs are still in their formative years in terms of security. Because of its open architecture and minimal protection, VANETs are vulnerable to security threats. Machine learning methods, such as decision trees, can aid in this filtering process and may be able to identify and isolate the attacker [2]. Security has already benefitted from blockchain technology. • Requires network reliability [2]: Improved hardware and software architecture are essential for increasing network resilience. By lowering load capacity and node failures, better hardware can make data interchange and processing considerably more reliable. In addition to increasing transmission range, better software can reduce transmission delay. • VANET intelligence is a key trend area for future VANETs development. Intelligent VANETs can enhance vehicle safety, energy efficiency, and comfort. • Improved network connectivity: To satisfy the high communication demands of future VANETs, continuous connectivity between vehicles is required. Cars and other network elements should be able to communicate continuously and with a high degree of reliability thanks to connected vehicles. Network should be able to withstand communication system transmission failures. • New Architecture Requirement [9]: In future, building an integrated system architecture that can utilize numerous various technologies (e.g., IEEE 802.11p, DSRC, ITS G5, Wi-Fi, WAVE, 5G/6G) and heterogeneous vehicular networks will be a significant research topic of vehicular ad hoc networks.

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• High mobility and location awareness [7]: Future VANETs call for the vehicles taking part in communication to have these characteristics. To respond to an emergency circumstance in the network, each vehicle needs to know where the other cars are in relation to it.

7 Simulators for VANET Because VANET involves mobile vehicles sharing their data via a variety of communication modes, it combines networking with mobility [11]. VANET deployment and testing are labor-intensive and expensive processes. Before actually implementing a solution, simulation can be a helpful and less expensive stand-in. Therefore, VANET needs both types of simulators that offer networking and mobility services. As for modeling communication protocols and message exchange, network simulators are in charge, while mobility simulators are in charge of each node’s mobility [11]. The following discussion includes some of the most well-known network simulators. • NetSim: Wireless, mobile, and sensing networks all can be simulated with this discrete-event tool. The simulator offers three different license types: professional, standard, and academic. Support for VANET simulations is offered by pro and regular licenses. NetSim connects to SUMO in order to imitate VANETs. The WAVE standard for wireless communication between vehicles is handled by NetSim, and road traffic conditions are modeled by SUMO. • Veins: This open-source framework for simulating vehicular networks makes use of OMNeT+ + and SUMO. It is intended to serve as a platform for userwritten programs to run in, allowing for the modeling of new environments and applications. It requires SUMO and OMNeT++ to function properly and produce accurate results. Any mistake in one of them could lead to erroneous outcomes. • Eclipse MOSAIC: Also referred to as vehicle to everything (V2X), simulation runtime infrastructure (VSimRTI) [12] is an open-source multi-scale and multidomain simulation framework for the evaluation of novel approaches to networked automated mobility. The main goal of this simulator is to give users the ability to do numerous V2X simulations with their preferred simulator. • EstiNet [13]: The EstiNet add-on package VANET is optional. It includes a feature for building roads to mimic car traffic. Basic driving actions like overtaking, lane-changing, and following another car are all supported by EstiNet’s mobility simulator. EstiNet offers OBU and RSU frameworks for VANET simulation.

8 Conclusion The automobile industry is expanding quickly these days, thanks to significant technological advancements. Smart vehicles interact with one another, which helps to reduce traffic congestion and accidents on the road. However, a lot of the work is

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still only in research articles and has not yet been applied practically. A form of network utilized for the network of roads is called VANET. The need to work on that is really significant. The paper provides details on the VANET’s characteristics, current implementation issues, and applications. Finally, the paper discusses some prominent simulators that provide a simulation environment for VANET implementation and provides a notion of future scopes in VANET. This survey article that covers issues, challenges, and applications can benefit society by promoting the development and deployment of VANETs, which can improve road safety, enhance transportation services, and provide economic benefits.

References 1. Xia Z, Wu J, Wu L, Chen Y, Yang J, Yu PS (2021) A comprehensive survey of the key technologies and challenges surrounding vehicular ad hoc networks. ACM Trans Intell Syst Technol 12(4):1–30 2. Khan S, Sharma I, Aslam M, Khan MZ, Khan S (2021) Security challenges of location privacy in VANETs and state-of-the-art solutions: a survey. Future Internet, MDPI 13(96):1–22 3. Mahmood J, Duan Z, Yang Y, Wang Q, Nebhen J, Bhutta MNM (2021) Security in vehicular ad hoc networks: challenges and countermeasures. Secur Commun Netw 2021(9997771):1–20 4. Tahir MN, Katz M, Rashid U (2021) Analysis of VANET wireless networking technologies in realistic environments. In: 2021 IEEE radio and wireless symposium (RWS). IEEE, pp 123–125 5. Shrestha R, Bajracharya R, Nam SY (2018) Challenges of future VANET and cloud-based approaches. Wirel Commun Mob Comput2018(5603518):1–15 6. Evangeline CS, Kumaravelu VB (2019) Survey on VANET’s layered architecture, security challenges and target network selection schemes. ARPN J Eng Appl Sci 14(24):4248–4262 7. Hawbani A, Wang X, Al-Dubai A, Zhao L, Busaileh O, Liu P, Al-Qaness MAA (2021) A novel heuristic data routing for urban vehicular ad hoc networks. IEEE Internet Things J 8(11):8976–8989 8. Luo L, Sheng L, Yu H, Sun G (2022) Intersection-based V2X routing via reinforcement learning in vehicular ad hoc networks. IEEE Trans Intell Transp Syst 23(6):5446–5459 9. Kumbhar FH, Shin SY (2021) DT-VAR: Decision tree predicted compatibility-based vehicular ad-hoc reliable routing. IEEE Wirel Commun Lett 10(1):87–91 10. Pavithra T, Nagabhushana BS (2020) A survey on security in VANETs. In: 2020 Second international conference on inventive research in computing applications (ICIRCA). IEEE, 881–889 11. Weber JS, Neves M, Ferreto T (2021) VANET simulators: an updated review. J Braz Comput Soc 27(8):1–31 12. Schünemann B (2011) V2X simulation runtime infrastructure VSimRTI: an assessment tool to design smart traffic management systems. Comput Netw 55(4):3189–3198 13. Wang S-Y, Chou C-L, Yang C-M (2013) EstiNet openflow network simulator and emulator. IEEE Commun Mag 51(9):110–117

The Proposed Deep Learning Combined with Knowledge Graphs for Fake News Detections on Social Networks Quoc Hung Nguyen, Le Thanh Trung, Thi Thuy Kieu Phan, Thi Xuan Dao Nguyen, Xuan Nam Vu, and Dinh Dien La

Abstract Social media and social networks are large network which plays significant roles in our society. Recently, many studies have investigated how to differentiate between a trustworthy and an untrustworthy news’ source. Most investigations have studied separated techniques either use deep learning or graph knowledge to track face news on social networks. However, it is hard to improve face news detections in terms of performance accuracy. This paper has presented a method that combines knowledge graphs and deep learning models to detect fake news. The proposed model represents the news relationship as nodes on the knowledge graph to detect fake news on social networks. Our model has been tested using social networks by mapping interactions as representation of a knowledge graph from social network and relations in the large knowledge graph. Experimental results shows that the model is evaluated on real-world datasets to demonstrate this method’s effectiveness. Keywords Fake news detection · Deep learning · Knowledge graph · Social networks

1 Introduction Recently, media is increasingly diverse such as books, newspapers, magazines, radio, television, and social networks since huge data with various sources. It is hard for the problem of “fake news” appearing in many different forms on social networks. Recent studies have investigated fake news and true news spread differently on social Q. H. Nguyen (B) · L. T. Trung · T. T. K. Phan · T. X. D. Nguyen University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, Vietnam e-mail: [email protected] X. N. Vu TNU–University of Information and Communication Technology, Tan Thinh Ward, Thai Nguyen City, Vietnam D. D. La Ha Giang Department of Information and Communication, Ha Giang, Vietnam © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_20

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networks that can be exploited for automatic detection of fake news. Usually, fake news is created for self-seeking purposes, attracting views and likes of the online community, and making profits. However, much fake news is created to infringe upon national security, social order and safety, and the rights and interests of organizations and individuals. In addition, fake news brings private, fabricated, distorted, or mixed content online to terrorize the spirit and create public opinion on the online community to serve dark intentions, harm stability, politics and social order, and safety. A series of protests and riots is given around the world, causing political and social instability for a long time in many countries with the participation of fake news and social networks. In related works, Shu et al. [1] have figured out basic features to detect fake news on social networks as follows: user-based, network-based, and post-based as follows: (1) User-based: Fake news can be created and spread from malicious accounts such as Bots, Cyborg, and Trolls; (2) post-based: People are given in their opinions or feelings through social media posts; (3) network-based: Users use social networks to connect members with similar interests, topics, and relationships with each other. It is extracted special structure from users who post public posts on social network. Choudhary et al. [2] and Zhou and Zafarani [3] have reviewed fake news detections of existing machine learning algorithms such as neural network, convolutional neural net, Naïve Bayes, and support vector machine. The studies have proposed detecting and reducing fake news from different social media platforms like Facebook, Twitter, etc. Recently, fake news has become a major problem affecting people, society, the economy, and national security. A survey of fake news by Zhou and Zafarani [3] has given least three parameters including writing styles, linguistic elements, and expressions. Nonetheless, these studies have not concerned with fake news veracity specifically. Gonwirat et al. [4] proposed a combined deep learning model based on the ideal distance weighting method for fake news detection. The ideal distance weighting approach was used to measure the criteria weights of each model. The results indicated that the proposed technique works well in identifying fake news datasets. In the COVID-19 epidemic outbreak, fake news related to the epidemic is spreading on social networks. A study to detect fake news has been investigated by Dinh and Pham [5]. Research data in social networks is collected from themes of fake news, including political, crime, health, religious, entertainment, religiopolitical, and miscellaneous. Research has helped dig into the depths of fake news about COVID-19. An investigation of automatic detection fake news of Pérez-Rosas et al. [6] with dataset collected from crowdsourcing and crawled celebrity-oriented real and fake news. The investigations have indicated the classification model based on a combination of semantic information with these readability properties. They achieved an average accuracy of 70.5% in detecting made-up crowdsourced news and 78.5% in detecting celebrity news. Ahmad et al. [7] proposed a useful model to detect fake news on social media networks. The model LSTM-RNN on the text was applied in predicting rumor detections. The results showed that proposed features with its baseline features and classification concerning the precision, recall, and F1 measures. A proposal by the authors Aldwairi

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and Alwahedi [8] about fake news content is called Clickbait. Based on the contents to predict whether news in the online text is genuine or fake. Authors group Girgis et al. [9] used with LAIR dataset to experiment on RNN (Vanilla, GRU) and LSTMS engineering models. Some results achieved (0.217), followed by LSTM (0.2166) and finally vanilla (0.215) for detecting fake news [10]. The authors’ proposed strategy is the combination of a Naïve Bayes. This method determines the probability of words belonging to fake news, thereby determining the probability of believing is true or false. The authors proposed model for detection of fake news [11]. Kudarvalli and Fiaidhi [12] have proposed an automated system to detect fake news base analysis “tweets” on Twitter and use machine learning algorithms to analyze. Pham and Tien [13] proposed knowledge graph for clustering behaviors of customers on Facebook. A combined CNN-RNN-based deep learning method was proposed by Nasir et al. [14] to classify fake news. The proposed model uses CNN’s feature to filter the information and LSTMs to learn the long-run dependencies. In related works [15– 17], the studies have investigated the proposed context matching algorithm combined with knowledge reasoning [15] for identified users on social networks. The other investigations using graph neural network for recommendation system on social networks and enhance clustering classification performance in large datasets [16, 18]. Most studies have focused either separated deep learning or knowledge graph; hence, the performance of these studies has limited while solving the problems on social networks. In this research, we presented a method that combines knowledge graphs and deep learning models to detect fake news. The proposed model represents the news relationship as nodes on the knowledge graph to detect fake news on social networks. While the deep learning model trained datasets in order to find contents and ID user, which is mapped in the knowledge graph using Neo4j in order to find the right designation of sources (true/untrue news). The proposed model has been tested using social networks by mapping interactions as representation of a knowledge graph from social networks. To validate the proposed model, we used real-world datasets for evaluation.

2 The Proposed Model 2.1 Overview of the System Architecture The system architecture of the proposed model is shown in Fig. 1. Facebook data has been captured by using both Scrapy and Selenium tools. All Facebook datasets have been updated in database as called Data Lake. While captured these datasets, labeling posts, all of the user lists are updated in the database. The proposed model using CNN model with modifications of parameter as follows: input layer, convolution layer, activation, full connection, max pooling, and softmax layer. A special feature is that the combined model with user features is incorporated into

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Fig. 1 Proposed overall architecture

the vector after the max pooling to include in the fully connected layer. In adjusted parameters, ReLU (linear rectified) uses in the proposed model since the effective efficiency evaluated done in the experiment. The last layer is the softmax which is an activation function that takes out the probability distribution on the labels. The steps of the proposed model are as follows: • Step 1–Input Data: All datasets gathering from nodes and groups related to related topics, collected from the social networks via the API, collected by using Selenium and Scrapy tools. • Step 2–Preprocess Data: The step is a converted datasets including characters are removed and separated these words, built word2vec library used to extract data. • Step 3–Labeling Step: Label and normalize datasets. • Step 4–Extract from users and topics in the dataset. • Step 5–Apply CNN Model: The proposed CNN model is used for training datasets in order to find topics associated with influenced users. • Step 6–Apply both user aggregation together with its knowledge aggregation: Find users influenced with fake news, as shown in Fig. 2. • Step 7–Detect profile users based on the knowledge graph: Find user’s profiles and destination sources for fake news detection.

2.2 Terms and Definitions • Social Graph Let U = {u 1 , u 2 , . . . , u n } is the set of profile users, and C = {c1 , c2 , . . . , cm } is the set of topics, where n is the number of profile users and m is the number of

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Fig. 2 Architecture of user and knowledge graph aggregation

topics. While R ∈ R n × m is a fake news matrix topic. If u i matches content v j , ri j represents a rating score, otherwise ri j = 0. Let N (i ) be a set of profile users having relations with user u i in the social network graph, C(i) represents a set of topics voted by the profile user u i , and B( j) represents profile users influenced with their content v j . Social graph T ∈ R n × n where Ti j = 1 if u i has a relation to the user u j otherwise 0. • Social Networks in Knowledge Graph Knowledge graph G in a social network presents a triple of entity with its relation, for each entity ( , , ) where is relation, respectively. ε,  represent quantities of entities and relations, respectively. Social relations graph T and knowledge graph G, while predict the missing rates with value r of set user-content in R. Therefore, user fake news vector u i where pi ∈ R d , fake news vector v j is q j ∈ R d where d represents a dimension of the fake news vector.

2.3 The Architecture of User and Knowledge Graph Aggregation As shown in Fig. 2, the proposed architecture consists of two parts as follows: user aggregation and knowledge aggregation. Firstly, user aggregation is applied train the

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fake news vector. The second component is knowledge graph aggregation, which learns the fake news vector. Here are details for explanation: • User aggregation: User aggregation is to learn the fake news vector, where h i ∈ R d belongs to user u i . First, it is applied to item space h iI ∈ R d of graph with its interaction. The second step is an aggregation from the social space h iS ∈ R d as combined into the fake news vector h i . • Learning user fake news vector: To learn a better user fake news vector, social with its content can be considered together, because the social graph for interaction graph influenced with users. In the hidden layers, the fake news vector is as follows:   c1 = h iI ⊕ h iS

(1)

  c2 = σ W2 · c1 + b2

(2)

  h i = σ Wl · cl−1 + bl

(3)

• Knowledge aggregation in social networks: In knowledge graph, item (entity) has many relations in node as called a triple including head, relation, and tail. To adjust the size of an entity’s neighbors, we can find contents associated users. For each pair user u i and content (entity) v j , N g (v) represents each of entities with these relationship of content v j . Therefore, rei , e j is the relation score of both ei and e j . To measure distances among user with his/her relation in KG, we have indicated a function as follows: πru = g(u ⊕ v)

(4)

where u ∈ R d and r ∈ R d are represented the user and content v and d is the dimension vector. Weight πru represents the weight of relation r to user u. A calculation of the attention weights in attributes of these relations in the knowledge graph, as expressed by   πru = W2T · σ W1 . [u ⊕ v] + b1 + b2

(5)

To calculate item v, the linear combination of v’s neighborhood can be expressed by v uNg (v) =

 e ∈ N g (n)

π˜ ruv, e

π˜ ruv, e e

  exp πruv, e   = u e ∈ N g (n) exp πrv, e

(6)

(7)

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• Learning user fake news vector: To learn the fake news vector, user-space and fake news vector are integrated. In the hidden layers, the fake news vector is calculated as follows:   c1 = z Uj ⊕ z Kj

(8)

  z j = σ Wl . cl − 1 + bl

(9)

3 Experimental Results 3.1 Datasets The research automatically collected data on Facebook, Twitter, and labeled posts and labeled users on social networks. It can be categorized by the type of group and page on which they are published. The proposed model has been tested using dataset provided by Facebook data downloaded sets and Imperial College London [17] social networks fully extracted and clearly classified sample data. There are 25,177 train data classified and 5881 data for the test set in details as follows: • Data features: The data has been downloading and extracted from Scrapy tool the following features on Twitter including news, users, likes, comments, and these activities. • User profile (human profile, geolocation, language, time, word embedding …), • User activities (follows, lists, favorites, and statuses), • Social networks (social connections, interest users, followers and close friends, source devices, replies, citations, likes) likes and retweets for the tweet source) • Contents (embeds words of the tweet’s text and accompanying hashtags).

3.2 Experimental Results and Case Studies In the experiments, the proposed model has been trained with 20 epochs each time, always checking for improved loss function and saving the best result after each epoch. • Results of the paper: Accuracy achieved ROC AUC: 93.70 ± 1.85%, respectively. • Results of running the program with self-searched data: Accuracy achieved ROC AUC: 90.92 ± 1.68%, respectively. In case studies, a decision maker has tried fake news detection of COVID-19 news based on the Facebook data, as shown in Fig. 3. As can be seen in Fig. 4, knowledge graph has been applied to learn datasets in visualization to detect fake news.

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Fig. 4 Screen of knowledge graph in training for visualization

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Fig. 5 Screen of viewed nodes in knowledge graph in fake news detection

While learning the nodes done by Neo4j application in knowledge graph, the decision makers have focused on the person who influenced with contents and topics, as shown in Fig. 5. For each node, a person with his relations (locations, pages, times, groups, contents, news) has considered in knowledge graph. Hence, the decision maker can find the right person who has influenced with contents and topics in the fake news detection.

3.3 Result Discussions As shown in Fig. 6, the proposed model in experimental results in real-case studies shows that calculation of mention for keyworks “COVID-19” on social networks of both news and fake news associated with influenced users. Further testing experiments, the proposed model has shown an interaction between contents and users for fake news detection of keyworks “COVID-19” on social networks in order to detect how much influenced users to the news, as shown in Fig. 7. Figures 6 and 7 show some pick points for the mention and interaction of users including good news and fake news from 7 to 11 March 2020. The proposed model has illustrated the effectiveness in real-case study.

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Fig. 6 The evaluation chart of fake news detection model results with the keyword “COVID-19” on Feb-23

Fig. 7 Screen of the proposed model running for keyworks “COVID-19” on social networks

4 Conclusion This research has a method that combines knowledge graphs and deep learning models to detect fake news. Experimental results have demonstrated the users associated with contents that contribute to boosting the predictive results of the fake news detection. It is indicated that the proposed model enhances the prediction using deep learning integrated with knowledge graph for tracking fake news in real-time in social networks. To deal with large datasets from social networks, the proposed model has been implemented with platform of big data such as Neo4j, Hadoop, and Spark. Further investigation is to enhance the quality of associated user behavior for fake news detection, such as historical tracking, destinations posts, source destinations, and the enhancement of embedded graphs and deep learning model to predict behavior for recognitions of fake news on social networks. Acknowledgements This research is funded by the University of Economics Ho Chi Minh City (UEH), Vietnam

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References 1. Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newsl 19(1):22–36 2. Choudhary M, Jha S, Prashant, Saxena D, Singh AK (2021) A review of fake news detection methods using machine learning. In: 2021 2nd International conference for emerging technology (INCET). IEEE, pp 1–5 3. Zhou X, Zafarani R (2020) A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput Surv 53(5):1–40 4. Gonwirat S, Choompol A, Wichapa N (2022) A combined deep learning model based on the ideal distance weighting method for fake news detection. Int J Data Netw Sci 6:347–354 5. Dinh XT, Pham HV (2021) Social network analysis based on combining probabilistic models with graph deep learning. In: Communication and intelligent systems. Springer, Singapore, pp 975–986 6. Pérez-Rosas V, Kleinberg B, Lefevre A, Mihalcea R (2017) Automatic detection of fake news. In: Computation and language. arXiv:1708.07104, arXiv:1708.07104v1, https://doi.org/10. 48550/arXiv.1708.07104 7. Ahmad T, Faisal MS, Rizwan A, Alkanhel R, Khan PW, Muthanna A (2022) Efficient fake news detection mechanism using enhanced deep learning model. Appl Sci 12(1743):1–20 8. Aldwairi M, Alwahedi A (2018) Detecting fake news in social media networks. Procedia Comput Sci 141:215–222 9. Girgis S, Amer E, Gadallah M (2018) Deep learning algorithms for detecting fake news in online text. In: 2018 13th International conference on computer engineering and systems (ICCES). IEEE, pp 93–97 10. Jain A, Shakya A, Khatter H, Gupta AK (2019) A smart system for fake news detection using machine learning. In: 2019 International conference on issues and challenges in intelligent computing techniques (ICICT). IEEE, pp 1–4 11. Ozbay FA, Alatas B (2020) Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A 540:123174 12. Kudarvalli H, Fiaidhi J (2020) Experiments on detecting fake news using machine learning algorithms. Int J Reliable Inf Assur (IJRIA) 8:15–26 13. Pham HV, Tien DN (2021) Hybrid Louvain-clustering model using knowledge graph for improvement of clustering user’s behavior on social networks. In: Tran DT, Jeon G, Nguyen TDL, Lu J, Xuan TD (eds) Intelligent systems and networks. ICISN 2021. Lecture notes in networks and systems, vol 243. Springer, Singapore, pp 126–133 14. Nasir JA, Khan OS, Varlamis I (2021) Fake news detection: a hybrid CNN-RNN based deep learning approach. Int J Inf Manag Data Insights 1(100007):1–13 15. Pham HV, Nguyen VT (2020) A novel approach using context matching algorithm and knowledge inference for user identification in social networks. In: ICMLSC 2020: Proceedings of the 4th international conference on machine learning and soft computing, pp 149–153 16. Dong NT, Pham HV (2020) Graph neural network combined knowledge graph for recommendation system. In: International conference on computational data and social networks. CSoNet 2020: Computational data and social networks. Springer, Cham, pp 59–70 17. Retrieved from https://github.com/KaiDMML/FakeNewsNet/tree/master/dataset 18. Pham HV, Thanh DH, Moore P (2021) Hierarchical pooling in graph neural networks to enhance classification performance in large datasets. Sensors 21(6070):1–20. https://doi.org/10.3390/ s21186070

Algorithm-Driven Predictive Analysis of Blue-Chip Stocks in the Murky Indian Environment A. Celina and K. Kavitha

Abstract The analysis of market data for short-term trading accuracy is needed to execute trades in the market. This paper reviews different techniques to predict stock price movements. This is approached using separate algorithms to find a stable solution using fundamental, sentimental, and technical data analysis from data processing. The performance and throughput of the stock’s price are improved, which are vital for any individual interested in long-term investments with higher returns. Large datasets are extracted using differents algorithms such as random forest and support vector machine (SVM) to achieve it. Indicators such as Bollinger bands, super trends, and simple and exponential moving averages are applied. The integration of all three types of analysis finally increased stock price prediction accuracy, thus making the investment process smoother. Keywords Market analysis · Market prediction · Random forest algorithm · Classification · Data processing

1 Introduction 1.1 Overview of Stock Market Prediction Stock market price prediction is an exercise of the hidden talent of an individual to determine the optimum cost of a business enterprise stock or various financial devices traded on an alternate. The hit prediction of a stock’s future price stimulates large earnings [1]. The streamlined market price hypothesis reflects available stock expense records and other unconsidered cost adjustments that are not considered for setting new facts that are inherently unpredictable [2]. Other involved factors such as physical, psychological, rational, and irrational behavior jointly volatile the A. Celina · K. Kavitha (B) SRM Institute of Science and Technology, Kattankulathur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_21

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share prices [3]. The use of advanced technology and implementation of own skills immunize the system to have destiny price. Intrinsic price (true price) is the perceived or calculated cost of a corporation, along with tangible and intangible elements, the usage of essential evaluation. Zhang et al. [4] introduce a novel instantaneous frequency algorithm for predicting stock index movement. By analysing the frequency patterns in stock price movements, the algorithm aims to enhance the accuracy of stock market predictions. This innovative approach contributes to the field of stock market analysis and offers potential benefits for investors and traders seeking more reliable forecasts of stock index movements. Polamuri et al. [5] review and analyze a wide range of machine learning algorithms employed in stock market prediction, including support vector machines (SVM), artificial neural networks (ANN), decision trees, random forests, and ensemble methods. The researchers discussed the strengths, weaknesses, and specific use cases of each algorithm.

1.2 Classification of Stock Market Prediction Techniques Prediction methodologies are categorized into three broad groups. They are fundamental analysis, technical analysis (charting), technological methods, and sentimental analysis.

1.2.1

Fundamental Analysis

The goal of fundamental evaluation is to decide whether or not a company’s future cost is appropriately reflected in its cutting-edge inventory charge [6]. Fundamental analysis is performed with the various data such as earn per share and profit by earning ratio, but to make financial forecasts.

1.2.2

Technical Analyais

Direction of prices using previous market data can be studied through the methodology called technical analysis [7]. It evaluates the latest buying and selling actions and tendencies to try to determine what is next for an agency’s stock price [8]. Less attention is provided to the primary fundamentals such as stock price. Technical analysts mostly depend on the inventory charts to clearly examine a company’s stock price. For instance, technicians may additionally search for a assist stage and resistance level while assessing a inventory’s subsequent flow. Siew and Nordin [9] by employing regression techniques, the researchers can capture the underlying patterns and relationships between independent variables and stock prices. This allows them to create predictive models that can potentially assist investors and traders in making informed decisions.

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Fig. 1 Architecture of system

1.2.3

Sentimental Analysis

The physical attributes of a market in order tothe mindset of the investors can be studied through sentimental analysis. Sentimental evaluation begins from the assumption that most of the people of investors are wrong [10]. In different phrases, the inventory market can disappoint, while “hundreds of investors” consider charges are headed in a specific course. Sentimental analysts, in other terms, are also known as opponents whose only purpose is to invest contradictory to the customer’s perception of the marketplace.

2 Architecture Diagram Figure 1 describes the architecture of the system. The system gets the input from the share market dataset using natural language processing to refine the data and algorithms to process the dataset. Figure 2 describes the rule-based share market analysis and prediction model which is used for the prediction.

3 Objectives 1. To study completely distinctive strategies to expect stock really worth movement. 2. To find the performance and accuracy of stock’s price.

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Fig. 2 Rule-based share market analysis and prediction model

4 Acquiring Knowledge on Data It includes a storehouse of stock marketplace information and a log related to a person’s revel in. The inventory market information is represented in unique tables for each of the one-of-a-kind shares. They may be companies: 1. 2. 3. 4. 5. 6.

Stock data Data patterns constituted Base worth tables Growing gains amounting to the cycles of events using day-to-day records Day-to-day data, including events with width and peak records The collection of data every week amounts to per day event with the most notable event.

4.1 Stock Data Daily The arriving accounts show the steady circulation of the trends manifested to the contrarians briefing the amount of time saved while organizing the table over a day with the machine use. The period remains constant over this period of the event.

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4.2 Data Patterns Constituted on a Daily Basis A log’s open, close, elevation, and demotion price for a particular specific time should be mounted in a piece of day-to-day information. The yearly cost is not commensurable with the present-day price at all times. It is then transmuted into gains and amounts as rising fall and balance inclinations depending on marginal worth. The emblematic accounts are commensurable with the price of the log for a specific period. The arriving details are then transmuted into a file on completion of the day and then instigated in the table.

4.3 Base Worth Tables When these tables are erected using groundbreaking accounts encompassing over 5 years, this worth is not affected during the day over a large scale. On the culmination of every particular worth, the computation of tables can be done taking into account the monthly information or data acquired from the tables.

4.4 Growing Gains Amounting to the Cycles of Events Using Day-To-Day Records Everyday data are operated to exemplify rigorous gains over the period of the day. Taking into account the rise and fall of a stock as an event, the cycles of distinctive episodes are bound to give a persistent profit or downfall. The episodic cycles rely on every other event and assist us in rationalizing or reshuffling activities or events.

4.5 Day-to-day Data, Including Events with Width and Peak Records The goal is to design an event with successive cycles of occasions with distinctive expansions. The events’ span amounts to the invoice’s stability over the period, and height amounts to the benefit or downfall that the stock made over the entire period. Therefore, width and peak records are as humongous as the stock invoice. The diversification of events over a day indicates that the range of events amounts to the rise and fall of the price during the course of the day.

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4.6 The Collection of Data on a Weekly Basis Amounts to Per day Event with the Most Notable Event To anticipate the price per week, the best means are to construct this desk. It comprises weekly transaction days, events in every hour of the day, range of most humongous events, and actual advantage or dropping of the rate on the termination of the week. Along with the investor, one organizes a desk including foregoing trial and transacting a particular stock simultaneously. Basically, it amounts to the transaction day, the stock’s opening price, the category of the transaction, whether it is a buy or a sale transaction, the transaction cost, and its outcome.

5 Analysis and Prediction 5.1 Functionalities 1. Moving averages 2. Super trend 3. Bollinger bands. 5.1.1

Moving Averages

Moving averages are a technical signal used in the market prediction. When a close look is given to the trends, we can observe the movement of the prices going up and down. In today’s fast-moving markets, the prices go up and then the moment drops later before rising up again hence giving a false indication. It increases the probability of inaccurate signal. The “noise” is filtered out from the irregular price moments and gives a stable moment to get the average value. It helps to get the trend of the market and also to confirm if there are any reversals. If the prices are above the calculated SMA and EMA, it is considered an uptrend; if below the lines, it is a downtrend. The points where the average line breaks imply a trend reversal (Fig. 3).

5.1.2

SMA

A simple moving average (SMA) is calculated by adding all the values which are given in a set of data and then dividing it with the total number of values present in the dataset. In other words, the prices present in NSE or any other financial stock exchange application are added to get the mean and then are divided by the total number of prices present in the set chosen.

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Fig. 3 Moving averages

5.1.3

EMA

An exponential moving average (EMA) differs from SMA by giving more importance to the data which is more recent in the provided financial stock exchange applications. For plotting the graph of moving averages, the first point is always calculated using SMA and then EMA is used; in this way, more accurate prediction is possible.

5.1.4

Super Trends

Super trend as the name tells is an indicant that follows the movement angled using the prices and the stock market positioning of this estimated cost, giving us a present trend. These trends are easy to understand and provide an exact examination reading about any extant trend. It provides us with the up and down trend so that the client and the vendor can perform well in the retail. UPPERBAND = (h + l)/2 + Multiplier * ATR, where h = high, l = low, ATR = avg. true range. LOWERBAND = (h + l)/2−Multiplier * ATR. LAST UPPERBAND = If the ((Present UPPERBAND < Prev. LAST UPPERBAND) and (Prev. Close > Prev. LAST UPPERBAND)) THEN (Present UPPERBAND) ELSE Prev. LAST UPPERBAND). LAST LOWERBAND = IF the ((Present LOWERBAND > Prev. LAST LOWERBAND) and (Prev. Close < Prev. LAST LOWERBAND)) THEN (Present LOWERBAND) ELSE Prev. LAST LOWERBAND). UPERTREND = IF (Present Close = 7 magnitude. The color map explains the age of events “red = < 24 h”, “orange = 1–7 days”, “yellow = 8–15 days”, “green = 16–30 days”, and “blue = > 30 days”. Figure 2 explains the Monitoring Station, Transmission parameter, Receiving parameter of various cities and emission of alert in India. Using a radio signal for synchronization, the temporary stations were able to keep their overall timing accurate to within 0.1 s. However, GPS is used to keep the digital seismic stations on the same schedule. Three-component digital seismograms were used to obtain P-wave (0.01 s) and S-wave (0.05 s) arrival times with high precision (0.01 s and 0.05 s, respectively).

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Fig. 1 Map of earthquake in India (Dated: 20th October 2022) Fig. 2 Block diagram of processing and receiving of seismic signals and alert emission

Since seismic source parameter assessments first began in the early 1970s [13], moment–magnitude relations, often known as Mw relations, have been an important part of the research that has been done on earthquake mechanisms. Exaggerated information regarding the time series was provided in Fig. 3. In recent years, moment magnitude has been utilized extensively in estimation of seismic b-value for location that has been researched [14]. All earthquake magnitudes are converted to a catalogue of Mw > = 4.0:

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Mw = 0.67 Ms + 2.07, 3.0 0-a properly minimum value, the step of learning. (4) Repeat steps 1–3 until g( f (wj)) >ε, where ε>0—a proper minimum constant.

4 Result Analysis The proposed work and all development have been led in the WINDOW environment using the 64-bit Python and done on the evaluation situation of a 2 GHz-Core i7 CPU, with 8 GB RAM (read-only memory), WINDOW 8 OS (operating system). The bank sector credit database is a public domain database available in the well-defined data repository of the Kaggle site [12]. Table 2 mentioned columns are; The result of the optimization SPCA method performance has been defined on the banking sector credit database [12]. The proposed work, feature extraction, optimization, and pre-processing phase are so vital for the complete performance of parameter of classification models it must be modified for databases from several domains. Methods for FE (feature engineering) and DM (data mining) are constantly developing. As an outcome, the feature extraction and selection (optimization) method is

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Table 2 Bank sector credit database [12] Parameters

Details

Credit policy

1: if the user meets the credit underwriting criteria of leadingClub.com 0: otherwise

Motive

The motive of the loan (“credit card”, “debt_consolidation”, “educational”,”major_purchase”,”small_business”, etc.)

Interest rate

Loan int_rate, as a proportion (rate 11% would be stored as 0.11)

Installment

Monthly installments are owed by the borrower if the loan is funded

FICO

Fico credit score of the borrower

Table 3 Comparison analysis: run time (sec)

Methods

Run time (s)

Optimized SPCA method

2.8

Statistical of feature [5] method

3.1

Improved owl search [13] method

3.7

implemented as a more effective approach for enhancing the running time in making decisions. These methods’ main purpose is to reduce dimensionality, which is key to optimizing model difficulty and over-fitting. The dimensionality decrease is one of the significant features of training classification models. It enhances the evaluation efficiency of the proposed model and optimizes the generalized error rate presented due to noise by irrelevant feature sets. The comparative analysis of proposed Optimized SPCA and other methods is shown in Table 3 and Fig. 3. According to Fig. 3, the mentioned techniques have more runtime compared to the proposed (optimized SPCA) model. Table 3 defines the run time comparison, and this proposed method takes a minimum time to run than other techniques.

5 Conclusion and Future Scope This article’s main purpose is to define feature extraction and selection algorithms to reduce relevant features that measure a bank’s credit risk, and clarification to solve the issues in this way. The optimized SPCA method has been used for different categories of data. This approach doesn’t need difficult evaluations to reduce the run time. The credit risk represents depends on the possibility of default of the other party of the bank in gathering its duties under the terms of the contract. Banks require handling the entire profile CR, including individual transaction risk. The proposed model is efficient and identifies the probability of default of a bank loan applicant. It can help banks to avoid large losses. This implemented model is built using the DM (data mining) methods available in the Python package and the database is taken from the Kaggle site. As the text pre-processing phase is the most vital and time-consuming

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process, feature extraction and selection methods in Python were utilized to make the data ready for use. Different Python functions and packages were used to prepare the data. The parameters derived from the feature selection define the run time of the implemented method. Further improvement will work on using a classification-based method with dissimilar preparations to offer better expectation outcomes. A credit risk prediction system will collect the relevant feature sets to improve the classification accuracy and minimize the computation costs connected with machine learning models.

References 1. Allen NB, Frame WS, Miller NH (2005) Credit scoring and the availability, price, and risk of small business credit. J Money Credit Bank 191–222 2. Roger SM, Jordao F (2003) What is a more powerful model worth. Moody’s KMV 3. Stein RM (2005) The relationship between default prediction and lending profits: integrating ROC analysis and loan pricing. J Banking Finance 29(5):1213–1236 4. Andreas B, Leippold M (2006) Economic benefit of powerful credit scoring. J Bank Finance 30(3):851–873 5. Ampountolas A, Nyarko NT, Date P, Constantinescu C (2021) A machine learning approach for micro-credit scoring. Risks 9(3):50 6. Sharifi P, Jain V, Poshtkohi AM, Aghapour V (2021) Banks credit risk prediction with optimized ANN based on improved owl search algorithm. Math Problems Eng 7. Yang D, Li G, Cheng G (2007) On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34(4):1366–1375 8. Hamdy A, Hussein WB (2016) Credit risk assessment model based using principal component analysis and artificial neural network. MATEC Web Conf 76:02039 9. Mishra SP, Sarkar U, Taraphder S, Datta S, Swain D, Saikhom R, Laishram M (2017) Multivariate statistical data analysis-principal component analysis (PCA). Int J Livestock Res 7(5):60–78 10. https://towardsdatascience.com/stochastic-gradient-descent-clearly-explained-53d239905d31 11. Boyko N, Khomyshyn I, Ortynska N, Terletska V, Bilyk O, Hasko O (2022) Analysis of the application of stochastic gradient descent to detect network violations. COLINS 12. Karthickaravindan (2018) Decision trees and random forest, Kaggle. Kaggle. Available at: https://www.kaggle.com/code/karthickaravindan/decision-trees-and-random-forest/ notebook#Comparison-between-Decision-Trees--and-Random-Forest-Project (Accessed: 7 Nov 2022) 13. Walusala SW, Rimiru R, Otieno C (2017) A hybrid machine learning approach for credit scoring using PCA and logistic regression. Int J Comput (IJC) 27(1):84–102

Enhancing Security of IoT Data Transmission in Social Network Applications R. Hemalatha and K. Devipriya

Abstract The development of ad hoc networks leads to enormous growth of information technology industries. With the current growing need for wireless connectivity, IoT systems use ad hoc solutions to communicate between devices and objects. The Internet of Things technology has gained immense popularity in recent years. Devices, things and sensors have acquired a virtual identity that allows them to connect and communicate with each other, society and a specific user of the system. Globally most of the networks are open networks. For social network, Internet of Things devices act as nodes, where the node transmission secured data won’t be assured. There is a highly possibility to the intruders to steal the information from the node. Based on this background, this paper proposed a data transmission model for social network with enhanced security and minimal energy consumption. The proposed method enables the secure data transmission after clearly analysing the nature of the node and behaviour of the malicious node. This will prevent the data communication with malicious node and enable reliable communications. The simulation results reveal that the proposed method significantly improves the secured communication in IoT social networks. In the proposed method, the performance is increased by 50–75% when compared to the previous method. Keywords Internet of Things · Social network applications · Energy minimization · Secure data transmission

R. Hemalatha · K. Devipriya (B) PG & Research Department of Computer Science, Tiruppur Kumaran College for Women, Tiruppur, Tamil Nadu, India e-mail: [email protected] R. Hemalatha e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_31

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1 Introduction “Internet of Things” is defined as the blend of various processed data sensing devices, namely GPS, laser scanner, infrared scanner, etc. Its work is to create a network that has all the connected items as a result the system can automatically track, locate, identify, trigger and monitor corresponding events of the objects in real time. The “Internet of Things” is considered as world’s information industries third wave after computers, the Internet and mobile communication networks. The “Internet of Things” concept has changed the thinking pattern of previous era. The idea of the past is to differentiate physical and IT Infrastructure, namely data centre, airport, buildings, broadband and roads. In addition to it reinforced concrete, broadband and cable are incorporated into unified infrastructure, it became possible in the “IoT” era. When it is considered, the infrastructure is similar to new construction site, on which the world works. It includes production operation, economic management, personal life and social management. This enables new scenarios for citizens, companies and public administrations with enormous potential to change the daily aspects of users. For individuals, the most palpable effect of IoT will be at work and at home, with home automation that will allow optimizing tasks, as well as devices to improve health conditions and assistance as an example. It will allow companies to improve production processes by optimizing available resources, which translates into increased profits. However, with new technology in addition to benefits IOT also adds risks and challenges. Since IOT has access to the communication and information system the privacy and security needs a greater attention. At first glance, the similarities in the above challenges with more “traditional” computing may make it seem appropriate to tackle them in the same way, but it is their differences that indicate that the approach cannot be done the same as in IoT and requires further examination. From now on you should pay special attention to some aspects: The major limitations of the IoT architecture are the security concern. Most of the areas using this architecture carries sensitive information like hospitality centres and military bases. The system is in the initial stage of development so it have some poor architectural designs and technical complexity. The IoT devices are manufactured by different vendors across the globe and each one has the different methodology and unique design patterns and here comes the chaos. The IoT architecture needs universal integrity and serviceability. 1. RFID (radio frequency identification, radio frequency identification) A noncontact automatic identification technique called radio frequency identification uses radio frequency signals to automatically identify the target item and collect pertinent data. The identification method can operate in a variety of challenging environments without requiring manual intervention. High-speed moving objects and numerous tags can both be recognized by RFID technology simultaneously, and the process is quick and simple. Combining RFID technology with Internet, communication and other technologies can realize item tracking and information sharing on a global scale. A new kind of passive electronic card known as

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an RFID (radio frequency identification, or radio frequency identification) electronic tag contains an antenna and an integrated circuit (IC) on a plastic substrate. It is waterproof, antimagnetic, has a large quantity of data storage and is wirelessly passive. It is a recent invention and one of the key technologies that, in a few years, will displace barcodes and usher in the "Internet of Things" age. Through electromagnetic field induction, energy, timing and data are wirelessly transmitted among the scanner (i.e. PCE machine) and the electronic tag (i.e. PICC card). Within the appreciable range of the antenna of the PCD machine, multiple PICC cards may appear at the same time. How to precisely find one another is the main problem to be solved by the anti-collision (i.e. anticollision, also called anti-collision) technology of A-type PICC cards. A network information system that integrates distributed information gathering, information transmission and information processing technology is known as a wireless sensor network (WSN). It is a key technology for both national economic growth and national security because of its suitability for moving targets, which has been widely valued. Through the various sensors positioned on objects and around every corner, as well as the wireless sensor network made up of them, the Internet of Things can now sense the entire physical world. While servers and workstations are protected in rooms and offices with nearby staff, in an IoT setup, sensors and other devices can be located anywhere, subject to theft, damage and intrusions. A potential attacker has a greater chance of physically accessing devices to find their vulnerabilities. In IoT it is very common, as mentioned in the previous point, that since a device can be anywhere, it does not have a stable power line, instead having batteries. In this case, so that the device can work autonomously for as long as possible before exhausting its resources, systems with low energy consumption and low processing power are used, which may not be able to make use of the libraries, as effective as a personal computer, server, or more powerful system. This inconvenience has lasted through the time that IoT implementations have been carried out, giving very discreet advances. With the increase in connected devices, the amount of information generated increases and the cost of maintenance rises, and redundancy measures may be reduced to prevent its loss. Connected devices are used and seamlessly integrated into the world around us. They can collect data, communicate and interact with other devices without supervision, in a way that is transparent to the user, since with such a potentially large number of devices it becomes impossible to permanently check if the interconnections are compromised. The promotion of the Internet of Things will serve as another catalyst for economic growth, creating yet another chance for the industry that has limitless potential. The number of sensors, electronic tags and supporting interface devices used by animals, plants, machines and items will likely far outnumber the number of mobile phones once the "Internet of Things" becomes widely accepted. Hundreds of millions of sensors and electronic tags will be required in recent

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years, according to the present demand for the Internet of Things. This will significantly boost the production of information technology components and increase the number of employment opportunities. There are two key elements that must be present for an IoT to be genuinely effective. Scale is one. The intelligence of things can only be relevant at large scales. A second factor is flexibility. Typically, objects are not motionless but rather in motion. Conversations can happen at any moment even during high-speed motion, but objects must be kept in motion. 8. Ubiquitous devices like wearables can join and leave your network at any time. This, combined with multi-protocol communication features, makes traditional information security measures insufficient for the IoT. Based on these aspects consequently, a technology-based social characteristics nodes is described by the network system. The impacts of normal and malicious nodes are analysed, and the communication path is selected. The main aim of this work is to develop a node based secured data communication in IoT social networks that allow a network of low-cost devices to be scalable and preserve the integrity of the data it transports and stores with minimal energy consumption and host the ability to encrypt the information sent overcoming the challenges posed for security in the IoT field mentioned above. The reminder part of the paper is organized as follows. Section 1 provides the introduction about the Internet of Things (IoT) networks. Section 2 provides the various literatures work done in the area of secured IoT communications and routing protocols. Section 3 describes the problem statement along with the objective of the work. Section 4 details the proposed algorithm. Section 5 provides the simulation results along with its discussion and comparative analysis. Section 6 concludes the paper along with cited references.

2 Literature Survey Protocols play a vital role in the full implementation of IoT. There have been many academic studies on IoT protocols in recent years. In studies on existing IoT protocols, it is seen that these protocols can provide superiority in different areas compared to each other. In this section, studies on performance comparisons of IoT application protocols in terms of bandwidth and latency and researches in terms of interoperability are discussed. In the era of the 6th generation system (6G, the sixth generation mobile communication system), the market for new mobile devices, vertical mobile devices, Virtual Reality devices and forcing operators to undertake the needs of multiple types of scenarios, provide customized services and focus on user personalization was provided by Lin et al. [1]. Vertical market participants who lack network infrastructure participate in the new collaborative business model as tenants, renting the physical resources of infrastructure network providers (InPs, infrastructure network providers) together with

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mobile virtual network operators with limited capacity or coverage proposed by Zhu and Xiang [2]. Network slicing builds multiple dedicated, virtualized and mutually isolated logical networks based on the user’s customized requirements for network capabilities, based on a general-purpose infrastructure hardware platform, and realizes on-demand allocation of resources proposed by Du et al. [3]. In order to meet the above challenges, artificial intelligence (AI, artificial in-intelligence) key technology machine learning (ML, machine learning) is widely used to solve complex dynamic environments in wireless networks it is proposed by Li [4]. The deep reinforcement learning (DRL) algorithm combined with deep learning and reinforcement learning has become one of the most promising algorithms in ML technology, and resource allocation based on DRL has become a research hotspot which is proposed by Li [5]. Xiong et al. [6] realized access network slice resource allocation based on DRL and obtained better performance than traditional optimization algorithms from the perspective of optimizing InPs revenue. Wei et al. [7] provided a multidomain slice management framework to manage the life cycle of slices deployed across multiple infrastructures, but did not integrate intelligent resource collaborative scheduling methods. Zhang and Zuo [8] proposed an AI-assisted network slicing architecture, using a two-layer controller, placing a local controller on a single RANs and assisting a centralized controller placed on a central cloud. This layered architecture is only applicable to access network slicing resources in order to realize the intelligent management of multi-tenant slicing, a two-layer hierarchical end-to-end slicing of “centralized control” and “distributed autonomy” is proposed. The intelligent management solution aims to optimize the long-term income of InPs, uses the DRL algorithm to achieve optimal resource allocation among tenants, obtains E2E slice status information in real time and adjusts slice resources with the goal of ensuring service quality. Guo et al. [9] proposed an anonymity analysis method for collusion attacks. The attacker consists of multiple malicious nodes, and each malicious node occupies a different position. From the perspective of the attacker, the sender is located before the first malicious node; the receiver is located after the last malicious node. The item information transmission mechanism studied in this paper is initiated by a non-malicious person, and the receiver is also a non-malicious person. In the stage of information processing [10]: Literature in order to realize the IoT architecture, information service system and IoT, Ken et al. suggested a global IoT system architecture based on RFID and IoT technology. This architecture was combined with this proposal to give the design scheme of the IoT information service system. A source is the Internet administration protocol; the literature [11, 12] Wang and Li. systematically introduces the key technologies of the Internet of Things such as coding, code conversion, name resolution service, information release system, middleware and network management and analyses the Internet of Things through a large number of examples. Based on the development status at home and abroad, some design schemes are given, which play a guiding role in the research of the Internet of Things. Huai et al. finally proposed a recursive hierarchy [13], which is currently a better choice to solve the scalability problem of the data centre network. The most typical structures in the recursive hierarchical structure are DCell, FiConn, BCube. The structure of the smallest recursive unit of these structures is exactly the

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same, and they all use a switch to connect multiple servers, but the biggest difference in connection between them is that their recursive laws are very different. Wei et al. [14] used the virtual technology to virtualize the collection layer of the attribute structure to provide the scalable data centre network topology of the system VL2. After the collection layer is virtualized, all servers will be similar to exist in a local area network, which will greatly improve network performance and service efficiency, The authentication and supervision of users’ legitimate use of resources is known as access management. Currently, the role-based access control mechanism (also known as RBAC) and its extension model serve as the primary foundation for the access control of information systems. The RBAC mechanism is primarily made up of Sandhu’s 1996 base model RBAC96. A user is first assigned a role by the system, such as administrator, ordinary user, etc. After logging in to the system, access to resources is realized according to the access policy set by the user’s role. Obviously, the same role can access the same resources. The RBAC mechanism is an access control method for systems such as Internet-based OA systems, banking systems and online stores and is user-based proposed by Su et al. [15]. In the perception stage, [16] Feng Deng-Guo et al. proposed a low-cost RFID anonymous authentication protocol based on the analysis of the security requirements of the RFID protocol and a general composable security model, and proves the security of the protocol under the determined security goal; Zhang and Zhao [17] analysed the shortcomings of the RFID security protocol based on cryptographic technology and used the provable security theory to conduct security research on the RFID security protocol model; the literature Lu and Li [18] gave an improved anti-collision algorithm in the RFID system, when a large number of tags simultaneously. During recognition, the algorithm estimates the number of tags to be recognized according to the previous round of collisions and then classifies them or changes the frame size to reduce the probability of tag collisions, thereby improving recognition efficiency. The recursive law of BCube is to use hypercube (Hypercube) proposed by Kang et al. [19]. In this recursive law, all servers at the same location in different recursive units of the same level are connected to each other through a switch. BCube also requires each server to be equipped with multiple ports, but it has a hypercube with high connectivity, small diameter and good reliability. Harold Abelson et al. [20] proposed a network to compare the pros and cons of the two topologies, the experiment compares the two network structures in the case of all-to-all communication in the switch. The change trend of the aggregated bottleneck bandwidth of the system, under the condition of fault changes. As can be seen, it is very difficult to state that a particular protocol has an advantage over the other in every field in the Internet of Things. Different protocols can be used according to the network topology used, security needs, scalability and bandwidth usage. In addition, the diversity of middleware makes it difficult to connect to IoT devices and interpret the collected data. The heterogeneous structure of the IoT ecosystem and an ever-increasing growth trend brings the need for different protocols.

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3 Problem Statement IoT network systems are typically implemented in open mode. It is very difficult to identify the node’s status. The malicious nodes in the network reduce network efficiency overall and have an impact on data communication between nodes by lengthening the delay and using more energy. In order to solve the problem of secure data transmission in IoT network, this paper proposes a new method, Secure Routing Method (SRM). The network impact of each node is evaluated then selection of normal node is done to avoid the malicious node for data transmission. This will improve the relaying of data to the malicious node and reduce the delay in the communication between nodes. In this way, the secure data transmission in social network is achieved and the service quality is improved.

4 Proposed Secure Routing Protocol In this research, to create a social network randomly some relationships are created in the network system. The network nodes systems are split into four types of social network system nodes, which are Malicious Node (MN), General Node (GN), Normal Node (NN) and Friendly Node (FN). A sample social network communication model is shown in Fig. 1. (1) Class Malicious Nodes (MN) are having the duplicate and fake data packets which will be injected to deteriorate the performance of the network system by relaying fake data to the nodes. (2) Class General Nodes (GN) are active nodes in the network, and these nodes are any time converted into fake nodes; i.e. malicious nodes when fake data are injected. (3) Class Normal Nodes (NN) are nothing but active nodes, and these nodes may collaborate with malicious nodes and vulnerable to attacks any time.

Malicious Nodes

General Nodes

Normal Nodes

Friendly Nodes

Fig. 1 Social network communication system model at time period t1, t2 and t3

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(4) Class Friendly Nodes (FN) are genuine nodes which are not converted to malicious node even if they have communicated with malicious nodes. The network performance will be improved by sending data via the friendly nodes. All malicious, general, normal and friendly nodes are included in the total amount of active nodes. Total number of nodes at time t is defined in Eq. (1). T (t) = MN(t) + GN(t) + NN(t) + FN(t),

(1)

where T (t) is the total number of nodes at a regular interval of time t. MN(t) is total number of malicious nodes at time period t; GN(t) is the total number of general nodes at time period t; NN(t) is the total number of normal nodes at time period t; and FN(t) is the total number friendly nodes at time period t. Initially, (MN(0), GN(0), NN(0), FN(0)) = (MN°, NN°, NN°, FN°) p R4 and the parameters α, β, θ1 , θ2 , ω, λ, ϕ incorporated in the model are all positive. Where all the variables lie in the range between 0 and 1, θ1 ≥ 1, θ2 ≥ 1. The values of the units α, β, θ1 , θ2 , ω, λ, ϕ are vary and depend on the time and state of the nodes but θ1 and θ2 are constants greater than the value one. Let’s take into consideration that the quantity of total hubs T (t) = T con , where θ 1 denotes the time at which the malicious node behaves maliciously. After θ 1 , to reduce transmission the network is attacked by the malicious nodes. After θ 2 cycles, θ 2 represents the time for a malicious node and is transformed into neutral node. Ʌ represents the probability of malicious node that is transmitted from normal node. ω denotes the node proportion to damaged stage from unbiased stage. ϕ denotes the ratio of friendly hubs to malicious hubs. β denotes the transmission friendly hubs probability. α is the ratio of hubs to friendly state from neutral state. λ(GN(t) MN(t)) NN(t) denotes the fraction of nodes that change from normal to neutral after malicious hub broadcast. However, after θ 1 , malicious hub is formed from neutral node. As a result, the beginning of the saturated malicious transmission rate ω GN (t−θ 1 ) denotes the fraction of unbiased hubs that is transformed into malicious hubs after θ 1 with a delay. According to the friendly nodes delivery ratio in a certain time period, the establishment of the delivery loss ratio ω MN (t–θ 2 ) indicates that after the delivery period θ2 . Friendly nodes proportion that transforms into normal nodes. ⎧ dMN ⎪ ⎪ ⎪ dt = ωN (t − θ1 ) − ϕ N (t) ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ dGN = λ(N (t) + M(t))O(t) − α N (t) − ωN (t − θ ) 1 dt ⎪ ⎪ dO ⎪ ⎪ = −β O(t) − λ(N (t) + M(t))O(t) + ϕ M(t − θ2 ) ⎪ ⎪ ⎪ dt ⎪ ⎩ dF = ϕ M(t) + α N (t) + β O(t) − ϕ M(t − θ2 )

(2)

The sum of all social network system communities is 1. The proportion of nodes in classes GN, MN, NN, FN at time period t is represented as GN(t), MN(t), NN(t),

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FN(t), where the dO/dt in Eq. (2) refers to the change in the energy consumption at a regular interval of time t. The node state M indicates the state if malicious node, the state N indicates the normal node, the node state G indicates general nodes and the node state F indicates the friendly nodes. Apply Eq. (2) to the hub i in the social network system, then the equation simplified as in Eq. (3). ⎧ ) ( Mi (t) = 1 − ϕ t Mi (t − 1) + ωt Ni (t − 1 − θ1 ); ⎪ ⎪ ⎪ ) ( ⎪ ⎪ ⎪ ) ( ⎪ t t Ni (t − 1) ⎪ ⎪ Oi (t − 1) − ωt Ni (t − 1 − θ1 ); N (t) = 1 − α Ni (t − 1) + λ ⎪ ⎨ i +Mi (t − 1) ) ( ⎪ ⎪ ⎪ O (t) = (1 − β t ) O (t − 1) − λt Ni (t − 1) ⎪ Oi (t − 1) + ϕ t Mi (t − 1 − θ2 ); ⎪ i i ⎪ +Mi (t − 1) ⎪ ⎪ ⎪ ⎪ ⎩ Fi (t) = 1 − Oi (t) − Ni (t) − Mi (t), (3) where α t indicates the transition of hubs to friendly state from neutral state, β t indicates mediation with friendly nodes, ωt indicates the node proportion that change from neutral to hostile, λt indicates mediation with malicious nodes and ϕ t indicates the node probability changing from malicious nodes. Initially, the probability values are random between 0 and 1. During a repeating period at time t, the parameter values are initialized between two given intermediate values a and b. In a social system network, there are Tcon nodes, and the nodes are numbered sequentially 1, 2, 3, …, T con . If each node has t con attributes for each node, I represented it as yi1 , yi2 , yi3 ,…,yitcon . In the system, there are malevolent nodes that will collaborate with another hubs. When malevolent nodes work together with another hubs, they will influence some but not all of their characteristics. Collaboration between nodes is given below: (1) If a malicious hub cooperated by a normal hub, attribute changes may occur. (2) The neutral state that the neutral nodes dummy data packet initially enters will last for a while, known as the latency interval, before leaving. Other nodes are not given neutral dummy packets. However, if other nodes work well with neutral hubs, their fictitious data packets will be passed to other hubs. (3) The node begins to behave maliciously once the fake data packets’ incubation time is over. The fake data packets from a malicious node will be passed to other nodes if it works well with other nodes. (4) A certain probability exists for the transformation of a hub in a neutral condition into an ordinary node. Even when it successfully collaborates with a malicious node, such a node will trust mechanism is established and forward the data after performing a trust assessment. The node may return to its initial configuration once the trust evaluation is finished. (5) Nodes can create a trust mechanism while they are in a normal or neutral condition. Through this method, it is highly likely that only non-malicious nodes

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will be chosen for cooperation, though it is still possible for malicious nodes to cooperate as well. The characteristics of the relay node and the node selected affect a node’s delivery frequency. Choosing a friendly node relay allows for efficient data transfer; in the absence of one, results of network delay. The search procedure that connects the aforementioned scheme to Eq. (1)’s global solution optimal for the optimization issue has the following implications: Equation (2)’s optimization formula’s solution (search) space correlates to the social network system. The optimization formula (2) has one node that correlates to a tentative solution; the temporary result set for this node is Y 1 , Y 2 ,…, Y Tcon . The conditional result variable Y i of the optimization problem correlates to the attribute of hub i (i = 1, 2,…,T con ), specifically the attribute j, variable yij of the tentative solution Y i is corresponded by the node i. As a result, the number of variables in the preliminary solution Y i equals the number of attributes in node i. So, hub i and test result Y i are ideas that are equivalent. The Node Delivery Index (NDI), which corresponds to the objective function value in the formula for the optimization issue, represents the Node Delivery Ratio (NDR) (2). A hub with a hub with a high delivery ratio or a high NDI value correlates to a successful test solution. A hub with a low delivery ratio, also known as a hub with a low NDI value, correlates to a poor test solution. The procedure for determining the node’s NDI value for the optimization issue formula (2) is NDI(Yi) = Smax−S(Y i ), i = 1, 2, …, Tcon. The probability of the node states at time period t is indicated as general node probability GNi (t), malicious node probability MNi (t), normal node probability NNi (t), friendly node probability FNi (t) are calculated using the proposed method. The delivery ratio of hub i will keep rising during the arbitrary search process if the value of NDI in period t−1 is lower than the node’s NDI value in period t, which indicates that node i is getting closer to the overall suitable answer. In contrast, if the node’s NDI number in period t is equal to or less than that in time t−1, means that node’s delivery ratio will drop and the system will begin to lag in period t−1. The algorithm becomes universally convergent thanks to this iterative random search. Proposed Secure Routing Method Step 1: Initialize the node states of GG (t), GM (t), GN (t), GF (t), randomly select D nodes from each set nodes GG, GM, GN and GF. G G (t) = {Yi1 , Yi2 , Yi3 , . . . , Yi D }; G M (t) = {Yi1 , Yi2 , Yi3 , . . . , Yi D }; G N (t) = {Yi1 , Yi2 , Yi3 , . . . , Yi D };

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G F (t) = {Yi1 , Yi2 , Yi3 , . . . , Yi D } Step 2: The node transformations from O–O, N–M(p) and F–O(p) to are as follows. Form an excellent set for each node state of M, N, O and F as GEG (t), GEM (t), GEN (t), GEF (t), G EG (t) = {Yi1 , Yi2 , Yi3 , . . . , Yi D ;} G EM (t) = {Yi1 , Yi2 , Yi3 , . . . , Yi D }; G EN (t) = {Yi1 , Yi2 , Yi3 , . . . , Yi D }; G EF (t) = {Yi1 , Yi2 , Yi3 , . . . , Yi D }; The nodes having high NDI values at t−1 time period are placed in the excellent set for at time period t. Step 3: The transformation of nodes from one state to another state. 3.1 O–O Transformation Let the attribute j weighted sum of DM normal hubs in the set GM t −1 and its actual value, and the difference between its actual value and the mean sum of the attribute j of DM normal hubs be the position value of the corresponding attribute j of the hub i, which is σij (t), is calculated as, ⎧ ωk yik j (t − 1) ⎪ ⎪ DM ⎪ ∑ ⎪ DN ⎪ ∑ ⎪ | | ⎪ σi j (t) = ⎨ | > 0; λk yik j (t − 1), |G t−1 − 0 k=1 k=1 ⎪ ⎪ ⎪ ⎪ σ = y − 1), SRM_ Si (t) ⎪ i j (t) i j (t ⎪ ⎪ | | ⎩ = SRM_ S(ti − 1), |G t−1 | = 0, 0

where yij (t) is the amount of attribute j that depends to the hub i in the time duration t−1 and σ ij (t)is the actual amount of the attribute j for the hub i in the period t. 3.2 N-M(m)Transformation Let the related node i attribute j in the neutral state receive the D malevolent hubs attributes j in the set GM t −1 and their average value according to their states, resulting in a change in that node’s state, that is σ ij (t), is calculated as,

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⎧ ∑ | | 1 ⎪ | > 0; ⎪ yk j (t − 1), |G t−1 ⎨ σi j (t) = || t−1 || M G k∈G t−1 M ⎪ ⎪ ⎩ σ (t) = y (t − 1), SRM_S(t ) = SRM_S(t − 1), ||G t−1 || = 0 ij ij i i M The amount of state attribute that hub i was strike by the fictitious data in duration t is only linked to the position amount in duration t−1, even though N−M(p)’s state transition needs to be delayed. The hub i receives false data, so even though the attack takes some time, its own attributes are continuously changing prior to the strike. 3.3 F–O(m) Transformation The attribute j of the D hub in the set GNt −1 and its mean position amount is given to the associated attribute j of the hub i that has changed into a desired position in order to get rid of the trust mechanism, that is σ ij (t), ⎧ ∑ | | 1 ⎪ | > 0; ⎪ yk j (t − 1), |G t−1 ⎨ σi j (t) = || t−1 || 0 G t−1 k∈G 0 ⎪ ⎪ ⎩ σ (t) = y (t − 1), SRM_ S(t ) = SRM_S(t − 1), ||G t−1 || = 0. ij ij i i 0 Despite the fact that the positional transformation of F–O(p) necessitates a substantial slow down, position value in period t−1 is linked to the position amount of hub i in pseudo-data strike in duration t. In all the state transformations, the growth operators are evaluated as per SRM. ⎧ Yi (t)

(ti ), NDI(σi (t)) > NDI(Yi (t)); i = 1, 2, . . . , Tcon . Yi (t − 1), NDI(σi (t)) ≤ NDI(Yi (t));

After comparison with the matching prior node rounds, the nodes with better performance are added to the following node rounds, while the nodes with subpar delivery ratios are left alone until they are changed.

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5 Simulation Results The proposed method is simulated using social network environment with parameters specified in Table 1. The obtained results are compared with the five standard methods, namely node-oriented secure data transmission algorithm (NOSDT) (Li and Wu 2020) and fuzzy routing-forwarding algorithm (FCNS) Kanghuai et al. (2018) for the performance comparison. The CBR/UDP refers to the constant bit rate in the UDP connection utilizing parametric calculations such as energy consumption (EC), packet drop ratio (PDR), packet drop ratio (PDR%) and routing overhead (RO); the efficacy of our proposed design is assessed. The simulation parameters used for the assessment are displayed in Table 1.

5.1 Packet Delivery Ratio (PDR) The changes in the harmful nodes’ mobility can improve the performance of the suggested task. About 10% of nodes are harmful generally, according to analysis. The implemented task typically has a packet delivery ratio (PDR) of between 75 and 50%. The network executes more quickly and experiences fewer linkage failures and packet losses when the troublesome nodes are removed. Figure 2 illustrates the packet delivery ratio (PDR) against detrimental nodes by contrasting the FCNS and NOSDT with the proposed SRM routing scheme. (Proposed SRM). There will be a rise in the percentage of malicious sites, which will cause more packet loss. When the current algorithms are examined, FCNS tends to lose 35% of its harmful nodes while NOSDT tends to lose 58%. The suggested approach offered SRM improves by 67% based on their trust value harmful nodes are eliminated. Table 1 Simulation of starting parameters

Parameter

Value

Simulator

NS 2.34

Scenario size

1000 × 1000 m2

Simulation time

250 s

Number of nodes

100

Misbehaving nodes

0–40%

Number of connections

15

Pause time Message size Node cache

5s 100kB 50 Mb

Traffic type

CBR/UDP

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Packet Delivery Ratio(%)

Fig. 2 Packet delivery ratio (PDR) against harmful nodes

FCNS 90 80 70 60 50 40 30 20 10 0

NOSDT

0

Proposed SRM

10 20 30 No. of Harmful Nodes

40

5.2 Routing Overhead (RO) Routing overhead (RO) is the quantity of routing packets sent for maintenance and also for route discovery. The routing Overhead (RO) in the proposed method helps to reduce the loss in data packets during the path discovery. The routing overhead analysis for the suggested work is shown in Fig. 3. The improvement of the proposed protocol usually ranges from 4.03 to 6.2, according to comparisons made between the various techniques. The RO range of the FCNS protocol typically extends from 4.8 to 9.9, whereas the NOSDT ranges from 4.8 to 6.5. Because there are fewer harmful nodes in the proposed work, the link quality between nodes will be better, resulting in less routing overhead. The proposed SRM improves routing overhead even more. (RO). It is obvious that malicious nodes are more likely to easily create harmful nodes. There are fewer route failures because proposed SRM only chooses safe nodes with strong links, necessitating the rerouting of fewer control messages.

5.3 Energy Consumption (EC) The energy consumption of the suggested protocol decreases as the number of harmful nodes rises. While when the number of harmful nodes increases, the FCNS and NOSDT protocols waste excessive amounts of energy. Figure 4 displays a plot for the number of harmful nodes and energy usage. According to the different FCNS and NOSDT protocol observations, the energy consumption of these protocols ranges from 311.96 to 313.5 J, while the proposed SRM technique has an energy consumption of 309.24–310.10 J. This will help to prevent route failure caused by dangerous nodes. Because there were fewer harmful nodes, the proposed approach produces results within a certain accuracy range.

Enhancing Security of IoT Data Transmission in Social Network … Fig. 3 Routing overhead of harmful nodes

FCNS

NOSDT

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Proposed SRM

12 Routing Overhead

10 8 6 4 2 0 0

Fig. 4 Energy consumption (EC) against harmful nodes

10 20 30 No. of Harmful nodes

Energy Consumption (J)

FCNS

NOSDT

40

Proposed SRM

322 320 318 316 314 312 310 308 306 304 0

10 20 30 No. of Harmful nodes

40

5.4 Packet Drop Ratio (%) The ratio between the number of lost packets and the overall number of sent packets is known as the packet loss ratio. It is monitored between malicious nodes and packets that have been dropped. Figure 5 plots the measured values. Figure 5 shows that as the number of malicious nodes increases, the packet drop rate also increases, which has a major effect on network efficiency. The proportion of fraudulent websites will increase, leading to an increase in packet loss. When comparing the two techniques, FCNS has a 60% decline tendency while NOSDT has a 55% decline tendency. According to the recommended method, SRM increases by 55% as malicious nodes are removed based on their trust value. The recommended SRM yields the best results because it prevents communication between the malicious nodes.

Fig. 5 Packet drop ratio (PDR %) against harmful nodes

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Packet Drop Ratio(%)

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FCNS

NOSDT

Proposed SRM

70 60 50 40 30 20 10 0 0

10 20 30 No. of Harmful Nodes

40

6 Conclusion Finally, to conclude the work, the proposed method is simulated with respect to the standard social network protocols FCNS and NOSDR, analysing the possible advantages and disadvantages that it could present to solve the previously raised challenges. The node characteristics are analysed and classified into multiple categories to evaluate the impact on the social network. The major part of the problem is to avoid the malicious node during relaying the data via attacked node. The proposed algorithm evaluates the nodes influence on the social network and select the best efficient nodes for the communication. The simulation proves that the proposed method improves the performance of social network based on the malicious node elimination using node influence evaluation. In future, the optimization of NDI parameter is important to reduce the initial time delay of the network.

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A Study on Existing EHR Models Used for Validating the Clinical Records Priyanka Sharma, Tapas Kumar, and S. S. Tyagi

Abstract EHR was a statistical database of electronic health records for individuals or demographics that can be accessed through diverse healthcare environments. Planning for long-term retention and handling of electronic health information is an important factor in the planning process. The majority of commercial EHR models were designed to integrate data from vast ancillary facilities like radiology, pharmacy, and laboratory with different clinical care components. Electronic health records (EHRs) were digital representations of hospital records that were renovated in real time. An (electronic health record (EHR) was a comprehensive statement on a person’s health. EHRs relief in the provision of evidence-based care, the tracing of a patient’s treatment development, and the enabling of improved healthcare judgments. This paper aims to review the comparison of different EHR models that are being used throughout the world. The usage of various machine learning approaches in existing EHR has also been explored in this study. A thorough introduction of EHR is provided initially, followed by a focus on some related work that explains the adoption principles employed by various authors in EHR. In addition, discuss the different EHR models and the methodologies they have used and challenges in existing EHR models, their advantages, need, and then finally the challenges of EHR. This paper can be concluded in a way that to create an effective framework of potential data exchange agreements between health services and patients more public dialogue around electronic health records is needed. Quantitative research, such as polls of a wide variety of customers and doctors, may also be performed in order to obtain better outcomes. In next paper, we intend to propose framework of such EHR model. Keywords Electronic health record · Statistical method · Machine learning techniques · Deep learning models · Regression model P. Sharma (B) · T. Kumar Department of Computer Science Engineering, Faculty of Engineering, MRIIRS, Faridabad, India e-mail: [email protected] S. S. Tyagi Department of Computer Science Engineering, Faculty of Engineering, IIMT, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_32

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1 Introduction Medical documents are reported from Egyptian era since 1600–3000 BC. Paper medical records were not distinctively available till the years 1900–1920. Recording a patient’s medical background and treatment is referred to as a medical record, health record, and medical chart. The topic “health record” is very popular because a patient’s medical records should contain lifestyle and health record in addition to episodic medical experiences. Health records used to be printed on paper and held in files separated into parts depending on the form of note, with just one copy available. Modern computing technologies laid the basis for developing the EHR in the 1960s and 1970s. EHRs have modified the layout of health records and therefore changed health care. They have made patients’ health records that easy to understand and accessible from almost everywhere in the world. Thousands of articles have been conducted on EHR acceptance, clinical decision support’s (CDS) capability to enhance or not enhance the evaluation methods, clinical performance or healthcare process, clinical trial patient recognition, adoption/ implementation, and unexpected effects. Data from millions of patients’ electronic health records (EHRs) is now regularly processed through various healthcare facilities. They provide medical images, physician reports, medication prescriptions, laboratory test findings, patient demographic records, diagnosis, and other data items. Due to the variability of data types, label and data availability, and data consistency, creating reliable analytic models from EHR data is difficult. Traditional health analytics modelling relies on time-consuming tasks like ad hoc feature engineering and expert-defined phenotyping1. The models that result are often constrained in their generalizability across organisations or datasets. EHRs were never meant to treat multi-institution, life-long health records. Patients’ data are scattered among numerous organizations when life experiences cause them to leave one supplier’s information silo and go to another. They risk simple access to previous data when a result, as not the patient but the provider holds main stewardship (by using either default agreements in the course of delivering care or by formal legal means in over twenty-one countries) [1]. Providers have up to 60 days to respond according to the HIPAA Privacy Rule, (but not always to comply) to a request for upgrading or deleting an incorrectly inserted record [2]. Aside from the time delay, record management can be difficult to start because patients are not motivated or permitted to revisit their entire record [1, 2]. As a result, patients communicate with records in a fragmented way that represents the essence of how they are treated. The EHRS is broadly used worldwide and has evolved into a valuable method for everyday patient practice and healthcare administration [3]. It is recognized as one of the present and future developments in healthcare innovation [4]. It has been stated that implementing EHRS improves the consistency, effectiveness, and protection of healthcare facilities [5]. Hospital information systems based on the computer first appeared in the USA in the late 1960s and were quickly adopted by large hospitals and local hospitals.

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However, no laboratory results were processed by the system to assist in diagnosis. Morris Collen, a leader in the usage of services based on hospitals, expanded the scheme in 1972 to include the storage and presentation of lab research outcomes as component of medical healthcare. Since then, EHRs have grown and strengthened with multifunctional capabilities that aid decision making and diagnosis [6]. Numerous surveys [7] have shown the increasing advantages of EHRS in increasing the standard of medical care in primary care practices and lowering healthcare costs in recent years. Although medical advancement is regarded as one of the most significant tools for transforming healthcare services at all times, EHRS, which has shown exciting promise as an emerging innovation, will take part in a crucial character, in the future in providing disease surveillance, data processing, maintaining patient safety, quality control, quality patient care, and several other areas [8]. There should be a wide range of potentials and strengths for enhancing EHRS efficiency in the future [9]. Despite the many advantages that EHRS clearly provides, surveys have shown that acceptance remains limited. In 2005, only 24% of doctors in outpatient environments were using EHRS [10]. An EHR is an information of individuals that contains their complete health records in an electronic format, including data such as medication details, immunization details, allergies, laboratory results, diagnosis, medical examination, and history. During lifetime, the record is entered manually by medical representatives. Figure 1 depicts an electronic health record system [11]. This contains information about some of the Institution’s departments. Patient information from other agencies may be used, based on the scope of the EHR scheme.

Fig. 1 Simple electronic health record system [12]

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Table 1 EHR (electronic health record) [13] Category

Description

Service outcome

• Includes the results of every assistance provided by the source, such as the result of a treatment or test • Simplified at every assignment as well as a contributor

Advisors note

• Includes freeform text along with any notes the counsellor wants to get through the scheduled time • Refreshed at each physician appointment

Diagnosis

• The identification of the client by the physician in conditions of stage and disease • Refreshed at each physician appointment

Assessment

• This section includes the advisor’s evaluation of the client’s stage and disease • Reorganized at every scheduled time along with the consultant

Referral

• This section comprises the advisor’s recommendation (source plus maintenance demanded, example, treatment, consult, test) • Simplified at every assignment along with the consultant

Lifestyle

• Holds data on caffeine and alcohol usage, stress level, smoking action, diet, and exercise • Simplified at every assignment as well as the advisor

Symptom

• Every indication identified by the client is listed there, along with the date of onset and frequency • Renovated at every appointment by the consultant

Client information

• Includes photo, gender, age, and name • Providing in the preliminary appointment together with the advisor

Health history

• It includes a common health profile, the date of the most recent physical, and the health status of your parents (as well as reason of demise if departed) • Supplied in the preliminary position along with the consultant

The different types of information collected in our simple EHR are described in Table 1.

2 Related Work The big IT initiatives in the healthcare context over the last decade have been to promote widespread use of EHRs, EHR-based applications, and clinical technologies that provide a range of capabilities [14, 15]. Furthermore, the EHR is regarded as the centre of health info [16] and the central component of an integrated “Health Information Technology (HIT)” [17] among IT functions in the healthcare field. As a result, EHR and HIT adoption have become a popular topic in the healthcare industry [18]. As a result, several researches have used different hypotheses to analyse the implementation of EHR programs by various users and in various health environments [19, 20].

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Wu et al. [21] recommended that clinical decision support be combined into computer-based health records to eliminate physician mistakes and unnecessary variance in procedures, as well as increase patient safety and results. The use of computers to process information has increased in importance, and it is now being combined into healthcare data processing. As a result, vast volumes of medical data, known as electronic health data, have been developed, including referral information, medical history, insurance claim data, patient–doctor encounters, and patient health records. Lin et al. [22] suggested using EHR as a data base to develop a time-to-event method for detecting chronic diseases. This suggested method assists the practitioner in predicting a patient’s chronic illness, anticipating whether serious complications will arise, and preparing for future treatments to minimize incident risks. In contrast to other prediction modelling experiments, the proposed time-to-event modelling allows for the integration of a meaningful range of functions. Data abstraction approaches can be integrated to reduce data dimensionality and improve estimation precision. The system’s flaws are that it does not treat therapeutic texts, and guideline-based feature selection entails a lot of manual labour and is not as flexible as automated feature selection systems. Keshavjee et al. [26] create a literature-based integrative structure for electronic health record application in their report. McGinn et al. [27] take a user-centric approach to electronic health record, and their research is confined to Canada and countries of similar socioeconomic levels. Both reports are not directly for hospitals, but they do involve regional or national EHR initiatives and small clinics. This systematic analysis focuses on hospital-wide, single-hospital electronic health record applications and describes observational research (with primary data collected) that represent this situation. Pettigrew’s method for interpreting strategic transition is used to categorize the conclusions from the selected papers. This paradigm has been extensively used in corporate case study analysis [28] and articles on the adoption of healthcare technologies [29]. It derives information by examining three interconnected dimensions—content, process, and context—that form administrative change. Since introducing an electronic health record artefact is an organization-wide effort, Pettigrew’s framework [30] is seen as relevant. This structure was chosen because of its emphasis on organizational reform, its simplicity, and its comparatively large dimensions, which allow for a wide variety of results to be used. The methodology organizes and focuses the study of the selected papers’ results. Table 2 lists some of the experiments in the area of electronic health, as well as the theories or theory that underpin the studies, the most important findings, and the subject variable that is being tested. Patients became the study’s target demographic [31–38].

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Table 2 Electronic health models References

Theory

Dependent variable

Findings

[31]

TAM, plus several other constructs

Cyberspaces utilize performance as a basis of data

• The emphasis placed on doctors’, other health professionals’, and PU’s importance placed on scripted media in explorations for health record, trust placed in the data accessible and concern for personal health, are the main predictors of usage behaviour

[32]

Privacy, TAM, and trust

Intention to adopt • PU, trust, and PEOU are significant electronic health predictors

[33]

UTAUT2 extended model

Developmental intent and utilize conduct in EHR portals

• Behavioural intention and habit are predictors of use behaviour • Performing probability, strength expectation, self-perception, and habit are predictors of behavioural intention

[34]

Extended TAM in health information technology (HIT)

HIT behavioural intention

• PU, PEOU, and perceived threat substantially influenced health consumer’s behavioural intention

[35]

Personal empowerment

Internet use behaviour as a source of information

• 3 behaviours encourage the internet to find health information: community logic, consumer, and professional

[36]

TAM (qualitative research)

Electronic health services behavioural intention

• Though knowledge is not a TAM structure, it appeared to have changed behavioural intent • PEOU did not seem to be an issue • PU seemed to be relevant

[37]

Concern for information privacy (CFIP), elaboration likelihood model (ELM),

EHR behavioural intention

• Issue involvement and positively framed arguments create more favourable attitudes toward EHR behavioural intention • Privacy concern (CFIP) is negatively associated with likelihood of adoption

[38]

Motivational Electronic health model (MM), behavioural integrated model intention (IM), TAM

• IM does not have a superior presentation than MM or than TAM when forecasting behavioural intention • Users’ perceived ease of use (PEOU), intrinsic motivation (IM), users’ perceived technology usefulness (PU), and extrinsic motivation (MM) have substantial constructive impact on behavioural intention

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3 History of EHR Health notes were there since the starting of healthcare industry. In starting, the health journal usage was to make the note of the disease and its reason for occurrence. Medical notes were stored on hardbound sheets in the starting of twentieth century [39]. The government passed a law establishing about the healthcare in the early 60s and 70s of 19th era, leading to a rapidly developing healthcare trend [39]. Around the same time, more third-party payers began to enter the healthcare market, healthcare litigation became more common, healthcare delivery became more relevant, and the government enacted more strict regulations [39]. This is why patient history became a true need in hospitals, and the first EHR was introduced [40] Healthcare providers and physicians have been unable to adopt electronic health records. In 2009, it was reported that only about 8% of hospitals have an EHR [41]. The lack of state guidelines, the high cost of the programs, and the fact that healthcare providers spend a considerable amount of money and time dealing with patient privacy provisions and government laws are some of the reasons for the slower implementation of EHRs [42]. In a speech to the “National Institutes of Health” in 2005, President Bush said of today’s healthcare system, “We have a 21st-century medical profession but a 19thcentury paperwork system” and “Electronic medical records are going to be one of the great advances in medicine”, he continued [43]. The American Investment and Recovery Act of 2009 was approved by Congress in 2009. The US government provides grants to all healthcare providers who use EHRs to supplement paper-based programs under this act. Facilities who successfully introduce an EHR scheme will be eligible for cash benefit bonuses, and those that do not implement EHR technologies will be penalized. Physicians and hospitals would be eligible for Medicaid and Medicare bonus incentives if they become significant recipients of health record by 2014. [44]. The national government intends to invest $27 billion in grant payments to encourage doctors and hospitals to use and share electronic health records. The conditions for practical usage are key parameters that will be enforced in three phases. Meaningful use is a set of guidelines established by the government as a least requirement for healthcare providers to be eligible for inducement costs [45]. The EHR must satisfy five additional goals and 14 core specifications from a set of ten to meet the practical use criterion [45]. Electronic pharmacy orders, computerized physician order entry, and quality data reporting are among the criteria. Healthcare providers must now be capable to exchange their electronic health records with other healthcare providers electronically.

4 EHR Models and Methods This section of the paper contains the EHR models and its methods which are given below.

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4.1 Statistical Method 4.1.1

Regression Model

Regression surveys are utilized in mathematical modelling to forecast a result variable’s potential status dependent on one or more individual variables. The probability of illness may be the result predictor. Any aspect that influences the outcome or dependent variable may be used as an independent or explanatory variable. One kind of regression model in which single or many independent variables decide a result is logistic regression. A binary dependent variable is used to assess the result. Multivariate logistic regression is used where there are several dependent variables, while univariate logistic regression is used where there is just one dependent variable. Using a collection of risk factors, the logistic regression model is released by McKown et al. [46] to forecast Significant Adverse Kidney Outcomes within 30 days (MAKE30) (e.g. intensive care unit, admission service, race, gender, age, etc.). They predicted MAKE30 using EHRs. Shahid et al. [47] used logistic regression based on EHR to create two statistical models (one for admittance and the more for expulsion) for forecasting early rehospitalizations. Tibshirani [48] suggested “least absolute shrinkage and selection operator (LASSO)”, which is other linear regression-based prediction model. Together a bound on the number of the correct values of the variables, it minimizes the normal sum of squared errors. Wu et al. [49] used EHR data to develop risk prediction models using LASSO and logistic regression (LASSO + LR) for breast cancer. In a research by Krishnan et al. [50], LR was used to diagnose diabetes early in administrative claims results.

4.1.2

Cox Proportional Hazards Model

The other form of regression model is the Cox proportional hazards model [51]. Statistics are widely used in medical science to analyses the association between multiple indicator (or explanatory) factors and patient survival period. Since correcting for other parameter factors, the model predicts therapy impacts on patient survival. It is possible to measure the probability (or threat) of mortality for people using the prognostic component, which is associated with the result of a health problem in the absence of medication. No assumptions about the hidden survival time distribution are required in this model.

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4.2 Machine Learning Models 4.2.1

ANN

Artificial neural networks (ANNs) have been used in various fields, including healthcare, and electronic health record (EHR) systems. ANNs are a type of machine learning algorithm that can be trained to identify patterns and relationships in large datasets. In context of EHR, ANNs can be used for a variety of tasks, such as predicting patient outcomes, identifying potential medical errors, and automating tasks such as image recognition and natural language processing. One of the main benefits of using ANNs in EHR systems is that they can help healthcare professionals make more informed decisions by providing them with accurate and timely information. For example, ANNs can analyse patient data to predict which patients are at risk of developing a particular disease or condition, allowing doctors to intervene earlier and potentially prevent the onset of the disease [52, 53].

4.2.2

SVM

Support vector machine (SVM) is a popular machine learning algorithm that can be used in various applications, including electronic health records (EHR). EHRs are digital records of patients’ health information that are generated and maintained by healthcare providers. SVM can be used in EHR to analyse large datasets and identify patterns and insights that can be used for diagnostic, treatment, and prognostic purposes. By optimizing the marginal gap that distinguishes both groups while minimizing classification errors, an SVM can perform classification tasks. The distance among the judgment hyperplane and its closest instance, which is a component of that class, is the class’s marginal distance. Every data point is first designed as a point in an n-dimensional space (where n represented as several features), with the value of every aspect being the coordinate value. To complete the classification, we must first locate the hyperplane that separates the two groups by the greatest margin. It has been used for classification in the areas of healthcare and bioinformatics.

4.2.3

Decision Tree and Random Forest

Decision trees and random forests are machine learning algorithms that can be used in electronic health records to predict patient outcomes, such as disease progression, treatment response, and risk of complications. A decision tree is a simple yet powerful algorithm that works by recursively partitioning the data into subsets based on the values of different features. At each node of the tree, the algorithm selects the feature that best separates the data into homogeneous groups, and creates a split based on a threshold value. This process continues until a stopping criterion is met, such as a

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maximum depth of the tree or a minimum number of samples per leaf node. Once the tree is trained, it can be used to predict the outcome of new patients by following the decision path from the root to a leaf node, where a prediction is made based on the majority class of the training samples in that node. A random forest is an ensemble method that combines multiple decision trees to improve the accuracy and robustness of the predictions. The algorithm works by creating a set of randomized trees, where each tree is trained on a random subset of the data and a random subset of the features. During the prediction phase, each tree in the forest independently generates a prediction, and the final prediction is obtained by taking the majority vote of all the trees. This approach reduces overfitting and improves generalization performance, especially when dealing with high dimensional and noisy data.

4.3 Deep Learning Models 4.3.1

RNN

RNNs are a feedforward neural network extension that can be used to model sequential data including natural language text [54], event sequences [55], and time series [56]; the RNNs recurrent structure, in particular, can imprisonment the multifaceted chronological subtleties in longitudinal electronic health record, creation them the chosen architecture for a variety of electronic health record modelling tasks, such as computational phenotyping [57] sequential clinical case prediction [58], and disease classification [59, 60]. Since the existing state of the secret coating is dependent on the feedback at the current time and former state of the obscure coating, the RNN’s hidden states act as memory. The RNN will now accommodate variable-length sequence input as well.

4.3.2

Autoencoders (AEs)

AEs is a nonlinear transformation-based unsupervised dimensionality reduction model. AEs are a favoured class of models for medical term embedding (e.g. embed various medical codes in a shared space) [61]. An AE uses an encoder to map inputs to an inside code description and then uses a decoder to map the low-dimensional interpretation in return to the input space. The restoration function is the combination of decoder and encoder. The reconstruction loss is minimized in a standard implementation of the AE, allowing AEs to concentrate on collecting critical data properties while decreasing the dimension size. To capture regular patterns and stable structures in the data, autoencoders were used to model electronic health records in an unsupervised way in [62].

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CNN

CNNs use local data properties (compositionality and stationarity by local statistics) and pooling and convolutional layers to extract abstract patterns in video, voice, and image processing. CNNs, for example, vastly improved the accuracy of automated skin lesions detection from image data [63]. The convolutional layers bind several local filters with their input data (raw data or previous layers’ outputs) to generate translation invariant local features. To prevent overfitting, pooling layers gradually minimize the output size. Both pooling and convolution are done locally in this case, so that the representation for one geographic function does not affect other regions in image processing. Since temporal electronic health record data is often insightful, modelling it with CNNs necessitates thinking about how to capture temporality. Table 3 describes the comparison between different models in electronic health record in terms of their benefits and limitations.

5 Need of EHR A longitudinal automated data of patient health information created by experiences of various people in every domain setting, according to the concept of EHRs. Laboratory data, radiology reports, vital signs, immunizations, prescriptions, patient demographics, progress notes, problems, and previous medical history are all included in this data. The ability to quickly navigate computerized documents and the absence of bad penmanship, which has historically afflicted the medical chart, are two of the most basic advantages associated with EHRs [65]. Clinical decision support (CDS) software and health information exchange (HIE) and computerized physician order entry (CPOE) systems are three functionalities of EHR systems that carry great potential in lowering costs and increasing the value of care at the level of healthcare system. The HITECH Act of 2009 established the “meaningful use” standards, which include these and other EHR capabilities [66]. A clinical decision support framework is one that helps a physician in getting decisions on health treatment. A CDS system’s features include possible patient problems, cross-referencing a patient’s allergy to a medication, warnings for drug reactions, and offering up-to-date drug information that the computer flags. Any of these functionalities offers a way for treatment to be provided in a more secure and effective way as medical expertise continues to expand. If more CDS programs are implemented, such medical errors should be avoided, and patients should get more effective and healthy treatment overall. Providers may use CPOE programs to type instructions (for physical therapy, radiology, lab testing, and medications) on paper rather than into a computer. The computerization of this procedure prevents potentially fatal medical mistakes caused by doctors’ sloppy handwriting. It also speeds up the purchasing process by eliminating the need for nurses and pharmacists to obtain validation or request missing information from incomplete or illegible orders. Previous evidence shows that when an electronic physician order entry system is utilized lonely, severe

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Table 3 Comparison between benefits and limitations of different models used in EHR [64] S. no.

Method

1

Regression model • It interprets model parameters in a probabilistic way • Models based on LR are simple to update • There are no conclusions to be taken concerning the distribution of independent variables

Benefits

Limitations • When there are complex relationships between input variables, it does not show good accuracy • Due to sampling bias, an LR model can exaggerate the accuracy of its predictions

2

Cox proportional hazards model

• It is not necessary to draw any conclusions about the survival distribution • It will look at multiple variables at the same time

• If the assumptions are not followed, the risk and analyses assessments will be biased • It is not a good idea to use a basic Cox model to detect violations of proportionate hazard theories since it may lead to incorrect conclusions

3

ANN

• Both classification and • Pre-processing of predictor regression problems benefit variables is needed • To train the network for a from this tool complicated categorization • This approach necessitates less trouble, this technique systematic statistical necessitates a significant amount preparation, and several of computing time training algorithms are • Since the consumer cannot access available in the literature the exact decision-making • When the interactions between process, it is referred to as a variables are dynamic and “black box” technology nonlinear, it is acceptable for predicting outcomes

4

SVM

• Both semi-structured and unstructured data, such as images and texts, works well • In SVM, the probability of overfitting is lower • The ability to manage a large number of feature spaces • It introduces the kernel, which allows for nonlinear transformation

5

Decision tree and random forest

• It is capable of producing • When the appropriate value for stable classifiers that can be the ancestor changing or attribute tested using statistical tests is absent, it is difficult to • Data preparation is minimal, determine which branch to take and it can accommodate a • The ultimate decision tree is variety of data types, including determined by the order in which categorical, numeric, and variables or attributes are selected nominal data • It is easy to comprehend and perceive

• The individual impact, final model, and variable weights are difficult to comprehend and perceive • Larger, more dynamic databases can take longer to train • SVMs would not function as a classifier if the points on the borders are not descriptive due to noise

(continued)

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Table 3 (continued) S. no.

Method

Benefits

Limitations

6

Random forest

• It will calculate the attributes or variables are most relevant in the classification • It fits well for large datasets • Random forests are nearly often more predictive than decision trees

• Overfitting can easily occur • In estimating variable significance, it prefers factors or attributes that can take a wide range of distinct values • Inside the random forest, the number of decision trees must be specified

drug errors can be minimized by as much as 55% 12, and by as much as 83% when paired with a clinical decision support system that provides warnings depending on what the practitioner orders. The use of a CPOE device, particularly when connected to a CDS, will increase care effectiveness and efficiency. EHRs make it easier for patients to share patient records through HIE until clinical data is available online. Health information exchange is the method of exchanging patient-level EHR between various associations, and it will help to improve healthcare quality [67]. HIE will eliminate expensive duplicate testing requested when one provider does not have open to medical data located at a further source’s location by permitting for safe and possibly real-time exchange of patient record. Patients’ record is usually held in a number of places where they get treatment. This may involve a physician specialists’ offices, and primary care physician’s office, one or two clinics, and other locations including emergency centres and hospitals. Over the course of a lifespan, a large amount of data accumulates in a number of locations, many of which are contained in silos. Providers have traditionally relied on mailing or faxing related data to one another, making it impossible to reach in “real time” when and where it is required. Health information exchange allows for sharing this data by electronic health records, resulting in more high-quality and cost-effective treatment.

6 Roles for EHR 6.1 Supports Enquiry and Learning • Promotes patient education. • Assists in health audit. • Promotes clinical study.

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6.2 Supports Population Healthcare • • • • • • • • • •

Increases the efficiency of healthcare professionals. Improves and demonstrates and cost-effective practice. Offers explanation for diagnoses and actions. Maintains supervision and value increase. Supports policy development. Supports a legitimate account of events. Accommodates future developments. Reduces routine reporting and facilitates management tasks. Gives testimony for assessment of programs and development. Supports continuing professional assessment.

6.3 Supports Communication • • • • • • •

Supports selective retrieval of information. Enables record access where and when needed. Enables record transfer. Supports electronic data interchange (EDI) and email generation. Allows automatic reports. Accesses medical knowledge databases. Supports case management, continuing, and collaborative care.

6.4 Supports Consumer Healthcare • • • • •

Accommodates decision support. Identifies deviations from expected trends. Describes preventative measures. Serves as the basis for a historical account. Expects potential health difficulties and activities.

6.5 Supports Consumer Involvement • • • • •

Keeps own confidentiality and strengthens privacy. Accommodates self-care and consumer decision support. Retrieves info for the customer. Ensures accountability of health professionals. Gives a customer position of data.

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7 Advantages of EHR There are various advantages of EHR; some of them are described in Fig. 2.

7.1 Enhanced Revenue When health maintenance appointments are due, EHR devices may be programmed to give alerts to both clinicians and patients. This will assist with adjusting patient flows and increasing income under different payment agreements [68].

7.2 Decrease in Billing Errors/Improved Charge Capture Electronic health record programs facilitate to ensure that charges for medical supplies, medicines, and hospital care are timely and accurately captured. They also have evidence of actual drug and facility management, which aids charge capturing

Fig. 2 Advantages of EHR

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[69]. According to certain analysts, faulty coding costs healthcare providers 3–15% of their future income.

7.3 Improved Cash Flow Billing and improved charge capture and will result in a decrement in billed for missed or disallowable charges [70] and reduction of overdue days in accounts receivable.

7.4 Improved Patient Safety EHR has the ability to improve patient safety in the similar way as it can improve productivity, e.g. an electronic health record that uses automatic physician order entry will help physicians determine the root effects of unfavourable outcomes in hospitals and outpatient situations after they happen and minimize medication errors in hospitals. Furthermore, EHR will help caregivers quickly recognize and alert specific patients of significant drug treatment transitions, such as the latest Vioxx withdrawal [71].

7.5 Improved Quality EHR methods have the ability to increase care value, especially when they are used in conjunction along with built-in elements like CDSS and computerized physician order entry. EHR has been related to positive outcomes in hospitals, including improved disease management, better infection prevention, and improved prescribing habits. Improvements of consistency are also possible in the outpatient environment.

7.6 Enhanced Capability to Perform Research Electronically accessible information for electronic health record applications can make it possible to evaluate patterns quantitatively and define data-established best ways. Statistics from electronic health records may be de-recognized and incorporated into broader data archives for analysis to increase public health, patient care, and medical information.

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7.7 Improved Productivity Electronic health record often strengthens workflows by optimizing resource efficiency and minimizing delicacy [72]. As a result, when people are not required to delay their own activities while waiting for other to complete theirs, they become more active. Overall, decreasing duplication of efforts, and optimizing resource efficiency can result in cost savings and increased production.

8 Application of EHR See Fig. 3.

8.1 Administrative Applications Administrative applications are needed in all EHRs. Registering the person who is coming to hospital is an integral aspect of the application, that is a part of the electronic health record. Details of patients are reported on the EHR during admission of the patient, wherein the patient’s main complaint, name, gender, age, contact details,

Fig. 3 EHR applications

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location, insurance information, and employer. The registry scheme gives the patient, a patient identity quantity that can only be utilized by one healthcare source.

8.2 Laboratory Systems Already many research laboratory in healthcare environments use “lab information systems (LIS)”, which are normally connected to the electronic health record for the sharing of test results and patient data. The proper interface should be with almost all lab research tools. Lab instructions, plans, test results, and other tasks are also stored in such automated systems.

8.3 Computerized Physician Order Entry The program is a standard for all EHRs. Respective personnel use this program to order radiology supplies, test centre, and drugstore and other respective orders. It offers major savings to medical representatives by encouraging doctors to demand samples online somewhat than on black in white formats. It guarantees that the demand raised are accurate and informs the right location. It also informs the medical representatives of procedures that must be done. The medical guidelines by the respective government also define the criteria for such program features [45].

8.4 Radiology Systems Another department of data organizations that edge with the electronic health record is radiology information systems (RIS). Image monitoring, scheduling, test reports, radiology instructions, and patient records are all stored in RIS, same as in laboratory. There is system such as picture archiving communications systems (PACS) used in radiology systems which takes care of maintaining and preserving optical radiography files.

8.5 Clinical Documentation Medical reporting is a major component of an electronic health record, so healthcare professionals, physicians, and nurses keep track of a lot of records about their patients. Medication administration records (MAR), evaluations, clinical reviews, and clinical notes are all examples of this data. Transcription records, discharge summaries, vital indications, and use control are all part of clinical documentation.

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8.6 Pharmacy Systems Today’s pharmacy systems are highly automated, where machines recording the prescriptions and remotely connected med carts. For ensuring that the drug administration, correct time, right patient, correct dosage, pharmacies use bar coding on drugs and patients. It is today’s need that pharmacy services unite with EHRs as this is where medicine allergies and medication reactions are monitored. E-prescribing is an important feature of HER’s pharmaceutical applications [73]. Bad handwriting on prescriptions and pharmacy orders from physicians is one cause of medication errors. Through submitting the medication directly to the hospital pharmacy or the retail pharmacy, e-prescribing solves this challenge. EHRs are an excellent method for reducing or eliminating medication defects.

9 Challenges The creation and introduction of electronic health records (EHRs) are fraught with difficulties. It necessitates adequate resources, as well as ample and well-trained staff, which includes experts from various fields such as educators, health consultants, IT experts, Physicians, and so on. One of the most difficult aspects of implementing EHRs is keeping medical information safe. Concerns have been raised about the database’s misuse and the danger to cyber-security. Only approved users should have access to data to preserve the integrity and security of the patient’s information. Some steps, such as encryption, password-protected files, and cloud storage, will help with EHR security concerns. Firewalls, antivirus tools, and other EHR authentication mechanisms can be used to ensure data confidentiality. Another difficult challenge is creating a user-friendly interface. A poorly built interface can result in decreased time utilization, low healthcare delivery quality, and even pose a risk to the patient’s security. Doctors’ approval of EHR is often seen as a hurdle. This is attributed to the hours spent by doctors entering details online, time that might otherwise be spent treating patients. Doctors are often opposed to the concept of implementing transparency through the use of EHRs, as well as administrative and financial liabilities. Medical identity theft is now getting more prevalent. An individual can illegally access and use another EHR person’s identifying information in order to obtain medical treatment for the ailment. Other EHR obstacles to the effective implementation of EHRs in India include electronic monitor of data, communication silos within the healthcare system, software development team, lack of common understanding between the healthcare team, absence of consciousness of procedures connected to safety of health info, poor management practices, absence of synergy amid the healthcare benefactors to usage the EHR system, infrastructural demands, absence

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Table 4 Challenges in the EHR [74] S. no.

Challenges

Description

1

Privacy concerns

Patient health data confidentiality is also an open field

2

Co-ordination and infrastructure

Hospitals in the private and public sectors lack cooperation and supporting resources (including software and hardware)

3

Computer literacy

Computer literacy is weak among government workers and the population in private hospitals. To use the EHR properly, you must first complete system training

4

Standards

The majority of systems do not conform to guidelines for the sharing of information and representation. It would be made more difficult by the fact that patients and nurses speak a variety of languages

5

Policy

There is no concerted government strategy to facilitate the introduction of EHR. There is a lack of consistency in HIT’s new policies

6

Cost

The most significant impediment is the high cost of execution. These systems are only affordable to hospitals or doctors with a large IT budget

7

Legacy system

But for a few well-known private large-scale clinics, the bulk of patient reports are captured on paper. Converting this paper-based document to an electronic medium is a challenging process

of consistency in the EHR software, and lack of computer literacy between the healthcare professionals are not a mandatory prerequisite. Another problem is that, based on the EHR system selected, increased clinician documentation time can result. Any nurses and doctors may be reluctant to reform and want to return to the old paper-based schemes. Malfunction to adjust clinical procedures when introducing an EHR will sabotage any hoped-for productivity gains. Some of most significant barriers and challenges to EHR adoption are shown in Table 4.

10 Limitation of Current Study While EHR systems have become more widespread in recent years yet they still have some limitations which includes interoperability which is one of the biggest challenges facing EHR systems which lacks of interoperability between different systems, this can make it difficult for healthcare providers to share patient information between systems which can lead to incomplete or inaccurate patient records. Other

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limitation is usability of EHR systems can be complex and difficult to use particularly for healthcare providers who are not tech savvy; this can in turn lead to frustration and decreased efficiency as well as potential errors in patient care. Other limitations are data security, cost, and standardisation. Although EHR systems have potential to improve patient care and outcomes, their limitations must be addressed in order to fully realize their benefits.

11 Conclusion and Future Scope It is well established that technology advancements have resulted in an increase in the condition of wellbeing care facilities offered to patients, and as a result, automated systems have emerged. EHRs, which are used in a number of medical fields, are a new application of these automated technologies. As a major healthcare breakthrough, EHRS will continue to increase the efficiency of healthcare services. Health data for a single person is recorded in an electronic presentation in “electronic health records (EHRs)”. EHR procedures are digital tools that healthcare assistance givers (such as clinics and hospitals) use to continue way of patient role EHR records. The implementation of EHRs would certainly strengthen the competence of healthcare systems, but many considerations, such as a confidentiality, security, guidance, cost, and lack of standards, will continue to be a source of concern. In this paper, we are providing an indication of the different standards for electronic health record. In addition, we discussed the need of EHR, rolls for EHR and also discuss about several advantages and challenges of an EHR. To create an effective framework of potential data exchange agreements between health services and patients, more public dialogue around electronic health records is needed. Quantitative research, such as polls of a wide variety of customers and doctors, may also be performed in order to obtain better outcomes.

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Usage of AI Techniques for Cyberthreat Security System in Android Mobile Devices Phaneendra Varma Chintalapati, Gurujukota Ramesh Babu, Pokkuluri Kiran Sree, Satish Kumar Kode, and Gottala Surendra Kumar

Abstract Due to technology advancements, every daily work has been automated since the beginning of the digital era. Technology hasn’t yet given individuals adequate resources and protections, though. The issue of protecting the connected devices becomes increasingly important as the Internet connects more and more devices globally. Identity theft, data theft, fraudulent transactions, compromised passwords, and system breaches are now commonplace in the headlines. The most recent developments in artificial intelligence gave a jolt to the escalating threat of cyber attacks. Almost all branches of engineering and research are using AI today. AI’s involvement not only automates a certain task but also vastly increases efficiency. It follows that cybercriminals would find such a delectable buffet to be quite tempting. Thus, the traditional cyberattacks and threats have evolved into ‘intelligent’ threats. Modern artificial intelligence (AI)-assisted approaches are used by the most recent generation of cyberthreats to launch multi-level, powerful, and potentially dangerous attacks. Different issues arise while trying to protect against recent and developing hazards with current cyberdefence technologies. As a result, a cyberthreat security system for Android-operated mobile devices is described in this study. On Android mobile devices, the machine learning (ML) and deep learning (DL) algorithms can quickly detect threats.

P. V. Chintalapati (B) · G. R. Babu · P. K. Sree · S. K. Kode · G. S. Kumar Department of CSE, Shri Vishnu Engineering College for Women(A), Bhimavaram, Andhra Pradesh, West Godavari District, India e-mail: [email protected] G. R. Babu e-mail: [email protected] P. K. Sree e-mail: [email protected] S. K. Kode e-mail: [email protected] G. S. Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_33

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Keywords Android mobile devices · Artificial intelligence · Cyberthreats · Cybersecurity · Machine learning and deep learning

1 Introduction Malware developers are currently attracted to Android-powered mobile devices, and the volume of work increases quickly [1]. Utilising smartphones with the most recent specs and enhancing associated Android applications is made possible by the quick growth of technology. Sandboxing techniques and authorisation systems were generally programmed into Android systems to lessen the hazard posed by Android vulnerabilities while applications development by customers as Google Play Store didn’t detect malicious attacks, and later, applications are published. Nowadays, it’s difficult to find a business, organisation, or family that doesn’t use technology and the Internet. The sheer amount of digital devices and programmes that we use on a daily basis overwhelms us as humans. Many of us even lack the ability to govern how we use technology. Others may not use the Internet frequently enough to keep up with the quick developments in technology, while some people may be addicted to it and unable to quit using it. Some people may choose to spend hours on their phones or laptops over socialising. We can’t help but feel compelled to utilise the Internet for everything because it is such a potent instrument. Without a doubt, technology has improved our efficiency and productivity in a number of ways. However, we must take into account how it affects our social and personal lives as well as our mental and physical health. Economic and societal advancements are being greatly accelerated by artificial intelligence. It has moreover emerged as one of the fundamental technologies of the digitalisation, opening up both potential and hazards [2]. The majority of malware development focuses on mobile devices that are hacked and transformed into bots. This gives hackers the ability to access affected machines with a connected device and build botnets. Botnets have been used to carry out a variety of harmful activities, including distributed denial of service (DDoS), spam transmission, and data theft, among others. Modern techniques (such as a multi-stage payload or self-defence) were used to perform malicious botnet attacks, creating malware that is difficult to detect. As a result, it generates the main hazards, which necessitate the programme for advantageous strategies to recognise these attacks. Therefore, it was important for the present to pay attention to developing high potential and capability cybersecurity resolutions [3]. A sort of crime known as cybercrime involves the use of digital means to perpetrate fraud, steal information, or cause harm. In essence, unlawful operations that are started via computers, such as hacking, phishing, malware distribution, online stalking, and identity theft, among many others, are referred to together as cybercrime. One of the most lucrative crimes in the modern era is cybercrime. Cybercriminals steal data, manipulate data, and compromise key infrastructure every year for

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billions of dollars in profit. Cybercrime has changed significantly over time and will continue to do so, much like any other type of crime. The creation of security plans to guard against unauthorised access, modification, and smashing of computing resources, networks, programmes, and information is known as cybersecurity. Recent cybersecurity concerns occurred and were promptly modified as a result of more data and communication technology considerations. It was crucial for the automatic detection of phases in a host-based cyberattack. This makes automated forensics possible, resulting in speedy attack detection, risk assessment, and ultimately remediation. This suggests that the attacker has a thorough understanding of their victim [4]. The most recent cyberthreats must be protected from by a strong cyberdefence system [5]. However, cybercriminals used cutting-edge techniques to increase the strength and scope of their attacks. However, there remained a need for quick, robust, and adaptable cyberprotection systems capable of quickly identifying various damages. The adoption of AI technology has grown throughout the most recent time and plays a significant role in both identifying and preventing cyberrisks [6]. In a good sense, AI can be used for general security against cyberdangers (defensive AI). AI improves the accuracy of malware, spam, and phishing email detection. The level of IT security can be greatly raised by doing this. AI can simultaneously increase the efficiency and scalability of cyberattacks. Offensive AI is the term used to describe the use of AI as a disruptive force. Due to new issues and vulnerabilities of Android applications that attackers can use right away, it also causes difficulties for researchers and developers of the protection process for these applications. The view of Android applications for digital ecommerce, e-business, savings, and online banking was mixed with sensitive information sent via cellular networks, which was important to determine the information security goals of the application. By using the identification of harmful attacks against Android applications to ensure that protection access didn’t occur in this network, ML and DL models were discovered. Due to their precise and dependable findings, ML and DL classifiers have recently attracted a lot of interest. To differentiate between typical and anomalous botnet attacks, several Machine Learning methods and the DL algorithms are used to find Android botnets. This study presents an AI-based cyberthreat security system for Android-powered mobile devices. The effectiveness of the provided approach was also investigated. Vulnerabilities created by consumers during the creation of applications because Google Play Store failed to recognise malicious assaults after applications were published. Nowadays, it’s difficult to find a business, organisation, or family that doesn’t use technology and the Internet. The sheer amount of digital devices and programmes that we use on a daily basis overwhelms us as humans. Many of us even lack the ability to govern how we use technology. Others may not use the Internet frequently enough to keep up with the quick developments in technology, while some people may be addicted to it and unable to quit using it. Some people may choose to spend hours on their phones or laptops over socialising. We can’t help but feel compelled

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to utilise the Internet for everything because it is such a potent instrument. Without a doubt, technology has improved our efficiency and productivity in a number of ways. However, we must take into account how it affects our social and personal lives as well as our mental and physical health. Economic and societal advancements are being greatly accelerated by artificial intelligence. It has moreover emerged as one of the fundamental technologies of the digitalisation, opening up both potential and hazards [2]. The majority of malware development focuses on mobile devices that are hacked and transformed into bots. This gives hackers the ability to access affected machines with a connected device and build botnets. Botnets have been used to carry out a variety of harmful activities, including distributed denial of service (DDoS), spam transmission, and data theft, among others. Modern techniques (such as a multi-stage payload or self-defence) were used to perform malicious botnet attacks, creating malware that is difficult to detect. As a result, it generates the main hazards, which necessitate the programme for advantageous strategies to recognise these attacks. Therefore, it was important for the present to pay attention to developing high potential and capability cybersecurity resolutions [3]. A sort of crime known as cybercrime involves the use of digital means to perpetrate fraud, steal information, or cause harm. In essence, unlawful operations that are started via computers, such as hacking, phishing, malware distribution, online stalking, and identity theft, among many others, are referred to together as cybercrime. One of the most lucrative crimes in the modern era is cybercrime. Cybercriminals steal data, manipulate data, and compromise key infrastructure every year for billions of dollars in profit. Cybercrime has changed significantly over time and will continue to do so, much like any other type of crime. The creation of security plans to guard against unauthorised access, modification, and smashing of computing resources, networks, programmes, and information is known as cybersecurity. Recent cybersecurity concerns occurred and were promptly modified as a result of more data and communication technology considerations. It was crucial for the automatic detection of phases in a host-based cyberattack. This makes automated forensics possible, resulting in speedy attack detection, risk assessment, and ultimately remediation. This suggests that the attacker has a thorough understanding of their victim [4]. The most recent cyberthreats must be protected from by a strong cyberdefence system [5]. However, cybercriminals used cutting-edge techniques to increase the strength and scope of their attacks. However, there remained a need for quick, robust, and adaptable cyberprotection systems capable of quickly identifying various damages. The adoption of AI technology has grown throughout the most recent time and plays a significant role in both identifying and preventing cyberrisks [6]. In a good sense, AI can be used for general security against cyberdangers (defensive AI). AI improves the accuracy of malware, spam, and phishing email detection. The level of IT security can be greatly raised by doing this. AI can simultaneously increase the efficiency and scalability of cyberattacks. Offensive AI is the term used to describe the use of AI as a disruptive force.

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Due to new issues and vulnerabilities of Android applications that attackers can use right away, it also causes difficulties for researchers and developers of the protection process for these applications. The view of Android applications for digital ecommerce, e-business, savings, and online banking was mixed with sensitive information sent via cellular networks, which was important to determine the information security goals of the application. By using the identification of harmful attacks against Android applications to ensure that protection access didn’t occur in this network, ML and DL models were discovered. Due to their precise and dependable findings, ML and DL classifiers have recently attracted a lot of interest. To differentiate between typical and anomalous botnet attacks, several machine learning methods and the DL algorithm are used. To find Android botnets, ML and DL models are employed. This study presents an AI-based cyberthreat security system for Android-powered mobile devices. The effectiveness of the provided approach was investigated using a variety of ML and DL models.

2 Literature Survey Cyberthreat detection based on ANN utilising event profiles is presented by Lee et al. [7]. The suggested solution separates the many protection events that have been recorded into distinct event profiles and uses DL-based identification to more accurately identify cyber-risks. The results of this investigation’s examination demonstrate that the offered strategies can use learning-based algorithms for identifying network intrusions and even display actual time utilisation, outperforming more conventional ML techniques. The AI-based cyberthreats and vulnerability identification, prevention and prediction model is presented by A.M.S.N. Amarasinghe, W.A.C.H. Wijesinghe, D.L.A. Nirmana et al. [8]. An automatic system with a method to enforce vulnerabilities and a large database of well-known vulnerabilities was the suggested application. AI-based generative algorithms carry out the clean-up process and improve accuracy while CNN identifies vulnerabilities. Artificial intelligence was discussed in Veranyurt et al. [9] discussion of DOS/ DDOS attack detection. The author’s goal was to study how different machine learning algorithms and artificial neural networks could detect denial of service threats in this paper (ANN). The Knowledge Discovery and Data Mining Tools Competition (KDD 99) dataset and the information gathered from lab experiments will be used for the evaluation. The evaluation of the ML and ANN models’ performance in identifying network layer DOS attacks will be the study’s main focus. The benefits of AI in cybersecurity were highlighted by Calderon et al. [10]. Machine learning techniques are able to mine the data to identify the sources of botnets, and AI-based approaches can improve the IDPS systems’ rate of identification. Cybersecurity experts need to be aware of consistency between advantages

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and dangers because the application of artificial intelligence may result in a variety of negative effects. The use of AI to combat cyberthreats in banking was described by Soni et al. [11]. Artificial intelligence creates an exact mode for the banking industry so that it can identify transactional fraud. Artificial intelligence and the field of cybersecurity were clearly related. Fraud detection models that are based on artificial intelligence can both stop and identify many sorts of cybercrimes. The prevention of poisoning assaults on AI-based threat intelligence systems has been studied by Khurana et al. [12]. The reliability of Reddit postings is evaluated in this analysis to ensure the validity of data gathered by AI systems, and security analysts use these systems to define potential risks by looking at data disseminated on social media websites, forums, blogs, etc. A Master Attack Methodology for an AI-Based Automated Attack Planner for Smart Cities is presented by Falco et al. [13]. This implementation enables both novice and expert attackers to identify attack paths. In the initial phase of protection, which is a challenging framework for cyberassault, they propose and produce a trail for an automated attack generation technique that might give clear, flexible, and consistent attack trees. Applied AI Methods to Prevent Cyberassaults by Anwar et al. [14]. Since neither humans nor artificial intelligence can demonstrate total accomplishment in this field, a thorough understanding of the cyberenvironment of associations where AI is combined with human knowledge is required to improve the growth of cybersecurity. Klein et al. [15] discussed the use of AI in cybersecurity. Artificial intelligence techniques will improve the implementation of its full security and offer the greatest defence against more complex cyberthreats. Artificial intelligence has legitimate hazards associated with it, in addition to the increased likelihood that it will be used in cybersecurity. Artificial intelligence approaches must be used in a socially controlled manner in order to significantly reduce the risks and problems involved.

3 Usage of AI in Cyberthreat Security System In this work, cyber threat security system using artificial intelligence for Androidoperated mobile devices is presented. The block diagram of presented system is shown in Fig. 1. Examinations were implemented with two standard datasets: Canadian Institute for Cyber Security (CICAndMal2017) and Drebin datasets. The cybersecurity datasets were standard mobile malware dataset that includes both constant and modern specifications of record files. The datasets are formed from different network runs utilising CICFlowMeter-V1 and CICFlowMeter-V3. The Drebin dataset is obtained from 15,037 approaches of Drebin program that includes two hundred and fifteen specifications and injections of 5560 malware and 9476 common approaches. The Android datasets had various formats and features; hence, preprocessing was most significant for controlling the dataset.

Usage of AI Techniques for Cyberthreat Security System in Android … Fig. 1 Block diagram of presented system

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Datasets

Pre-Processing

Artificial Intelligence

Deep Learning Algorithms

Algorithms

Features Extraction

Evaluation Metrics

User Access Authentication

Network Situation Awareness

Dangerous Behavior Monitoring

Abnormal Traffic Identification

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Minimum–Maximum Normalisation Method: Normalisation was calculating application for shift as well as rescales dataset outcomes. The minimum–maximum normalisation technique is implemented to calculate information in the range of 0 and 1. The normalisation technique is implemented for overlap of complete datasets utilising following equation: V' =

 V − min(A)  newmax( A) − newmin( A) + newmin( A) max(A) − min( A)

where min(A) and max(A) were the minimum and max information, respectively; new_min(A) and new_max(A) were recent outcomes of min and max utilised for calculation of information, as well as ‘V ’ is normalised information. AI has the best detecting impact in specific situations and can process massive amounts of information quickly. However, it might be disturbed and might not precisely reflect the current situation. Cybersecurity makes use of the interactive ML used in AI. The support vector machine (SVM) was a supervised ML model used to resolve challenging problems with linear and nonlinear techniques. Support vector machines (SVMs) were used to analyse the impact of the position and situation of the hyperplane as well as to sketch hyper plane among data points that were close to hyperplane (SV). If the distance between the data points is close to the hyperplane, the support vector can be used most effectively. SVM offers a variety of linear and nonlinear functions, and radial basis function (RBF) was appropriate for distinct models since network information has a challenging structure. One of the most effective traditional ML models is linear regression (LR), which uses the minimal mean squared error function to describe the line or hyperplane that best fits the training data. A feed-forward ANN called a multilayer perceptron (MLP) makes a pair of outputs and offers a pair of inputs. A directed graph connecting the input nodes and output layers in an MLP is made up of multiple layers of input nodes an MLP with just the input, hidden, and output layers of nodes. Convolutional neural network and long short-term memory, both deep learning (DL) AI methods, were combined to create convolutional neural network-long shortterm memory (CNN-LSTM). With trainable mass and bias features, the convolutional neural network has invisible neurons. That was commonly used to study information in a grid structure, providing a different framework from the one that was previously in place. Due to the input data stream following a single path from input to production layer, that network was known as a feed-forward network. The feature extraction process was a crucial part of the machine learning workflow since it required the developer to give the models only relevant data, allowing them to specify the precise response and improving the algorithm’s capabilities. The process of turning unprocessed data into numerical parameters that could be used for saving the data in a real dataset is called feature extraction. By creating current attributes from supplied data, the feature extraction goal seeks to reduce the number of features in the dataset (and get rid of actual attributes). The recently reduced collection of

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attributes should be able to draw more accurate conclusions from the data in the actual feature set. The datasets are divided into training data (80%) and testing data (20%). It was demonstrated how to divide training and testing at random. The Android malware datasets are used to implement the training level to suit the model. The test level is set up to evaluate current applications using available data. The protection of applications running on Android-powered mobile devices is ensured by the most effective application of ML and DL algorithms. In this method, convolution neural network-long short-term memory (CNNLSTM) is used to validate the performance of the presented system in order to improve user authentication, network situation awareness, harmful nature observation, and anomalous traffic detection. ML models such as linear regression, SVM and DL, MLP algorithms, and CNN-LSTM are also used. Using a mobile device and one or more authentication methods, user authentication verifies a user’s identification for security access. By allowing the transfer of credentials from a human to a machine during network interactions, user authentication ensures user authenticity and validates the detection of a user trying to reach a network or mobile. Based on threat intelligence, big data, visualisation, and other technologies, network situation awareness is a platform for monitoring and displaying clients’ current network performance and security status. Network situational comprehension was concerned with the network’s layout and content, which may not correspond to what people believe. What technologies are available and how do they provide the crucial data? Recent applications for inner side risk obstruction and identification involve dangerous nature observation. Unusual network traffic included traffic brought on by criminal intent as well as traffic from various risky attacks, Internet worms, and scans. The identifying segment receives information from routers or observation systems. Accuracy, precision, and sensitivity are used to calculate how well the presented ML and DL algorithms perform in each category.

4 Result Analysis In this part, the output examination of cyberthreat security system using artificial intelligence for Android-operated mobile devices is discussed. The performance evaluation of presented algorithms on standard Android malware dataset is regulated using the Python programming language. The statistical investigation evaluates output of presented algorithms. The performance metrics are evaluated and depend on the following values. The true positive (TP) indicates the number of examples that were achieved and divided as positive sentiment, false positive (FP) was number of examples which were not exactly divided as negative sentiments, true negative (TN) indicates number of examples which were divided as negative sentiment, and false negative (FN) indicates number of examples which were not exactly divided as positive sentiments.

452 Table 1 Performance evaluation of different ML and DL algorithms

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Algorithms

Accuracy (%)

Precision (%)

Sensitivity (%)

LR

82.4

84.3

80.6

MLP

91.7

90

92.7

SVM

94

95.2

93.4

CNN-LSTM

95.4

97

96.5

Accuracy: It is ratio of exactly divided samples to complete classified samples and it is expressed a Accuracy =

TP TP + FP + TN + FN

(1)

Sensitivity: It is called as recall. It is described as ratio of exactly classified positive samples to complete number of positive samples (i.e., TP + FN). Sensitivity =

TP TP + FN

(2)

Precision: The precision can be described as number of TPs to total positive predictions (i.e., TP + FP). Precision =

TP TP + FP

(3)

The performance of various ML and DL algorithms is shown in Table 1. In ML algorithms, the SVM algorithm has better performance than LR algorithm, whereas in DL algorithms, the CNN-LSTM has better results compared to MLP. The CNN-LSTM has better results than SVM, MLP and LR. The comparison between these four algorithms is shown in Fig. 2. Fig. 2 Performance comparison of different Ml and DL algorithms

120 100 80 60 40 20

Usage of AI Techniques for Cyberthreat Security System in Android … Fig. 3 Performance comparison graph

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120 100 80

User authentication

60 40

Network situation Awareness

20 Traditional approaches

Presented cyber threat security system using AI

dangerous behavior monitoring Abnormal traffic identification

Compared to traditional mobile devices, presented cyberthreat security system has better results in terms of user authentication, understanding of network condition, and harmful nature observation and abnormal traffic detection for Android-operated mobile devices. Figure 3 shows the performance comparison between traditional approaches and presented cyberthreat security system using AI for Android-operated mobile devices. Therefore, presented cyberthreat security system using AI effectively monitors the dangerous behaviour, identifies the abnormal traffic, and provides user authentication as well as awareness about network situation. This system has greater results for Android-operated mobile devices in order to detect and monitor the abnormalities and threats.

5 Conclusion This work presents an artificial intelligence-based cyberthreat security system for Android-powered mobile devices. In this method, various ML and DL algorithms are used to compute and confirm the performance of the offered system several ML and DL techniques, including CNN-LSTM-memory, LR, SVM, and multilayer perceptrons. This method makes use of the Darebin dataset and the CICAndMal2017 dataset. In order to develop a precise cyberthreat security system that may aid in the protection of Android-powered mobile devices against threats, the SVM and conventional neural network-long short-term memory algorithms obtained high accuracy execution. For Android-operated mobile devices, the provided system performs better than traditional mobile devices in terms of user authentication, knowledge of network status, observation of dangerous nature, and abnormal traffic detection.

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References 1. Alkahtani H, Aldhyani THH (2022) Artificial intelligence algorithms for malware detection in android- operated mobile devices. Sensors 22:2268. https://doi.org/10.3390/s22062268 2. Kant D, Johannsen A (2022) Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs). Soc Imag Sci Technol. https://doi.org/ 10.2352/EI.2022.34.3.MOBMU-38 3. Zhang Z, Ning H, Shi F, Farha F, Xu Y, Xu J, Zhang F, Raymond Choo KK (2021) Artificial intelligence in cyber security: research advances, challenges, and opportunities. Springer. https:/ /doi.org/10.1007/s10462-021-09976-0 4. AbuOdeh M, Adkins C, Setayeshfar O, Doshi P, Lee KH (2021) A novel AI-based methodology for identifying cyber attacks in honey pots. In: The 35th AAAI conference on artificial intelligence (AAAI-21), 2021. Association for the Advancement of Artificial Intelligence 5. Alavizadeh H, Jang-Jaccard J, Alpcan T, Camtepe SA (2021) A Markov game model for AI-based cyber security attack mitigation. arXiv:2107.09258v1 [cs.GT] 20 Jul 2021 6. Truong TC, Diep QB, Zelinka I (2020) Artificial intelligence in the cyber domain: offense and defense. Symmetry 12:410. https://doi.org/10.3390/sym12030410 7. Lee J, Kim J, Kim I, Han K (2019) Cyber threat detection based on artificial neural networks using event profiles. IEEE Access 7. https://doi.org/10.1109/ACCESS.2019.2953095 8. Amarasinghe AMSN, Wijesinghe WACH, Nirmana DLA, Jayakody A, Priyankara AMS (2019) AI based cyber threats and vulnerability detection, prevention and prediction system. In: 2019 international conference on advancements in computing (ICAC), 5–6 Dec 2019, Malabe, Sri Lanka 9. Veranyurt O (2019) Usage of artificial intelligence in DOS/DDOS attack detection. Int J Basic Clin Stud (IJBCS) 8(1):23–36. ISSN:2147-1428 10. Calderon R (2019) The benefits of artificial intelligence in cyber security. Econ Crime Forensics Capst. https://digitalcommons.lasalle.edu/ecf_capstones 11. Soni VD (2019) Role of artificial intelligence in combating cyber threats in banking. Int Eng J Res Develop 4(1):7. https://doi.org/10.17605/OSF.IO/JYPGX 12. Khurana N, Mittal S, Piplai A, Joshi A (2019) Preventing poisoning attacks on AI based threat intelligence systems. IEEE, 978-1-7281-0824-7/19 13. Falco G, Viswanathan A, Caldera C, Shrobe H (2018) A master attack methodology for an AIbased automated attack planner for smart cities. IEEE Access. https://doi.org/10.1109/ACC ESS.2018.2867556 14. Anwar A, Hassan SI (2017) Applying artificial intelligence techniques to prevent cyber assaults. Int J Comput Intell Res 13:883–889. ISSN 0973-1873 15. Wirkuttis N, Klein H (2017) Artificial intelligence in cyber security, vol 1, no 1. Academia

Essential Amino Acids of Lectin Protein of Selected Pulses: A Comparative Analysis Arti Chauhan, Nihar Ranjan Roy, and Kalpna Sagar

Abstract Protein is an essential part of human diet. As per the National Academy of Medicine, 7 g of protein is required every day for every 20 lbs of body weight. Millions of people around the world consume inadequate amounts of protein, particularly young children. Protein deficiency has a variety of serious consequences, including stunted growth, loss of muscle mass, weakened immune systems, heart and respiratory system weakness, and even death. Pulses are found to be a reasonable and decent source of protein which contain a number of bioactive proteins including lectin, histone H1 and actin. The main focus of this study is to analyse the content of essential amino acids among the selected pulses so that a person could have a proper intake of a protein diet which can be helpful in abating dietary diseases. Essential amino acids are the ones that the human body cannot synthesise and must be obtained from daily diet. This work mainly focuses on essential amino acids content in lectin protein and an effort has been made to analyse lectin protein of different pulses viz. Cajanus cajan (Pigeon pea), Vigna mungo (Black gram), Lathyrus sativus (Indian pea), and Vigna aconitifolia (Moth bean) to understand which supplies more essential amino acids or a good source of protein. Lectin protein of Vigna aconitifolia provides a good amount of essential amino acids (53.8%) followed by Cajanus cajan (51.7%). These results were further compared with Oryza sativa (Rice), to identify dietary combination results as people of India are habituated to take rice and pulses together. It was found that Oryza sativa can be used with these pulses to increase essential amino acids content in the human diet. Further, our finding showed that pulses can provide a proper balanced protein diet if properly included, hence can be used to abate dietary diseases.

A. Chauhan School of Engineering, GD Goenka University, Gurugram, India N. R. Roy School of Engineering and Technology, Sharda University, Greater Noida, India e-mail: [email protected] K. Sagar (B) KIET Group of Institutions, AKTU, Ghaziabad Delhi-NCR, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_34

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Keywords Biomolecule · Pulses · Deficiency diseases · Dietary diseases · Protein · Essential amino acid

1 Introduction The human diet is split into five nutritional groups: proteins, carbohydrates, fats, vitamins, and minerals. Whenever an individual does not get enough of essential protein nutrients that are amino acids, the body cannot function properly and that they are at a risk for dietary deficiency diseases [1]. Thus, it’s very important to calculate protein intake on an individual level so that a balanced diet can be maintained. Kwashiorkor and Marasmus are known to be protein deficiency diseases. These diseases are very widespread in developing countries where people lack nutritious food. A technical report on average protein consumption/requirement for a population has been developed by the Food and Agriculture Organization (FAO) of World Health Organization (2007) [2]. Amino acids such as lysine, methionine, threonine, and tryptophan are most limiting within the world’s diet [3]. Pulses are a significant source of livelihood generation for immeasurable resourcepoor farmers practicing agriculture within the semi-arid and sub-tropical regions [4]. They function as a low-cost protein to meet the needs of the large section of the people. They have, therefore, been justifiably described as ‘the poor man’s meat’ [5]. The main aim of this research work is to analyse and quantify the content of essential amino acids among the selected pulses so that a person could have a proper intake of protein diet which can be suggested to abate dietary diseases [6]. Further, a comparative analysis has been done among pulses viz. Cajanu scajan, Vigna mungo, Lathyrus sativus, and Vigna aconitifoliain order to analyse essential amino acid composition in lectin protein. The selected pulses were also compared with oryza sativa for essential amino acid composition, and secondary structure were analysed to get functional inference. This paper is organised as follows: Sect. 2 focuses on the relevant and recent work. The proposed work and methodology are presented in Sect. 3. Results of the experiments and discussion are presented in Sect. 4, and finally, the paper is concluded in Sect. 5.

2 Literature Review In this section, we discuss the nutritional importance of pulses and some of the recent work done on these with different angles such as maintaining nutritional value, effect of processing techniques, change in structure for flavouring to name a few. Pulses are highly recommended in some chronic diseases such as type-2 diabetes and have several advantages and are of great commercial importance too. A detailed

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documentary on pulses, its advantages, nutritional value, and commercial importance is available in [7] by Narpinder et al. Pulses are prime source carbohydrates, proteins, fibre, vitamins, and minerals. It has been found that they help in reduction of disease like various cancers, HDL cholesterol, hear diseases, and type 2 diabetes. They have nutritional and antinutritional compounds having beneficial properties. Lectin protein is the one that reduces certain forms of cancer, obesity by activating innate defence mechanisms [8, 9]. A good source of protein lectin possesses strong antimicrobial properties. Antimicrobial mechanisms of lectins include the pore formation ability, followed by changes in the cell permeability and latter indicate interactions with the bacterial cell wall components. A detailed review and evaluation of lectins for its antimicrobial properties are presented by Coelho et al. [10]. In silico study performed by [11, 12] to understand lectin structure and its docking of carbohydrates found that specific sites of carbohydrate can provide information about the antibiosis property of lectin. Jimenez et al. [13] suggests that lectin plays two major functions in plants. One as a great source of protein that even boosts plant growth; second, they play a defensive role in plants and protect them from pathogens. Other than this, if we see digestive properties of pulses as many suggest that plant-based proteins are not digestible, a study performed by [14] shows that all pulses have high digestibility values. Studies performed by [15] taking different preparatory methods in account such as household cooking method, canning method, and stabilisation method showed that prominent decline in protein content and fibre content of the pulses were due to the canning. Pulses are decent source of bioactive compounds1 that plays a significant role in metabolic activities. Their concentration varies from pulses to pulses. A detailed study on this is presented by Singh et al. [16]. Pulses are a good source and have a significant role in abating the malnutrition problem in developing and underdeveloped countries. A global environmental impact of the same with increasing demand is studied in [17]. Bechthold et al. [18] suggest that healthy food choice of an individual has a great impact on the risk of metabolic disorders. A recent study by Oscar et al. [19] analyses the effect of extrusion, baking and cooking on protein nutritional parameters especially on pulses of different variety and ways to improve its digestibility and retain protein quality and composition. Azarpazhooh et al. [20] in their recent work have presented a study of different processing techniques for maintaining composition and nutritional profile of pulses and dry beans. The process considered are dehulling, soaking, size reduction, fermentation, germination, cooking, extrusion cooking, and high pressure cooking. Shevkani et al. [21] and studied antioxidative and antimicrobial properties of pulse proteins and their uses in sports nutrition and increasing demand of gluten free nutrition with a concern on safety and nutritional value.

1

Bioactive compounds are usually the non-nutrient food constituents that typically occur in small quantities [16].

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Yogesh et al. [22] have presented a detailed study of anti-nutritional compounds present in different pulses including their fractions, significance and beneficial and adverse effect on human health. In a recent study by Zha et al. [23], authors have studied ways for improving functionality and flavour profile of pulse proteins to overcome unpleasant flavour problem. Boeck et al. [24] in their recent work have emphasised on usage of pulse-based proteins in making plant-based yogurt due to high protein content and beneficial amino acids in pulses. Avezum et al. [25] have presented the effect of germination process from physiological and biochemical angle, by considering the genotype, environmental conditions, hormone control, and the metabolic transition from seed to seedling.

3 Proposed Work A comparative study of lectin protein of different pulses viz. Cajanus cajan (Pigeon pea), Vigna mungo (Black gram), Lathyrus sativus (Indian pea), and Vigna aconitifolia (Moth bean) has been done to understand which supplies more essential amino acids or a good source of protein. Further, comparison has been performed with Oryza sativa (Rice), to identify dietary combinations. Lectin is the common protein in all the pulses. Thus, the amino acid sequences of lectin associated with different pulses species including Cajanus cajan, Vigna mungo, Lathyrus sativus, and vigna aconitifolia are retrieved from NCBI with accession number AEW50184.1, CAM12258.1, CAD27485.1, AEW50187.1, respectively, and also of Oryza sativa indica with accession number AAD27889. Further, to understand the phylogenetic relationship between these pulses, the amino acid sequence was aligned with each other using clustal omega, and to assess the amino acid composition and physiochemical properties, protparam was employed. That is present on Expasy. GOR (short for Garnier–Osguthorpe–Robson) was used to forecast locations of alpha helix as well as beta-strand from amino acid sequence. Further, comparison and analysis of the secondary structure of these amino acid sequences were done.

4 Result and Discussion The sequences of lectin protein were retrieved from NCBI. The sequence length was found to be 275 amino acids in Cajanus cajan, 206 amino acids in Vigna mungo, 210 amino acids in Lathyru ssativus, and 268 amino acids in Vigna aconitifolia, respectively. These sequences were then analysed the quantity of essential amino acid present in the lectin protein using the protparam tool. The result showed that lectin protein of Cajanus cajan contains 51.7% of essential amino acids, Vigna mungo contains 47.1% of essential amino acids, Lathyrus sativus contain 50.1%, and Vigna

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Fig. 1 Amino acid composition of lectin protein of Cajanus cajan, Vigna mungo, Lathyrus sativus, and Vigna aconitifolia with lectin protein of Oryza sativa

aconitifolia contains 53.8% essential amino acids, respectively. It was clear from the above result that Vigna aconitifolia and Cajanus cajan were found to contain the maximum percentage of essential amino acids. These pulses also showed more percentage of amino acids when compared with lectin protein of Oryza sativa with 19.5% of essential amino acids (Fig. 1). Out of the total essential amino acids, a comparative study was done between lysine, methionine, threonine, and tryptophan which are known to be the most limiting factor in the world’s diet (Fig. 2). The result showed that all the pulses were found to have all the essential amino acids, but methionine is the only amino acid which were found to be absent in vigna mungo and lathyrus sativus, whereas maximum amount of methionine was present in Oryza sativa. Glutamic acid amount was also checked in these pulses, and it was found that Vigna mungo and Cajanus cajan contain more amount of glutamic acid, i.e., 5.8% and 4.4%, respectively, followed by Vigna aconitifolia (4.1%), Oryza sativa (3.5%), and least amount was found to be present in Lathyrus sativus (3.3%) (Fig. 3). To understand the phylogenetic relationship of the selected pulses. A multiple sequence alignment was performed using clustal omega. The result showed that the lectin protein sequence of Lathyrus sativus and Vigna aconitifolia is more similar to each other followed by Cajanus cajan and Vigna mungo. Below is the phylogenetic tree shown for the same (Fig. 4 and 5). GOR along with secondary structure analysis was executed to perform comparison of secondary structural changes in these significant positions of amino acids (Fig. 6). It was observed that alpha helix contributes more towards the stability of Lathyrus sativus that is 9.56% and composition of extended strand and random coil in Lathyrus sativus are 36.65% and 53.78%, respectively. In Vigna aconitifolia alpha helix contributes 8.96%, extended strand (34.70%) and random coil (56.34%). In cajanus cajan and Vigna mungo alpha helix contribute 7.27% and 4.30%, extended strand

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Fig. 2 Comparative analysis of lysine, methionine, threonine, tryptophan amino acids in Cajanus cajan, Vigna aconitifolia, Vigna mungo, Lathyrus sativus, and Oryza sativa

Fig. 3 Amount of glutamic acid in Cajanus cajan, Vigna aconitifolia, Vigna mungo, Lathyrus sativus, and Oryza sativa

Fig. 4 Phylogenetic relationship between Cajanus cajan, Vigna mungo, Lathyrus sativus, and Vigna aconitifolia

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Fig. 5 Multiple sequence alignment of Vigna aconitifolia, Lathyrus sativus, Cajanus cajan, and Vigna mungo using clustal omega

36.00% and 35.44% and random coil composition contribute towards secondary structure is 56.73% and 60.19%, respectively. Vigna aconitifolia and Oryza sativa offer a decent amount of essential amino acids than other pulse species. So, they were analysed for the secondary structure resemblance using GOR (Fig. 7). Results showed that there are some significant changes at certain positions of amino acids. Some portion of amino acids is conserved. In Oryza sativa alpha helix composition is 3.00%, extended strand (18.50%) and random coil (78.50%). A comparative in silico analysis of Vigna aconitifolia, Vigna mungo, Cajanus cajan, and Lathyrus sativus showed that Vigna aconitifolia and Cajanus cajan provides sufficient amount of essential amino acids than Vigna mungo and lathyrus sativus. These pulses were then compared with Oryza sativa and it was found that Oryza sativa can be used with these pulses to increase essential amino acids in human diet and these results corroborated with the findings of [6] (Figs. 1 and 2). A similarity search using clustal omega showed that Vigna aconitifolia and Lathyrus sativus are more like each other (Fig. 4), but still Vigna aconitifolia provides more essential amino acids than Lathyrus sativus. The protein content in one plant does not provide enough certain amino acids, which may be present in another plant and when taken in combination they can complete the essential amino acids requirement of the human body [19]. Our analysis also showed that Vigna acontifolia provides sufficient amounts of alanine (6.3%) and methionine (1.1%) and also provides a low

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Fig. 6 GOR result of lectin protein of Vigna aconitifolia, Lathyrus sativus, Cajanus cajan, and Vigna mungo

amount of glutamic acids. This combination of amino acids can help to abate kwashiorkor because according to previous study high levels of glutamic acid and low levels of alanine were the hallmark of kwashiorkor [20]. Thus, combining different pulses altogether in the diet with other sources of food such as rice, vegetables, and chapatis (source of carbohydrate) can provide enough amino acid and other nutrients to fulfil daily requirements.

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Fig. 7 GOR result of lectin protein of Vigna aconitifolia and Oryza sativa

5 Conclusion Lectin protein of Vigna mungo and Lathyrus sativus lacks methionine. On the other hand, Vigna aconitifolia, Cajanus cajan, Oryza sativa supplies sufficient methionine with other essential amino acids. Oryza sativa can be included with these pulses to increase essential amino acids content in the human diet. Thus, combining these foods in our diet can relieve us from protein deficiency to abate dietary diseases. Also, as informed by [11] that massive demand in animal-based protein food will have a negative impact; hence, pulses can be a very affordable choice to complete protein intake in diet as compared to other sources of protein. Some people think that lectins are to blame for a variety of issues, including joint pain, inflammation, and other GI issues. Other people claim that lectins are both safe and healthy for us, providing fibre and other nutrients. Also, antioxidant properties of lectins help in defending cells from damage brought on by free radicals. They even take longer to digest and absorb carbohydrates due to their unique carbohydrate binding property, which may lessen the likelihood of sudden spikes in blood sugar and high insulin levels. Other than legumes, whole grains, and nuts are lectin-rich foods that have been linked to lower incidence of type 2 diabetes, heart disease, and weight loss in numerous large population studies [21]. These foods are abundant in healthful fats, protein, fibre, minerals, and B vitamins. Because of this, eating these foods has much greater health benefits than any potential risks from their lectin content. Also, to reduce the side effects of lectin soak the lectin-rich legumes before cooking.

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This study confirmed the content of essential amino acids in different pulses which can help in daily requirement of protein intake with other benefits of lectin protein.

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A Survey on Human Behavioral Cybersecurity Risk During and Post Pandemic World Hanna Paulose and Ashwani Sethi

Abstract COVID-19 pandemic also gave rise to cyber pandemic when the incidence of cybercrimes increased manifold all over the world. As the existing health infrastructure crumbled in the face of the severity of the COVID-19, so did the prevalent technical architecture, procedures, policies and practices of cybersecurity at various enterprises in the face of vigorous cyber-attacks. The strict lockdown(s) and quarantine protocols enforced during the pandemic affected the mental condition and human behavior adversely. Loss of lives and livelihoods further aggravated the mental condition causing so many people to become angry, anxious and paranoid. At the same time, the need for remote working increased the dependence on technology networks which proliferated immensely without due regard to cybersecurity architecture. Rapid pace of adoption of emerging technologies necessitated by lockdowns and the enhanced metal stress due to pandemic considerably increased the attack surface for social engineering-based attacks. In this paper we have carried out a systematic literature survey of the threat landscape both, during and post pandemic world in view of human behavioral changes. We have then recommended a mitigation framework incorporating necessary regulations at national level, policies and security culture at organizational level, procedures at operational level and training at individual levels to mitigate the risks associated with the changed threat landscape. Keywords COVID-19 · Mitigation framework · Cybersecurity · Ransomware attacks · Crypto-currencies · Metaverse · Vulnerabilities · Information communication technologies · Machine learning

H. Paulose (B) Guru Gobind Singh College of Engineering and Technology, Gurukashi University, Bhatinda, India e-mail: [email protected] A. Sethi Gurukashi University, Bhatinda, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_35

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1 Introduction The digital technology has proliferated all walks of human life reshaping day-to-day human behavior and interaction [1]. It has also become an essential pre-requisite for growth of nations. Cyber space transcending the international borders has emerged as a strategic space for nations to dominate and for malicious elements to commit fraud on people. Outbreak of COVID-19 pandemic has accelerated the dependence on and development of the digital networks due to the need for remote working from home, day-to-day digital transactions in finance, business supply chains, online education, entertainment and telemedicine [2]. The International Telecommunication Union has reported that as many as 4.9 billion people or 63high-profile cyber-attacks like ransomware attacks also increased manifold. Emergence of crypto-currencies aided and abetted such malicious attacks as the criminals took the ransom in such currencies. With the advent of Metaverse, there is an increased tendency of the youth to live in the virtual world. Inherently, the cyber-criminals have been exploiting the vulnerabilities of the human beings and the information and communication technology (ICT) systems to commit the cybercrime. The cyber-attacks are capable of paralyzing critical services and infrastructures. Threats to supply chains and ransomware attacks have increased manifold which need to be urgently addressed by the global business leaders and governments [4].

1.1 Motivation The disruption in economic and social lives of people during COVID-19 pandemic has produced significant changes in the mental outlook and behavior of human beings. They have become more defensive and paranoid which makes them highly vulnerable to social engineering-based attack vectors. On an another plain, the emerging ubiquitous technologies are reshaping the human interaction. The malicious actors world over are exploiting the general lack of familiarity and awareness about the use and consequences of emerging technologies.

1.2 Our Research The existing cybersecurity strategies are primarily defensive and relate to ICT equipment, networks and procedures. There is insufficient attention given to the risks associated with human behavior in their approach. These are not enough to combat the increased frequency of cyber-attacks. Machine learning and advanced automation are constantly providing new tools to the threat actors. This paper presents an in-depth examination of the shifting threat landscape and the developing hazards in the aftermath of the COVID-19 pandemic. We have then

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outlined the approaches for developing substantive solutions for ensuring cybersecurity to mitigate risks due to human behavioral element.

2 COVID-19 Impact on Mental Health World Health Organization (WHO) declared COVID-19 outbreak a public health emergency of international concern (PHEIC) on 30 January 2020. Coronaviruses, also known as CoV, are a diverse group of viruses that can cause a range of illnesses from mild conditions such as the common cold to more severe diseases. First case of COVID-19 was detected in India in January 2020, and the number of cases peaked in 2021. As of January 2022, the total number of COVID-19 cases detected in India exceeded 40 million since the first case. The consequences of COVID-19 in India have been tremendously disruptive, causing significant economic setbacks and loss of human life [5].

2.1 Governments’ Response As the nations and the healthcare infrastructure were ill-prepared all over the world to meet the onslaught of pandemic, the respective governments resorted to strict lockdowns and remote working from home. In 2020, India implemented a strict lockdown in response to the COVID-19 pandemic, leading to a significant migration of millions of workers from cities to their home villages [6]. The stringency index of India’s March 25 lockdown was 100, which indicates harshness of the lockdown imposed [7]. Need to quarantine the patient also led to acute isolation.

2.2 Adverse Impact of lockdowns on Mental Health COVID-19 pandemic and the various hurried governments’ policies to combat it have left mental, psychological and socioeconomic scars on millions of people across the world. Many people have experienced negative effects on their mental health due to COVID-19, including suffering from the illness itself or witnessing the suffering of loved ones. The lack of healthcare facilities, oxygen and inadequate pandemic management by the government has also contributed to these adverse effects. Also, indiscriminate imposition of lockdown(s) by the state authorities and the necessity of keeping a safe distance increased the isolation of the people further. Public have grown more anxious, depressed and helpless. This has developed a defensive attitude which impairs judgment and action. The common mental disorders which could make the people vulnerable, confused and paralyzed when confronted with a cyber threat are [8]:

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Anxiety Stress Depression Suicidal thinking Self-harming behavior Psychoses.

Even the present state of the mind of the Russian President Putin leading to his invasion of Ukraine could be attributed to his isolation during COVID-19 [9].

3 Digital Infrastructure During and Post Pandemic Information and communication technologies (ICTs), along with the internet, have played a crucial role in ensuring the continuity of business operations, employment, education, access to basic services for citizens, entertainment and social interactions. Organizations have made use of cloud services to rapidly upgrade their IT infrastructure. Digital platforms and services have enabled countless innovations that helped mitigate the health, social and economic costs of the tragedy and build resilience against future crises [10]. Table 1 displays the breakdown of online activity for a 60s time span in 2021, based on data collected by Lori Lewis and published on AllAccess. Table 1 Estimate of data created in internet [11] Application S. no. (a) (b) (c) (d) (e) (f) (g) (h) (i)

Instagram WhatsApp and Messenger LinkedIn TikTok YouTube Emails Tinder Money transactions Netflix subscribers

Amount of data 6,95,000 stories 69 m messages 9132 connections 5000 downloads 500 hours of content upload 197.6 m 2 m swipes US $1.6 m 28,000 subscribers watching

3.1 Lack of Digital Skills, Infrastructure and Poor Literacy One factor that has a detrimental impact is the lack of digital skills, which hinders a significant number of individuals from accessing the internet entirely, while also

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undermining the effectiveness of devices and services for others. Insufficient digital literacy can also leave individuals vulnerable to the negative aspects of connectivity, including cyber-attacks, fraudulent activities, misinformation and harmful content. New and emerging technologies are expanding development opportunities. Yet, their ever-evolving properties and characteristics also expand the attack surface, creating new vectors and vulnerabilities that can be exploited for malicious ICT activity [12]. The year 2021 has highlighted the serious digital vulnerabilities of our ICT systems. The following have influenced the cybersecurity: 1. Automation and machine learning 2. Remote/hybrid working.

3.2 Impact on Cybersecurity COVID-19 radically changed societies and businesses [13]. The COVID-19 pandemic created a series of exceptional circumstances related to cybercrime [14]. Lockdown aggravated the cyber threat in the domain of networking of IT systems. On one hand, it forced people and companies to connect on virtual private networks while working from home [15], while on the other it increased their dependence on online transactions for their daily needs manifold. With more time at their disposal, many started indulging into various online activities. Advent of “Metaverse” offered limitless opportunities for excitement. This has opened up a vast field of operations for the cyber-criminals. From operating as a lone wolf, the cyber-criminals have evolved into formidable organized international gangs. The World Health Organization has observed a significant rise in the number of cyber-attacks aimed at its employees since the beginning of the COVID-19 outbreak [16]. By sending fraudulent email and WhatsApp messages they attempt to trick one into clicking on malicious links or opening attachments [17]. The recent spate of “ransomware” attacks indicate that it has emerged as a major form of cyber-attack. This trend has imposed major cybersecurity costs on people, companies and nations. In addition to the cyber-criminals, the countries all over the world have embarked on cyber warfare as the major part of their war strategy. Russia has embarked on cyber warfare against Ukraine to destabilize that nation. China is relentlessly increasing its cyber warfare capabilities [18]. The data security has also become essential for national safety [19].

4 Threat Landscape During and Post Pandemic Cyber-attacks are increasing in scope, scale, severity and sophistication. While ICT threats manifest themselves differently across regions, their effects are also be global. Poor cybersecurity makes too many of us excellent targets. Several countries are enhancing their ICT capabilities for military objectives, indicating a growing possi-

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bility of utilizing ICTs in prospective state-to-state conflicts. Malicious ICT activity by the state poses a significant risk to international security and stability, economic and social development, as well as the safety and well-being of individuals [20]. The cyber-attacks that the organizations are most concerned about are: 1. 2. 3. 4. 5. 6. 7.

Ransomware Social engineering Online scams and phishing Malicious insider activity Denial of services and infrastructure breakdown New malware attacks through existing botnets Data harvesting malware attacks.

The cost of breaches to an organization is also very high, amounting to an average of US $ 3.6 million per incident [21]. Although most companies recover post-breach, some have to shut down their business operations. Even those that recover from an incident pay a very high price both financial and in terms of reputation. Furthermore, the companies need 280 days on average to identify and respond to a cyber-attack [22]. In the recent case of Emotet malware attack even though the attack was disrupted in January 2021, it again appeared in November 2021 through existing botnets. Therefore, it can be said that Emotet-like malware will again reappear in 2022. The techniques and capabilities of attackers are rapidly developing to launch multistage ransomware attacks. At the same time new security vulnerabilities are appearing in the most popular software tools and systems [23].

4.1 State or Non-state Actors The hackers may be state or non-state actors. They exploit the common human vulnerabilities of greed and lust through social engineering and commit crimes. Criminal organizations are known to recruit individuals to carry out phishing, social engineering, SIM swapping and malware attacks with the goal of gaining control over bank accounts [24]. Typical prices for services such as social media account hacking average US$230, while website hacking ranges from US$394 to US$230 [25]. Many of them are operating from Russia [26].

4.2 Ransomware Attacks Ransomware attack represents one of the most potent and growing international cyber threats today [27]. Most of the ransomware group operate a Ransomware as Service(RaaS) model [28]. RaaS makes it easy for even less-skilled cyber-criminals to operate. It also makes attribution difficult. In ransomware attacks the hackers lock files or data of people in their computers or even lock them out of the networks.

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They even threaten to publish their data on internet. They thus hold them to ransom demanding a huge amount of money. One of the biggest ransomware attacks took place in the US in May 2021 on the Colonial Pipeline System [29]. More than twothirds of South Caroline’s gas stations went out of fuel. In the first half of 2021, there was a substantial surge in ransomware attacks, with the worldwide volume of attacks rising by 151%. The US Federal Bureau of Investigation (FB) has warned that there are now 100 different strains of ransomware in circulation globally. There were on average, 2700 attacks per organization in 2021 a 31% increase over 2020.

4.3 Crypto-Currencies Hackers demand and collect huge ransom payments in unregulated crypto-currencies which they then cash out of the crypto asset ecosystem. Bitcoins account for 98% of ransomware payment [30]. Bitcoin is easy to acquire and use. A crypto-currency transaction consists of a payer sending funds to payee, with both parties identified only by an account number or address. When buying or transferring bitcoin, individuals use a bitcoin wallet or a bitcoin ATM. While bitcoin operates on a public block chain that allows anyone to see all bitcoin transactions [31], there is no direct way to determine the account owner. These need to be regulated.

5 Mitigation Framework To effectively address cybersecurity threats, it is recommended that organizations and nations focus on cyber resilience. This means that instead of solely relying on prevention strategies, there must be a focus on building systems that can withstand and quickly recover from attacks. Cybersecurity must be a shared responsibility between business and cybersecurity executives, and everyone must be trained to use strong authentication protocols to protect sensitive data. As technology continues to evolve, cybersecurity measures must also evolve to keep up with the emerging threats. The digital world’s future will be influenced by a range of technologies, including artificial intelligence, robotics, quantum computing, the Internet of Things, cloud computing and blockchain [32]. While automation and machine learning have the potential to transform cybersecurity, their potential risks and vulnerabilities must also be considered. It is essential to allocate sufficient budgets for secure infrastructure and manpower training. Cyber threats transcend international boundaries, and hence, there is a need to develop a global ecosystem of cybersecurity, which would require cooperation and collaboration among nations, organizations and individuals. Overall, effective cybersecurity approaches should focus on building resilient systems, incorporating strong authentication protocols and considering the potential risks and vulnerabilities of emerging technologies [33, 34].

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In the near future, the field of cybersecurity is expected to be significantly impacted by the increasing utilization of automation and machine learning. However, while these new technologies offer many advantages, they also come with new risks and vulnerabilities that need to be carefully considered by all leaders before adopting and implementing them. This includes the need to provide sufficient budget for securing infrastructure and training personnel. Moreover, the rising frequency and severity of cyber-attacks have eroded the trust that organizations have in one another, highlighting the need for a cyber resilience posture to be adopted. As a result, there is a growing recognition of the need for a global ecosystem of cybersecurity that brings together various stakeholders, including governments, businesses and academia, to address this problem collectively [35]. Therefore, there is a need for developing an international ecosystem of cybersecurity. By establishing such an ecosystem, it is hoped that cybersecurity risks can be better managed and mitigated, and that global efforts can be coordinated more effectively to address the threat posed by cyber-attacks that transcend international boundaries. The various elements of this ecosystem are given in Table 2 [36].

5.1 Cybersecurity Approaches 5.1.1

Stringent Policies and Strategies

The issue of ransomware attacks is complex and requires multifaceted solutions. One approach is the implementation of stringent policies and strategies. This involves better securing networks, more regulation of crypto-currencies to prevent criminals from hiding their transactions, and strict law enforcement to identify, charge and arrest offenders who transcend international boundaries. To confront the threat of ransomware attacks, the development of “Resilient Cyber Security Policies” [2] is required, which is a transnational collective endeavor involving public and private sector investments. In this regard, the G-7 Interior and Security Ministers formed an Extraordinary Senior Officials Forum on Ransomware in December 2021. Numerous organizations, including but not limited to the European Commission, Europol, Interpol and the United Nations Office on Drugs and Crime, have joined forces to create a comprehensive cybersecurity initiative. This collaboration, known as the Global Forum on Cyber Expertise, involves various entities such as the Council of Europe, European Union Agency for Cybersecurity, Financial Action Task Force and G-7 Cyber Expert Group. The aim of this Counter Ransom Initiative is to urgently address the threat from criminal ransomware networks [38]. The Counter Ransomware Initiative includes the following [39]: • Find practical policy solutions like employee training, offline back-ups, cyber insurance to limit financial liability and seek out platform based cybersecurity solution that stops known ransomware threats across all attack vectors. • Develop proposals on technical assistance.

- Management of assests during rest, removal, transferand deposition - Protection against data leak - Adequate capacity to ensure availability Configuration management and back-ups conducted, maintained and tested as per scheduled timelines - Local and remote maintenance and repair of organizational assets are performed, logged to avoid any unauthorized access

Confidentiality, integrity and availability of - Policy on employment of Integrity information is protected based on the risk checking for hardware and software strategy integrity - Air-gap in the development, testing and production platforms

Policy, process and procedure for cybersecurity to manage scope, roles and coordination of organizational entities along with maintenance and repair methodology

The threat detection, response and recovery Response mechanism, reputation activities are coordinated with internal and management and disaster recovery plans are external parties, viz. coordinating centers, deliberated, formalized and implemented ISPs, attacking systems, victims and customers

(c)

(d)

(e)

- System development life cycle is formulated for the organization along with maintenance and repair methodology - Incident Response and Business Continuity and Incident Recovery and Disaster Recovery strategies are formulated - Vulnerability Management Plan is formmalized

- Policy on network segregation and network segmentation - Principles of least privilege and separation of duties are ensured - Authentication policies, viz., single factor, multifactor are listed which commensurate with the risk of the transaction

Assets access is limited to authorized users, processes and devices and is managed consistent with the assessed risk of unauthorized access to authorized activities and transactions

(b)

- Activities are monitored, and malicious code are detected - Incidents are contained - Detection plans are continuously updated and communicated

- The identities and credentials are issued, managed, verified, revoked and periodically audited for authorized devices, users and processes

- Physical IT and communication devices and systems which are to be connected to the network, software, programs, external applications and human resources within the organization are required to be studied and inventoried in detail

- Identifying the critical infrastructure and prioritizing based on classification, criticality and business value - Listing of cybersecurity roles and responsibilities of each element including workforce and third pary stake holders of the organization - Formulation of policy and procedure for the organizational cyber threat landscape

The security, objectives, policies and stability in the use of personnel and devices are increased by identifying information, human assets, devices, systems and facilities that enable the organization to achieve the goals are managed consistent with their relative importance to organizational objectives and the organization’s risk strategy

(a)

Operational measures

Measures at executive level

Function and category

S. no.

Table 2 Mitigation framework [37]

Training of all human resources with respect to response and recovery plans

-Training to manage and protect physical access to assets - Training on remote access management - Periodic training of all users are trained on the reponsibilities and roles

- Training to be imparted to the organizations element to correct identification and documentation of the resources - Cybersecurity roles and responsibilities of each entity to be clearly trained

Training aspects

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Advance policy cooperation. Raise public awareness. Global Partnership. International cyber policing and investigative network. Enhance the ability to swiftly recover from a cyber incident. Prepare for swift and timely incident response and recovery when an attack occurs.

5.1.2

Zero Trust Security Model

The Zero Trust security model [40] is a strategy that has become increasingly popular in addressing the challenges posed by mobility, IT usage and cloud platforms. The guiding principle for Zero Trust, as defined by John Kindervag, is to "never trust, always verify". The strategy is founded on the recognition that if a hacker manages to breach an authentication point, such as a login or a firewall, they can misuse the trust within the system to maneuver around and focus on sensitive data. Additionally, an insider who starts within a trusted zone can escalate privileges. By always verifying, we can identify and stop these types of frequent attacks.

5.2 Solutions to Ransomware Attacks The tangible ways to reduce the end user getting affected by the cyber-attack are as follows [41]: (1) (2) (3) (4) (5)

Employee cyber training Offline back-ups Cyber insurance Developing a layered security model Cyber resilience.

5.3 Human Behavioral aspects and Cybersecurity Effective cybersecurity in any ecosystem requires managing and securing the fundamentals of Information Security, namely confidentiality, integrity and availability. The active role of cybersecurity is to protect individual data, information communication and technology (ICT) infrastructure and devices, while the role of ICT is to ensure quality and efficacy of services, confidentiality, usability and data protection. Employee behavior plays a critical role in information security. The KnowledgeAttitude-Behavior (KAB) model investigates employee knowledge of policy and procedures, attitudes toward policy and procedures and self-reported behaviors. A framework with controls that could potentially influence or impact employees’ riskrelated behaviors is listed in Table 3.

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Table 3 Human behavioral mapping with IS controls Human cognition and Scenario emotions

Technical controls

Physical controls

Greed

Luring user

Authentication, authorization [33]

Segregation, four eye Reward, punishments [42] and legal implications

Honey trap

Luring user/blackmail

Honeynet, network profiling

Quarterly auditing, access control

Need to know

Trust

Connect through interests and background

Authentication

Profile checking

Case studies

Curiosity

Loaded flash drive, scripted topics

Access control

Segregation of networks and data

Periodic training

Leadership/ motivational influence

5.4 Cyber Resilience Cyber resilience refers to an organization’s capacity to predict, endure, recover from and adjust to various stresses, failures, hazards and threats that affect its cyber resources within the organization and beyond. The ultimate goal of cyber resilience is to allow the organization to continue pursuing its mission, maintaining its desired way of operations and promoting its culture with confidence [43, 44]. In other words, cyber resilience means the ability to both enable and protect an organization’s critical ICT infrastructure while absorbing the disruptions and shocks from severe cyber-attacks. To achieve cyber resilience, cyber and business leaders must incorporate the latest technological advancements and human behavioral vulnerability aspects and develop visibility into both their own extended networks and third-party supply chains. They must also keep offline records of critical business data and deploy hot-standby ICT infrastructure to minimize downtime in the event of a cyber-attack. Cybersecurity refers to the measures taken to protect an organization’s network and information system from malicious agents seeking to gain access. On the other hand, cyber resilience refers to an organization’s ability to recover from a cyberattack.

5.5 National Cyber Security Strategy The need for a strong and effective National Cyber Security Strategy cannot be overemphasized in today’s world. With the increasing number of cyber-attacks and threats from both state and non-state actors, it is imperative for India to have a comprehensive and action-oriented plan to ensure the security of its critical infrastructure such as power, water, transportation and communication systems.

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As per reports, approximately 4 million instances of malware are identified on a daily basis, and India is considered among the most targeted countries by cyberattacks worldwide, making cybersecurity a critical concern [45]. With 1.15 billion phones and over 700 million internet users, the need for a centralized apex governance organization that will be responsible for cybersecurity cannot be overemphasized [46]. Despite the challenges, India has made significant progress in cybersecurity and currently ranks among the top 10 countries in the Global Cybersecurity Index. However, reports indicate that by 2025, there will be a massive gap of about 1.5 million job vacancies in the cybersecurity sector, which may pose a significant challenge to the nation’s cybersecurity efforts. Therefore, there is a need for a concerted effort from the government and other stakeholders to address this gap and ensure that India has the necessary skilled workforce to secure its cyberspace.

5.6 Joint Cyber Partnerships During her visit on 31 March 2022, Liz Truss, the former British Foreign Secretary, signed a strategic partnership agreement with India aimed at enhancing security cooperation in the area of cybersecurity. In a similar move, India and Australia have established a Joint Working Group on cyber and cyber-enabled critical technology, including 5G, to implement a five-year plan of action from 2020 to 2025, aimed at cooperation in the spheres of digital economy and critical information infrastructure [47].

5.7 Nationalizing or Privatizing Cybersecurity—A Policy Choice The case study of the ransomware attack on the Colonial Pipeline highlights the need for evolving cybersecurity policies. While the company was able to contain the attack, it had to stop all operations and pay a ransom of $4.4 million. It was the company and not the hackers who shut down the pipeline because it could not run its services profitably due to the damage done to its business processes [48]. It is a common business practice for many companies to have an online presence, but unfortunately, many of them take only basic cybersecurity measures and are vulnerable to cyber-attacks. Dealing with such issues falls under the purview of the company’s board of directors, given the possibility of a ransomware attack leading to insolvency. Given that commercial organizations play a crucial role in the country’s critical infrastructure such as water, electricity, fuel supply, communications, logistics, food supply, health care, schools and other essential services, it should be a criminal

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offense for them to maintain lax cybersecurity. While it is up to these organizations to manage commercial risks, national security is the responsibility of the government and not private firms. The digital threat is too significant to be left to individual firms that may not take it seriously [49]. Governments worldwide, including the United Nations, need to develop policies to combat cyber threats, as it cannot be left solely to businesses.

6 Conclusion and Future Work In this paper we carried out detailed research and survey on the impact of behavioral changes due to COVID-19 pandemic on the cybersecurity with a view to recommend the approaches for developing a cybersecurity mitigation framework for dealing with it effectively. The framework proposed in the paper could significantly reduce the risks associated with human behavior that affect the cybersecurity of organizations and nations. Acknowledgements We express our gratitude to Guru Gobind Singh College of Engineering and Technology, Gurukashi University, Bhatinda, India, for supporting the research work and providing the necessary infrastructure and resources for the development of the framework.

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11. Lori L (2021) A minute on the internet in 2021@ONLINE. [Online]. Available: https://www. allaccess.com/merge/archive/32972/infographic-what-happens-in-an-internet-minute 12. Rizvi S, Orr R, Cox A, Ashokkumar P, Rizvi MR (2020) Identifying the attack surface for IoT network. Internet of Things 9:100162 13. Donthu N, Gustafsson A (2020) Effects of Covid-19 on business and research, pp 284–289 14. Lallie HS, Shepherd LA, Nurse JR, Erola A, Epiphaniou G, Maple C, Bellekens X (2021) Cyber security in the age of Covid-19: a timeline and analysis of cyber-crime and cyber-attacks during the pandemic. Comp Secur 105:102248 15. Ahmad T (2020) Corona virus (covid-19) pandemic and work from home: challenges of cybercrimes and cybersecurity. Available at SSRN 3568830 16. Khan NA, Brohi SN, Zaman N (2020) Ten deadly cyber security threats amid Covid-19 pandemic 17. Alkhalil Z, Hewage C, Nawaf L, Khan I (2021) Phishing attacks: a recent comprehensive study and a new anatomy. Front Comp Sci 3:563060 18. Deni JR (2022) European strategies in post-pandemic peer competition: implications for America. Defence Stud 22(4):644–665 19. Alık N (2022) Emerging cyber security threats: India’s concerns and options. Int J Polit Secur 4(1):170–200 20. Chattopadhyay P, Wang L, Tan Y-P (2018) Scenario-based insider threat detection from cyber activities. IEEE Trans Comput Soc Syst 5(3):660–675 21. Bouveret A (2012) Cyber risk for the financial sector: a framework for quantitative assessment. International Monetary Fund 22. Kamiya S, Kang J-K, Kim J, Milidonis A, Stulz RM (2021) Risk management, firm reputation, and the impact of successful cyberattacks on target firms. J Fin Econ 139(3):719–749 23. Chen H, Pendleton M, Njilla L, Xu S (2020) A survey on ethereum systems security: vulnerabilities, attacks, and defenses. ACM Comput Surv (CSUR) 53(3):1–43 24. Böhme R, Clayton R, Collier B (2021) Silicon den: cybercrime is entrepreneurship. In: Workshop on the economics of information security (WEIS) 25. Anderson R, Barton C, Bölme R, Clayton R, Ganán C, Grasso T, Levi M, Moore T, Vasek M (2019) Measuring the changing cost of cybercrime 26. Naseir MAB (2011) National cybersecurity capacity building framework for counties in a transitional phase. Ph.D. dissertation, Bournemouth University 27. Kaloudi N, Li J (2020) The AI-based cyber threat landscape: a survey. ACM Comput Surv (CSUR) 53(1):1–34 28. Alhawi OM, Baldwin J, Dehghantanha A (2018) Leveraging machine learning techniques for windows ransomware network traffic detection. In: Cyber threat intelligence. Springer, pp 93–106 29. Hobbs A (2021) The colonial pipeline hack: exposing vulnerabilities in us cybersecurity. In: SAGE business cases. SAGE Publications, SAGE Business Cases Originals 30. Hassan NA (2019) Ransomware revealed: a beginner’s guide to protecting and recovering from ransomware attacks. A press 31. Lee JY (2019) A decentralized token economy: how blockchain and cryptocurrency can revolutionize business. Business Horizons 62(6):773–784 32. Zhuang P, Zamir T, Liang H (2020) Blockchain for cybersecurity in smart grid: a comprehensive survey. IEEE Trans Indust Inform 17(1):3–19 33. Singh J, Chaudhary NK (2022) Oauth 2.0: architectural design augmentation for mitigation of common security vulnerabilities. J Inform Secur Appl 65:103091 34. Satapathy U, Mohanta BK, Jena D, Sobhanayak S (2018) IEEE 13th international conference on industrial and information systems (ICIIS). IEEE 2018:303–308 35. Ruhl C, Hollis D, Hoffman W, Maurer T (2020) Cyberspace and geopolitics: assessing global cybersecurity norm processes at a crossroads. Carnegie Endowment for International Peace 36. Lezzi M, Lazoi M, Corallo A (2018) Cybersecurity for industry 4.0 in the current literature: a reference framework. Comp Indust 103:97–110

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Extraction of Patterns for Cervical and Breast Cancer Protein Primary Sequences Charan Abburi, K. S. Vijaya Lakshmi, Chimata Meghana, and K. Suvarna Vani

Abstract Breast cancer and cervical cancer, the most common forms of cancer in women worldwide, are on a fast and steady rise, accounting for more deaths in women than any other cancer in the developing world. Previously, the cervical and breast cancer’s were diagnosed using the interior images of the human body. They classify the images based on deep learning and then detect the cancer in women. Our approach is, first we need to know the protein ids that includes in the cervical and breast cancer. A PDB id generally be four characters long, and begin with a numeral. Then we will extract the c-alpha atoms information that includes the x, y, z coordinates of atoms in 3-dimensional space. These coordinates are used to generate the distance map and sparse matrix. A protein contact map which represents the distance between all possible amino acid residue pairs of a 3-dimensional protein structure using a binary 2-dimensional matrix is known as sparse matrix. Using the sparse matrix we will generate the contact map based on threshold value. Later we need to extract the significant patterns from the protein contact map. Finally, our main aim is to extract the patterns from the cervical and breast cancer. Keywords Breast cancer · Cervical cancer · PDB · Contact map · Sparse matrix

C. Abburi (B) · K. S. V. Lakshmi · C. Meghana · K. S. Vani Department of CSE, Siddhartha Engineering College, Vijayawada, VR, India e-mail: [email protected] K. S. V. Lakshmi e-mail: [email protected] C. Meghana e-mail: [email protected] K. S. Vani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_36

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1 Introduction Bioinformatics mainly consists of biological studies that use computer programming as part of their methodology. It is an emerging field undergoing rapid growth in the past few decades. Computers became essential in molecular biology when protein sequences became available after determining the sequence of insulin. The bioinformatics plays a major role in understanding the biological processes of human body. Bioinformatics has been applied to protein research and endeavoured great contributions in sequence, structure analysis of proteins. Proteins are of large biomolecules, or macro molecules, they are formed by combining two or more amino acid residues [1]. Proteins differ from each other mainly in their sequence of amino acids, which is dictated by the nucleotide sequence of genes, and which results in forming protein folding into a specific 3-dimensional structure which determine its activity. The side chains of standard amino acids, mentioned in the list of standard amino acids, have a great variety of chemical structures and properties. In protein chain the individual amino acids are called as residue and linked chain which consists of carbon, nitrogen, and oxygen atoms is the protein backbone or main chain. As the atoms were located in the 3-dimensional space. We extract the location of the atoms and find the distance between these atoms in 3-dimesnional space. We can also generate the contact map using the 3d structures and extract the information from them [1, 2]. This information will be helpful in further analysis regarding these cancers.

1.1 Protein Structure A protein primary structure is obtained by sequence of amino acids in a peptide or protein. The sequence of amino acids in a protein is called its primary structure. There are four different levels in protein structure. They are primary, secondary, tertiary, and quaternary structure. A peptide bond is formed with the loss of water molecule from the amino acids. The amino acids and peptide bond looks like (Fig. 1).

1.2 Primary Sequence A protein primary structure is nothing but sequence of amino acids in a peptide bond or protein. The sequence of amino acids in a protein is called its primary structure. Peptide bonds that are made during the protein biosynthesis are more strong and process hold the primary structure together (Fig. 2).

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Fig. 1 Amino acids and peptide bond

Fig. 2 Represents primary sequence

1.3 Secondary Structure The secondary structure consists of helices and beta sheets that are formed from the primary structure with the oxygen and hydrogen bond (Fig. 3). Fig. 3 Represents secondary structures

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Fig. 4 Represents secondary sequence

1.4 Secondary Sequence The secondary sequence is generated from the primary sequence. The generation is nothing but the getting the position of the helices and beta sheets from the given protein data bank file and replacing the primary sequence letters with helices (H) and beta sheets (T). The general form of the secondary sequence looks like (Fig. 4).

1.5 Sparse Matrix The sparse matrix is also commonly known as the distance matrix. As it finds the distance between the atoms. A matrix is generated by finding the distance from one carbon atom to all the c-alpha atoms present in protein data bank that is known as sparse matrix.

1.6 Threshold Value The threshold value acts as a backbone in generating the contact map from the sparse matrix. The set of threshold values, is comprised of different ranges of values where the behaviour of the protein will varies in some important way. For every threshold value we need to generate the contact map. Here the threshold value ranges between (5 and 13). The range of the threshold value is taken from the previously existing papers related to cervical and breast cancer.

1.7 Contact Map A protein contact map is nothing but the representation of 0’s and 1’s. Based on the distance matrix, i.e. sparse matrix generated and threshold value the contact map is generated. The contact map consists of zeros and ones and it will be same as a 2-dimensional matrix from which we can extract the required patterns for any of the cervical or breast cancer protein ids (Fig. 5). A. Dataset Collection The dataset is taken from the open source Research Collaboratory for Structural Bioinformatics PDB (RCSB PDB). The portal consists of Protein Data Bank

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Fig. 5 Contact map generated using tool

Table 1 Represents collected PDB ids

Cervical cancer

1npo, 2gk1, 3b5h, 3f81, 4bgh, 5cqh, 5j6r, 5y9f, 6igf

Breast cancer

1ljy, 1tft, 5tx3, 6c2t, 6cz2, 6l8u, 6rlc, 6ztf, 7s4a

(PDB) files which consists of all the information related to proteins. There will be different proteins that were present in both the cervical and breast cancer. So we consider the below represented protein ids for extracting the patterns of cervical and breast cancer related protein primary sequences. The protein ids we have considered are shown Table 1. B. Existing Solutions In recent years the study related to cervical and breast cancer has been increased. Mainly the study is done related to the protein ids and also extracting some information from them [1–3]. These studies include in generating the protein contact map and extracting the features from them. These studies mainly focuses on the tertiary structures of the atom, i.e. 3d structures [1]. In the process of generation of the contact map, the structures obtained parallel to the diagonal and anti-parallel to the diagonal are called beta sheets. Some of the structures may also present in the contact map other than helices and beta sheets. These are generally termed as off-diagonal contact maps [2], the patterns can also be extracted from these off-diagonal contact maps. The previous study also involves in generating the contact maps for all-alpha, all-beta atoms, extracting the significant features from them [3]. These studies related to cervical and breast cancer are helpful for us and our proposed approach will mainly focuses on the c-alpha atoms rather than all the available atoms.

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C. Proposed System Contributions • Our proposed system will first extract the coordinates of the c-alpha atoms in 3-dimesnional space. • Generate primary sequence by converting the 3 letter c-alpha atom code into single letter. • Next step is to generate the secondary sequence from the primary sequence by identifying the position of the helices and beta sheets for that particular protein. • Finally, our aim is to extract patterns of width 3, 4, 5 from the helices that are present in the contact map. The length of the pattern is fixed to 3.

2 Literature Survey Before entering into the study related to the previous protein ids. Let us recall the previously existing methods [4, 5] that were used in the detection of cervical and breast cancer. Mostly, deep learning techniques and interior images were frequently used in detection of the cancer. As we discussed the CT-images are used in the cancer detection [4]. They apply various algorithms and CNN techniques to study the image and diagnose whether a person is suffering with cancer or not. But every time this method may not give the accurate results. The images may vary from person to person and also machine to machine. To get the accurate results the image must be in the proper format to process it, for getting accurate results. The interior images gives the complete information of what is happening inside our body and how it differs from a person who is not suffering with that cancer. Different approaches were used [4, 5] on the images to improve the accuracy of the model. These techniques are goes on decreasing as these were treated as traditional methods, modern tools and techniques were developed for the diagnosis of cancer. These image diagnosis plays an important role in the diagnosis of the cancer. The study related to protein ids became a major step in the bio informatics. Every study related to this will be helpful in improving detection of the cancer. So, one of the approach aimed in generating the protein contact map using the protein primary sequences [1]. Generation of the contact map can be done by using 3d structures they are helices and the beta sheets. This approach directly extracts the fasta sequence from the pdb file. By using this in 3d structures we can directly generate the contact map by using some tool. Then after generation of the contact map various machine learning techniques were applied for extracting the features from it. The generation of contact map from the protein sequence will be more helpful without calculating the distance between the atoms. More accurate results in this study will be more helpful in further analysis regarding the proteins or contact maps. The study related to protein and contact maps gives utmost priority to the extraction of patterns from them. The contact maps gives the representation of all the atoms that are present in that particular protein. The structures that are parallel, anti-parallel are identified as helices and beta sheets. All the remaining atoms are represented in off-diagonal, we can also extract the patterns from them [2]. Every study related to

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protein focuses only on the diagonal, by extracting the patterns from the off-diagonal [2] will give more accurate results in determining the cancer or further analysis. This approach [2] uses triangle sub division method for the extraction of the patterns. Extraction of significant patterns indicates that the patterns have no fixed length and fixed width. If somehow it looks like a pattern then it will be extracted from it. This study [3] considers all-alpha, all-beta, alpha + beta, alpha/beta atoms were considered and extracted the position of the atoms in which they are present. From these coordinates generated the contact maps, extracted the significant patterns from them. This approach [3] also focuses on the off-diagonal interactions, extracting the features from them. This model gets more accuracy when compared to other approaches in detecting the significant patterns from contact maps. The keynote step in our proposed approach is finding the Euclidean distance between the atoms in 3-dimensional space. The study [6] proposes the concept of finding the Kinetic Euclidean Matrices, the main aim of this approach is finding the distance between the two points that are moving. This implies that the Euclidean distance gives the exact distance between the two points that can be used in determining the distance between the atoms. The approach [6] uses various noise removing techniques in removing the missed values and finding the accurate distance between the two points. The main aim of this is finding the distance with respect to the time. Based on the time how the points will vary from one position to the other. Pattern mining is the final step in the study related to the cervical and breast cancer protein ids. Various pattern mining techniques were discussed [7]. The pattern mining is not only useful in medical related, and it has many applications. One of the major application is market basket analysis. This mining techniques is used in that for finding the most frequent items that are sold together. This paper [7] surveyed various papers in mining and proposed different approaches that are being used. Theses pattern mining techniques will play a major role in extracting the patterns from the protein contact maps. Unlike deep learning, artificial intelligence also plays an important role in diagnosing the cervical and breast cancer at an early stage [8]. Previously the CT-image is used by deep learning in the detection of the cancer [4]. The proposed approach [8] uses the histopathological images in detecting the cancer at an early stage. This paper reviews various machine learning and deep learning techniques that are applied to histopathological images in detection of the cancer at an early stage. As in the detection of the cancer imaging plays an important role in determining it.

3 Related Work The study related to cervical and breast cancer protein structures, like how the protein look, how this will be helpful in further analysis of the cancer. Previous studies [1–3] have been considered regarding the protein, i.e. extracting the features, identifying different atoms from the pdb file. So, we thought to work only on the c-alpha atoms. Then we read the previous studies related to cervical and breast cancer. We collected

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some pdb id’s and started to work. Firstly, we extracted the positions in which calpha atoms are present. Then primary sequence, secondary sequence, contact map generation, and finally pattern extraction. Our idea is to confine the width of the pattern to only 3,4,5. The length of the pattern is fixed to 3. So, we prepared some of the patterns and count the patterns in the generated contact map. Firstly, width 3 patterns are just like they must contain three 1’s in every row. It should not contain any other 1’s by its side. As we know that the helices are parallel to the diagonal. Next, we also tried to extract the pattern of width 4. As the helices are parallel to the diagonal, the pattern also looks like goes on decreasing. Finally, we extracted the patterns with width 5. All these patterns that are extracted will be helpful in further analysis of the project. If the patterns that is to be extracted, should have a minimum length of 3. The length of the pattern can be of any length. But the minimum length it should maintain is 3. From the previous studies we come to know that helices are parallel to the diagonal. So, based on this work we started to implement the project and count the number of patterns in the contact map. We mainly focused only on the helices, which are thick bands that were parallel to the diagonal. The pdb files contain more information about the beta sheets also. But while generation of the secondary sequence we only represent the helices with ‘H’, beta sheets can also be represented as ‘B’. But, our main goal is to extract the patterns from helices only. Other than the helices all the remaining beta sheets and all-alpha, all-beta are represented by using ‘T’. The contact map generated consists of a header that gives information about the name of the pdb file, date at which the contact map is generated. So, based on this information we extract the patterns from the protein contact map.

4 Methodology D. Methodology The basic architecture for the project as seen in the diagram (Fig. 6).

Fig. 6 Represents architecture of the project

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The above diagram represents the overall architecture of the proposed system. Now let us see in detail about the each step in the system process. The architecture is mainly divided into 5 steps: 1. 2. 3. 4. 5. 1.

Extracting coordinates of c-alpha atoms. Generate primary and secondary sequence. Calculate the Euclidean distance. Generate contact map based on the threshold value. Extract the patterns from contact map. Extracting Coordinates of c-alpha Atoms Our region of interest is in extracting the patterns from the contact map that is generated from the c-alpha atoms. A pdb file consists of information related to each and every atom that is present in the protein structure [1–3]. Now in order to extract the coordinates, we need to first identify the c-alpha atoms in the pdb file. To identify them, the c-alpha atoms are denoted with the symbol ‘CA’. So, we first identify the location of the atoms in the pdb file and then that atom with symbol ‘CA’ is identified. From there we extract the x, y, z coordinates for every c-alpha atom that is present in that pdb file. These extracted coordinates will be helpful in generating the contact map. All the atoms must be properly extracted in order to identify the helices and beta sheets in the protein contact map. 2. Generate Primary and Secondary Sequence The primary sequence and secondary sequence can be generated from the calpha atoms. Firstly, to generate the primary sequence we need to convert the three letter code into a single letter character [9]. As each of the c-alpha atom is represented by using a three letter code. In order to get primary sequence we have to map those three letter character into a single letter character. There will be open source available for converting 3-letter code into single letter code. The sequence of all the single characters that are obtained from the three letter code represents the primary sequence of c-alpha atoms. As the individual amino acids will for the peptide bond with the loss of water molecule. These amino acids are attached side by side then, the beta sheets are formed. If the amino acids are attached in parallel manner, then they are called as helices. We have generated the primary sequence, next in order to generate the secondary sequence we should identify the position of helices and beta sheets in the given protein. So, based on their position we need to replace the primary sequence with ‘H’ and ‘B’ to represent helices and beta sheets. Other than helices and beta sheets we need to place ‘T’ which indicates the terminal. By replacing all the above in the primary sequence then the secondary sequence is generated. 3. Calculate the Euclidean Distance The important step in calculating the distance between one c-alpha atom and all other c-alpha atoms. As the distance between these atoms is found in 3dimensional space. The Euclidean distance will give the accurate distance the

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two points. The distance that is calculated is represented in the form of a 2dimesnional matrix. A particular row represents the distance from one atom to all other atoms that are considered by us. The Euclidean distance is calculated by using the formula (Fig. 7). The above formula is used to calculate the distance between each and every c-alpha atoms. 4. Generate Contact Map Based on the Threshold Value Once the distance matrix or sparse matrix is obtained, we just need to generate the contact map based on the threshold value. The threshold value is nothing but a limit up to which the distance between the c-alpha atoms need to be considered. The range of threshold value can be considered from the previous studies [3] related to the protein ids. Before directly generating the contact map, we need to change the sparse matrix or distance matrix as lower triangular matrix. In order to change we need to replace each and every row with each and every column. By doing this we will be obtaining the triangular matrix. Based on the number of values that are considered in the range of threshold values, we will obtain that many number of contact maps. For every threshold value we will get a contact map. Steps for generating the contact map: 1. 2. 3. 4. 5. 6. 7. 5.

Start. Consider each threshold value from given range. Consider the distance matrix. Now iterate through each and every value in the distance matrix, i.e. every row and every column element. If the value is greater than the threshold value, then replace the value with ‘0’. Otherwise, replace the value with ‘1’. Complete the process and generate contact map for each and every threshold value. Stop. Extract the Patterns from Contact Map The final step, extracting the patterns from the protein contact map. For the extraction of the contact map we have used baker-bird algorithm [1]. This algorithm is a string-based searching algorithm. This algorithm first searches for the given string in the particular row, then it searches the given string in the column wise. This algorithm just returns the count of number times that particular pattern is present in that contact map. There will be two steps in this algorithm one is row matching step and other is column matching step.

Fig. 7 Distance formula for 3d coordinates

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The algorithm as follows: Input: Contact map. Output: Count of patterns. Step 1: Step 2: Step 3: Step 4: Step 5: Step 6: Step 7: Step 8:

Extract the length of the pattern and text length. Now traverse the pattern row by row. Now create a dictionary for each row that the pattern occurs in the text. Read the pattern and assign a unique name to each row by using AC automation. Represent pattern as 1D vector and construct 1D KMP automation. Then, it checks for the matching rows in the given text by using AC automation. Finally, column matching step using the KMP to find the pattern matching columns. Stop.

By using the above algorithm we can find the total count of the patterns with specified width and length. These patterns considered are of width 3, 4, 5. The length of the pattern is minimum of 3, then it will be treated as helix. If the length of pattern is three, then it will be recognized as helix otherwise it is not treated as helix. The predicted patterns can be represented in the form of a table.

5 Results and Analysis The project’s final outcome is all about extracting the patterns from the protein contact map. The width of the pattern is fixed to 3, 4, 5. The length of the patterns should start with 3 (minimum length of helix) and can be of any length. Figure 8 represents reading all the pdb files from the local system into the console.

Fig. 8 Reading cervical and breast cancer pdb files

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Fig. 9 Contact map, primary and secondary sequence

Next step is in generating the contact map, primary sequence and secondary sequence. The generated contact map and sequences are placed in a text file. Figure 9 represents the generated contact map. In the above represented figure, first line represents the header of the file that includes name of the protein, date at which the protein was generated and the type of the protein. Next, in the second line it represents the primary sequence that is generated from the 3-letter code of c-alpha atoms are converted into single letter code. Next line represents the secondary sequence obtained from the primary sequence, the position of helices will be replaced by ‘H’ and remaining are represented as terminal ‘T’ in the primary sequence. Next line represents the count of the protein contact map, i.e. number of rows present in the contact map. Finally the contact map is shown, the diagonal in the contact map represents the helices and remaining are all other atoms that are present in the protein id. The anti-parallel to the diagonal represents the beta sheets in the protein contact map. Table 2 shown below represents the individual pattern count of each protein id.

Extraction of Patterns for Cervical and Breast Cancer Protein Primary … Table 2 Represents the pattern count of each protein

Protein id

Width 3 count

Width 4 count

495

Width 5 count

2gk1

8

11

9

1np0

16

18

6

3f81

23

36

27

3b5h

50

37

46

5cqh

35

40

30

4bgh

34

63

26

6iwd

23

56

18

6igf

84

90

62

5j6r

78

90

71

5y9f

88

93

68

6 Conclusion and Future Work The major objective of this project is to extract the patterns from the protein contact map. This extraction of patterns from cervical and breast cancer will be helpful in study related to the cervical and breast cancer. The dataset is taken from the open source RCSB portal. The dataset that is taken is based on the previous studies related to the cervical and breast cancer. We first analysed the file, how c-alpha atoms are present in the file. This will be helpful in extracting the coordinates from the atoms. Further in future we extend our project by generating the contact map directly by using the primary sequence. By generating by using the primary sequence, this will reduce time in extracting the coordinates finding the distance there by generating the contact map. We can also extend our project for detecting whether a person with cervical cancer has a chance of developing breast cancer over lifetime. This analysis will be helpful in identifying the cancer in women and take a precaution against it. As a women is suffering with a breast cancer then there is a chance that the child born also suffers with the same cancer over the lifetime. So, this study will be helpful in identifying before and save millions of life in the future.

References 1. Deepika P, Suvarna Vani K (2019) Extraction of contact maps for cervical and breast cancer proteins 3d-structures. Int J Sci Res (IJSR) Res Gate Impact Factor 0.28 2. Swaroopa MO, Vani K (2012) Mining dense patterns from off diagonal protein contact maps. Int J Comp Appl 49:36–41 3. Jalapally P, Suvarna VK, Madala S (2021) Detection of significant patterns in cervical and breast cancer proteins. Annals of RSCB, pp 19350–19357 4. Wang Y, Yang F, Zhang J et al (2021) Application of artificial intelligence based on deep learning in breast cancer screening and imaging diagnosis. Neural Comput Applic 33:9637–9647 5. Shaw N, Bhoi J, Jee G, Saha P (2022) An efficient ensemble-based deep learning model for the diagnosis of cervical cancer. In: 2022 IEEE 12th symposium on computer applications and

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C. Abburi et al. industrial electronics (ISCAIE), 2022, pp 33–37. https://doi.org/10.1109/ISCAIE54458.2022. 9794537 Tabaghi P, Dokmani´c I, Vetterli M (2020) Kinetic euclidean distance matrices. IEEE Trans Signal Process 68:452–465. https://doi.org/10.1109/TSP.2019.2959260 Upadhyay P, Pandey MK, Kohli N (2018) A comprehensive survey of pattern mining: challenges and opportunities. Int J Comp Appl 180. https://doi.org/10.5120/ijca2018916573 Nassif AB, Talib MA, Nasir Q, Afadar Y, Elgendy O (2022) Breast cancer detection using artificial intelligence techniques: a systematic literature review. Art Intell Med 127 https://www.fao.org/3/y2775e/y2775e0e.htm Bhatnagar D, Pardasani K (2012) A matrix model for mining frequent patterns in large databases. Int J Eng Sci Technol

The Implementation of Artificial Intelligence in Supply Chain Elisabeth T. Pereira

and Muhammad Noman Shafique

Abstract Artificial intelligence (AI) and big data are two emerging technological concepts in supply chain management. The current study has merged AI and big data as artificial intelligence-driven big data analytics capability (AI-BDAC). The integration of resource-based theory (RBT) and contingency theory (CT) has provided to formulate conceptual framework. The efficient use of AI-BDAC in the supply chain improves internal integration (II) organizational processes and achieves supply chain agility (SCA). The ultimate goal for utilizing AI-BDAC is to increase supply chain performance (SCP). Data has been collected data from Chinese logistic companies having sample size 150 using survey method. Partial least square-structural equation modeling (PLS-SEM) results showed that AI-BDAC has direct effect on SCP. Moreover, II and SCA have mediating effects between AI-BDAC and SCP. It is concluded that using advanced technologies like big data and AI will support organizations to improve their II to attain SCA, which will boost SCP, especially in emerging economies. Additionally, the improvement in SCP will also increase speed and efficiency in logistic industry, and reduce the wastage, and cost; these are social benefits. Keywords Supply chain performance · Artificial intelligence · Big data analytics · Supply chain integration · Supply chain agility

1 Introduction Google, Pfizer, and other 91.5% of US companies have invested in AI technologies [1]. Additionally, 30% of Japanese and 50% of European manufacturing organizations have deployed AI technologies [1]. Moreover, 84% of organizations can gain E. T. Pereira · M. N. Shafique (B) University of Aveiro, 3810-193 Aveiro, Portugal e-mail: [email protected] M. N. Shafique University of Buner, Buner 19281, Pakistan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_37

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a competitive advantage through AI, machine, and deep learning [1]. In addition, adopting AI technologies will bring 26% of economic benefits in China [1]. The adoption of advanced information technologies (IT) in supply chain management (SCM) has gained scholars’ attention [2–5]. Moreover, the implementation of AI in supply chain [6] and bibliometric analysis [7] provides the future directions for research of AI in SCM. Additionally, AI in the supply chain in the pandemic situation has been focused in recent literature [8]. But there are still gaps in literature and practice of AI in SCM. The current study has contributed in three aspects: First, the implementation of AIBDAC is scant in supply chain literature. Second, in emerging economies, there are still problems in logistic industries that could be solved through the implementation of advanced IT. Third, there is a need to develop a comprehensive framework to analyze the SCP through AI-BDAC, SCA, and II. The proposed gaps have been filled by combining two theories, i.e., RBT and CT. Both theories provided the foundation to develop a comprehensive framework for SCP. Moreover, the study has focused on the Chinese logistics industry, which is an emerging economy. In addition, data has been analyzed through PLS-SEM using structural equation modeling techniques. Furthermore, this study has contributed to the literature and provided guidelines for organizations to increase their SCP.

2 Literature Review and Hypotheses Development RBT has founded on the concept that all organizations have unique resources and capabilities which are difficult to imitate. Moreover, these resources and capabilities differentiate organizations from their competitors to make a competitive advantage. Therefore, organizations should emphasis on their own unique organizational resources and capabilities to sustain and grow in the market [9, 10]. CT grounded on the environmental condition, which was ignored in RBT because RBT “lacks context insensitivity” [11]. The combination of RBT and CT enables organizations to utilize their unique resources and capabilities according to the environment, enabling them to gain more market growth [12]. Big data is high volume and velocity, and big data analytics (BDA) is the technique to analyze big data. The association between BDA, collaboration, trust, and performance found in SCM literature [3, 5, 13]. Using advanced AI techniques in BDA can boost SCP, especially operational performance. Accordingly, the relationship between AI-BDAC and SCP has been developed [14]. SCA is the organizational unique capability to respond in the rapid change in supply chain requirements. Consequently, the association between SCA and SCP has been established [14]. Using AI-BDAC is a unique organizational capability that can enhance SCA and SCP [14]. Moreover, a positive and significant relationship between AI-BDAC and SCA has been found in the previous study [14]. The mediating effect of SCA between AI-BDAC and SCP has also been established [14].

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Supply chain integration is a completely integrated system involving internal and external integration. The external integration is to integrate customers and suppliers. In contrast, II is to integrate all internal organizational processes [15]. II is also the departmental integration that will align all the organizational processes, boosting organizational performance, especially SCP. A relationship between II and SCP was found in a previous study [15]. Additionally, the literature focused on the positive and significant relationship between SCA and II [16]. The organizations have more II, and their operations are more interlinked with each other they are in a better position to gain SCA, flexibility, trust, and resilience from manufacturing and humanitarian perspective, but the logistic perspective has been ignored in supply chain literature. The relationship between blockchain and II was found in previous literature [17]. Blockchain and artificial intelligence are two advanced information technologies. So, following the same logic, the relationship between AI-BDAC and II has developed. Moreover, the mediating effect of II between the internet of things and SCP was found in previous literature [18]. Therefore, on the same logic, the mediating effect of II between AI-BDAC and SCP has developed in this study. The integration of RBT and CT has provided the foundation to understand organizational inimitable resource and capabilities of II, SCA, and AI-BDAC in dynamic supply chain environment. Additionally, the intensive literature review on AI-BDAC, II, SCA, and SCP has grounded the following conceptual framework mentioned in Fig. 1. H1 : AI-BDAC has positive effect on SCP. H2 : II has positive effect on SCA. H3 : SCA has mediating effect between AI-BDAC and SCP. H4 : II has mediating effect between AI-BDAC and SCP.

Fig. 1 Conceptual framework

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3 Methodology 3.1 Instrument and Data Collection Deductive approach based on a cross-sectional data collection technique using a survey method and a questionnaire technique based on an adapted instrument has been used in this study. All items have been measured on the Likert scale, AI-BDAC has four items, II has six items [15, 17], SCA has three [12, 19], and SCP (operational performance) has six items [15]. All the factors have factor loading greater than 0.7 [20]; Fig. 2 shows items loadings on the path of each items have values above then threshold value. Data was collected from Chinese logistics companies using WeChat from January 2022 to February 2022. The total number of respondents is 150 employees from Chinese logistic industry.

Fig. 2 Path coefficients of SCP

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3.2 Common Method Bias (CMB) It is the method bias that has been calculated through the full collinearity method using Inner Variance Inflation Factors (VIF). All the variables have been considered as independent variable one by one. The inner VIF values of each independent variable are less than 3.3 [21], and results showed no CMB issue.

4 Results 4.1 Measurement Model Partial least square—structure equation modelling (PLS-SEM) technique has used for data analysis. It consists of an inner measurement model and an outer measurement model. Inner measurement models are calculated through reliability and validity. Reliability has been analyzed through internal consistency and composite reliability; threshold values for both are 0.70 [22]. Validity has measured through convergent validity and is the theoretical relationship between variables, which was measured through an AVE threshold value is 0.50 [22]; results mentioned in Table 1. Discriminant validity is the theoretical difference between constructs. It has been analyzed using the Fornell and Larcker criterion method, which shows the square root of AVE must be higher than correlational values [20], mentioned in Table 2. The robustness of discriminant validity has measured using Hetrotrait-Monotrait, and its threshold value is 0.90 [23]. All the HTMT values are less than 0.9, indicating constructs are valid. Table 1 Reliability and average variance extracted

α

Constructs

CR

AVE

AI-BDAC

0.826

0.884

0.655

II

0.909

0.93

0.687

SCA

0.944

0.964

0.900

SCP

0.888

0.915

0.642

Table 2 Fornell-Larcker criterion method Constructs

AI-BDAC

AI-BDAC

0.809

II

SCA

II

0.524

0.829

SCA

0.373

0.477

0.949

SCP

0.659

0.749

0.501

SCP

0.801

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Table 3 Hypotheses testing Hypotheses

β

T Statistics

P Values

Decision

AI-BDAC → SCP

0.345

2.936

0.003

Accepted

II → SCA

0.388

4.079

0.000

Accepted

AI-BDAC → SCA → SCP

0.022

2.077

0.038

Accepted

AI-BDAC → II → SCP

0.265

3.226

0.001

Accepted

4.2 Structural Model The structural model has been measured through path coefficients using PLS-SEM. It is hypotheses testing. In hypotheses testing, path coefficients (β) values and their significant values are important to decide whether to accept or reject hypotheses [21]; results showed that all the hypotheses got accepted, as mentioned in Table 3. The path coefficients have illustrated in Fig. 2. The outer values showed items loadings on paths of each item, and regressive values (R2 ) mentioned in circles.

4.3 Model Fit The PLS-SEM model has calculated the fit through standardized root mean square residual (SRMR) and normed fit index (NFI). The SRMR value is less than 0.1 [24, 25], and NFI nearer to 1 [26] is threshold values of model fit. The SRMR is 0.063, and NFI is 0.825; values showed model fit.

5 Discussion and Conclusion The conceptual framework has grounded in the lens of RBT and CT to develop four hypotheses. In this study, H1 : AI-BDAC has positive effect on SCP, and results (β = 0.345, p < 0.05) got accepted, which is aligned with previous literature [14]. Second, H2 : II has positive effect on SCA; results (β = 0.388, p < 0.05) that got accepted are supported through literature [16]. Third, H3 : SCA has the mediating effect between AI-BDAC and SCP results (β = 0.022, p < 0.05) got accepted, supported by the previous study [14]. Fourth, H4 : II has the mediating effect between AI-BDAC and SCP (β = 0.265, p < 0.05), got accepted. In this research, Chinese logistics industry has been focused. Because the Chinese logistics industry is very fast growing, hyper-competition, and customer demand for on-time delivery is needed to implement the advanced IT technologies in their operations practically. Moreover, implementing AI in SCM will enable organizations to strengthen their SCA and II. Moreover, the AI-BDAC is the key factor in increasing SCP [14].

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This study has focused on AI-BDAC, II, SCA, to analyze the SCP. In the future studies, supplier integration, customer integration, buyer—supplier relationship can also considered. Moreover, this study has focused on empirical evidence, while in future systematic analysis, bibliometric analysis or meta-analysis can considered. Furthermore, the current study has only focused in Chinese logistic industry, while in the future, the comparative analysis between China and India can be conducted or time series or experimental technique instead of cross-sectional can be conducted. The present research has contributed to the literature because it has developed the mediating relationship of II between AI-BDAC and SCP, which was not found in previous literature. Second, the present study provides guidelines to organizations that they must adopt advanced IT in their operations to gain a competitive advantage because modern technologies will support organizations to save their resources and integrate their operations to improve their SCP. Additionally, the improvement in SCP will reduce delivery time, cost, wastage, damages and increase the speed of logistics which will be benefited for society. Funding This work was financially supported by the research unit on Governance, Competitiveness and Public Policy (UID/CPO/04058/2019), funded by national funds through FCT—Fundação para a Ciência e a Tecnologia, Portugal.

References 1. Thrive my way, https://thrivemyway.com/artificial-intelligence-stats, last accessed 15 Nov 2022 2. Shafique MN, Khurshid MM, Rahman H, Khanna A, Gupta D, Rodrigues JJ (2019) The role of wearable technologies in supply chain collaboration: a case of pharmaceutical industry. IEEE Access 7(1):49014–49026 3. Shafique MN, Khurshid MM, Rahman H, Khanna A, Gupta D (2019) The role of big data predictive analytics and radio frequency identification in the pharmaceutical industry. IEEE Access 7(1):9013–9021 4. Shafique MN, Rashid A, Bajwa IS, Kazmi R, Khurshid MM, Tahir WA (2018) Effect of IoT capabilities and energy consumption behavior on green supply chain integration. Appl Sci 8(12):2481 5. Shafique MN, Rahman H, Ahmad H (2019) The role of big data predictive analytics acceptance and radio frequency identification acceptance in supply chain performance. In: International conference on innovative computing and communications. Springer, pp 65–72 6. Pournader M, Ghaderi H, Hassanzadegan A, Fahimnia B (2021) Artificial intelligence applications in supply chain management. Int J Prod Econ 241:108250 7. Riahi Y, Saikouk T, Gunasekaran A, Badraoui I (2021) Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Syst Appl 173:114702 8. Dubey R, Bryde DJ, Blome C, Roubaud D, Giannakis M (2021) Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. Indust Market Manage 96:135–146 9. Barney J (1991) Firm resources and sustained competitive advantage. J Manag 17(1):99–120 10. Penrose E, Penrose ET (2009) The theory of the growth of the firm. Oxford University Press

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11. Brandon-Jones E, Squire B, Autry CW, Petersen KJ (2014) A contingent resource-based perspective of supply chain resilience and robustness. J Supply Chain Manag 50(3):55–73 12. Dubey R, Bryde DJ, Foropon C, Tiwari M, Dwivedi Y, Schiffling S (2021) An investigation of information alignment and collaboration as complements to supply chain agility in humanitarian supply chain. Int J Prod Res 59(5):1586–1605 13. Dubey R, Gunasekaran A, Childe SJ, Roubaud D, Wamba SF, Giannakis M, Foropon C (2019) Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. Int J Prod Econom 210:120–136 14. Dubey R, Bryde DJ, Dwivedi YK, Graham G, Foropon C (2022) Impact of artificial intelligencedriven big data analytics culture on agility and resilience in humanitarian supply chain: a practice-based view. Int J Prod Econ 108618 15. Flynn BB, Huo B, Zhao X (2010) The impact of supply chain integration on performance: a contingency and configuration approach. J Oper Manag 28(1):58–71 16. Tarigan ZJH, Siagian H, Jie F (2021) Impact of internal integration, supply chain partnership, supply chain agility, and supply chain resilience on sustainable advantage. Sustainability 13(10):5460 17. Kamble SS, Gunasekaran A, Subramanian N, Ghadge A, Belhadi A, Venkatesh M (2021) Blockchain technology’s impact on supply chain integration and sustainable supply chain performance: Evidence from the automotive industry. Ann Oper Res 1–26 18. De Vass T, Shee H, Miah SJ (2018) The effect of “internet of things” on supply chain integration and performance: an organisational capability perspective. Australasian J Inform Syst 22(1) 19. Altay N, Gunasekaran A, Dubey R, Childe SJ (2018) Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view. Product Plann Control 29(14):1158–1174 20. Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24 21. Kock N (2015) Common method bias in PLS-SEM: a full collinearity assessment approach. Int J e-Collaboration (IJEC) 11(4):1–10 22. Bagozzi RP, Yi Y (1988) On the evaluation of structural equation models. J Acad Mark Sci 16(1):74–94 23. Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 43(1):115–135 24. Henseler J, Sarstedt M (2013) Goodness-of-fit indices for partial least squares path modeling. Comput Statist 28(2):565–580 25. Hu L-T, Bentler PM (1998) Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychol Methods 3(4):424 26. Lohmöller JB (1989) Predictive versus structural modeling: Pls versus ml. In: Latent variable path modeling with partial least squares. Springer, pp 199–226

Analytical Investigation of Different Parameters of Image Steganography Techniques Ravi Saini, Kamaldeep Joshi, and Rainu Nandal

Abstract Data is the most valuable asset in the current age of digital era. A person can lose billions of dollars if unauthorized users steal some data. Image steganography is the solution for these types of problems. It uses the image as the cover object for the security of data. Every steganography approach revolves around five key parameters. The key parameters are the key parameters of hiding capacity, perceptual transparency, robustness, tamper resistance and computational complexity. The blended approach using XOR operation, adapted approach using pixel mutation and parity checker method is used for analytical investigation of different parameters. The relationship between different parameters shows that if we increase or decrease one parameter, then it will affect another parameter. After analysis of some results, we have found that if hiding capacity is increased, then the imperceptibility of image steganography automatically decreases. This analytical investigation is helpful in developing new approaches in the field of steganography as we can trade off between different parameters according to the user’s requirement. Keywords Steganography · Imperceptibility · Data hiding · Image

1 Introduction Digital data have to be conveniently shifted via the network due to the Internet’s quick development of technology. Attacks and unauthorized network access to information have grown to be bigger problems recently. For these problems, information hiding offers a solution. The science of information concealment has existed since the dawn of mankind. Through the ages, it has been utilized by the general public, spies, ruling parties, etc. However, digital data information is still quite new. Information-hiding techniques were previously given less impressive by research scientists and start-up companies than cryptography, although this formula is quickly changing [1]. These R. Saini (B) · K. Joshi · R. Nandal UIET MDU Rohtak, Rohtak, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_38

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days, finding a secure private communication channel is crucial for commercial and military goals connected to market strategy, copyright protection and other factors. It’s crucial to find other ways to connect secretly. Because it relies on obscurity to maintain the secret, steganography is extremely important in this situation [2]. The Greek words for your steganography are “steganos” (covered) and “graphs” (Writing). Data concealment in various cover media is both an art and a science. Steganography has a biological and physiological foundation. Steganography is a very old branch of study. After Trithemius’ work on the subject, “Steganography” was published in the 1500s, the term “steganography” began to be used [3]. For steganography purposes, individuals in the past employed wooden tablets, invisible ink, microdots, etc. [4]. Secret covert code insertion can be accomplished by encoding a secret object into a transparent medium, such as an image object, video object, or audio object while maintaining the transparency of the medium’s quality in the eyes of the viewer [5]. Simmons initially discussed the concept in 1983 [6]. Anderson [7] provides a theory of steganography that is easier to understand. Steganography is distinct from cryptography in that the latter focuses on hiding the message’s content, whilst the former hides the message’s very existence [8]. Many alternative ways have been developed, and images make great carriers for concealed information [9, 20]. We would achieve better outcomes in terms of image quantum Fourier steganography [10–12]. The secret covert object could be encrypted initially and then clubbed into a graven object. A stego image is an image that contains encrypted data embedded inside it. The graven object and the stego object alteration is so little that the human sense is unable to see the difference [13, 14]. Yadav et al. [15] proposed a new method for inserting the data using the concept of even and odd parity. It is a straightforward and practical approach. Joshi et al. [16] proposed another blended approach of image steganography using the XNOR operation. It provides the finer value of key parameters. Saini et al. [17] proposed an adapted approach using a bit extraction and augmentation process. This method also provides the finer value of PSNR and other metrics. Pandey et al. [18] proposed another approach which uses different channels for inserting different bits. Kumar et al. [19] proposed another method of image steganography which uses graphical matrix for data hiding. Shah et al. [21] proposed another method of secret communication which uses genetic algorithm. It provides better results in terms of different parameters.

2 Parameters of Stegaography There are five key parameters of image steganography. The different parameters of steganography are described subsequently. Hiding capacity is the first and most important parameter for the steganography technique. Hiding capacity is the total amount of data that can be hidden in the cover media. The robustness of the steganography technique is also another crucial parameter. The robustness of any steganography approach is the ability of the approach such that its hidden data remain unchanged if the cover file goes through some processing required for it. Tamper resistance is

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the number of hindrances or obstructions in the path of a third party or attacker to alter or change the hidden data once the data is hidden in the cover media. The next parameter of the steganography approach is perceptual transparency. Perceptual transparency means there should be a very minute difference between the cover file and the stego file so that it is not seen by the human eye and does not create any suspicion about covert communication. Computational complexity is another parameter of the steganography approach. The computational complexity should be less for a good steganography approach. It is the amount of time and effort taken for the implementation of the steganography approach.

3 Results and Analysis We will analyse the different parameters of image steganography techniques. Proposed method 1 [17], proposed method 2 [16] and parity checker method [15] are used for analytical investigation of different parameters. We have taken six dataset images, Lena, Baboon, Candy, Building, Penguine and Rose, for analysis. The corresponding images are shown in Figs. 1 and 2.

3.1 Hiding Capacity Versus Imperceptibility Hiding capacity or payload data is taken in Kilobytes (KB). The PSNR value can check the imperceptibility of the technique. PSNR is given by the following equation: PSNR = 10 log10 (I 2 ∗ MSE)

(1)

PSNR denotes the peak signal-to-noise ratio, I denotes the peak value of the pixel, and MSE is the mean square error given by the following equation: MSE =

r ∑ c ∑ (xi j − yi j )2

(2)

i=1 j=1

We have analysed PSNR for a different set of payload data. The hiding capacity of 10 KB to 60 KB has been taken for calculation of the result. Figure 4 shows graphical relationship between imperceptibility and hiding capacity for Lena and Baboon image for different sets of data.

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Fig. 1 Different test images

Fig. 2 Hiding capacity versus imperceptibility for Lena image and Babbon image

3.2 Imperceptibility Versus Robustness Imperceptibility can be checked by bit error rate (BER). BER is given by the following equation. n ∑ BER = (xi − yi ) (3) i=0

The various methods robustness has been analysed against image processing assaults, such as the inclusion of various noises, cropping, a variety of filters and compression assaults. The average, median and Gaussian filters are applied to the six test images with different sizes of masks, and their BER value is calculated. The resultant values

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Fig. 3 Robustness versus imperceptibility for different cover images for 10, 20 KB data

Fig. 4 Robustness versus imperceptibility for different cover images for 30, 40 KB data

of BER for different dataset images for different filters and 10–40 KB data insertion are given in Figs. 3 and 4. Figure 3 shows graphical relationship between imperceptibility and robustness for six test images for 10 and 20 KB data insertion. Figure 4 shows graphical relationship between imperceptibility and robustness for six test images for 30 and 40 KB data insertion.

3.3 Robustness Versus Tamper Resistance Tamper resistance can be measured as the level of different attacks up to which the message is recovered. Different image processing assaults are the inclusion of various noises, cropping, a variety of filters and compression assaults. The results are shown in Tables 1, 2 and 3.

Table 1 Level of different noises for which message is lost Test images Guassian noise level Speckle noise level Lena Babbon Candy Building Penguine Aeroplane

Variance = 0.091 to 1 Variance = 0.089 to 1 Variance = 0.092 to 1 Variance = 0.091 to 1 Variance = 0.092 to 1 Variance = 0.089 to 1

Variance = 0.087 to 1 Variance = 0.088 to 1 Variance = 0.086 to 1 Variance = 0.085 to 1 Variance = 0.084 to 1 Variance = 0.086 to 1

Salt-and-pepper noise level Density = 0.072 to 1 Density = 0.077 to 1 Density = 0.079 to 1 Density = 0.081 to 1 Density = 0.082 to 1 Density = 0.078 to 1

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Table 2 Level of different noises for which message is partially lost Test images

Guassian noise level

Speckle noise level

Salt-and-pepper noise level

Lena

Variance = 0.051 to 0.090 Variance = 0.047 to 0.086

Density = 0.042 to 0.072

Babbon

Variance = 0.047 to 0.088 Variance = 0.045 to 0.088

Density = 0.043 to 0.076

Candy

Variance = 0.044 to 0.091 Variance = 0.046 to 0.085

Density = 0.048 to 0.078

Building

Variance = 0.052 to 0.090 Variance = 0.045 to 0.084

Density = 0.047 to 0.080

Penguine

Variance = 0.053 to 0.091 Variance = 0.048 to 0.083

Density = 0.048 to 0.081

Aeroplane

Variance = 0.049 to 0.088 Variance = 0.046 to 0.085

Density = 0.048 to 0.077

Table 3 Level of different noises for which message is recoverable Test images Guassian noise level Speckle noise level Salt-and-pepper noise level Lena Babbon Candy Building Penguine Aeroplane

Variance = 0 to 0.050 Variance = 0 to 0.046 Variance = 0 to 0.043 Variance = 0 to 0.051 Variance = 0 to 0.052 Variance = 0 to 0.048

Variance = 0 to 0.046 Variance = 0 to 0.044 Variance = 0 to 0.045 Variance = 0 to 0.044 Variance = 0 to 0.047 Variance = 0 to 0.045

Density = 0 to 0.041 Density = 0 to 0.042 Density = 0 to 0.047 Density = 0 to 0.046 Density = 0 to 0.047 Density = 0 to 0.047

3.4 Robustness Versus Computational Complexity The robustness of the steganography technique is also another crucial parameter. The robustness of any steganography approach is the ability of the approach such that its hidden data remain unchanged if the cover file goes through some processing required for it. For example, if we consider the example of the image as cover media, then there are many image processing operations that can be applied to the image, like translation, rotation, scaling, compression, blurring and cropping. It is the amount of time and effort taken for the implementation of the steganography approach. Spatial domain and transform domain steganography are two different types of steganography techniques. Transform domain techniques are more robust than spatial domain techniques, but their computational complexity is more than spatial domain techniques. So, we can say that there is an inverse relationship between robustness and computational complexity. If we increase the robustness, then computational complexity will decrease, and if we decrease the robustness, then computational complexity will increase. The following equation shows the relationship between computational complexity and robustness: Computaional Complexity α

1 Robustness

(4)

The relationship between computational complexity and robustness is given by Fig. 5.

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Fig. 5 Computational complexity versus robustness

4 Conclusion This paper presents an analytical investigation of different key parameters of image steganography techniques. The analysis of results from Fig. 2 shows that there is an inverse relationship between hiding capacity and imperceptibility. If we increase one parameter, then the other parameter will automatically decrease. The result analysis of imperceptibility versus robustness, robustness versus tamper resistance and robustness versus computational complexity depicts the inverse relationship between different parameters. This analysis is helpful in developing a new technique to give weights to different key parameters and adjust their value per user’s requirements.

References 1. Ahn S, Hopper LVNJ (2004) Public-key steganography. In: Advances in cryptology (Eurocrypt 2004), vol 3027. LNCS, Springer Berlin Heidelberg, pp 323–341 2. Amirtharajan R, Akila R, Chowdavarapu PD (2010) A comparative analysis of image steganography. Int J Comp Appl 2(3):41–47 3. Amirtharajan R, Ganesan V, Jithamanyu R, Rayappan JBB (2010) An invisible communication for secret sharing against transmission error. Univ J Comp Sci Eng Technol 1(2):117–121 4. Anderson RJ (1996) Stretching the limit of steganography. In: Information hiding. Springer Lecture Notes in Computer Science, vol 1174, pp 39-48 5. Anderson RJ, Petitcolas FAP (1998) On the limits of stegnography. IEEE J Select Areas Commun 16(4):474–481 6. Arnold M (2000) Audio watermarking: features, applications and algorithms. In: Proceeding of IEEE international conference on multimedia and expo., New York, vol 2, 1013–1016 7. Bandopadhyay SK, BhattaCharya D, Ganguly D, Mukherjee S, Das P (2007) A tutorial review on steganography

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Automatic Recognition of Speaker Labels Using CNN-SVM Scheme V. Karthikeyan, P. Saravana Kumar, and P. Karthikeyan

Abstract The most natural method of interaction is speech. To recognize the speaker, we employ a method known as “automatic speaker recognition,” which takes into account the speaker’s voice’s several characteristics, including pitch, timbre, tension, frequency, etc. Additionally, it communicates details regarding the manner in which voice is created and conveyed. The method is applicable to forensics, monitoring, and authenticity applications. The problem with the current approach is the vast number of features employed for voice recognition. In this work, we are planning to use support vector machine (SVM) as a classifier and reinforcement learning as a linear regression to recognize the speaker’s voice purely based on a limited set of attributes selected from the convolutional neural network. Building a clear, unambiguous and delegated automatic spokesman identification organism is the major goal of this endeavor. We will only test our algorithm on a small voice dataset due to space restrictions. To increase accuracy, the system is trained and evaluated using a large number of numerical speech data samples. Here, 125 examples will be used to help identify each phrase. The NIST and ELSDSR speaker datasets were used to conduct an analysis of the hybrid network models that were suggested earlier. The findings of our experiments demonstrated that the CNN-SVM model that we proposed achieved a high recognition accuracy of 95% (avg). In the same vein, the findings show that the deep network model that was proposed is superior to other models in terms of its ability to correctly identify speakers. Keywords SVM · Deep learning · MFCC · LPC · DFT · CNN

V. Karthikeyan (B) ECE Department, Mepco Schlenk Engineering College, Sivakasi, India e-mail: [email protected] P. S. Kumar ECE Department, AAA College of Engineering and Technology, Amathur, Sivakasi, India P. Karthikeyan ECE Department, Velammal College of Engineering and Technology, Madurai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_39

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1 Introduction Speech processing includes the two fields of speaker recognition and speaker verification. The method of identifying which linked voiced speaker provides a specific phrase is known as speaker/voice identification [1–3]. On the other hand, speaker/ voice verification is the practice of approving or disapproving a speaker’s claim to authenticity. The two primary modules in speech identification algorithms are attribute extraction and matching. The process of feature extraction is used to extract a small amount of data from the audio or speech signal that can then be utilized to characterize every individual. By comparing extracted features from an unknown speaker’s vocal speech input with those from a predetermined collection of speakers, feature matching calls for a specific technique to identify the speaker [4–8] (Fig. 1). Every speaker recognition technique must work on two distinct levels. The first is known as the registration stage or the learning stage, and the second is known as the performance stage or the pattern discovery [6, 7]. Each recorded voice must contribute a little portion of their speech during the early phase so that the architecture can learn or construct a foundational structure for that specific speaker. The significance of speaker-specific characteristics in the identification scenario is laid out in Table 1, which can be found here. When introducing speaker validation mechanisms, a specific speaker-specific criterion is also examined using the training set of data [8–12]. During the execution

Fig. 1 General classification of speaker recognition

Table 1 Various speaker features used for SR Feature type

Example

Source feature

Glottal pulse contour

Time domain features

Zero crossing rate, autocorrelation etc.

Dynamic features

Velocity, acceleration rate features, Fused Features, etc.

Prosodic features

Pitch, energy contours,

Frequency domain features

Spectral-Kurtosis, Spectal_Skewness, formants, Power spectral density, MFCC, GTCC, LPC, etc.

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stage, the provided audio data is compared to the gathered reference components, with the outcome being the recognition or confirmation [2] (Fig. 2). Due to numerous factors, including human speech data transformation, physical state (for example, the speaker has a cold), communication rates, etc., audio patterns in training and testing sessions might drastically differ [13–16]. Over and above speech utterance uncertainty, there are other problems that make it difficult to use speech recognition or detection methodologies [17, 18]. Examples of these problems include voice data quantification risks and differences in the environment where recordings are made (for instance, when a user uses a variety of gadgets like android phones or headphones). This paper continues as follows: Sect. 2 summarizes unsupervised and deep learning approaches for voice signal and speaker identification. Section 3 discusses our pipeline, key technologies, and use. Section 4 covers our test and evaluation measures. Section 5 concludes the work.

Fig. 2 Audio card recording and writing

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2 Literature Work Because of its low cost and non-invasive nature, speech is a biomarker that is receiving a lot of attention as a potential indicator in the development of new application scenarios [19]. Speaker-specific information has the potential to be a rich source for a variety of data-driven applications in the forensic domain, ranging from diagnosis to verification. This is a must for the quick and accurate development of medical procedures for screening, diagnosing, and treating [20–22]. As biometric systems are used more frequently, the necessity for robustness in a variety of applications grows more crucial. How well a speaker identification system works to identify speakers depends on how unpredictable its recording system is [23, 24]. According to Chougule et al., the broad usage of automatic speaker recognition technology in applications that take place in the real world requires that the technology be robust against a variety of actual settings [25]. By examining audio signal and traits taken from speaker sounds, an ASR may identify speakers. As a crucial component of audio biometrics, ASR has reemerged as a fruitful research topic. The issue of selecting best features is receiving increasing interest in the field of pattern recognition, and numerous strategies have been devised. Heuristic and exhaustive approaches can both be used to find the most appropriate subset of attributes [2, 4, 26]. The FOCUS engine is an illustration of an exhaustive algorithm. It commences with an empty set and quantifies endlessly till it discovers the smallest collection of attributes that can capture the signal. As with the relief algorithm, the algorithm may also be heuristic. Each attribute is given a relevance weight, which is updated. It is unable to eliminate unnecessary characteristics [10, 12, 27] and always chooses the majority of the unique features. The best features can also be excluded using a stochastic model [28–30]; however, this can take a while when dealing with large amounts of data. Methods for selecting features can also be divided into three categories: embedded selection methods, filter methods, and hybrid approaches [31–33]. To enhance the functionality of an algorithm or method, embedded approaches like decision trees and L1 regularization are employed. An induction algorithm receives the attributes that have been selected via filter approaches. These techniques include variance threshold, chi-square assessment, and gain ratio, among others [3, 8, 17]. The feature selection algorithm functions as a wrapper before the induction technique in wrapper methods. They cost more to compute than filtering techniques. This includes techniques like simulated annealing, sequential extraction of features, and sequential backward minimization. Karthikeyan et al. [33] employed a neural network support vector machine (NN-SVM) hybrid approach to recognize the speaker [34]. Using adversarial training, Codosero et al. [35] presented a model that was built on an X-vector and an auto encoder. This model was intended to identify the anonymous speaker [35]. Researchers are seeking to extract information from speech that goes beyond linguistic content [36]. In this paper, the best audio features are selected using the deep learning approach to improve the recognition accuracy of the supervised classifier.

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3 Materials and Methods The speaker identification system is described in Fig. 3 as a component of the biometric authentication mechanism. This proposal’s primary focus is on speaker verification and related field research. The LabVIEW2009 infrastructure is used to construct the speaker identification scheme employing formant identification with MFCC and LPC. The system has two sessions. Registration comes first, followed by testing [37]. The signal is first pre-emphasized during the registration session. To compare the features with the query, the features are extracted and saved in a file. A voice print of an unidentified spokesperson is considered during the testing session. Studies on the datasets (NIST and ELSDSR) included in the.wav files have been done, and it has been found that the technique is efficient up to a level of 95% (avg). Fig. 3 Proposed work block diagram

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3.1 Speech Feature Extraction This section’s major goal is to convert the audio signal information to an equivalent feature description (at a substantially marginal data rate) for upcoming research and advancement. The voice data analytics front end is what this is commonly referred to as. The details about each speaker are a gradually changing database. Figure 4 is an illustration of a human speech signal. Its traits are essentially unchanged when examined during intervals between 5 and 100 ms, which is a relatively short time frame. Additionally, the speaker data features change at long intervals (on the order of 1/5 s or more) to mimic the various speech sounds being uttered [4, 5]. Therefore, the most often used method to encode the speech output signals is the short-time Fourier transform (STFT). Linear prediction coding (LPC), mel-frequency cepstrum coefficients (MFCC), and other features for the speaker detection system can be used to distinguish the speaker information utilizing an extensive range of probabilities. The well-known and more widely recognized feature is MFCC, and this study makes use of it [8, 11]. The phonetically important aspects of speech data have been collected using filters that are split linearly at lower frequencies and nonlinearly at higher frequencies. MFCCs are dependent on the observed divergence of the human ear’s vital available bandwidth by means of frequency. The mel-frequency spectrum, which is flat under 1000 Hz and nonlinear above 1000 Hz, is used to depict this. The method for extracting MFCCs is then described in more depth [26].

Fig. 4 Example for clean speech signal

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Fig. 5 Schematic of the MFCC processor

3.2 Mel-Frequency Cepstral Coefficient The block diagram of the MFCC processing unit is depicted in Fig. 5. Typically, the speech signal data is recorded at Nyquist intervals greater than 10 kHz. In order to minimize the causes of aliasing in the A–D conversion, this Nyquist frequency was chosen. These processed signals are capable of acquiring all statistics up to 20 kHz, which masks the energy of the individual speaker-produced speech impulses. The MFCC processor’s primary design goal is to replicate how an individual’s ear functions. Additionally, it is demonstrated that MFFCs are less vulnerable to the aforementioned fluctuations than the audio waveforms itself [11, 13].

3.3 LPC The second phase of the work, speaker signal analysis is represented in Fig. 6, the output of the pre-processed data is subjected to additional signal processing and extraction of features using the LPC formant detection mechanism. Speech cords that are used to vocalize vowels and uttered consonants intermittently pulsate to generate phonetic flow. Phonetic pulses build up the glottal flow. The pitching interval is equivalent to the duration of a phonetic pulse [2, 8]. The pitching, normally mentioned to as the fundamental frequency, is the inverse of the pitch interval. To comprehend that formants and pitch shift over time, experts examine formant patterns and pitching curve. The second effort explains well how analyze audio signals for formant patterns and pitch curves using the math works graphical development platform from data acquisition system [2].

Fig. 6 Audio track system

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Fig. 7 LPC-based formant detection

Pitch and formant patterns can be measured by means of a range of approaches. The linear predictive coding (LPC) approach is the most commonly employed technique. This method simulates the vocal tract by means of an all pole model. The workflow for formant extraction using the LPC approach is illustrated in Fig. 7. Deploying the window w(n) separates the incoming audio signal s(n) into audio chunks x(n). The LPC approach is used in each audio chunks of x(n) to compute the components of an all pole vocal folds representation. After performing the discrete Fourier transform (DFT) on the coefficients A(z), the formants are evaluated by identifying the peaks of 1/A(k) [30].

3.4 Proposed Neural Network Model-SVM Design For the suggested work, a CNN with four convolutional layers and a dropout is used. Since the NIST and ELSDSR databases consists of spokesman .wav audio files, this system employs kernels of size 5 for each filter and only accepts input from 2 channels [31]. Following the convolutional layers, 2 pooling layers are both configured using the max-pooling function to extract the features. Due to the ReLU function’s ease of implementation and benefit of quicker convergence, it is utilized as the activation function. In order to output the potential outcomes of 13 alternative labels, we want to employ the Softmax activation function at the network’s conclusion. However, since our loss function is Negative Log Probability, we only need to change SoftMax to Log SoftMax in order to link them to an appropriate date format. We also require a method for selecting the qualities that should be eliminated from all attributes in order to implement feature selection on this system. An easy-to-follow algorithm is employed in this case. When excluding one attribute from the learning algorithm, efficiency rates are calculated [32]. We only set the input weight of one feature to “0” to eliminate it from consideration. These networks are then rated according to their detection rate. The system will drop the feature and recalculate when it can obtain accuracy that doesn’t decrease by greater than R% with one more parameter discarded. Otherwise, the algorithm will stop [38].

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Algorithm: CNN-SVM 1

Set the Initial Parameters of CNN and SVM

2

Extract the MFCC and LPC features from the ELDSR samples

3

Construct the Multi layered CNN

4

While not best features solution Do

5

Select the best speaker-specific features

6

Layer I: Prepare the initial audio features (MFCC & LPC) and weights from step 1

7

Layer II: Adjust the features and weights according to minimum error

8

Layer III: Select the best speaker features using the upper layers of CNN

9

Layer IV: Construct the SVM using the selected features

10

Layer V: Classify the obtained data and measure the efficiency and error rate

11

Evaluate the performance if satisfied then go to step 12 otherwise go to step 5

12

End-While

3.5 Evaluation Measures The term “accuracy” refers to how well a model performs across all classes. Accuracy is considered when each class is given the same weight. Mathematical calculations can be made to determine the ratio between the quantity of accurate forecasts and the entire number of speaker samples [33]. Accuracy =

Correctly identified speaker samples Total audio samples in the dataset

In order to optimize the model parameters in deep learning, a special parameter called the Loss function is used. It is a way of determining how fairly a system reflects a database. To put it another way, the loss function measures the actual difference between the output value that was anticipated and the output value that was actually produced. Loss Function = Speaker_ Idprdicted − Speaker_ Idoriginal

4 Results and Discussion The suggested speaker identification system is built with a CNN and SVM. CNN is used as an adaptable feature selector in the hybrid approach, while SVM is used as a recognizer in the hybrid model. The best features from the speaker-specific MFCC and LPC coefficients are automatically selected by the convolutional neural network,

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which then outputs the best-fit attributes for the supervised classification. The CNN classifier in the regular combination model is trained with raw speech input but with normalized voiceprint sizes. The SVM classifier, on the other hand, is learned with generated, best-fitted features. Through the implementation of a rejection mechanism, both models have attained the level of dependability that was intended for them. On the ELSDSR and NIST datasets, respectively, the performance of the hybrid models that were proposed for use was evaluated [31, 39]. CNN-SVM comprises two steps in this task: training and testing. During the training process, we generate a dataset for future reference.

4.1 Speaker Dataset The proposed speaker recognition algorithm is implemented in version 2022 Q3 of the research tool LABVIEW. The suggested hybrid approach is evaluated using the Neurosciences Institute’s speech data base (NIST) and ELSDSR. In the first trial, all 640 speakers are employed for training and testing (446 males and 194 females). The subsequent round of studies involved 240 speakers (142 males and 98 females) drawn from the same database. Each speaker is taught using the clean speech from the NIST database, whereas testing is conducted using white and channel noises [39]. The English Language Voice Database for Speaker Recognition (ELSDSR) contains speech data from 22 speakers, including 12 men and 10 women [31]. The ratio of the training database to the testing database is 80–20%.

4.2 Experimental Results In this section, the output response of the presented hybrid network model for the datasets, ELSDSR and NIST, is explained. During the testing phase, identify the individual. The results of the proposed work under various stages are illustrated in Figs. 8, 9, 10, 11, 12 and 13. The speaker identification system is referred to in this study as a multimodal strong authentication neighborhood. This work focuses mostly on the investigation in this sector and speaker recognition. The system has two sessions. Registration comes first, followed by testing. Pre-emphasizing the signal is done during the enrollment phase initially. Possibilities are retrieved and stored in a large file so they can be evaluated to the inquiry. An unidentified speaker’s voiceprint is recorded during the testing phase. A value of ninety-four percent (95%) has been determined by experimentation on the ELSDSR dataset audio files, proving that the system is accurate up to that point. The outcomes of the proposed work are outlined in Tables 2 and 3, respectively. Comparing with the results shown in Table 1, it is shown that the more complex CNN and SVM structure has an advantage of starting from a higher standard and reaching the maximum accuracy rate than the conventional models. This indicates

Automatic Recognition of Speaker Labels Using CNN-SVM Scheme

Fig. 8 Recorded .wav file

Fig. 9 Feature extraction output

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524

Fig. 10 Weight updation in the hidden layer neurons

Fig. 11 No. of epochs versus error

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Fig. 12 Response for speech signal

that the proposed work is more stable and more capable of distinguishing patterns from the beginning. However, after reaching the 95%, it is hard to have further improvement due to some limitations, which results in the same highest result as my network. But overall, it is relatively not easy for ELSDSR to achieve a accuracy rate above 93%, but our proposed work achieve higher identification rate with minimum error value. To achieve success with the spokesperson voiceprint recognition, experiments were conducted using the well-known NIST and ELSDSR databases. The hybrid model that was proposed achieved the best results, according to the findings of the experiments that were conducted and the comparisons that were made with other works found in the same database (see Fig. 14): For the experiments performed on the NIST database, the following error rates and recognition rates were used: an average error rate of 4.25% without rejection (associated to the best error rate of 7%), and a prediction rate of 94.40% (related to a prediction rate of 91.51% from other conventional schemes from the literature). Both of these rates compare favorably to the most recent error rate, which was 7%. In our studies, the results demonstrated that the hybrid CNN-SVM scheme performed better than other unicast classifiers,

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Fig. 13 Efficiency of Speech signal

Table 2 Evaluation measures for the ELSDSR dataset

Baseline methods

Accuracy

Base work (SVM)

21.9350

78.0650

Base work (neural network)

17.5615

81.1564

6.5612

93.6468

Hybrid NN-SVM [34]

Table 3 Evaluation measures for the NIST dataset

Loss

Conventional CNN [33]

6.3124

92.6145

Proposed work (CNN + SVM)

4.0129

95.7412

Baseline methods

Loss

Accuracy

Base work (SVM)

20.3963

76.3576

Base work (neural network)

13.6358

80.6574

NN-SVM [34]

7.9646

93.9465

Conventional CNN [33]

6.7516

93.4218

Proposed work (CNN + SVM)

4.2536

94.3902

including SVM, NN, and CNN. It is evident that combining CNN and SVM in this manner resulted in an enhancement in the precision of the identification. The presented CNN + SVM strategy is an efficient solution for the speaker identification system since it preserves the relevant information while simultaneously

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Percentage of Accuracy and Loss

100 SVM

90

Neural Network

80 70

NN-SVM

60

Conventional CNN Proposed work

50 40 30 20 10 0 Loss

Accuracy

Loss

NIST

Accuracy ELSDSR

Evaluation Measures against the Dataset Fig. 14 Comparative analysis of the proposed method’s performance

reducing the number of redundant characteristic features. This work suffers from the limits of having a longer training and testing duration per epoch as well as the need to optimize the parameters. Future work will concentrate on integrating certain techniques with each other in order to improve the accuracy of speaker identification systems. Examples of such systems include DWT&LPC and MFCC&LPC&LPCC, both of which are examples of systems in which development can occur at this stage. These systems focus on reducing the number of features, eliminating irrelevant, noisy, and redundant data, and achieving an acceptable level of recognition accuracy. In further work, the network’s learning speed, its reliability in the face of a wide variety of environmental noise situations, and its stability are the aspects that need to be enhanced.

5 Conclusion This work has been proposed an automatic speaker recognition system using MFCC and LPC features with combined CNN-SVM classifier. It reveals that we are able to recognize a particular speaker from a number of speakers. MFCC gives better performance in feature extraction and high noise reduction in the given speech signal and LPC provides the accurate peak values of the tones. The convolutional neural network is used to select the best feature for the classifier SVM. Based on the minimum noise margin and the decision planes, the CNN-SVM classifiers recognize the speaker label with the accuracy rate of 95% for the ELSDSR and NIST dataset. In future, combination of techniques (MFCC, LPC, DWT, and LPCC) can be used for feature

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extraction. Hybrid techniques continuously provide improved results. In future, we can extend this work to recognize the speaker even if one imitates another.

References 1. Brümmer N, Du Preez J (2006) Application-independent evaluation of speaker detection. Comput Speech Lang 20(2–3):230–275 2. Orság F (2002) Some basic techniques of the speech recognition. In: Proceedings of 8th conference student EEICT, p 5 3. Deshpande MS, Holambe RS (2010) Speaker identification using admissible wavelet packet based decomposition. Int J Sign Process 6(1):20–23 4. Chakroborty S, Saha G (2009) Improved text-independent speaker identification using fused MFCC&IMFCC feature sets based on Gaussian filter. Int J Sign Process 5(1):11–19 5. Mary L, Yegnanarayana B (2008) Extraction and representation of prosodic features for language and speaker recognition. Speech Commun 50(10):782–796 6. Kaur K, Jain N (2015) Feature extraction and classification for automatic speaker recognition system—a review. Int J Adv Res Comp Sci Softw Eng 5(1):1–6 7. Swathy MS, Mahesh KR (2017) Review on feature extraction and classification techniques in speaker recognition. Int J Eng Res Gener Sci 5(2):78–83 8. Gupta K, Gupta D (2016) An analysis on LPC, RASTA and MFCC techniques in automatic speech recognition system. In: 2016 6th international conference-cloud system and big data engineering (confluence). IEEE, pp 493–497 9. Prokoski FJ, Riedel RB, Coffin JS (1992) Identification of individuals by means of facial thermography. In: Proceedings 1992 international carnahan conference on security technology: crime countermeasures. IEEE, pp 120–125 10. Deller Jr JR (1993) Discrete-time processing of speech signals. In: Discrete-time processing of speech signals, pp 908–908 11. Karthikeyan V, Suja Priyadharsini S (2021) A strong hybrid AdaBoost classification algorithm for speaker recognition. S¯adhan¯a 46:138. https://doi.org/10.1007/s12046-021-01649-6 12. Rabiner L, Juang BH (1993) Fundamentals of speech recognition. Prentice-Hall, Inc. 13. Noll AM (1967) Spectrum pitch determination. J Acousti Soc AM 41:293–309 14. Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circ Syst Video Technol Spec Issue Image Video Based Biometr 14(1):1782–1793 15. Barney A, Shadle CH, Davies POAL (1999) Fluid flow in a dynamic mechanical model of the vocal folds and tract. I. Measurements and theory. J Acoust Soc Am 105(1):444–455 16. Rodman DR (2013) Computer speech technology, Boston. Mass.: Artech House. Carnahan Conference on Security Technology: Crime Countermeasures, Atlanta 17. Humphrey EJ, Reddy S, Seetharaman P, Kumar A, Bittner RM, Demetriou A, Yang L et al (2018) An introduction to signal processing for singing-voice analysis: High notes in the effort to automate the understanding of vocals in music. IEEE Signal Process Magazine 36(1):82–94 18. Oppenheim AV (1999) Discrete-time signal processing. Pearson Education India 19. Rios-Urrego CD, Vásquez-Correa JC, Orozco-Arroyave JR, Nöth E (2021) Is there any additional information in a neural network trained for pathological speech classification? In: International conference on text, speech, and dialogue. Springer, Cham, pp 435–447 20. Demir KC, May M, Schmid A, Uder M, Breininger K, Weise T, Maier A, Yang SH (2022) PoCaP corpus: a multimodal dataset for smart operating room speech assistant using interventional radiology workflow analysis. In: International conference on text, speech, and dialogue. Springer, Cham, pp 464–475 21. Maurya A, Kumar D, Agarwal RK (2018) Speaker recognition for Hindi speech signal using MFCC-GMM approach. Proc Comp Sci 125:880–887

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22. Lin YY, Zheng WZ, Chu WC, Han JY, Hung YH, Ho GM, Chang CY, Lai YH (2021) A speech command control-based recognition system for dysarthric patients based on deep learning technology. Appl Sci 11(6):2477 23. Arasteh ST (2020) Generalized LSTM-based end-to-end text-independent speaker verification. arXiv preprint arXiv:2011.04896 24. Khan MA (2021) HCRNNIDS: hybrid convolutional recurrent neural network-based network intrusion detection system. Processes 9(5):834 25. Chougule SV, Chavan MS (2015) Robust spectral features for automatic speaker recognition in mismatch condition. Proc Comp Sci 58:272–279 26. Liu H, Setiono R (1998) Incremental feature selection. Appl Intell 9(3):217–230 27. Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. In: Aaai, vol 2, pp 129–134 28. Liu H, Setiono R (1996) A probabilistic approach to feature selection-a filter solution. In: ICML, vol 96, pp 319–327 29. Blum AL, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97(1–2):245–271 30. Smith SW, The scientist and engineer’s guide to digital signal processing: Chapter 22. Audio Processing Speech Synthesis and Recognition, California Technical Publishing, San Diego 31. Feng L (2004) Speaker recognition, informatics and mathematical modelling. Technical University of Denmark, DTU, English Language Speech Database for Speaker Recognition (ELSDSR) 32. Setiono R, Liu H (1997) Neural-network feature selector. IEEE Trans Neural Netw 8(3):654– 662 33. Karthikeyan V, Suja Priyadharsini S (2022) Modified layer deep convolution neural network for text-independent speaker recognition. J Experiment Theor Art Intell. https://doi.org/10.1080/ 0952813X.2022.2092560 34. Karthikeyan V, Priyadharsini SS, Balamurugan K et al (2022) Speaker identification using hybrid neural network support vector machine classifier. Int J Speech Technol 25:1041–1053. https://doi.org/10.1007/s10772-021-09902-3 35. Perero-Codosero JM, Espinoza-Cuadros FM, Hernández-Gómez LA (2022) X-vector anonymization using autoencoders and adversarial training for preserving speech privacy. Comp Speech Language 74:101351 36. Al-Ali AKH, Dean D, Senadji B, Chandran V, Naik GR (2017) Enhanced forensic speaker verification using a combination of DWT and MFCC feature warping in the presence of noise and reverberation conditions. IEEE Access 5:15400–15413 37. Yoon SM, Park K, Bae JH, Oh YH (2008) Feature vector classification by threshold for speaker identification. In: IEEE Odyssey 2008. IEEE 38. Wubet YA, Lian KY (2021) A hybrid model of CNN-SVM for speakers’ gender and accent recognition using English keywords. In: 2021 IEEE international conference on consumer electronics-Taiwan (ICCE-TW). IEEE, pp 1–2 39. Nist speaker recognition evaluation (2012) http://www.nist.gov/itl/iad/mig/sre12.cfm

Automatic Subjective Answer Evaluator Using BERT Model Sanyam Raina, Heem Amin, Shrey Sanghvi, Santosh Kumar Bharti, and Rajeev Kumar Gupta

Abstract Subjective answers might get tedious to check and take time and effort. Although there has been much research in this field, there needs to be an all-developed and deployable method that considers all factors like keyword matching, context analysis, and relative scoring. We have developed an algorithm for Subjective Answer Evaluation using Tf-idf scoring, BERT-based context analysis, and assigned scores based on relevance. We calculate the Tf-idf value for the keywords, and then if they match with the keywords from the evaluator’s answer, we add the Tf-idf value to generate a total score for each answer. Further, we use transfer learning from BERT and assign scores based on context relevance. Finally, we use a combination of both scores to calculate relative scoring and then give the results as relative scores using a custom algorithm. Comparing these scores to manual scores, i.e., checked by the evaluator, we obtained an accuracy of 90%. Keywords Tf-idf · BERT · Transfer learning · Context analysis · Subjective answer · Relative scoring · Answer evaluation

1 Introduction Written exams are pivotal in assessing a student’s knowledge and learning. Whether handwritten or typed, written exams are essential for the assessment of many students at the same time. The problem occurs when answer sheets are piled up at the teacher/evaluator’s desk. A single individual sometimes must go through more than a hundred answer sheets, which creates issues like over time-consumption, fatigue, and unfairness.

https://www.pdpu.ac.in/. S. Raina (B) · H. Amin · S. Sanghvi · S. K. Bharti · R. K. Gupta Pandit Deendayal Energy University,Gandhinagar, Gujarat, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_40

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Keeping these problems in mind, we decided to develop an approach to check subjective answers, which minimizes the above issues and gives more accurate results. In our proposed model, student and evaluator answers are fed. Our system uses Tfidf to calculate the relevance of keywords used in the students’ answers by comparing them with the evaluator’s answer. Further, to make sense of the answer, we propose to use transfer learning from the BERT model and analyze the context of students’ answers. Once we have scores from both, we find the product and assign scores based on a relative scoring algorithm.

2 Literature Survey Subjective Answer Evaluation [4, 7] has been a challenging area for research as there are several factors like context understanding, synonyms, antonyms, keyword matching, and grammar. There are numerous developments in this field that make use of BERT, NLTK library, and other relevant tools. ASSESS [8] discusses sentence-by-sentence comparisons between the user’s and evaluator’s answers. They use a semantic learning approach using Google’s Universal Sentence Encoder algorithm to create sentence embeddings. Although this approach is better from the semantic point of view, particular keywords are not evaluated, so the accuracies of answers are affected. We considered another research that selected smaller chunks of data for comparison. Most of these suggest keyword extraction and comparison approaches. “Subjective Answer Evaluator Using Machine learning” [2] discusses a method to compare keywords from the question with the student’s answer keywords. Another research talks about forming vectors after preprocessing, applying the Tf-idf formula, embedding, and comparing using the cosine formula [10]. Further, the “Subjective Answer Evaluation System” by Agrawal Lalit and others suggests including grammar and spelling to find a cumulative score to check the answers. These approaches are limited to keyword matching and do not explore the context of the solution. We included keyword matching using Tf-idf calculations and context analysis of each answer. Tf-idf is a widely used NLP concept that helps us find keywords’ relevance in documents. Ramos [12] has explained in his paper how Tf-idf can be used to determine word relevance in document queries. We have used this concept to assign a score to every answer based on the relevance of keywords with the keywords of the evaluator. We investigated transform learning using BERT [5, 13] to approach context analysis. We explored the libraries (listed in tools and techniques) to customize and create context analysis scoring according to our application. In the paper Bert-Elmo-based deep neural network architecture for English named entity recognition task [1], they focused solely on creating rich semantic and grammatical word representation vectors using BERT and ELMO models and the BiLSTM-CRF (BLC) model. Similarly, [9] uses BiLSTM and talks about a different approach. Both papers have the same problem—they do not talk about keyword relevance, which in our opinion, is essential.

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Fig. 1 Pipeline model of subjective answer evaulator

Several studies [3, 6, 11] discuss the use of context analysis to interpret written material. We made the decision to move forward with BERT context analysis.

3 Proposed System We first go through the cleaning and preprocessing of the data; for that, we use the tokenization method to separate the words and then implement lemmatization followed by Parts of Speech (POS) tagging. This helps us identify the keywords and remove stop words (Fig. 1). From here, two tasks are run parallelly on the same data: Tf-idf and BERT.

3.1 Term Frequency—Inverse Document Frequency (Tf-idf) Term Frequency: TF(t, d) is the relative frequency of the term t within that document d, i.e., the number of times t occurs in d, divided by the total number of times the word t occurs in the entire corpus. n td T f (t, d) = ∑ D i=0

t d D n td

the word the document total number of documents in the corpus total number of t in d.

n ti

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Inverse Document Frequency: IDF(t, D) gives us the importance of the word t across the entire corpus, thereby giving us the rarity. This value helps us in ignoring the words that are not relevant. ) ( D I d f (t, D) = log |{d ∈ D : t ∈ d}| t d D |{d

the word the document total number of documents in the corpus ∈ D : t ∈ d}| total number of d in which t occurs.

Tf-idf is the product of TF and IDF. This value is computed for every single word in the corpus. A customized Tf-idf score is generated by keyword relevance matching for every single document in the corpus, which is used for final scoring purposes. T f − Id f = T f ∗ Id f

n td ← nt D ← T f (t , d) ← nnttdD D← Ndt ← ( ) I d f (t, D) ← log |NDdt | T f − Id f ← T f ∗ Id f

▷ Count of t in document d ▷ Count of t in the entire corpus ▷ D: Total number of documents in the corpus ▷ Total number of d in which t occurs ▷ calculated Tf-idf value

In the final phase, an aggregate score is generated per document based on the cumulative Tf-idf value of the words in that document and the BERT value. This score is further sent to the grading algorithm, which grades each paper on a scale of 1–10, for which you can refer to the table relative scoring.

3.2 Bidirectional Encoder Representations from Transformers (BERT) We convert the data into BERT-readable format and then use transfer learning to find the context and relevance of each answer. After the text preparation for BERT is done, the data is sent for embedding. This embedding is represented in numerical form (between 0 and 1). This extra step is taken to include the meaningfulness of the answer instead of only keywords. Calculating the distance between the embedding:

Automatic Subjective Answer Evaluator Using BERT Model while i < texts.length() do text1 ← texts[i] embed1 ← target_wor d_embeddings[i] while j < texts.length() do text2 ← texts[ j] embed2 ← target_wor d_embeddings[ j] cost_dist ← 1 − cos (embed1, embed2) list_o f _distance ← (text1, text2, cost_dist) end while end while Table 1 Relative scoring Lower range marks Avg + (1.5 * sd) < Avg + (1.0 * sd) < Avg + (0.5 * sd) < Avg < 4 Avg − (0.5 * sd) < Avg − (1.0 * sd) < Avg − (1.5 * sd) 200

Complete Infrastructure destroyed

len, (xii) Width of the storm in miles as wid, (xiii) Hours as hour, (xiv) Minutes as minutes, and (xv) Number of people lost their lives in the storm as Casualties. The heat map shown in Fig. 4 portrays the correlation and covariance among the scalars and vectors. As seen from Fig. 4, the white diagonal across the matrix depicts the high correlation between the scalars and vectors and thus, optimal to be used as input parameters into Machine Learning algorithms for carrying out prediction. The graph

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Fig. 3 Confusion matrix

shown in Fig. 5 projects a graph of Tornado Magnitude in EF scale versus Year, from 1950 to 2018 by the data derived from the dataset by Storm Prediction Centre, NOAA, USA. As the Enhanced Fujita Scale is based on the scale of destruction caused, it can be analyzed from the obtained graph that from 1950 the curve depicts that EF is decreasing continuously, and post-1996, the curve goes flat. The reason for this phenomenon is the developments that have taken place in the arena of prediction of storms as well as government measures to prevent infrastructure destruction.

Fig. 4 Obtained correlation matrix of parameters

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Fig. 5 Reduce in impact post-1996

5 Conclusion Storms in the past have wreaked havoc in coastal regions across the globe. This work has been carried out to predict storms in order to reduce the loss of life and property, which ensures the well-being of humanity. In this work, the dataset used was from the Storm Prediction Centre, NOAA, USA. The data was in the CSV format consisting of 60,144 rows and 22 columns, tracking tornadoes that have hit the USA from 1950 to 2018. Figure 4 explains the heat map, which has been plotted using different hypertuning parameters as well as data preprocessing. In this case, the dataset is reduced from 22 to 15 vectors. Based on this, we have plotted the heat map which represents the correlation and the covariance. It also explains the probability of the distribution of vectors. Multiple machine learning algorithms have been implemented, viz. (i) KNN, (ii) decision tree, (iii) random forest, (iv) Support Vector Machine, and (v) logistic regression, out of which random forest emerged as the best among all, boasting an accuracy of 82.83%, on the other hand, KNN, lowest among all with an accuracy of 70.06%, which has been shown in Fig. 2. The Enhanced Fujita Scale (EF) has been used as a unit to measure the magnitude of tornadoes, which is with respect to the destruction caused by storms. Analyzing the graph projected in Fig. 5, it can be inferred that proactive government measures and

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the development of prediction methodologies prove to be effective in reducing the destruction caused due to storms.

References 1. Kim M, Park MS, Im J, Park S, Lee MI (2019) Machine learning approaches for detecting tropical cyclone formation using satellite data. Remote Sens 11(10):1195 2. Lin GF, Jhong BC, Chang CC (2013) Development of an effective data-driven model for hourly typhoon rainfall forecasting. J Hydrol 495:52–63 3. Kim SW, Melby JA, Nadal-Caraballo NC, Ratcliff J (2015) A time-dependent surrogate model for storm surge prediction based on an artificial neural network using high-fidelity synthetic hurricane modeling. Nat Hazards 76(1):565–585 4. Marchigiani R, Gordy S, Cipolla J, Adams RC, Evans DC, Stehly C, Papadimos TJ et al (2013) Wind disasters: a comprehensive review of current management strategies. Int J Crit Illness Inj Sci 3(2):130 5. Zhang W, Villarini G, Vecchi GA, Murakami H (2019) Rainfall from tropical cyclones: highresolution simulations and seasonal forecasts. Clim Dyn 52(9):5269–5289 6. Scheuerer M, Möller D (2015) Probabilistic wind speed forecasting on a grid based on ensemble model output statistics. Ann Appl Statist 9(3):1328–1349 7. Wei J, Jiang GQ, Liu X (2017) Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013). Ocean Dyn 67(9):1179–1193 8. Bengtsson L, Hodges KI, Roeckner E (2006) Storm tracks and climate change. J Clim 19(15):3518–3543 9. Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G (2018) Could machine learning break the convection parameterization deadlock? Geophys Res Lett 45(11):5742–5751 10. Foote GB (1984) A study of hail growth utilizing observed storm conditions. J Appl Meteorol Climatol 23(1):84–101 11. Hsiao LF, Yang MJ, Lee CS, Kuo HC, Shih DS, Tsai CC, Lin GF et al (2013). Ensemble forecasting of typhoon rainfall and floods over a mountainous watershed in Taiwan. J Hydrol 506:55–68 12. Ginger JD, Henderson DJ, Leitch CJ, Boughton GN (2007) Tropical cyclone Larry: estimation of wind field and assessment of building damage. Aust J Struct Eng 7(3):209–224 13. Huang Y, Jin L, Zhao HS, Huang XY (2018) Fuzzy neural network and LLE algorithm for forecasting precipitation in tropical cyclones: comparisons with interpolation method by ECMWF and stepwise regression method. Nat Hazards 91(1):201–220 14. Hsieh PC, Tong WA, Wang YC (2019) A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change. Hydrol Sci J 64(14):1793–1802 15. Adams-Selin RD, Ziegler CL (2016) Forecasting hail using a one-dimensional hail growth model within WRF. Mon Weather Rev 144(12):4919–4939 16. Anderson-Frey AK, Richardson YP, Dean AR, Thompson RL, Smith BT (2017) Selforganizing maps for the investigation of tornadic near-storm environments. Weather Forecast 32(4):1467–1475 17. Zhang W, Fu B, Peng MS, Li T (2015) Discriminating developing versus non-developing tropical disturbances in the western North Pacific through decision tree analysis. Weather Forecast 30(2):446–454 18. Roth V, Steinhage V (1999) Nonlinear discriminant analysis using kernel functions. Adv Neur Inform Process Syst 12 19. Park MS, Kim M, Lee MI, Im J, Park S (2016) Detection of tropical cyclone genesis via quantitative satellite ocean surface wind pattern and intensity analyses using decision trees. Remote Sens Environ 183:205–214

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A Comparative Study for Prediction of Hematopoietic Stem Cell Transplantation-Related Mortality Rishabh Hanselia and Dilip Kumar Choubey

Abstract Multipotent hematopoietic stem cells are transplanted during hematopoietic stem cell therapy (HSCT) in order to replicate inside of a patient and produce more healthy blood cells. These cells are often taken from bone marrow, peripheral blood, or umbilical cord blood. Patients with particular blood or bone marrow malignancies, like multiple myeloma or leukemia, are those who have the procedure the most frequently. Though a lifesaving procedure, it comes with its risk; hence, to minimize the risk, prediction of survivability and the factors affecting it is crucial. In this study, the authors have done an extensive and rigorous analysis of various works done in predicting the mortality of patients undergoing hematopoietic stem cell transplantation that involves the use of machine learning and data mining techniques and have compared them. This study gives an overview of the available machine learning and data mining techniques in improving risk prediction for patients undergoing HSCT that provides an alternative to traditional risk scores and indexes. Though these techniques already outperform the in-use risk scores and indexes, they can be still improved upon by the use of a specialized and large amount of data. Keywords HSCT · HCT · Allo-HSCT · EBMT · ML · Data mining

1 Introduction Undifferentiated cells are stem cells. They can grow in the body to become different specialized cells. Typically, bone marrow, peripheral blood, or umbilical cord blood is the sources of stem cells. A patient’s own stem cells may be used during an R. Hanselia (B) · D. K. Choubey Department of Computer Science and Engineering, Indian Institute of Information Technology Bhagalpur, Bihar, India e-mail: [email protected] D. K. Choubey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_49

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autologous stem cell transplant, an allogeneic stem cell transplant, or a syngeneic stem cell transplant (from an identical twin). HSCT, often known as a bone marrow transplant or HPSCT, involves giving patients with malfunctioning or depleted bone marrow healthy hematopoietic stem cells. In cases like immune deficiency syndromes, hemoglobinopathies, and other disorders, this serves to enhance bone marrow function and enables either the destruction of tumor cells with malignancy or the generation of functional cells that can replace the defective ones. Before the transplant, the recipient’s immune system is typically damaged by radiation or chemotherapy. Major side effects of allogeneic HSCT include infection and graft-versus-host disease. HSCT is still a risky operation with numerous potential problems; it is only performed on individuals with terminal illnesses. The usage of the operation has expanded beyond cancer to include autoimmune disorders and inherited skeletal dysplasias, particularly malignant infantile osteopetrosis and mucopolysaccharidosis, as survival rates after the procedure have grown. Even though in recent years there has been a decline in mortality rates after transplantation, but they are still substantial. Risk scores and indexes like the European Group for Blood and Marrow Transplantation (EBMT) risk score and the Hematopoietic Cell Transplant-Comorbidity Index (HCT-CI) have been developed to aid clinical decisions. But, these methods are based on a standard statistical approach and have sub-optimal predicting accuracies. This along with ever-increasing medical data requires better prediction models. This demand can be met by machine learning models and various other data mining approaches. This study focuses on comparing and understanding the various works by researchers in predicting the mortality rate for patients undergoing HSCT with the use of machine learning algorithms and data mining.

2 Literature Review Jahan Ratul et al. [1] used various machine learning algorithms along with Hyperparameter Optimization to predict the mortality of children undergoing HSCT and also compared the performance of the various algorithms used. Karami et al. [2] used various machine learning algorithms to identify the factors affecting the survivability of patients with acute myeloid leukemia. Pan et al. [3] talk about the application of machine learning algorithms to predict the relapse in childhood acute lymphoblastic leukemia (ALL) which is an important factor in the survivability of the patient. Iwasaki et al. [4] used three different machine learning algorithms (DynamicDeepHit, Random Survival Forest, and AdaBoost) to predict the overall mortality, relapse-free mortality of patients with hematologic diseases that underwent their first allo-HSCT between 1986 and 2016. Goswami et al. [5] developed a three-factor two-stage system that categorizes patients of multiple myeloma patients that were undergoing autologous stem cell transplantation in high-risk and low-risk groups of relapses. Okamura et al. [6] developed an interactive web application that enables

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the use of a graphical user interface to provide personalized prognostic predictions for patients that had an allogeneic hematopoietic cell transplantation which uses the Random Survival Forest model. Taati et al. [7] used data mining techniques that involved Collaborative filtering, logistic regression, random forest, and SVM to identify patients with high survival probabilities that had bone marrow transplants. Choi et al. [8] predicted the long-term survival of patients suffering from hematologic malignancies after they underwent allo-HCT using five different machine learning models and concluded that gradient boosting machine was the best performing. Iwasaki et al. [4] show the use of ensemble learning involving various machine learning algorithms to establish a predictive model to predict GVHD-free, relapsefree survival of patients. Eisenberg et al. [9] used gradient boosting machine to develop a model that integrates baseline data and CMV lab data for time-dependent mortality prediction for patients that had hematopoietic cell transplantation. Shouval et al. [10] used Alternating Decision Trees to develop an interpretable model for predicting the mortality of patients that had their allo-HSCT to show that ML-based models can outperform risk scores and indexes that are already being used for clinical predictions. Shouval et al. [11] made use of the EBMT dataset to understand and discover the factors that affect the prediction of HSCT-related mortality. They made use of five different ML algorithms. Shouval et al. [12] made use of Alternating Decision Trees to create a novel predictive model for allo-HSCT mortality after 100 days. A data mining study for the same was showcased. Nazha et al. [13] developed a model that provides patients with personalized prognostics for different points of time in the cycle after hematopoietic cell transplantation. In the same way, [14] have used functional link convolutional neural network for the classification of diabetes mellitus. Choubey et al. [15] presented a hybrid intelligent system for diabetes disease diagnosis. Choubey et al. [16] have shown the implementation and analysis of classification algorithms for diabetes. Choubey et al. [17] present comparative analysis of classification methods with PCA and LDA for diabetes. Choubey et al. [18] have evaluated the performance of classification methods with PCA and PSO for diabetes. The above-discussed works have used a various soft computing and computational intelligence techniques to predict diabetes. Researchers used both public as well as collected real-world data and compared and analyzed their proposed algorithms with the existing algorithms. Analyses as well as future work of each algorithm was discussed. Similarly, [19] discussed soft computing approaches for breast cancer detection; researchers [20] conducted a comparative analysis for leukemia using machine learning and data mining methods; and [21] conducted a comparative analysis for leukemia using soft computing approaches. So, after reviewing these existing articles, idea came to us to review the work on prediction of hematopoietic stem cell transplantation-related mortality. Table 1 illustrates the summary of the existing works. Figures 1, 2, and 3 show the performance in graphical form for the models used in the various works that has been studied for this article and mentioned in Table 1. Figure 1 represents accuracy, Fig. 2 represents AUC, and Fig. 3 represents C-index. These figures represent paper reference number versus accuracy for Fig. 1, AUC for Fig. 2 and C-index for Fig. 3.

Decision tree, random forest, logistic regression, k-nearest neighbors, gradient boosting classifier, AdaBoost, XGBoost

UCI machine learning repository: bone marrow transplant: children dataset

Leukemia sample bank at the University of Texas M. D. Anderson cancer center

Guangzhou Women and Children’s Medical Center, Guangzhou, China

1

2

3

SVM, logistic regression, random forest, decision tree

Decision tree, random forest, logistic regression, Naive Bayes, W-Bayes Net, and gradient boosted tree

Techniques used

Dataset used

Paper reference no.

Table 1 Summary of the works Future scope

85.17% accuracy, 0.93 AUC

94.73% accuracy

Performance

The first study to Use of a larger dataset, use 82.9% accuracy employ machine of strong predictors, MRD, learning models based and some gene fusions on medical data from the Electronic Medical Record to forecast childhood ALL relapse

Achieves best-in-class – performance rate of prediction of survival of patients of AML

Prediction through Deep learning techniques this method requires fewer data, resources, and time consumption

Advantages

(continued)

Machine learning applications for childhood acute lymphoblastic leukemia relapse prediction

Prediction of survival rate of AML patients using machine learning algorithms

Prediction of survivability of children undergoing hematopoietic stem cell transplantation using different machine learnings

Purpose

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AdaBoost, Random Survival Forest, and dynamic-DeepHit, fivefold cross-validation

Seventeen transplantation centers in Japan

Department of Spectral clustering, medical fast and frugal tree oncology of All India Institute of Medical Sciences (AIIMS)

Osaka City University Hospital

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5

6

Random survival forest model

Techniques used

Dataset used

Paper reference no.

Table 1 (continued)

Interactive tool that can provide various types of patient-specific prognosis prediction curves in allo-HCT

Performance

Web application developed is preliminary, predictive performance for new allo-HCT candidates in other institutes is not guaranteed, susceptible to selection bias

0.70 AUC for 1-year overall survival, 0.72 AUC for progression-free survival, 0.73 AUC for relapse, and 0.77 AUC for non-relapse mortality

-

The validation set size can C-index O.631 be increased (final risk scores into two groups due to small validation set)

Future scope

Extremely conclusive Unavailability of on the effect of stem cytogenetic/FISH data, cell rescue sample paucity

The model outperformed other cutting-edge competitive risk models in terms of the C-index across all risk categories (C-index: 0.631)

Advantages

(continued)

To develop an interactive web application for plotting personalized prognosis prediction curves in allogeneic hematopoietic cell transplantation using machine learning

To develop a staging system to predict the risk of relapse in patients with multiple myeloma undergoing autologous stem cell transplantation

Establishment of a predictive model of GvHD-free, relapse-free survival after allogeneic hematopoietic stem cell transplantation using a machine learning algorithm

Purpose

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Collaborative filtering, tenfold cross-validation, logistic regression, random forest, SVM

Shariati Hospital, Tehran University of Medical Sciences

Asan Medical Center, Seoul, South Korea

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Gradient boosting machine (GBM), random forest, deep neural network, logistic regression, and adaptive boosting (AdaBoost)

Techniques used

Dataset used

Paper reference no.

Table 1 (continued)

Offers a tailored strategy for choosing patients or donors who are better candidates for allogeneic HCT and factors connected to transplantation

Confirmed feasibility of identifying the individuals with very high chances of survival with high accuracy

Advantages

Use of a larger patient cohort, external validation using data from a greater number of patients

Explicit modeling of the binary properties of dissected features into matrix factorization, incorporating a generative model on the distribution of missing values into the prediction process, and also the collection of further records

Future scope

71.2% accuracy, 0.788 AUC, SD of 0.03

92% accuracy for 74 recipients and 97% accuracy for 31 recipients

Performance

(continued)

Create and test a machine learning-based predictive model for allogeneic HCT survival in patients with hematologic malignancies

To identify patients with high odds of survival in bone marrow transplant records using data mining

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Cox proportional Hazard (Cox-PH), dynamic-DeepHit, random survival forest, XGBoost, AdaBoost, extra tree classifier, bagging classifier, and gradient boosting classifier

Kyoto Stem cell transplantation group

Department of hematology and stem cell transplantation, West-German Cancer Center, University Hospital Essen

Acute Leukemia working party registry, European group for blood and Marrow transplantation

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The developed model had superior performance than physicians at predicting CMV reactivation

A new predictive model for survival analysis employing a stacked ensemble of various machine learning algorithms that demonstrated superior risk stratification versus existing techniques

Advantages

Alternating Decision A novel prediction Trees (ADT), tenfold model based on ten cross-validation variables that performed better than the EBMT score

Gradient boosting machine using LightGBM, logistic regression, fivefold cross-validation

Techniques used

Dataset used

Paper reference no.

Table 1 (continued)

Utilization of algorithms designed for survival modeling, accounting censored data, and incorporation of additional biologic, genetic, and clinical features

Integration of additional features in the model, Improvement in mortality prediction

Individualized prediction and treatment require the use of external validation utilizing larger data and more in-depth patient information

Future scope

0.701 AUC for 100 days survival, 0.657 AUC for 2 years survival

0.92 AUC for mortality

67% accuracy

Performance

(continued)

Improve risk prediction of allogeneic HSCT by utilizing ML algorithms

Development of a machine learning model that integrates both baseline patient data and time-dependent laboratory measurements to predict mortality and CMV reactivation

To comprehend the risk for GRFS and each component of GRFS, a novel analytical approach that handles right-censored data and competing hazards correctly

Purpose

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Center for International Blood and Marrow Transplant Research Registry

14

Random Survival Forest

AL Registry, Alternating Decision European Group Tree (ADT), tenfold for Blood and cross-validation Marrow Transplantation

13

The model can provide survival probabilities at different points of time which are personalized for patients

Can be readily used online, provides personalized 100 days’ OM-risk estimation

AdaBoost, A novel experimental Alternating Decision system Tree (ADT); logistic regression (LR); multilayer perceptron (MLP); Naïve Bayes (NB); random forest (RF)

Acute Leukemia working party (ALWP) registry, EBMT

12

Advantages

Techniques used

Dataset used

Paper reference no.

Table 1 (continued)

Larger dataset is required to improve the C-index, requires external validation to ensure reproducibility of the model

Aim for making more precise predictions for long-term outcomes, managing censored data, use of an external dataset for validation

Involvement of more inputs in the dataset like genetic, clonal, and biologic factors

Future scope

0.62 C-index for OS, and 0.68 C-index for time to relapse

AUC, 0.701

AUC 0.67

Performance

To create a custom prediction model that can predict post-transplant survival outcomes for MDS patients based on clinical, molecular, and transplant-related data

To facilitate transplant-related mortality risk prediction with the use of machine learning algorithms

To analyze the factors involving the performance of ML algorithms used for the prediction of HSCT-related mortality

Purpose

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Accuracy

Accuracy in %

100

94.73 85.17

97

92

82.9

80

71.2

67

8

9

60 40 20 0 1

2

3

7(for 74 7(for 31 receipients) receipients)

Paper Reference No. Accuracy (in %)

Fig. 1 Accuracy for the models

AUC

AUC 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0.93

0.92 0.7

0.72

0.73

0.77

0.78

0.7

0.65

0.67

0.7

Paper Reference No. AUC

Fig. 2 AUC for the models

3 Discussion and Future Directions The authors in this study reviewed various existing research works done on the prediction of survivability of patients undergoing HPSCT. The authors discussed the various techniques used and compared their performance, their motivation, and how their study has contributed to predicting the survivability of patients.

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Comparison of C-Index

0.7

0.68

C-Index

0.68 0.66 0.64

0.63

0.62

0.62 0.6 0.58 4

14 (for OS)

14 (for time to relapse)

Paper Refrence No. C-Index

Fig. 3 C-index for the models

Traditionally, the medical decision regarding the risk of HPSCT is done by the medical expert along with occasional aid from the statistical models providing the expert with a risk score/index to make an informed decision. As seen in the various works of the researchers, it is evident that machine learning models have better prediction capabilities while maintaining interpretability. The use of ML modelbased risk prediction methods enables if not better, comparable risk prediction capabilities while reducing the requirement for data, resources, and time consumption. They also provide an opportunity for use of interactive tools that provide patientspecific prognostic details. One of the works also showed the ability to offer a tailored strategy for choosing patients or donors who are better candidates. Besides this, the use of such methods which are based on ML has the benefit of ease of access and use. Though the machine learning models presented by the various researchers in their work outperformed the traditional statistical methods, they show room for improvement, and these improvements were discussed in the comparative study done by the authors of this comparative review. Like the use of a larger dataset and strong predictors was seen as a common scope of improvement for the explored work in this review. The addition of more features was also one of the improvements that could be incorporated. Also, the rise in medical data and its ever-increasing complexity of it give an opportunity for the use of deep learning models for better handling of large and complex data.

References 1. Jahan Ratul I, Habiba Wani U, Muntasir Nishat M, Al-Monsur A, Ar-Rafi AM, Faisal F, Ridwan Kabir M (2022) Survival prediction of children undergoing hematopoietic stem cell transplantation using different machine learning classifiers by performing chi-squared test and hyper-parameter optimization: a retrospective analysis. arXiv e-prints, arXiv-2201

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2. Karami K, Akbari M, Moradi MT, Soleymani B, Fallahi H (2021) Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques. PLoS ONE 16(7):e0254976 3. Pan L, Liu G, Lin F, Zhong S, Xia H, Sun X, Liang H (2017) Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia. Sci Rep 7(1):1–9 4. Iwasaki M, Kanda J, Arai Y, Kondo T, Ishikawa T, Ueda Y, Takaori-Kondo A et al Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning. Blood Adv 6(8):2618–2627 5. Goswami C, Poonia S, Kumar L, Sengupta D (2019) Staging system to predict the risk of relapse in multiple myeloma patients undergoing autologous stem cell transplantation. Front Oncol 9:633 6. Okamura H, Nakamae M, Koh S, Nanno S, Nakashima Y, Koh H, Nakamae H (2021) Interactive web application for plotting personalized prognosis prediction curves in allogeneic hematopoietic cell transplantation using machine learning. Transplantation 105(5):1090–1096 7. Taati B, Snoek J, Aleman D, Ghavamzadeh A (2013) Data mining in bone marrow transplant records to identify patients with high odds of survival. IEEE J Biomed Health Inform 18(1):21– 27 8. Choi EJ, Jun TJ, Park HS, Lee JH, Lee KH, Kim YH, Lee JH et al (2022) Predicting longterm survival after allogeneic hematopoietic cell transplantation in patients with hematologic malignancies: machine learning–based model development and validation. JMIR Med Inform 10(3):e32313 9. Eisenberg L, XplOit consortium, Brossette C, Rauch J, Grandjean A, Ottinger H, Turki AT et al (2022) Time-dependent prediction of mortality and cytomegalovirus reactivation after allogeneic hematopoietic cell transplantation using machine learning. Am J Hematol 97(10):1309–1323 10. Shouval R, Nagler A, Labopin M, Unger R (2015) Interpretable boosted decision trees for prediction of mortality following allogeneic hematopoietic stem cell transplantation. J Data Min Genom Proteom 6(4):2 11. Shouval R, Labopin M, Unger R, Giebel S, Ciceri F, Schmid C, Nagler A et al (2016) Prediction of hematopoietic stem cell transplantation related mortality-lessons learned from the in-silico approach: a European society for blood and Marrow transplantation Acute Leukemia working party data mining study. PLoS One 11(3):e0150637 12. Shouval R, Labopin M, Bondi O, Mishan Shamay H, Shimoni A, Ciceri F, Mohty M et al (2015) Prediction of allogeneic hematopoietic stem-cell transplantation mortality 100 days after transplantation using a machine learning algorithm: a European Group for Blood and Marrow transplantation Acute Leukemia working party retrospective data mining study. J Clin Oncol 33(28):3144–3151 13. Nazha A, Hu ZH, Wang T, Lindsley RC, Abdel-Azim H, Aljurf M, Saber W et al (2020) A personalized prediction model for outcomes after allogeneic hematopoietic cell transplant in patients with myelodysplastic syndromes. Biol Blood Marrow Transplant 26(11):2139–2146 14. Jangir SK, Joshi N, Kumar M, Choubey DK, Singh S, Verma M (2021) Functional link convolutional neural network for the classification of diabetes mellitus. Int J Numeric Methods Biomed Eng 37(8):e3496 15. Choubey DK, Paul S (2016) GA_MLP NN: a hybrid intelligent system for diabetes disease diagnosis. Int J Intell Syst Appl 8(1):49–59 16. Choubey DK, Paul S, Shandilya S, Dhandhania VK (2020) Implementation and analysis of classification algorithms for diabetes. Curr Med Imag 16(4):340–354 17. Choubey DK, Kumar M, Shukla V, Tripathi S, Dhandhania VK (2020) Comparative analysis of classification methods with PCA and LDA for diabetes. Curr Diabetes Rev 16(8):833–850 18. Choubey DK, Kumar P, Tripathi S, Kumar S (2020) Performance evaluation of classification methods with PCA and PSO for diabetes. Netw Model Anal Health Inform Bioinform 9(1):1–30 19. Sharma D, Jain P, Choubey DK (2020) A comparative study of computational intelligence for identification of breast cancer. In: International conference on machine learning, image processing, network security and data sciences, 2020, pp 209–216

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A Strategic Review on MIR Photodetectors: Recent Status and Future Trends Bhaskar Roy, Md. Aref Billaha, Ritam Dutta, and Debasis Mukherjee

Abstract In our daily life, infrared photodetectors (IRPDs) play a pivotal role in numerous applications, viz. medical/imaging, ecological sensing, and night vision for commercial as well as defense sectors. Especially in semiconductor industrial sectors, majorly the mercury cadmium telluride (HgCdTe) and compound material-based photodetectors are used nowadays. Currently, activities in infrared detector research are focused on upgrading the IRPD performance and making the IR detectors cheaper, simplifying the fabrication processes, increasing the operating temperature, and more convenient to use. This research work mainly focuses on the elaborate study of various photodetectors, viz. lead sulfide (PbS), Lead Selenide (PbSe), mercury cadmium telluride (HgCdTe), Lead Tin Telluride (PbSnTe), Schottky-barrier photoemissive devices, silicon and germanium detectors, III–V and II–VI ternary alloy detectors, indium antimonide (InSb) photodiodes, quantum-well and infrared photodetectors, and their applications. It has also been observed that when compared to other III-V room-temperature detectors currently available, it is the photodiode, which has a wider spectral response. Keywords Bandgap · Eigen-energy · Quantum well · Infrared detector · Mid-infrared

B. Roy (B) · D. Mukherjee Brainware University, Kolkata, West Bengal 700125, India e-mail: [email protected] Md. Aref Billaha Jalpaiguri Polytechnic Institute, Jalpaiguri, West Bengal 734007, India R. Dutta Poornima University, Jaipur, Rajasthan 303905, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_50

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1 Introduction Friedrick William Herschel discovered infrared radiation in the early nineteenth century. Since then, many attempts have been made for spectrum detection and spectrum analysis, which resulted in the first IR photo detection in the early twentieth century. This infrared radiation ranged from 0.8 to 12 μm and was classified as NIR, SWIR, MWIR (3.0–5.0 μm), LWIR, and VLIR. The mid-wavelength infrared wavelength (MWIR) ranges are crucial for free-space communications and astronomy because it allows transmission without significant losses [1]. Low-cost MIR detectors have enormous potential in emerging applications such as secure video surveillance, automotive intelligent night vision, smart phoneembedded night vision, and many other typical electronic devices. Recently, MIR detectors have received a lot of attention in sensing technology, beginning with affluent compound (III-V) elements include indium antimonide (InSb), indium gallium arsenide (InGaAs); the (II-VI) group of elements include silicon (Si), mercury cadmium telluride (Hg1−x Cdx Te), lead sulfide (PbS), and germanium (Ge) for short wavelengths (Fig. 1). Our focus has primarily been on the atmospheric windows’ wavelengths 3–5 μm (MWIR) because several gases absorb in this range [2, 3]. As a result, developing a low-cost alternative material for high-performance MIR detectors is highly desirable. Gas detection and measurement are commonplace in modern life as the emission of pollutants such as car exhaust and greenhouse gases that contribute to global warming. Many gases are hazardous and potentially flammable. Therefore, gas

Fig. 1 Spectral detectivity of dominant SWIR and MIR detectors [2, 3]

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sensing is required to guarantee safety, particularly in an industrial setting. Many industries produce and use unique gases in their manufacturing processes. The detection of combustible and toxic gases, as well as concentration measurement systems, is required for these plants to operate. Every gas-sensing system connects to a sizable global market. Most of this market is accounted for gas detection, while the remaining part is reported for gas sensing and analyzing. The most popular gas for gas detection is CH4 , with H2 S, CO, O2 , and CO2 following (Fig. 2). Catalytic sensors and IR sensors are the most practical in sensor technologies for toxic and combustible gas detection. Since the majority of important gases (Fig. 3) possess strong absorptions in the MIR spectra, MIR gas sensing has emerged as the suitable approach among various gas-sensing strategies. The most common measurement technique used for CO2 as well as for hydrocarbons and CO measurements is MIR absorption.

Fig. 2 Pie chart of global market shares of different gas-sensing systems in 2014 [4]

Fig. 3 Molecular gas absorption bands in the infrared. Above a wavelength of 3 μm, the strongest absorptions occur in the MIR range [4]

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MIR gas sensing has been outperforming other technologies considerably at a challenging cost with respect of sensitivity, selectivity, and stability. Because many contaminants and toxic gases and liquids have essential absorption bands in MIR range, photodetectors functioning in the 3–5 μm spectral regions have a wide range of possible applications in optical gas sensors based on optical absorption (Fig. 3). Moreover, the high value of detectivity of the device at room temperature makes it attractive for optical gas sensing. The model allows the device’s performance to be optimized in terms of detectivity, responsiveness, dark current, and quantum efficiency. In carbonated drinks, e.g., hydrocarbons (3.3 μm), CO2 (4.25 μm), and saccharine (3.9 μm) (soft drinks) [5] and CO (4.6 μm) from automobile exhaust gases, all require precise monitoring in a diverse situation (e.g., oil rigs, coal mines, landfill sites, car exhausts) and concentrations ranging from ppb to nearly 100% [6]. MIR gas-measuring systems have an advantage over others because many gases have significantly strong absorption features only in the MIR (e.g., CO2 – shows strong infrared absorption, at 4.25 μm). Several gases, like CO2 and CH4 , comprise much stronger fundamental absorptions in the MIR than in the other bands. There is also current interest in volatile organic compound (VOC) monitoring and measurement, as they can be used to diagnose a variety of metabolic disorders [7]. In general, detector material is chosen primarily based on wavelength of interest, performance criteria, and operating temperature. Photodetectors characteristics will mainly depend on the absorption, dark current, response time, quantum efficiency, responsivity, mobility and detectivity. The absorption should be as high as possible so that current is generating much more and it reaches to the collector consequently photocurrent increases. The dark current should be as small as possible as detectivity which is basically how sensitive the detector is for a small current to give its response or responsivity. Narrow bandgap semiconductors were rapidly progressing, which would help to extend wavelength with improved sensitivity. These alloy systems are nearly ideal for a wide range of IR detectors (high optical absorption coefficient, high electron mobility, and low thermal generation rate) due to its essential properties of narrowgap semiconductors. These devices could serve as the basis for an optical sensor to monitor carbon monoxide levels in the environment at 4.6 μm or as a replacement for PbSe photoconductors. Comparatively less work has been done on the modeling of mid-infrared photodetectors than in near-infrared regions based on narrow bandgap III–V semiconductor materials [8]. There are many latest mature materials, viz. II– VI, III–V, QW nanostructures (Fig. 4), which can be used in IRPD devices, which have also been investigated in this study [1]. The most important detectors that are generally used in infrared or visible light detection are intrinsic detectors (photoconductive) and QW detectors (Table 1). The fabrication and characterization of a metal–semiconductor–metal (MSM)structured AlGaN/GaN photodetector operating at room temperature in 2.5–5 μm mid-infrared wavelength regions have been reported by several researchers earlier. Because of fundamental advantages, mainly simple structure, and fabrication, devices are frequently fabricated in either Schottky or MSM configurations [3]. The main disadvantage of MSM photodetectors is their inherent low sensitivity. To overcome

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Fig. 4 Schematic structure of GaAs/AlxGa1-xAs QWIP

this limitation, the heterojunction configuration is used with modern technologies, and demand for good quality materials AlN, GaN, InN, and their alloys AlGaN, InGaN is essentially a condition [9].

2 Material Study To describe any device structure’s physics, the material properties play a pivotal role in determining its electrical behavior. But, we have focused our study on the photoconductive infrared quantum-well devices that use carrier excitation mainly as other structure may not be useful in MWIR regime and suitable materials also may not be available. Due to the utilization of intrabank processes, these infrared detectors have the advantage of implementation with wide bandgap materials that are chemically stable. As a result, GaAs, InGaAs, InSb, InAsSb, InAs, GaInSb dominate initially. Table 2 shows the dark current densities, which compares some common nitridebased photodetectors (III–V) with various specifications. From this, it is evident that the lowest dark current density is obtained in p–i–n photodetectors. In 1963, the narrow band gap III–V (InAs1-x Sbx ), IV-VI (Pb1-x Snx Te), and II–VI (Hg1-x Cdx Te) semiconductor alloys’ material systems have been introduced, and these materials have given IR detector designers’ unmatched flexibility. Additionally, IR detector

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Table 1 Comparison of various infrared detectors [3] Detector type

Advantages

Disadvantages

Photon intrinsic (photoconductive) IV–VI (PbS:1–3.6 μm, PbSe:1.5–5.8 μm, PbSnTe)

– Easier to prepare – Stable materials

– Much higher thermal expansion coefficient – Large permittivity

Photon intrinsic (photoconductive) II–VI (HgCdTe) (2-16 μm)

– Simple bandgap shaping – Well-developed theory and exp. – Multi-color detectors

– Non-uniformity over a large area – High growth cost and processing – Surface instability

Photon intrinsic (photoconductive) III–V (InGaAs, InAs, InSb:2–6 μm, InAsSb)

– Good material and dopants – Advanced technology – Possible monolithic integration

– Heteroepitaxy with large lattice mismatch – Long wavelength cutoff to 7 mm (at 77 K)

Quantum wells (GaAs/ AlGaAs, InGaAs/AlGaAs) Type I

– Matured material fabrication–growth process – Multi-color detectors – High uniformity – Low cost – Covers MWIR to WLWIR and THz

– Thermal generation high – Complicated growth and design – Quantum efficiency is low – Normal incident angles cannot absorb – Elevated temp. performance poor

Quantum wells (InAs/ InGaSb, InAs/InAsSb) Type II

– Low recombination rate – Better wavelength control

– Wavelength control easy – Sensitive Interface

applications can cover all IR bands thanks to the bandgap energy tunability. Because narrow-gap semiconductors have important characteristics like high optical absorption, high electron mobility, and low thermal generation rate, these alloy systems are almost perfect for a variety of IR detectors. PbSnTe was one of those materials that was vigorously pursued alongside HgCdTe. At the turn of the century, two leading IR systems, based on InSb/III-V and HgCdTe detectors, were very advanced and widely viable. Table 2 III-V based on nitride photodetector characteristics with various configurations Device

Dark current (A/ cm2 )

Responsivity (mA/)

Detectivity (cmHz1/2 W−1 )

References

p–i–n-GaN

4 × 10–11 at 10 V

140 at 360 nm at 20 V

1.7 × 1014 at 20 V

[10]

MSM-GaN

10–6 at 1 V

192 at 361 nm at 3V

6.4 × 109 at 3 V [11]

p–i–n-AlGaN/GaN

3 × 10–11 at 6 V

65 at 267 nm at 0V

4.9 × 1014 at 0 V [12]

MSM-AlGaN/GaN

~ 10–6 at 6 V

2 at 264 nm at 0 V 8.9 × 1010 at 0 V [12]

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It is necessary to use different semiconductor materials with various effective bandgaps for various IR wavelengths. We examine IRPDs based on fully developed material in this section, more specifically II–VI and III–V. One of the most vital semiconductor alloy systems, along with HgCdTe, is the II-VI material system and may be the material that is used the most frequently for IR detection in the 1–25 μm spectral range. The HgCdTe ternary alloy, with its high optical absorption coefficient, high quantum efficiency, and long carrier lifetimes, is the ideal IR material system with high operating temperatures and a composition-dependent tailorable energy bandgap. Because of these features, HgCdTe is flexible and can be used to make MWIR detectors. Two major detector materials are used in the mid-infrared range, namely mercury cadmium telluride (HgCdTe) and indium antimonide (InSb) [13]. The high quantum efficiency and wavelength tunability make HgCdTe more preferred material than InSb in spite of the maturity. In comparison to HgCdTe, the InAsSb ternary alloy is much more stable. Because of the high vapor pressure of Hg, it is hard to grow HgCdTe material, and it also has a slew of major drawbacks, including health risks, large fabrication, and processing costs [14]. InSb-based photodetectors limit the wavelength variation due to the fixed bandgap of InSb [15]. So, the application of infrared detectors based on HgCdTe and InSb is restricted due to their expenses and difficulty in increasing operating temperature [16]. As a result, various competing IR technologies, such as lead, InSb, and QW IR detectors, have frequently been proposed to replace HgCdTe in order to get around its limitations. InSb was the first material from the III–V system which is used in MWIR detection, which at that time is having the least energy gap among III– V binary alloys known. HgCdTe performs better at higher operating temperatures because InSb’s energy gap is better matched to the 3–5 μm band. The threshold wavelength is 3–5 μm for the compound InAs, similar to InSb but with a larger energy gap. However, in the 30–120 K temperature range, MWIR HgCdTe photodiodes perform notably better. InAsSb is the material in III-V family that stands out when compared to other materials with respect to IRPD. The InAsSb ternary alloy is more stable than HgCdTe and exhibits a weaker dependence between the band edge and composition. Other physical properties of InAsSb material outperform those of HgCdTe. However, InAsSb faces fabrication challenges due to lattice mismatch with the substrate. The most common Schottky-barrier detector for longer wavelengths is the PtSi detector, which can detect in the 3–5 μm spectral range. The development of photodetectors based on intraband or inter sub-band transitions in quantum-confined heterostructures, like QWIPs, has been prompted by the general limitations of III–V semiconductors [17]. We will begin with QW structures, the most mature nanotechnologies used to improve IRPD efficiency by using new section of materials and heterojunctions with unique electronic and optical characteristics as they have continued to dominate the field [1]. Optical transition in the quantum-well (QW) structures provides a selectable spectrum of emission and detection wavelengths, making them appealing for diverse applications in electronics and optoelectronics devices [18]. Moreover, the task of detecting wavelengths in near and mid-infrared ranges is very challenging.

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The low-temperature energy bandgap diagrams of various semiconductors in relation to their lattice constant matching are shown in Fig. 5. Strain compensation allows a close lattice constant of slim layers of semiconductors to be grown on top of each other within each of the gray zones. Figure 6 depicts the mechanism of designing such thin QWs and barriers to generate localized quantum states resonating with the targeted IR radiation at a particular energy difference. Recently, the typical device modeling of GaAs–AlGaAs-based quantum-well detector has been the pick of research that nearly matches natural lattice structures between GaAs and AlGaAs in the inter sub-band. In order to achieve this, we found that Cds/ZnSe-based optoelectronics would have to show unmatched performance. In this context, one of our authors [19] reported that the peak absorption strength is greater in the MW region than in the LW region, and thus, photocurrent monitoring is important when using CdS/ZnSe materials. At mid and long wavelengths’ regions, they demonstrated that the peak absorption coefficients occur and that the corresponding values are enough to estimate photocurrent, as shown in Fig. 7. His research group also calculated photocurrents for two IR regions, as shown in Fig. 8, for the LW and MW regions. The photocurrent generates due to the strong dependence on absorption causing response in MW which is to be clearly larger than the LW response. In another study, Aref et al. [20] used the CdS/ZnSe material to calculate the detector’s responsivity (R) using R = J M A/Pi , and the responsivity for different materials previously reported, including their results, is presented in Table 3. The CdS/ZnSe QWIP has significantly higher responsiveness than other reported QWIPs. By increasing the number of QWs, incident power, and doping concentration, photocurrent and thus responsiveness can be increased. As a result, we can conclude

Fig. 5 Lattice constant (nm) versus bandgap energy (eV) [1]

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Fig. 6 Energy band profile of the conduction band of a GaAs/AlGaAs QWIP under zero bias (above) and finite bias (below) is depicted schematically [1] Fig. 7 Absorption coefficient calculated as a function of wavelength [19]

that the CdS/ZnSe material is extremely useful for detecting infrared illumination at short to mid-wavelengths. More research can be done to investigate the dark current and the model’s detectability. This will help us understand the model’s overall behavior in the short to the mid-wavelength range. By means of theoretical modeling, Aref et al. [24, 25] examine the efficacy of CdS/ ZnSe-based multicolor QWIP. The mid-operational wavelength region’s absorption

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Fig. 8 Response characteristics of photocurrent in the MW and LW regions [19]

Table 3 Comparison of different models with other reported materials Parameter

Cds/ZnSe [20]

GaAs/AlGaAs [21]

AlGaAs/InGaAs [22]

SiGe/Si [23]

Peak responsivity (Rp)

190 mA/W

90 mA/W

30 mA/W

76 mA/W

strength is noticeably higher than the long-operational wavelength region’s absorption strength, according to measurements of the mid- and long wavelengths of infrared detection. The results also show that the suggested models calculated absorption performs better than QWIPs based on GaAs/AlGaAs. The absorption coefficient for bound-to-bound inter sub-band transitions using III–V-based QWIP, like GaAs/ AlGaAs, is considerably lower.

3 Conclusion Several novel nanotechnology approaches to IRPD detection are investigated and compared to traditional solutions. Mid-wavelength (3–5 μm) IRPDs are expected to see increased use in a variety of fields, including target detection and environmental sensing. One of the most significant IRPD research areas presently is imaging technology. Bulk detectors like AlGaAs, SiGe, and HgCdTe have been broadly commercialized due to their simple fabrication, flexibility in absorption wavelengths, coverage of roughly every IR band, and large responsiveness at specific temperatures. Low dark current and high-performance IRPDs at high temperatures, on the other hand, are critical in many emerging application areas.

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Infrared photodetectors that are well-developed are made of HgCdTe. This substance is frequently used for infrared detection due to its high absorption coefficient and slow thermal generation rate. The compound semiconducting materialbased quantum-well structured IR photodetectors began to receive more attention over time. Due to their affordability and simplicity of integration, high-performance silicon quantum-well-based photodetectors are more sought-after. For MID-IR detection, II–VI materials, viz. CdS, ZnSe, CdTe, and ZnTe, can be utilized in future research work.

References 1. Tan CL, Mohseni H (2018) Emerging technologies for high performance infrared detectors. Nanophotonics 7(1):169–197 2. Rogalski A (2012) Progress in focal plane array technologies. Prog Quantum Electron 36(2):342–473 3. Rogalski A (2003) Review Infrared detectors: status and trends. Prog Quantum Electron 27:203–210 4. Frost S (2015) Analysis of the global gas sensors, detectors, and analyzers market, NF 77-32 5. Technical Information SD12 (2014) Characteristics and use of infrared detectors, Hamamatsu photonics 6. Chakrabarti P, Krier A, Morgan AF (2003) Analysis and simulation of a mid-infrared double heterojunction photodetector grown by LPE. IEEE Trans Electron Devices 50(10):112–117 7. Erden F, Soyer EB, Toreyin BU, Cetin AE (2010) VOC gas leak detection using pyroelectric Infrared sensors. IEEE Int Conf Acoust, Speech and Signal Processing, pp 1682–1685 8. Lal RK, Jain M, Gupta S, Chakrabarti P (2003) Theoretical analysis of a proposed InAs/InAsSb heterojunction photodetector for mid-infrared (MIR) applications. IEE Proc Optoelectron 150(6):342–349 9. Allam Z, Hamdoune A, Boudaoud C (2013) The electrical properties of InGaN/GaN/AlN MSM photodetector with Au contact electrodes. J Electron Devices 17:1476–1485 10. Zhang Y, Shen SC, Kim HJ, Choi S, Ryou JH, Dupuis RD, Narayan B (2009) Low-noise GaN ultraviolet p-i-n photodiodes on GaN substrates. Appl Phys Lett 94:221109 11. Wang CK, Chang SJ, Su YK, Chiou YZ, Chen SC, Chang CS, Lin TK, Liu HL, Tang JJ (2006) GaN MSM UV photodetectors with titanium Tungsten transparent electrodes. IEEE Trans Electron Devices 53:38–42 12. Ozbay E, Biyikli N, Kimukin I, Kartaloglu T, Tut T, Aytür O (2004) High-performance solarblind photodetectors based on Alx Ga1-x N heterostructures. IEEE J Sel Top Quantum Electron 10:742–751 13. Harrison P (2005) Quantum wells, wires and dots, 2nd edn. Wiley-Interscience Publication, England, 1–510. ISBN: 978-0-470-01081-5 14. Tan CL, Mohseni H (2017) Emerging technologies for high performance infrared detectors. J Nanophoton 61:21–29 15. Levine BF (1993) Quantum well infrared photodetectors. J Appl Phys 74. https://doi.org/10. 1063/1.354252 16. Zhang S, Hu Y, Hao Q (2020) Advances of sensitive infrared detectors with HgTe colloidal quantum dots. MDPI J Coat 10:760–768 17. Germano M, Penelloet S (2019) Progress in symmetric and asymmetric superlattice quantum well infrared photodetectors. Ann Phys 531:1800462 18. Lyman JE, Krishnamoorthy S (2020) Theoretical investigation of optical inter sub band transitions and infrared photodetection in β-(Alx Ga1 − x )2 O3 /Ga2 O3 quantum well structures. J Appl Phys 127:173102. https://doi.org/10.1063/5.0001917

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19. Billaha MA, Bhowmick B, Choudhary SK (2021) Inter-sub band photo response Analysis of CdS/ZnSe QWIP. In: Advances in smart communication technology and information processing, pp 417–424 20. Billaha MA, Bhowmick B, Choudhary SK (2021) Investigating inter sub band photocurrent in CdS/ZnSe quantum well photodetector for infrared applications. Microsyst Technol 27(9):357– 363 21. Gadir MA, Harrison P, Soref RA (2020) Responsivity of quantum well infrared photodetectors at terahertz detection wavelengths. J Appl Phys 91:5820–5825 22. Alves FDP, Santos R, Amorim J, Issmael AK, Karunasiri G (2008) Widely separate spectral sensitivity quantum well infrared photodetector using interband and inter sub band transitions. IEEE Sens J 8:842–848 23. Kruck M, Helm T, Fromherz G, Bauer JF (1996) Medium wavelength, normal incidence, ptype Si/SiGe quantum well infrared photodetector with background limited performance up to 85 K. Appl Phys Lett 69:3372–3374. https://doi.org/10.1063/1.117263 24. Billaha M, Rakshit S, Roy B, Mondal B, Choudhary SK, Yadav KA (2019) CdS/ZnSe-based multicolor quantum well infrared photodetector for infrared application. Adv Comp Commun Control 501–507 25. Billaha M, Roy B, Sahoo N (2021) Effect of external electric field on photo-responsivity of CdS/ZnSe multiple quantum well photodetector. Superlattices Microstruct 157:107003

Smart Farming Monitoring Using ML and MLOps Yaganteeswarudu Akkem, Saroj Kumar Biswas, and Aruna Varanasi

Abstract Smart farming includes various operations like crop yield prediction, soil fertility analysis, crop recommendation, water management, and many activities. Researchers are continuously developing many machine learning models to implement smart farming activities. This paper reviewed various machine learning activities for smart farming. Once a machine learning model is designed and deployed in production systems, the next challenging task is continuously monitoring the model. A monitoring model is required to ensure that models still deliver correct values even underlying conditions changes. This paper reviewed machine learning operations (MLOps) process feasibility for smart farming to provide correct smart farming recommendations when any environmental factors or soil properties change by continuously monitoring the smart farming process by MLOps. Keywords Smart farming · Machine learning · MLOps · Machine learning models

1 Introduction Agriculture or smart farming includes various activities like fertilizers management, weed management, crop recommendation, seeds managements and many other activities. Researchers or scientists invent smart farming techniques to solve issues in farming. Machine learning and deep learning are current trending technologies which will automate farming in various ways. Smart farming with machine learning will provide smart suggestions to the farmers like which crop is recommended based on Y. Akkem (B) · S. K. Biswas National Institute of Technology Silchar, Cachar, Assam, India e-mail: [email protected] S. K. Biswas e-mail: [email protected] A. Varanasi SNIST, Yamnampet, Ghatkesar, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_51

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soil properties, weather conditions, based on the region and production year which crop will provide better yields and many more suggestions. Deep learning can be applied for identifying disease in crops by using image analysis and suggesting suitable fertilizers, water requirements for any crops and many other suggestions. Since machine learning and deep learning are extensively used in smart farming, there is requirement to automate the process. Machine learning requires complex activities like data preparation, data validation, data verification, model design, application programming interface (API) design and deployment of model in production environment. Once a model is deployed in production, there is a chance of incorrect predictions from model due to unseen data in productions. Sometimes model accuracy may reduce below the expected values. So there is need of continuous monitoring of model. And also due to the complex machine learning life cycle, there is requirement of continuous integration (CI)/continuous deployment (CD) of the model. Machine learning operations (MLOps) will be used for continuous monitoring of model and also if required model will be rebuild and re-deployed again in production environment. Smart farming using ML and DL includes various activities described in further sections. MLOps requirements and life cycle are described in Sect. 3. To implement API, Python framework Flask is used because it is simple to use and open source. Various articles are reviewed for Flask framework.

2 Literature Review 2.1 Literature Review of Machine Learning and Deep Learning in Smart Farming (Condran et al. [1]) Applying ML in agriculture is a challenging task. While data preparation in agriculture various impacts on data like dimensionality reduction, validation and other issues. Some methods were proposed to address challenges in ML activities. ML deployment requires Python frameworks to develop API. In order to use ML models, feasibility of Python Flask is demonstrated in Yaganteeswarudu et al. [2]. Similar to Flask usage in disease prediction explained by Yaganteeswarudu et al. [3], there is a requirement to consume Flask API in front end. Rubia Gandhi et al. [4] demonstrated ML life cycle in various agriculture processes. Since there is huge demand for food, there is requirement to analyze agriculture process like soil management and water management which are demonstrated clearly in Rubia Gandhi et al. [4]. Aruna Devi et al. [5] considered various weather parameters like temperature, humidity and rainfall to predict crop selection. If farmers select recommended crops, farmers can get better yields and profits for crops. Anantha Reddy et al. [6] proposed a method to increase crop productivity by using various machine learning algorithms. Ransom Reddy et al. [7] demonstrated various regression algorithms usage in recommending nitrogen in corn crop.

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Table 1 includes sources of various MLOps journals and machine learning and deep learning agriculture articles. From the following source of articles, it is demonstrated that MLOps is an important activity in agriculture using ML. Table 1 MLOps and smart farming article sources Methods

Description

Source

MLOps challenges Tamburri [8]

Author

Focuses on MLOps importance and key challenges for day-to-day activities of software monitoring

SYNASC

Building MLOps

Liu et al. [9]

MLOps framework design with open-source platforms instead of cloud

Elsevier

MLOps journey

Granlund et al. [10]

Applying best practices of software Springer engineering by using MLOps to the ML feature to avoid any problems in production environment

Python framework Yaganteeswarudu Flask et al. [2]

Explained clearly how to develop Flask API. Flask APIs are developed after model design and can be used for deployment

Diabetes model using Flask

Yaganteeswarudu et al. [3]

Model design and API development ICIPCN 2020 process clearly explained

ML for smart agriculture

Rubia Gandhi et al. [4]

Various ML techniques are discussed for agriculture management like soil management, weed management, water management, etc.

IEEE

ML for crop yield and crop selection

Aruna Devi et al. [5]

Random forest model implemented for crop selection by considering various weather parameters

IEEE

ML and deep learning for smart farming

Durai et al. [11]

By using deep learning and ML recommending pesticides and cultivation cost prediction

Decision Analytics Journal

Classification of soil

Raunak Jahan [12] By using machine learning classifying soil types

IJRASET

Crop yield with data mining

Beulah [13]

Data mining methods are explored for crop yield prediction

IJCSE

Sugarcane prediction using ML

de Almeida et al. [14]

Sugarcane prediction by applying machine learning life cycle

Computers and Electronics in Agriculture

ICCES

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2.2 MLOps Literature Review Tamburri [8] defined the process of MLOps as combination of data, ML and DevOps. The research explored various software quality challenges in ML operations and concluded that MLOps is one of the trending topics to make ML models more sustainable. Liu et al. [9] explored work related to CI/CD pipeline of machine learning. Explained importance of DevOps in ML and by using MLOps managing resource and storage relative activities are clearly explained. Krishnamurthi et al. [15] presented DL and ML models deployment with cloud tool. The importance of DL and ML is presented with a use case colorization of image. Granlund et al. [10] explained clearly complete life cycle of ML like developing ML model, testing ML model, deployment of model and continuous integration and monitoring of model, explored the challenges of deploying a medical product and also explained risk management in healthcare applications. Zhou et al. [16] demonstrated that machine learning deployment differs from other softwares because ML development has complex setup like training model and parameter tuning to increase accuracy. Through experimentation, difficulties in ML deployment and resources required for ML deployment were analyzed. Agrawal et al. [17] proposed a method for data preparation process of ML, since ML data preparation is an important process which may result in good or bad accuracy (Kumeno [18]). Due to increasing demand of ML and DL, there is a requirement to make deployment as easy as possible by considering software challenges. Munappy et al. [19] demonstrated that the process of data preparation in ML has various operations like data validation and data verification. The proposed data Ops can be used to overcome difficulties in data preparation.

2.3 Bibliometric Analysis Springer, IEEE transactions, Web of Science journals and Elsevier Journals articles are reviewed for machine learning and deep learning in smart farming. By using the keywords “MLOps for Machine Learning applications” and “MLOps for deep leaning applications” data retrieved from the databases, the above databases more than 3000 papers have been selected and grouped into different categories like 58% articles, 14% chapters and 7% international conferences, and others include reviews, abstracts and many others.

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Table 2 Sample data set for crop recommendation N

P

K

Temp

Humidity

pH

Rainfall

Label

51

56

18

28.12787838

64.2097765

6.706505915

70.86340755

Blackgram

91

94

46

29.36792366

76.24900101

6.149934034

92.82840911

Banana

Silt

Output

Table 3 Soil fertility data set pH

EC

OC

OM

N

P

K

Mn

Sand

CEC

7.75

0.4

0.02

0.01

76

24

275

4.6

84.3

6.8

7.81

Fertile

8.38

1.09

0.03

0.05

97

12

95

4.2

91.6

4.2

7.21

Non-fertile

Table 4 Crop yield production state wise State_ name

District_name

Crop_ year

Season

Crop

Bihar

SITAMARHI

2010

Rabi

Peas & beans

Odisha

DEOGARH

2006

Summer

Rice

Area

Production

Cat_crop

278

288

Pulses

3000

5000

Cereal

3 Machine Learning Life Cycle in Smart Farming 3.1 Crop Data Set Extraction and Preparation First step in smart farming [20] is data preparation. Data engineers will perform basic Extract Transform Load (ETL) operations. Agriculture data may present in various database sources like Oracle or any relational databases or Excel sheets. In this article, data sets are collected from publicly available websites like Kaggle or UCI Machine Learning Repository. Table 2 represents crop recommendation data set based on various parameters like temperature, rainfall, humidity, nitrogen, etc., recommending crop to the farmers. Table 3 represents soil fertility based on various soil parameters like potassium, nitrogen, sand, etc., verifying whether soil is fertile or not. Table 4 represents state wise crop yield production in India based on season, area, district and state, predicting crop production.

3.2 Agriculture Data Validation Data validation for agriculture model design using ML includes verifying consistency of data and accuracy of data. Data validation is important because accuracy of model depends on quality of the data. Data validation checks whether data is authorized data, data is quality data or not, and data meets the requirements or not. Many users

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like data scientist, business analyst and others are working with data required data validation. Only valid data will produce accurate results.

3.3 Model Training and Model Evaluation Once data is prepared and validated, next step is identifying type of algorithm required to handle the data set. Based on the crop inputs and outputs, algorithms required can be decided, for example in crop recommendation system identifying crop based on various crop parameters. Here it is a classification problem because outcome variable is having fixed values like wheat, rice, soybean, etc. In crop yield prediction outcome, variable is having continuous values, so regression algorithms can be applied. Given data can be divided into training data and test data. Training data is used to train the agriculture model. Different algorithms will be applied on training data like random forest, decision trees, k-nearest neighbors and many more. Model will be evaluated based on accuracy score if it is a classification problem and mean squared error or root mean squared error if it is regression algorithm.

3.4 Maintaining Code Repository To develop machine learning model, Python language is used. There are lot of uses by using Python for machine learning development. Python will provide predefined packages like NumPy, scikit-learn, Pandas and many more due to which number of lines code can be reduced. Once developer develops code, there is requirement to place code in repository so that other developer can integrate their code also. GitHub, Team Foundation Server (TFS) and many other repositories are available to maintain code in repository. While storing code in repository, the responsibility of developer is to check any conflicts and resolve conflicts if any before storing code in repository.

3.5 Model Deployment Development of model is one important task in agriculture model design. Once agriculture model is developed, the next task is to consume model. To consume a model, an API can be designed by using Python frameworks like Flask and analyzed various articles [2, 3] about Flask development and its usage in consuming machine learning model. Model can be deployed in production environment for its usage. For deploying model, various cloud platforms are available like Amazon Web Service (AWS), IoT deployments and many other platforms available. Once an agriculture

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model is deployed in production, farmers can use these models for future prediction. Deployed models can be used for crop recommendation prediction, crop yield prediction, language translation and many more services.

3.6 Monitoring and Retraining if Required When model is deployed in production environment, there is possibility for overfitting or underfitting or model may perform well after deployment. It is developer’s responsibility to monitor model after deployment in production, monitor continuously model predictions and compare with actual predictions. Model may over fit or under fit due to quality of data which may not be good. When model performs well in development environment and when deployed in production accuracy reducing, then model is called as overfitted model. Smart farming model which is not performing well in development or production then model is called as underfitted model. If a model is overfitted or underfitted, then retraining of model is required. Retraining the model will include from starting of the model, checking quality of model, training and testing.

4 Smart Farming Models Monitoring Using MLOps 4.1 Smart Farming Models Without MLOps As shown in Fig. 1, in smart farming without using MLOps, various developers or data engineers in different stages are required. Once the requirement is available data scientists are required to understand the problem and frame a final problem statement. Data can be prepared from different data sources, analyzing quality of the data, validation of the data can be done by data engineer and data scientist (DS). Feature selection which includes finding important features which will results in good accuracy can be done by data scientist. Model building with different machine learning algorithms and selecting best algorithm will be done by machine learning engineers (MLE) and DS. To deploy a model, by using Flask an API is designed and consumed in front end. During deployment process, multiple teams will be involved like DevOps Teams, MLE and DS. Once a model is deployed, DS, MLE and DevOps team will continuously monitor model to check whether model performing well or not in production environment.

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Fig. 1 Smart farming architecture without MLOps

4.2 Smart Farming Models with MLOps Smart farming architecture with MLOps is described in Fig. 2. Initially, smart farming data is collected and basic data analysis is done. Initial smart farming model will be built and evaluate metrics of model and selecting best model. Code repository will be maintained so that all DS, DE and MLE will collaborate and store the code at central location. Once model is built, basic testing will be done and preparing packages required to deploy. A pipeline is designed for deployment. Once model is deployed, there is an automated pipeline which will monitor production model continuously, a service which will test production model. If there is any change in accuracy, again automated pipeline will start extracting data, data validation, data preparation and model training, model evaluation will be done continuously, and if model is giving expected results, then trained model will be used for predictions. This

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Fig. 2 Smart farming architecture with MLOps

MLOps architecture will take care of retraining the model if required and follows machine learning lifecycle automatically.

5 Discussions and Future Scope DevOps plays an important role for any software applications. Each software application required deployment process. By using MLOps, machine learning models will be automatically deployed. Future scope of MLOps in agriculture includes exploring more about cloud computing like integrating Amazon Web Service in MLOps and researching more about how to reduce complex architecture of machine learning by using cloud services. Since many farmers across the world will access smart farming models, there is requirement to explore load balancing in MLOps.

6 Conclusion MLOps is used to build automatic build pipelines. Due to MLOps, it performs automatic ML life cycle whenever there are any changes in model which will avoid model to be overfitted or underfitted. MLOps will reduce manual effort and reduce cost and time complexity. MLOps will invoke deployment pipeline automatically and

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monitor smart farming model continuously. Smart farming will provide suggesting to the farmers on crop recommendation, crop yield and so on in which required model should be more appropriate, and by using MLOps, models are able to provide appropriate suggestion since continuously, models are monitored.

References 1. Condran S, Bewong M, Islam MZ, Maphosa L, Zheng L (2022) Machine learning in precision agriculture: a survey on trends, applications and evaluations over two decades. IEEE Access 10:73786–73803. https://doi.org/10.1109/ACCESS.2022.3188649 2. Yaganteeswarudu (2020) Multi disease prediction model by using machine learning and flask API. In: 2020 5th international conference on communication and electronics systems (ICCES), pp 1242–1246. https://doi.org/10.1109/ICCES48766.2020.9137896 3. Yaganteeswarudu A, Dasari P (2021) Diabetes analysis and risk calculation—auto rebuild model by using flask API. In: Chen JIZ, Tavares JMRS, Shakya S, Iliyasu AM (eds) Image processing and capsule networks. ICIPCN 2020. Advances in intelligent systems and computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_27 4. Rubia Gandhi RR, Angel Ida Chellam J, Prabhu TN, Kathirvel C, Sivaramkrishnan M, Siva Ramkumar M (2022) Machine learning approaches for smart agriculture. In: 2022 6th international conference on computing methodologies and communication (ICCMC), pp 1054–1058. https://doi.org/10.1109/ICCMC53470.2022.9753841 5. Aruna Devi M, Suresh D, Jeyakumar D, Swamydoss D, Lilly Florence M (2022) Agriculture crop selection and yield prediction using machine learning algorithms. In: 2022 second international conference on artificial intelligence and smart energy (ICAIS), pp 510–517. https:// doi.org/10.1109/ICAIS53314.2022.9742846 6. Anantha Reddy D, Dadore B, Watekar A (2019) Crop recommendation system to maximize crop yield in Ramtek region using machine learning. Int J Sci Res Sci Technol 6:485–489. https://doi.org/10.32628/IJSRST196172 7. Ransom CJ, Kitchen NR, Camberato JJ, Carter PR (2019) Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations. Comput Electron Agric 164:104872. ISSN 0168-1699. https://doi.org/10.1016/j.compag.2019. 104872 8. Tamburri DA (2020) Sustainable MLOps: trends and challenges. In: 2020 22nd international symposium on symbolic and numeric algorithms for scientific computing (SYNASC), pp 17– 23. https://doi.org/10.1109/SYNASC51798.2020.00015 9. Liu Y, Ling Z, Huo B, Wang B, Chen T, Mouine E (2020) Building a platform for machine learning operations from open source frameworks. IFAC-PapersOnLine 53(5):704–709. ISSN 2405-8963. https://doi.org/10.1016/j.ifacol.2021.04.161 10. Granlund T, Stirbu V, Mikkonen T (2021) Towards regulatory-compliant MLOps: Oravizio’s journey from a machine learning experiment to a deployed certified medical product. SN Comput Sci 2:342. https://doi.org/10.1007/s42979-021-00726-1 11. Durai SKS, Shamili MD (2022) Smart farming using machine learning and deep learning techniques. Decis Anal J 3:100041. ISSN 2772-6622. https://doi.org/10.1016/j.dajour.2022. 100041 12. Jahan R (2018) Applying Naive Bayes classification technique for classification of improved agricultural land soils. Int J Res Appl Sci Eng Technol (IJRASET) 6:189–193. https://doi.org/ 10.22214/ijraset.2018.5030 13. Beulah R (2019) A survey on different data mining techniques for crop yield prediction. Int J Comput Sci Eng 7(1):738–744

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14. de Almeida GM, Pereira GT, de Souza Bahia ASR, Fernandes K, Marques Júnior J (2021) Machine learning in the prediction of sugarcane production environments. Comput Electron Agric 190:106452. ISSN 0168-1699. https://doi.org/10.1016/j.compag.2021.106452. 15. Krishnamurthi R, Maheshwari R, Gulati R (2019) Deploying deep learning models via IOT deployment tools. In: 2019 twelfth international conference on contemporary computing (IC3), pp 1–6. https://doi.org/10.1109/IC3.2019.8844946 16. Zhou Y, Yu Y, Ding B (2020) Towards MLOps: a case study of ML pipeline platform. In: 2020 international conference on artificial intelligence and computer engineering (ICAICE), pp 494–500. https://doi.org/10.1109/ICAICE51518.2020.00102 17. Agrawal P, Arya R, Bindal A, Bhatia S, Gagneja A, Godlewski J, Low Y, Muss T, Paliwal MM, Raman S et al (2019) Data platform for machine learning. In: Proceedings of the 2019 international conference on management of data, pp 1803–1816 18. Kumeno F (2019) Software engineering challenges for machine learning applications: a literature review. Intell Decis Technol 13(4):463–476 19. Munappy AR, Mattos DI, Bosch J, Olsson HH, Dakkak A (2020) From ad-hoc data analytics to dataops. In: ICSSP. ACM, pp 165–174. [Online]. Available: http://dblp.uni-trier.de/db/conf/ ispw/icssp2020.html#MunappyMBOD20 20. Akkem Y, Biswas SK, Varanasi A (2023) Smart farming using artificial intelligence: a review. Eng Appl Artif Intell 120:105899. ISSN 0952-197. https://doi.org/10.1016/j.engappai.2023. 105899

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques Amol V. Dhumane, Priyanka Kaldate, Ankita Sawant, Prajwal Kadam, and Vinay Chopade

Abstract Conditions related to the cardiovascular system (also known as CVDs) are the biggest cause of mortality according to WHO standards, accounting for 17.9 million deaths each year. The term “cardiovascular diseases” aka CVDs refers to a set of illness that affect both the heart and blood arteries. We collected, pre-processed, and transformed correct data for the training model. Relief and LASSO are used to select features. The highest accuracy value was 63.92% when feature selection was not used, however this value could be raised to 88.52% by employing backward feature selection in conjunction with a decision tree classifier. 78% accuracy has been achieved by the Relief-based selection technique. Top of Form Bottom of Form. The highest accuracy value was 63.92% when feature selection was not used, however this value could be raised to 88.52% by employing backward feature selection in conjunction with a decision tree classifier. According to the results of the experiments, using feature selection method is likely capable of accurately categorizing the condition using only a limited amount of features. Keywords Heart disease · Cardiovascular disease · Heart disease dataset · Machine learning · Data mining · LASSO feature selection · Relief feature selection · Classification algorithms · AdaBoost · Support vector machines · RF · KNN · Decision tree · Gradient boosting

1 Introduction Heart illness is fatal. Heart disease harms too many lives every year. Deterioration of cardiac muscle causes heart disease. Inability to pump blood effectively is another indication of heart failure. Coronary artery disease is heart disease (CAD). CAD is A. V. Dhumane · P. Kaldate · A. Sawant · P. Kadam (B) · V. Chopade Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_52

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caused by poor artery blood flow. Data mining, machine learning, deep learning, and other automated technologies can now diagnose heart issues. In this study, we’ll review machine learning methodologies. Cardiovascular illness is historically considered the most serious human disease. As cardiovascular illnesses and mortality rates rise, global healthcare systems face huge risks and burdens. Men are more likely than women to suffer from cardiovascular problems, especially in middle age or old age, but children are equally at risk. According to WHO figures, heart disease causes one-third of global deaths. Structure of the paper: We’ll cover research on predicting heart disease using classifiers and hybrid approaches in the next section. Section 3 explains how algorithms work. Section 4 describes the planned system and other performance measures. Section 4 explains (1) data preparation, (2) preprocessing, and (3) data preparation hybrid techniques like Bagging and Boosting. Section 4 explains the implementation’s results. Section 6 contains future recommendations and a conclusion.

2 Literature Survey on Machine Learning Algorithms 2.1 LASSO The title of the publication that Sonam Nikhar and her colleagues developed is “Prediction of Heart Disease.” [1]. Using ML methods this paper analyses the Naive Bayes and decision tree classifiers used in our evaluation, particularly for heart disease diagnosis. The decision tree improves Bayesian classification, according to studies using predictive data mining on a comparable dataset. Heart attack prediction using deep learning was created by Abhay Kishore et al. This paper provides a way for predicting heart attacks using deep learning and recurrent neural systems. Recurrent neural network uses deep learning to do new characterization processing. The article outlines the framework modules and assumption. The suggested model uses data mining and deep learning to reduce errors. This article provides instructions for a new heart attack prediction tool. Ashraf et al. coupled individual learning the algorithms with ensemble techniques as J48, multilayer perceptron, random tree, KNN, Naive Bayes, Bayes Net, and random forest in order to make predictions. J48’s accuracy was 70.77%. Using cutting-edge technologies, KERAS achieved 80% accuracy. Using the attention mechanism, an MT-recurrent NN with several tasks was constructed to predicted cardiovascular disease. The model’s AUC increases by 2–6% [2]. Latha et al. developed a model to assess the chance of acquiring heart disease by employing ensemble classification and feature selection strategies. The model calculated heart disease risk. Can improve poor classifiers estimate outcomes and assess heart disease risk. Ensemble classification improved our weak classifiers’ accuracy by 7%. Adding feature selection improved prediction speed and accuracy.

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2.2 Relief Amin et al. investigated a hybrid of voting with NB and LR to these risk factors: KNN, NB, LR, neural network, DT, and SVM. Also investigated was a hybrid voting method that included NB and LR. According to their research, the hybrid model which made use of the chosen attributes had an accuracy of 87.41%. Mienye et al. [3] used a classification trees, a method for randomly dividing the dataset using regression trees and a mean-based splitting strategy into less significant groups in order to construct a model for the prediction of cardiovascular disease. This model used mean-based splitting [3]. Research used this strategy. A homogeneous ensemble was constructed with the use of an accuracy-based weighted classifierensemble. This enabled the ensemble to classify the Cleveland and Framingham test sets with an accuracy of 93% and 91%, respectively.

2.3 KNN Ashok Kumar Dwivedi and colleagues suggested a variety of methods, includes ANN, Logistic Regression, KNN, SVM, and Classification Tree. Logistic regression is the most accurate algorithm. Megha Shahi and colleagues proposed a heart disease prediction system that makes use of data mining techniques. WEKA helps medical facilities diagnose patients’ illnesses and provide the best care. ANN, Naive Bayes, association rule, SVM, KNN, and decision tree were some of the algorithms used. SVM is more accurate and effective than other data mining methods, according to the study. In order to evaluate the reliability of cardiac disease diagnosis and prognosis, Boshra Brahmi and colleagues developed several data mining methods. The goal is to examine classification techniques like J48, decision tree, K-nearest neighbours, SMO, and Naive Bayes. The performance is then evaluated and compared using accuracy, precision, sensitivity, and specificity. J48 and decision tree seem to be the best heart disease predictors. Another study that is relevant to this topic is Jarad et al. “Heart’s Intelligent’s Disease Prediction System with MongoDB.” They used MongoDB with the Naive Bayes, Decision List, and KNN algorithms to diagnose a patient’s heart. They also used a sample dataset from UC Irvine with 13 of 76 factors. CP, Chol, bs, Restecg, sex, Trestbps, Exang, Oldpeak, age, slope, Ca, Thal. This research shows that the algorithms used are accurate, notably Naive Bayes (52.33% accuracy), Decision List (52% accuracy), and KNN (45.67 accuracy). Semen et al. [4] created a diagnostic model for the quick diagnosis of chronic renal disease by using a dataset that included 400 patients and 24 features. The model was helpful in the diagnosis. Recursive feature elimination (RFE) selected the most significant features. Classification was done using decision trees, random forests, SVMs, and K-nearest neighbours (KNN). Each classification method was effective.

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Random forest surpassed every other technique, scoring perfect on accuracy, recall, precision, and F1-score.

2.4 RF The conclusion was reached using standard clinical procedures, which may be carried out at any hospital or medical centre. The diagnostic process is given a boost in terms of confidence and precision thanks to the training of the model using actual data collected from both healthy and sick elderly people. The model also provides an increase in confidence as well as precision when identifying patients. The model also determines and improves the patient’s identification. The methodology helps medical professionals validate clinical records and minimize human error [2]. Random forest and support vector machine were used to improve a classifier model’s performance and accuracy in predicting disease. They could be cured with new medical techniques and therapies. If cancer is detected late, even cutting-edge medical technology won’t help [5]. In addition to standardizing parameters, combining multiple information mining strategies can increase structure precision. This investigation notifies the U.S. about diverse technologies used in separate publications, including a different count of qualities and varying accuracies based on execution instruments [6]. The genetic search method reduces 14 attributes to 6. Severity of sickness corroborated indeterminate findings [7]. Random forest classifiers are the best for predicting cardiomyopathy. Inaccurate data, missing numbers, screaming five knowledge, and outliers limit data mining and machine learning. To manage data quality, apply mathematics and machine learning. “Machine Learning Heart Disease Prediction.” by Karan Bhanot [Scientific Paper] he cited published material that claimed machine learning is used worldwide. Even healthcare isn’t an exception. Learning machines can diagnose locomotor diseases, heart ailments, and others by distinguishing their presence or absence. If medical professionals can predict such knowledge in advance, they can tailor their diagnosis and treatment to each patient. In this article, he outlines a study in which predicted heart disease using machine learning. “Predicting Heart Diseases in People” was the study’s title. K neighbours, support vector, decision tree, and random forest classifiers were used. The article “Heart Disease Prediction using Machine Learning” was written by Aman Preet Gulati and published. In this article, he predicted using the heart disease dataset. From that dataset, he obtains insights about each feature’s weight and how they’re related. This time, we want to determine if someone has a major heart problem [8]. “The Use of Random Forests in the Prediction of Heart Diseases” 2021. During the processing of the algorithms, various Python libraries of varying types are utilized. “Heart Illness Prediction using Machine Learning” achieved an accuracy of 86.9%

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Table 1 Comparison of existing and proposed system Parameters

Proposed system

Existing system

Estimation

On 1000 n_estimator, our model gives 0.89 accuracy

On 1000 n_estimator, existing model gives 0.83 accuracy

Imported libraries

– import numpy – import pandas – import matplotlib – import seaborn – train_test_split – LogisticRegression sklearn.model_selection sklearn.linear_model

– numpy – matplotlib – warnings train_test_split StandardScaler

for the prediction of cardiovascular disease. In the year 2020, Naive Bayes, logistic regression, random forest, and decision tree will be used. Random forest classification achieved 90.16% accuracy. Intelligent system for the prediction of coronary heart disease based on random forests and evolutionary processes (Table 1).

2.5 Comparative Study See Fig. 1.

3 Study of Algorithms 3.1 LASSO Least Absolute Shrinkage Operator(LASSO)—This operator’s minimal selection and shrinking functionality requires changing the absolute value of function coefficients. Certain features with zero coefficients and those with negative coefficients can be eliminated. LASSO performs well for features with less coefficients. Weight with high coefficients be included in the user-selected subsets. LASSO has optional features. The dependability of this feature can be improved by repeat the above technique many time and then prioritizing most prevalent (Fig. 2 and Table 2). The Pseudo-Code for the Newly Proposed Method of Diagnosing Heart Disease. 1. 2. 3. 4. 5. 6.

Start preprocessing the CVD dataset. Feature selection using traditional and suggested algorithms. Train the classifiers by making use of training dataset. Validate by using testing dataset. Calculate metrics for the performance evaluation. End.

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Fig. 1 Accuracy

3.1.1

Ensemble Methods of Machine Learning

Ensemble approaches combine many Decision Tree classifiers to improve classification results. Combining fewer competent learners creates a more capable learner, boosting the model’s accuracy and precision. Figure shows how ensembles work. Noise, uncertainty, and bias contribute to real versus observed outcomes. Ensemble approaches can help manage bias and uncertainty (Fig. 3).

3.2 Relief Relief is a dataset-feature-considering attribute-selection algorithm. The most important aspects should be larger. Relief uses KNN-style feature weighting. Kira and Rendell demonstrated feature selection algorithms. Ri is chosen randomly. Relief’s closest classmate was H, while her closest opponent was M. Ri, M, and H modify feature A’s consistency calculation W [A]. As this isn’t predicted, W [A] performance is decreased if Ri and H are far apart. W [A] is enhanced if Ri and M for attribute A differ since it can differentiate across classes. Each cycle repeats this process where m is a variable.

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Fig. 2 Flow diagram of LASSO Table 2 Results and conclusion of existing system Feature-name

Feature-code

Number of years

Age

00.001

Serum–(cholesterol)

Chol

00.000

(Type) chest pain

Cp

00.080

(Resting) bp

Trest Bps

00.002

Heart rate (max)

THALACH

ST depression originate (by exercise)

Old peak

00.022

Slope: peak exercise—(ST segment)

Slope

00.031

THAL

THAL

00.011

Score

−00.001

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Fig. 3 The working techniques of ensemble process

Fig. 4 Relief algorithm

3.2.1

Mathematical Model and Algorithm Steps

Algorithm (Fig. 4) Input: An attribute vector and value classes Output: the vector W represents our assessments of the quality of the characteristics.

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Pseudo-code: • • • • • • •

W[X]: = 0.0; set all weights. For j: = 1 to m do begin. Select an instance Yi ; randomly. Find nearest hit H and nearest miss M. For X: = 1 to a do. W[X]: = W[X]-diff (X, Yi , H)/m + diff (X, Yi , M)/m. End.

3.3 KNN KNN permutes the dataset’s values and predictions. Since exact parameters for each functional form are unknown, the KNN algorithm is non-parametric. It makes no assumptions about dataset or findings. KNN is a passive classifier since it remembers training data instead of learning and updating weights. Most computer work is done during categorization, not training. KNN works by determining which class a newly discovered feature most closely resembles, then adding to class.

3.3.1

Woking of KNN Algorithm

1. In the beginning, we select a K value for our KNN algorithm. 2. We take a distances measurement. Let’s focus on Euclidean distance. Find the K neighbours’ Euclidean distance. 3. We now examine every neighbour to the newly provided point we have made to determine nearest our point. Just find K nearest. 4. The class with the most results is now visible. We choose the maximum number and give that class our new point. 5. KNN is used this way. K-nearest neighbours, a simple algorithm that sorts new data or cases by similarity, works well in practice. If a new point shares features with its neighbours, it will join their class. KNN is utilized when searching for related things. “K” indicates how many of a new point’s neighbours must be forecasted. Because of its rapid learning, the KNN algorithm is called a lazy learner. When a prediction is needed, the training dataset is remembered and used (Fig. 5).

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Fig. 5 Flow diagram of KNN algorithm

3.4 RF In supervised learning, random forest is employed. It helps with regression and ML classification. It’s based on ensemble learning, which combines multiple classifiers to improve a model’s performance. This ensures model accuracy.

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“Random Forest” uses decision trees on subsets of the supplied dataset to increase its prediction accuracy. Random forest predicts based on the most popular guesses, not one decision tree. More trees in the forest increase accuracy and prevent overfitting. After creating a random forest from N decision trees, each tree is predicted. Random Forest Assumptions Certain decision trees may properly forecast the result because the random forest classifies the dataset using several trees. Each tree’s information points to the expected conclusion when combined. Testing the following two hypotheses may result in a better random forest classifier: • The classifier needs real values in the dataset’s feature variable to create an accurate prediction rather than making an assumption about the data analysis’s conclusion. • There should be just a very slight relationship between the forecasts from each tree.

4 Proposed System (Results and conclusion of proposed system). See Fig. 6.

4.1 LASSO See Fig. 7.

4.2 Relief Relief weights each feature in a dataset to extract them. The weights may then be adjusted. Less significant traits should be given less weight. Relief computes feature weights using KNN-like method (Figs. 8, 9 and Table 3).

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Fig. 6 Working diagram of proposed model

4.3 KNN See Fig. 10.

A. V. Dhumane et al.

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Fig. 7 Result of LASSO

Fig. 8 Result of Relief

4.4 RF See Figs. 11, 12 and Table 4.

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Fig. 9 Accuracy of proposed Relief

Table 3 The features used by Relief algorithms and their respective ranks in the existing model Feature-name

Feature-code

Score

Number of years

AGE

00.19

Serum (cholesterol)

CHOL

00.867

Fasting (blood sugar)

FBS

00.0233

Resting electro-cardiographic results

RESTECG

00.582

Heart-R (Max)

THALACH

00.543

Exercise

EXANG

00.0089

Major blood vessels count—coloured by Fluoroscopy (0–3)

CA

00.581

5 Application • Predicting Buying Behaviour. • Content Recommendation. • Healthcare Diagnosis.

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Fig. 10 Accuracy of proposed KNN

Fig. 11 Proposed system flow

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Fig. 12 Result of RF on different no. of estimators

Table 4 Result and used library

Parameters

Result

Estimation

On 1000 n_estimator, our model gives 0.89 accuracy

Imported libraries – – – – – – – –

import numpy import pandas import matplotlib import seaborn train_test_split LogisticRegression sklearn.linear_model sklearn.model_selection

6 Conclusion Future work will make the model com0070atible with alternative feature selection techniques and resistant to missing data. This research aimed to extend earlier work by establishing a practical, easy-to-use model in a creative and original way.

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References 1. Wu L, Garg SK, Buyya R (2015) Service Level Agreement (SLA) based SaaS cloud management system. In: IEEE 21st international conference on parallel and distributed systems 2. Ashraf M, Ahmad SM, Ganai NA, Shah RA, Zaman M, Khan SA, Shah AA (2021) Prediction of cardiovascular disease through cutting-edge deep learning technologies: an empirical study based on TENSORFLOW, PYTORCH and KERAS. Springer, Singapore, pp 239–255 3. Mienye D, Sun Y, Wang Z (2020) An improved ensemble learning approach for the prediction of heart disease risk. Informat Med Unlocked 20, Art. no. 100402 4. Dhar S, Roy K, Dey T, Datta P, Biswas A (2018) A hybrid machine of molecular & clinical medicine. Learning Approach for Prediction of Heart Diseases. In: 2018 4th international conference on computing communication and automation (ICCCA), vol 7, issue 4, pp 2020–2459. ISSN 2515-8260 5. Sharma S, Parmar M (2020) Heart diseases prediction using deep learning neural network model. Int J Innov Technol Exploring Eng 9(3):1–5 6. Muhammad Y, Tahir M, Hayat M et al (2020) Early and accurate detection and diagnosis of heart disease using intelligent computational model. Sci Rep 10:19747. https://doi.org/10.1038/ s41598-020-76635-9 7. Soni J, Ansari U, Sharma D, Soni S (2011) Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int J Comput Appl 17(8):43–48 8. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:101093 3404324

IoT-Based Home Automation System Using ESP8266 Jyoti Rawat, Indrajeet Kumar, Noor Mohd, Kartik Krishnan Singh Rana, Nitish Pathak, and Rajeev Kumar Gupta

Abstract All gadgets, sensors, and machines may now communicate with one another automatically thanks to the advent of the Internet of things, the surge in automation, and other factors. The usage of IoT to automate modern dwellings through the Internet is demonstrated in this study. Through Bluetooth or Wi-Fi, this system’s mobile-friendly application enables the users to manage their home’s appliances. This automated system also includes temperature, humidity, and other sensors that will provide a broad overview of the home’s environment. All linked appliances are accessible to and under the admin’s management. Due to its flexibility and ability to connect any number of devices and connections, the automation is affordable and can be expanded as needed. Keywords Home automation · ESP8266 · IoT · Smart home · Blynk

The original version of this chapter has been revised. The affiliation of the author Nitish Pathak has been corrected. The correction to this chapter is available at https://doi.org/10.1007/978-981-99-3315-0_69 J. Rawat VertexPlus Technologies Limited, Gopalpura Road, Jaipur, Rajasthan, India I. Kumar (B) · R. K. Gupta Graphic Era Hill University, Dehradun, Uttarakhand, India e-mail: [email protected] N. Mohd Graphic Era Deemed to Be University, Dehradun, Uttarakhand, India K. K. S. Rana DIT University, Dehradun, Uttarakhand, India N. Pathak BPIT, GGSIPU, New Delhi, India

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_53

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1 Introduction The Internet of things (IoT) has evolved since 2013 along with the advancement of contemporary technology, making smart gadgets smarter. Automation systems have grown throughout many industries since they are a crucial part of the dominance of many operations that are related to processes. In the modern period, everyone lives in an automated environment where the superiority of structure, including those in household, business, and other sectors, is now machine driven. House automation systems [1] are moving toward mechanization procedures where the machinery equipment can operate many different home systems with less human labor. It brings about the automatic governance of household hard wires with the use of commander and technology from absolutely diverse areas via laptops, desktops, tablets, or cell phones. The two categories into which automation systems are divided are industrial and household systems. They are automated home automation systems and industrial automation systems. Three types of home automation systems are home automation based on power lines BUS cable or wired wireless home automation is accessible. The application for home automation that applies Android, Dual Tone Multi Frequency (DTMF), RF, Arduino, along with touch screen is discussed in this paper [2]. One other huge advantage of home automation systems is the smoothness by which it can be used by them with a range of devices including desktop computers, tablets voice assistants, and laptops. Household automation using the ESP8266 has many advantages, including increased safety when handling appliances and lighting control, increased home security and awareness thanks to automated door locks and security cameras, increased comfort thanks to temperature adjustment, and the ability to save time, money, and precious resources. The low-power network and sensors attached to the devices enable remote monitoring and control of these devices. An integral component of the Internet of Things (IoT) Internet of things organization in this paradigm. It can be envisioned as a collection of remotely connected devices that communicate and assemble themselves in line with some predetermined principles. The resources available to these devices are constrained. Therefore, the optimal protocols for data transmission through wireless communication are lightweight ones like MQTT, CoAP, and so forth [3]. In the current development, the prototype’s communication method of choice is Wi-Fi, and the devices are managed by ESP8266. The options are affordable, manageable, and tiny.

2 Related Work IoT these days helps innovation progress. Internet of things is the organizational paradigm unified with household appliances, physical devices, and other installed devices that permit these objects to communicate with one another and exchange information over an organization without the need for a person or governor to be

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present. Only the foundation for home automation is impacted by IoT. The expression “Brainy house” is passed down; it has been in existence for over ten years. Household automation [4], mostly known as brainy home, implements its residents with amenity, satisfaction, preservation, and energy competence. A smart house includes wireless technologies including mobile networks, Wi-Fi, RFID, and Bluetooth. These have been used to enact several frameworks that detect, manage, and exchange remote information. The study describes a Bluetooth-based smart home communication system that works without a web network. To conquer the limitations of using Bluetooth signal (maximum reach 100 m, or about 328.08 feet), experts also presented a well-known invention known as Wi-Fi-based home control framework [5]. This technology allows real devices, such as actuators and sensors, to be permeate and governed by cell phones with availability of Bluetooth. A dedicated web server and PC have been used in a Wi-Fi-based household control system, which is difficult to use and not customizable. In place of Wi-Fi, GSM-based mobile networks have been used in household appliances as the board structure for extended reach communications. Home equipment can be spoken to and controlled using AT commands. The leading deficiency is that GSM lacks an appropriate Graphical User Interface (GUI). The designers has developed a clever home model for operating the home appliances without the ability to observe it using NearBus, MIT App Inventor, and Arduino. Additionally, pricey Raspberry Pi-based household automation framework is demonstrated in papers. They have not used any Internet of things platforms; hence, their framework is inadequate for accurately assessing the condition of household appliances. There is also no guarantee mechanism suggested. The authors of the article suggested a great IoT-based guarantee and household automation architecture. They implied the TI-CC3200 Launchpad board to create a PIR movement sensorbased security system [6] and a phone’s finger-squeezing user interface for managing house hardware. An Internet of things-based household automation framework which can also be regulated remotely from anywhere has been planned and implemented in light of this sizable number of concerns. The automation of a household implying the IoT has been major subject of various studies. However, they were carried out using different techniques. Some of the papers, expressing their creativity and applicability, are considered. Utilizing the ESP8266, an economical household automation system has been created. For this, a four-switch port standard remote ADSL modern switch is used by the Wi-Fi network. Both the secure Wi-Fi and the organization SSID were preconfigured. The method was used to maintain and secure all messages that were received by the virtual house before they were decrypted. ZigBee was useful in lowering the cost of the framework [7]. The authors developed a smart GSM-based home robotization system where all operations were managed by a mobile phone. Research is drawn to GSM-based home computerization because of the advancements in cell phones and GSM. The options we considered for home automation were SMS-based house robotization, GPRS-based household computerization, along with (DTMF)-double-tone multirecurrence-based home mechanization [8].

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Input yield ports on the Arduino BT board are connected to home appliances and gadgets using a Bluetooth-based household automation framework. The Arduino BT board’s program is written in the important level C language for microcontrollers, and a Bluetooth connection was used to establish the link. Additionally, password security was offered. The framework implied Python script; it is adaptable and portable along with it can be installed on any Symbian OS environment [9]. An automated system was created by the authors employing a complicated Arduino board that has greater capabilities and was utilized to do activities that might have been completed using a simpler and less expensive board [10]. For solar house monitoring and automation, a system with a straightforward and adaptable design is created. The platform chosen is the Emon CMS, which applies the (IoT) Internet of things principle that gather data from sensor nodes using a cloud server [11]. Figure 1 shows a comparison of home automation systems that have been published recently. Here’s how to explain it: Bluetooth established household automation system implying phones: The relay and Arduino BT board connects household appliances in an automation system based on Bluetooth [5]. Major connection is established through Bluetooth, and the highlevel interactive C language of microcontrollers, the programming language.

Fig. 1 Different household automation systems developed in recent years

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GSM-established household automation system implying phones: The GSMestablished household automation system makes use of serial communication and AT-command concepts. From their mobile phones, homeowners can get feedback on the state of any home appliances they are in control of, whether they are remotely turned ON or OFF [12]. ZigBee-established household automation system implying phones: The ZigBee technology majorly implied in the system’s design and implementation to audit and conduct household appliances. A basic wireless ADSL router with four-switch port is used in this Wi-Fi network. Network coordinators keep a record of and store the device’s performance. Additionally preconfigured [13, 14] are majorly used the network SSID and security Wi-Fi confines. RF module for household automation: The main objective of the household automation system is to be controlled by remote. For the same, a microcontroller on the transmitter side is linked along an RF remote, which transmits ON or OFF signals to the receiver where the connected items are. Wireless technology can be utilized to turn OFF or ON the load globally [15]. Cloud-established household automation system: The structure along with deployment of a household gateway that gather data from household hardware and deliver it to a cloud-established data server is the main objective of home automation using cloud-based systems. A safe and comfortable household automation system is provided along with being economical comfortable and dependable for the entire family [12, 16, 17]. Raspberry Pi household automation: By reading the email subject lines and the algorithm, a household automation system is created, also using wireless sensors and mobile devices along with using the Raspberry Pi that the Raspberry Pi agrees to be a platform that is more effective for implementing strong, affordable smart home automation. In many aspects, home automation using a Raspberry Pi is superior to existing home automation techniques [12, 18, 19]. Wi-Fi-established household automation system using cell phones: Wi-Fi technology is a powerful solution that can be managed remotely, offers home protection, and is more affordable than the antecedent systems. An assigned household automation system is made up of servers and hardware interface modules. The server, its dismay, and actuators are administered by the hardware interface module and is smooth adaptable to handle additional hardware modules which manage one hardware interface. The web server is an ordinary PC that has an integrated Wi-Fi card. The web server should support the ASP application and network 4.0, like IIS7.0 for Window OS, because the web server software was designed using asp.net technology [1, 20, 21]. Wireless household automation system establishing IoT: There are many ways to govern household appliances, including Internet of things-based household automation over the cloud, RF-based home computerization, home mechanization via Wi-Fi via applications from a mobile device, home automation using advanced control, Arduinos-based home automation by the android application-based controller, and contact screen-based household robotation [12, 22].

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Table 1 shows the recently published work for home automation and their summary.

3 Methodology The prototype’s construction and implementation of the suggested system, which is the entire operation of the system and is controlled by a mobile application, are shown in a flowchart in Fig. 2. The flowchart, which is managed by a mobile application, shows the entire construction process. Automating the process of actuating household appliances is the main goal of the effort. The program is created using the Arduino IDE and has been applied as an interface between the Node ESP8266 MCU and the microcontroller in the Blynk app to increase accuracy. Relays are set up with Google Assistant using IFTTT, a free web service that builds a series of straightforward conditional statements so that relays can be triggered, to increase versatility. An interface is created to manage the working and install them on the Wifi local reciever. The prototype is ready to be connected to the appliances through the relays after it has been deployed. Figure 3 depicts the prototype’s operation and details how it operates in the following steps: Step 1 Upload the code to the Esp88266 board. Step 2 The ESP8266 board will automatically attempt to connect to the Wi-Fi using the provided credentials when powered on by a 5-volt power supply after the code has been successfully uploaded to it. Step 3 A notice appears on the screen, and the Wi-Fi servers for the controlling links are activated if the Wi-Fi connection is successful. Step 4 On Wi-Fi, each link has a unique function, and hyperlinks are linked to the interface’s buttons. Step 5 When a certain button is pressed, the ESP8266 receives an instruction that sends it to the appropriate relay linked to the desired appliances. Step 6 If the command is valid and the connection is established successfully, the user can now operate all the appliances linked to the website, application, Blynk app, and Google help. Experimental Bed Setup The ESP8266 module is set up as an MQTT client using the Arduino IDE. On a Windows computer, an open-source MQTT vendor is utilized. To hear and broadcast GPIO control instructions for the ESP8266, the supported application is utilized as a subscription MQTT client. Blynk: The Blynk app has several widgets, buttons, text editors, and visualizations that may assist you with many tasks and connect many devices at once. It also offers a simple user interface (UI) for you to work with. The Blynk app can be thought of as a gateway for communicating with your gear. It is an IoT platform that utilizes the

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Table 1 Different work published by authors in existing work Author(s), Year

Communication Controller “User Interface interface”

Applications

Kodali, R. K, 2018, [7]

Wi-Fi

Temperature Low cost, secure, and motion remotely detection, controlled monitoring, and controlling

Spandana, 2021, [8]

Wired Xi O and Arduino wireless ZigBee

Arduino

“Web Application and android App”

Merits

“Android Energy “Energy efficient Application” management and highly and task scalable” scheduling with power and cost

Balasingam, Web server and S., 2022, [9] interface card

Raspberry Android Pi application

Controlling the Autonomous, shutter of the and quite window scalable

Kodali, R. K, 2016, [10]

Cloud-based data server uses Hadoop technology

Home gateway and routers

Smart device “Monitoring and controlling home appliances”

Kotiyal, B, 2016, [11]

“ZigBee wireless network”

Smart socket

PC or Android phone

Entrance Convenience, control safety, and management, power saving monitoring the power consumption, temperature, and humid

Dey, S, 2016, [13]

“Cloud-based data server “

“PCB circuits”

“Mobile application”

Monitor the home conditions and power consumption of appliances

Low power consumption and cost efficient

Kotiyal, B, 2016, [11]

“Micro Web Server”

Arduino Mega 2560 and the Arduino Ethernet shield

“Android application”

“Light, switches, temperature, humidity sensors, intrusion detection, smoke/gas sensor”

Feasibility and effectiveness

ElShafee, A., 2012, [15]

Bluetooth

Arduino

Python supported mobile

Controlling

“Secured and low cost”

“Effectively manage semi-structured and unstructured data, Reduce the computational burden of smart devices”

(continued)

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Table 1 (continued) Author(s), Year

Communication Controller “User Interface interface”

Applications

Merits

Harsha, S. S., 2017, [19]

SMS

Control appliances

Simplicity

Arduino

Smartphone

Fig. 2 Prototype construction steps

cloud and is incredibly helpful for Internet of things (IoT) with the aid of a Blynk app. ESP8266: It is a Wi-Fi MCU that is affordable and well integrated for Internet of things applications. It has a 32-bit Tensilica processor, high durability, compact, and power-saving architecture. Relay 4 channels: The ESP8266 is connected to a four-channel relay, and the relay’s output is linked to several household appliances in that order: a light, a led, a fan, and a coffee machine. Relay activates the switches that are linked to an immense voltage using low current and voltage. The relay’s four input pins are attached to an Arduino board, which receives a 5 V power supply and may activate up to a 10A, 250 V supply. IFTTT: A user can set up a response to events in the world using the services offered by the private company. If this, then perform specified task. Wi-Fi: Here, a hotspot is created using Wi-Fi (wireless fidelity), a wireless communication method, so that the SP8266 module can connect. To link the smartphone and ESP8266, the router will give the module a specific IP address. Mobile device and web application: Numerous platforms include Windows Mobile, iOS, Android Studio, and more, for developing mobile applications. Because the Android GUI is more simple and more complex than the Android Studio working framework, almost 90% of people worldwide use it. Although there are far easier frameworks available, you may design an application that is extremely simple to create, completely functional stand-alone, and self-sufficient [23]. Some of the frameworks do not even demand that you have coding experience. Drag and drop are all that is required. Users must understand how to utilize the tool. The online application has been used portably as a result of the data’s increased accessibility, as users can access it via the Internet from any location in the globe. These have led to an increased reliance on applications. The web application can be accessed whenever,

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Fig. 3 Work flow diagram

wherever, using whichever web browser in whichever operating system supports one. To achieve this, the suggested framework offers online apps as well as mobile phone applications for controlling home appliances. MIT App Inventor, which was made available by Google at first, is an integrated development environment for online applications [24]. Along with the aid of this, we will create a program that will enable us to manage and keep an eye on our equipment. The similar type of study is also found in study [25–27].

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4 Implementation This home automation model has a straightforward circuit as shown in Fig. 4. Use active low 5 V relay modules in this situation. For the command, the user must connect by the output pin that is specified. The circuit was supplied by an outdated 5 V mobile charger, according to the author. It is written in the HTML code that is contained within the stored function array, and it is passed through a link to a server that is provided locally over WiFi. The addresses used vary depending on the command. These URLs can also be used to configure the web page and app. Next, utilizing the Blynk App, a website, Google help, and MIT App Builder, users can simply control home appliances from a smartphone. In the Blynk app, users can also keep an eye on the current status of switches. The prototype’s circuit connection is represented schematically in the above diagram, which is Fig. 4. An Arduino board is linked to the ESP8266 controller through the GPU pin in the diagram, which instructs the module to behave as instructed. Even relays that are linked to appliances, such as a fan, a bulb, and a tube light, can be controlled by the Arduino by sending commands and signals to those relays. To control an appliance’s power, a relay is linked in series between the power source and the device. Next, it should have total control over the appliance’s electricity with the aid of a relay and an esp8266, enabling remote control.

Fig. 4 Circuit connection diagram

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5 Discussion and Future Work While the rest of the world has been working for years to automate every aspect of the home, India has lagged despite having access to these technologies. A highly useful and practical idea that makes people’s lives a little bit easier is home automation. When a person leaves the house, he or she frequently starts to wonder if they have turned OFF the appliances and many other inquiries. With home automation, these concerns can be quickly resolved on a person’s phone. In this work, a straightforward ESP8266-based home automation solution is suggested. Both opportunities are libel inconvenience, price, and smooth to usage. The notion behind the suggested system is quite smooth and allowed for a further malleable network composition. It aims on being a fully functional smart household prototype along with many proposal, including governance, automation, security, and monitoring. Additionally, it complies system that is continually updating and enhancing. This paper’s review of recent literature articles, commercial products, and open-origin household automation systems is one of its contributions. Every element of domestic lives will be automated in the future with the growth of sensor technologies and products. A feature is to be soon included, and the innovation potential will skyrocket.

6 Conclusion To render lives uncomplicated and extra economical, this paper presents an authentic case study of an actual household and every liniments the suggested system also including the applications has to offer. Any user can utilize the given to implement the suggested system. This article describes the system’s installation, application, and configuration of the same, as the devices and the features it supports that can be practiced to create a brainy home. A high-level, multi-interface, website-based home automation system is created and implemented. The suggested structure ensures straightforward and hassle-free control of household equipment from any location on Earth. By prohibiting hackers or gatecrashers, this architecture increases a home’s security. Removing fire and gas threats also protects property from accidents. This framework is extremely beneficial for people of all ages, those with special needs, and those who are employed. The entire framework has examined and has held up as was expected and conventional. The GUI may be customized to meet demands and is simple to grasp. Adaptability, unwavering quality, and energy efficiency are all guaranteed by the framework.

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Analyzing Critical Stakeholders’ Concern in Indian Fruits and Vegetables Supply Chain Using Fuzzy-AHP Rekha Gupta

Abstract The Indian fruit and vegetable supply chain is entrusted to several vital stakeholders’ right from the producer-farmer, the middleman-mandiman, and the government before the produce reaches the end-consumer. Each stakeholder is bothered by several concerns that need immediate attention for averting the enormous annual food wastage. Most of the earlier cited studies in Indian context have listed issues with respect to different stages of fruit and vegetable supply chain. The author, on the other hand felt that a targeted stakeholder’s specific dimension was a vital area to be explored to address the numerous issues clogging the vegetable and fruit supply chain. Hence, the article first attempts to identify key stakeholders for fruit and vegetable supply chain along the critical issues associated with each of the prominent stakeholder. Thereafter, it classifies the issues and attempts to prioritize the most vital critical issues to present a prioritized listing for all times to address. The Fuzzy-AHP method provides an elegant and simple approach to ranking the critical issues. The issues are then discussed with key challenges and recommendations. Keywords Indian fruit and vegetable supply chain · Stakeholders · Critical issues · Fuzzy-AHP

1 Introduction Supply chains play a vital role in fruit and vegetable produce sector and are vital for timely and reliably meeting the requirements of consumers in terms of quality, quantity, and price. From production to consumption, the supply chains also manage transportation of produce, handling issues, and storage facilities. The quick degradable aspect and the short life span of these products further drives the importance of these supply chains. Numerous models of supply chain are prevalent depending on the area, location and have different stakeholders with vital issues concerning R. Gupta (B) Lal Bahadur Shastri Institute of Management, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_54

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them. The Indian fruit and vegetable supply chain currently is highly inefficient and suffers from issues like fragmented chain, poor road infrastructure [24], inadequate cold store and refrigeration facilities [16], improper packaging and processing units [19, 21], poor distribution systems, etc. contributes immensely to produce wastage and losses [6, 8, 23] etc. On the other hand, the continuous supply of fresh fruits and vegetables is hugely facilitated by India’s diverse climate making it the second largest producer after China. Among the vegetables, India amply produces potatoes, onions, cauliflowers, brinjal, cabbages along with ginger and okra for which it is the largest producers. The fruits such as mangoes, bananas, and papayas are grown amply with production among the top highest countries of the world. Thus, it is believed that fruits and vegetables production could help Indian agriculture with better returns. They provide incomes that are 2–4 times higher than traditional crops and are also less water intensive crops. Rich in essential nutrients required for the body and under the category of protective food, fruits and vegetables provide for vitamins, minerals, proteins, and carbohydrates. Rise in incomes of middle class, changed habits and lifestyles based on health and nutrition, fruits and vegetables have carved a niche and preferential markets for them. This inadvertently demands in all stages of supply chain starting with the production, processing process, distribution methods along with changes in quality and safety of products, With a fragile supply chain besieged with multiple issues, increased public demand, quick profit, this supply chain promises better returns if issues are handled tactfully and carefully. The traditional supply chain boasts of numerous players each with its own set of issues and concerns. They need to be addressed with immediate attention if the supply chain is to be bought back to track. Each stakeholder is vital to the supply chain and issues need to be cornered, addressed, and removed. With an agrarian economy, this must be the prioritized concern of the government. Hence, this paper attempts to identify the most critical stakeholders and their critical issues and prioritizes the vital issues with the help of mathematical algorithm such as Fuzzy-AHP to present the critical issues as a viable listing for government to address to.

2 Objective of the Study Fruit and vegetable are labeled as one of the most upcoming sectors of Indian agricultural economy. Still the Indian farmers lack of interest and besetting of fruit and vegetable supply chain with its enormous issues have attracted the attention of government, private players, and research agencies toward it. Henceforth, several studies have come forth listing several issues with focus on post-harvest stage, as it is the critical stage of supply chain [5, 14] etc. However, the issues affecting the stakeholders have not been identified separately which is crucial for the actual

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development and implementations of solutions Therefore, study tries to focus on the below enlisted objectives for the above mentioned supply chain. • To identify the issues concerning the major stakeholders • To identify suitable factors through Factor Analysis • To rank the factors of each stakeholder using Fuzzy-AHP.

3 Methodology The traditional as well as modern Indian fruit and vegetable supply chains are investigated to examine the most prevalent supply chain models. An attempt is then made to list critical issues across major stakeholders in the predominant supply chain. The initial critical issues listing for all important stakeholders is derived from secondary literature review [1]. A series of publications in reputed journals were scrutinized to get an already established and accepted listing for the Indian fruit and vegetable supply chain. The listing is validated by a primary questionnaire survey conducted among the experts of a premier agriculture research institute. The validated questionnaire was then administered to the different stakeholders to ascertain the critical success variables. The factor analysis was performed to derive the critical success factors. The factors were prioritized using the Fuzzy-AHP algorithm to present a prioritized critical success factor listing and discussion.

4 Discussion In this section, the discussion revolves around the traditional Indian fruit and vegetable supply chain, major stakeholders in the supply chain, small and medium landholders and issues concerning each major stakeholder in the fruit and vegetable supply chain.

4.1 Variables Affecting the Fruit and Vegetable Supply Chain Key Players In this section, we first present the variables for each of the key players in the fruit and vegetable supply chain. The variables had initially been enlisted through the secondary literature review by a research study compilation of journals through a period of 2008 to 2020. The identified variables have been cross verified by a primary field survey questionnaire validated on a 7-point Likert scale. The farmers data included small land holders in three villages of Jhajner, Redi, Budhakeda of Saharanpur district in Uttar Pradesh state were considered by judgmental sampling

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technique. These farmers primarily grew sugarcane and crop rotated by growing fruits and vegetables. Middleman survey sample size was 50 from Azadpur mandi, one of the largest wholesaler fruit and vegetable market in Asia. A sample of 30 senior government officials involved in policy making and research in agriculture from PUSA institute in Delhi were administered a questionnaire for firsthand information regarding the government policies, rules, and regulation, for the perceived versus the actual benefits of the policies were interviewed. The factors drawn for various stakeholders in the supply chain are listed below along with the variables that were clubbed by using factor analysis using SPSS (Table 1). For most of the farmers interviewed, agriculture was their main occupation and earn their income primarily through the same. Often, women from the households also participated in the agricultural activities. The farmers in Uttar Pradesh (A state in India) are typically characterized by very small land holdings and often many working as contractual laborers. The farmers insisted on the presence of an agency for indicating the land available for agriculture to facilitate the landless farmers. Most of them grew two crops a year as the ones interviewed in this belt grew sugarcane and crop rotated with fruits and vegetables. The farmers insisted on land suitability surveys to optimize the selected crops for cultivation. Most of them used local, traditional seeds rather than using genetically modified seeds. The reasons pertained to ignorance, non-availability of good seeds and financial constraints. Usage of organic and chemical fertilizers as well as of pesticides was done. The prominent concerns were the timely and quality availability of seeds, fertilizers, and pesticides. The education level was basic and were often uneducated followed by complete ignorance of latest tools and techniques. The monthly income of farmers in the state of Uttar Pradesh farmer was found to be much lower than the income of farmers in well—off states like Panjab, Haryana, etc. Besides, there are several deterring factors dissuading farmers away from their interest in the cultivation of fruits and vegetables. First, the minimum support price does not exist for these as compared to traditional crops like wheat and rice. Pulses, fruits, and vegetables possess higher risk of failure as they are more susceptible to extreme weather conditions. Furthermore, the cultivation of fruits and vegetables requires more care and effort during cultivation with the need to dispose the produce quickly due to lack of storage facilities. Lack of proper storage and transport facilities has yet another impact—spoilage of produce resulting in lower price realization due to poorer quality of produce by the time it reaches markets. Fruits and vegetables experience a much higher degree of price volatility than grains. Part of the reason for this is the high level of mismatch between demand and supply of fruits and vegetables. Further, stored crops are seen as fixed deposits by farmers in different parts of the country which is not the case with fruits and vegetables.

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Table 1 Factor table for small farmer/contract farmer Factor no.

Factor name

Variables included

Related literature

Factor category and stage

F1

Input availability

Seed availability & affordability, Agency available for procurement, quality inputs, funds availability

Narsimh and Sudershan [9] Halder and Pati [4]

Tactical Pre-harvest

F2

Land availability

Land/land on lease available, landlord/land providing agency, land suitability, Land information providing agency

Narula [10], Modi Tactical et al. [7], Halder and Pre-harvest Pati [4]

F3

Extension services

Water availability, water availability locally, weather dependent/ pertinent water resources, fertilizer, pesticide availability, affordability

Narsimh and Tactical Sudershan [9, Halder Pre-harvest and Pati [4]

F4

Demand & market estimate

Past demand trends, Singh et al. [22], demand Forecast, Accurate Narula [10], Modi information et al. [7], Negi and Anand [11], Shukla and Jharkaria [20]. Report

F5

Information & technology availability

Information planning & disseminating agency, technology for information, timely availability

F6

Produce holding/ storage issue

Produce storage Singh et al. 22, Operational availability& affordability, Narula [10], 19, Post-harvest grading & wastage concern Negi and Anand [11], Halder and Pati [4]. Report

F7

Distribution & transportation network

Produce transportation available, affordable, wastage concern

Singh et al. 22, Operational Narula [10, 19], Negi Post-harvest and Anand [11], Halder and Pati [4]

F8

Economic viability of produce

Produce economically viable, sellable, quality & sustainable

Murthy et al. [8], Narula [10], Shukla and Jharkaria [20], Halder and Pati [4]

Tactical Post-harvest

F9

Timely availability of extension and other services

Timely availability of seeds. fertilizers, pesticides, transport and cold storage

Halder and Pati [4], Negi and Anand [11], Rais and Sheroran [15]

Tactical Pre-harvest and post-harvest

Tactical Pre-harvest

Singh et al. [22], [7], Tactical 19 Negi and Anand Pre-harvest [11], Vishwanadhan [24]

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R. Gupta

5 Mandiman Factors In rural economy, the mandiman provides money to small and marginal farmer using land and his labor as collateral without proper documentation. In rural areas, this informal form of credit accounts for 80% of the farmers loan for agricultural inputs like seeds, fertilizers etc. The middleman personally knows the farmers and provides the subsequent loan without repayment of the previous loan. The middleman’s mode of credit is tailor-made for farmer’s need frequently dictated by their agricultural, and even social (family functions), requirements. No player from the formal sector has ever dared to venture where the middleman routinely does. The main factors that were of concern to mandiman was to meet the demands of the consumers by a constant produce supply. Often the demand–supply requirements were not met because of varied reasons. This was a prima face concern to the mandiman who wanted exact forecasts to be available to them so that they could make appropriate provision for storage, distribution, and transport of the produce. They also demanded proper grading services be made available so that produce is appropriated sorted and timely distributed. Often the mandiman too were poorly educated about the government’s pricing policies and incentive schemes and thus were not able to pass the benefits to the producers (Table 2).

6 Farmers and Mandiman Expectations from Government The farmers look to Government for its intervention and reforms on several issues. The multidimensional help is wanted in field of irrigation, crop insurance, minimum support price, provide subsidized inputs, and education programs, etc. They want government to develop various means of irrigation, develop and produce new hybrid varieties of seeds for increasing yield, increase availability of fertilizers at subsidized rates, fix minimum support price for crops for a year, to provide crop insurance to protect crops from pests and natural hazards. They want government to provide various loan schemes on easy terms to buy machinery and other agricultural items. They suitably want awareness programs through radio, TV, etc. about the schemes so that they are aware of them to avail them. They want government to set up demonstration farms for farmers to learn adopt new and latest techniques in farming. The mandiman requested for right information at right time on introduction of new farm bills, policies, and procedures. They expect the government to train them to handle resources and their wastage efficiently. They expect reforms and policy development at regular intervals for efficiently managing the supply chain. With the factors in hand, it was decided to prioritize the factors within each category of Farmer, Mandiman, and Government, so that immediate and appropriate attention is paid to the most critical factors within each category. For this the technique of Fuzzy-AHP was used.

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Table 2 The factor table for the mandiman Factor no.

Factor name

Variables included

Related literature

F1

Input availability

Seed availability & affordability, agency available for procurement, Quality inputs, funds availability

Narsimh and Sudershan Tactical [9, Halder and Pati [4] Pre-harvest

F2

Land availability

Land/land on lease available, landlord/land providing agency, land suitability, land information providing agency

Narula [10], Modi et al. Tactical [7], Halder and Pati [4] Pre-harvest

F3

Extension services

Water availability, water availability locally, weather dependent/pertinent water resources, fertilizer, pesticide availability, affordability

Narsimh and Sudershan Tactical [9, Halder and Pati [4] Pre-harvest

F4

Demand & market estimate

Past demand trends, demand forecast, accurate information

Singh et al. 22, Narula [10], Modi et al. [7], Negi and Anand [11], Shukla and Jharkaria [20]. Report

Tactical Pre-harvest

F5

Information & technology availability

Information planning & disseminating agency, technology for information, timely availability

Singh et al. [22], Modi et al. [7, 19], Negi and Anand [11], Vishwanadhan [24]

Tactical Pre-harvest

F6

Produce holding/ storage issue

Produce storage availability & Singh et al. 22, Narula Operational [10], 19, Negi and affordability, grading & Post-harvest wastage concern Anand [11], Halder and Pati [4]. Report

F7

Distribution & transportation network

Produce transportation available, affordable, wastage concern

Singh et al. 22, Narula Operational [10], 19, Negi and Post-harvest Anand [11], Halder and Pati [4]

F8

Economic viability of produce

Produce economically viable, sellable, quality & sustainable

Murthy et al. [8, Narula Tactical [10], Shukla and Post-harvest Jharkaria [20], Halder and Pati [4]

F9

Timely availability of extension and other services

Timely availability of seeds. fertilizers, pesticides, transport and cold storage

Halder and Pati [4], Negi and Anand [11], Rais and Sheroran [15]

Factor category and stage

Tactical Pre-harvest and post-harvest

7 Fuzzy Analytic Hierarchical Process (F-AHP) Analytic Hierarchical Process [17, 18] forms a hierarchical system by integrating experts’ opinions and evaluation for solving multi criteria decision making problems. Yahya and Kingsman [25] applied AHP to supplier selection prioritization problem. However, experts’ opinion is often besieged with problems of uncertainty and vagueness, the impreciseness of human’s judgments can be handled better through the fuzzy sets’ theory developed by Zadeh [26]. Fuzzy-AHP method systematically solves the

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Table 3 The factor table for the government Factor no.

Factor name

Variables included

Related literature

Stage

F1

Produce demand & availability

Market demand information, fresh produce available, past demand information, demand trends for pricing

Narula [10]

Pre-harvest + post-harvest

F2

Grading & sorting, storage

Grading & sorting of produce, produce storage important, available, affordable

Narula [10], Rais and Sheroran [15]

Post-harvest

F3

Transportation & distribution

Distribution network available, transportation available/affordable

Murthy et al. [8, Rais and Sheroran [15]

Post-harvest

F4

Govt. rules and regulations

Information for pricing available, commission procedures & processes streamlined

Murthy et al. 8, Modi et al. [7]

Post-harvest

selection problem by combining the concepts of fuzzy set theory and hierarchical structure analysis.

7.1 Fuzzy Analytic Hierarchy Process (F-AHP) F-AHP technique is Fuzzy set theory applied to Analytic Hierarchy Process (AHP). The decision making problems frequently use AHP for solving various decision making problems having multiple criteria. The criteria are teamed up pair-wise and comparisons are done with objective (at the first level), the criteria and sub criteria (second and third levels) respectively with the alternatives at the fourth level. In F-AHP, the linguistic variables are used to determine the criteria and the alternatives pair-wise comparisons. Pedrycz and Laarhoven [13] defined the triangular membership functions for the same. For triangular membership functions, Buckley [2] defined the fuzzy priorities of comparison ratios. Chang [3] too used the triangular numbers in pair-wise comparisons. This research paper uses Buckley’s methods [2] for the criteria and the alternatives relative importance weights (Appendix).

8 Results Nine factors were rated for relative importance by 10 experts who were Scientist C category from PUSA Institute with minimum 10 years’ experience. Based on their opinions, the ranks of the factors were computed. The input information (what to grow, how to grow), Extension Services, and Land availability occupied the top three slots followed by others. Similarly, ranks were found for the Mandiman and the Government policies.

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For mandiman, the produce demand was ranked at the top priority followed by produce demand and availability followed by Grading and sorting issue. For Government, the top issue was crop insurance followed by research and development issue.

9 Conclusion India is an agrarian economy with agriculture being the largest employer of work force. Agriculture and allied activities of agriculture continue to provide employment to around 65% of the total workforce. The Gross Domestic Product of the country has its maximum contribution from Agriculture and allied activities. Hence, it is critical that attention is given to all critical success factors concerning all important stakeholders in the agri-supply chain. However, in times of resources and budgetary constraints, prioritization of factors would help concentrate on most vital factors so that majority of the concern is taken care of. And in times of abundance, all the factors are handled.

Appendix See Tables 4, 5, 6, 7 and 8.

(1/6, 1/5, 1/4) (1/4, 1/3, 1/ (1, 1, 1) 2)

(1/4, 1/3, 1/2) (1/6, 1/5, 1/ (2, 3, 4) 4)

(1/6, 1/5, 1/4) (1/6, 1/5, 1/ (2, 3, 4) 4)

(1/6, 1/5, 1/ (4, 5, 6) 4)

(1/6, 1/5, 1/ (4, 5, 6) 4)

(9, 9, 9)

(1, 1, 1)

(2, 3, 4)

(2, 3, 4)

(1/4, 1/3, 1/2) (1/6, 1/5, 1/ (2, 3, 4) 4)

Economic Viability

Knowledge

Storage

Distribution

Input

Extension services

Land availability

Timely availability

(2, 3, 4)

(1/8, 1/7, 1/ (2, 3, 4) 6)

(1, 1, 1)

(1/9, 1/9, 1/ (4, 5, 6) 9)

(1, 1, 1)

Knowledge

Weather Information

Economic viability

Weather information

Factors

(1, 1, 1)

(2, 3, 4)

(2, 3, 4)

(2, 3, 4)

(2, 3, 4)

(1, 1, 1)

(1/4, 1/3, 1/2)

(4, 5, 6)

(2, 3, 4)

Storage

(4, 5, 6)

(4, 5, 6)

(6, 7, 8)

(6, 7, 8)

(1, 1, 1)

(4, 5, 6)

(1/4, 1/3, 1/2)

(4, 5, 6)

(4, 5, 6)

Distribution

(1/4, 1/3, 1/2)

(1/4, 1/3, 1/2)

(1, 1, 1)

(1, 1, 1)

(1/8, 1/7, 1/6)

(1/4, 1/3, 1/2)

(1/8, 1/7, 1/6)

(4, 5, 6)

(1, 1, 1)

Input

Table 4 F-AHP expert criteria rating (10 expert: scientist C category and above, PUSA Institute) Land availability

(4, 5, 6)

(2, 3, 4)

(2, 3, 4)

(2, 3, 4)

(1/4, 1/3, 1/ (1, 1, 1) 2)

(2, 3, 4)

(1, 1, 1)

(1, 1, 1)

(1/8, 1/7, 1/ (2, 3, 4) 6)

(1/4, 1/3, 1/ (1, 1, 1) 2)

(1/6, 1/5, 1/ (1/4, 1/3, 1/ 4) 2)

(4, 5, 6)

(1/4, 1/3, 1/ (2, 3, 4) 2)

Extension services

(1/4, 1/3, 1/ 2)

(1, 1, 1)

(1/4, 1/3, 1/ 2)

(2, 3, 4)

(1/6, 1/5, 1/ 4)

(1/4, 1/3, 1/ 2)

(1/4, 1/3, 1/ 2)

(6, 7, 8)

(1/4, 1/3, 1/ 2)

Timely availability

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Table 5 Geometric mean criteria rating Criteria

Geometric mean fuzzy comparison values

Weather information

[0.33, 0.99, 1.19]

Economic viability

[3.59, 4.30, 4.97]

Knowledge

[0.25, 0.30, 0.40]

Storage

[0.52, 0.66, 0.88]

Distribution

[0.38, 0.32, 0.38]

Input

[1.49, 1.75, 2.00]

Extension services

[1.19, 1.46, 1.77]

Land availability

[1.1, 1.45, 1.83]

Timely availability

[0.52, 0.66, 0.88]

Table 6 Vector summation Criteria

Lower

Middle

Upper

Weather information

0.33

0.99

1.19

Economic viability

3.59

4.3

4.97

Knowledge

0.25

0.30

0.40

Storage

0.52

0.66

0.88

Distribution

0.38

0.32

0.38

Input

1.49

1.75

2.00

Extension services

1.19

1.46

1.77

Land availability

1.1

1.45

1.83

Timely availability

0.52

0.66

0.88

Table 7 Cummulative Vector summation All

9.87

11.89

Reverse

0.10

0.08

14.3 0.07

Increasing order

0.07

0.08

0.10

Table 8 Weight computation of the criteria & Mi & Ni Criteria

Lower

Middle

Upper

Mi

Ni

Rank order

Weather information

0.023

0.079

0.119

0.074

0.045

5

Economic viability

0.251

0.344

0.497

0.364

0.377

7

Knowledge

0.018

0.024

0.04

0.027

0.027

8

Storage

0.036

0.053

0.088

0.059

0.056

4

Distribution

0.027

0.026

0.038

0.03

0.038

6

Input

0.104

0.14

0.2

0.148

0.156

1 (continued)

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Table 8 (continued) Criteria

Lower

Middle

Upper

Mi

Ni

Rank order

Extension services

0.083

0.117

0.177

0.126

0.126

2

Land availability

0.077

0.116

0.183

0.125

0.119

3

Timely availability

0.036

0.053

0.088

0.059

0.056

4

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Analysis of Top Vulnerabilities in Security of Web-Based Applications Jyoti Rawat, Indrajeet Kumar, Noor Mohd, Ayush Maheshwari, and Neelam Sharma

Abstract During the last few years, web applications have emerged as the most famous manner for providing services on the internet. The world today runs on the internet. Ranging from online banking to ecommerce, everything is made available at your disposal. As their popularity is increasing, so is the complexity in their layout and implementation. This increasing complexity makes them number one goal for hackers on the web. New vulnerabilities are being discovered every day in the applications, network, and software. The goal of this paper is to summarize some of these well-known vulnerabilities, how various tools and techniques can be used to mitigate them, and what impact it has on the world. Keywords Web security · XSS · SQLi · IDOR · Clickjacking

1 Introduction Web apps have been evolving at a high speed, introducing new programming technologies and models, resulting in a constant shift in their security [1]. They are prone to attacks like DOS, XML, SQLi, XPath, and spoofing [2]. Web services are in demand among researchers and developers, but only some of them have been able to propose a strong foundation [3]. E-commerce businesses need security for J. Rawat VertexPlus Technologies Limited, Gopalpura Road, Jaipur, Rajasthan, India I. Kumar (B) · A. Maheshwari Graphic Era Hill University, Dehradun, Uttarakhand, India e-mail: [email protected] N. Mohd Graphic Era Deemed to Be University, Dehradun, Uttarakhand, India N. Sharma MAIT, GGSIPU, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_55

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its database, workflow, and even role-based access control [4]. These companies are changing ways to communicate with internal and external stakeholders, thus creating a target for attackers to manipulate their workflow [5]. Cloud computing is currently the most popular technology but is also becoming prone to attackers as its demand is growing [6]. Online transactions have led to various vulnerabilities that have been listed by OWASP [7] and even SANS “Top-20 Internet Security Attack Targets” [8]. Comprehensive analysis of the app and development of a reliable method is needed to ensure safety of internet applications. Neat tools and other interesting ways are there to handle mishaps and to expose these vulnerabilities [9–11]. But, most of the models focus on application side only [12]. Automated, black box testing tools are called scanners that examine these vulnerabilities [13]. There are tools like WebDefender [14] and SAST [15] that provides these features. Firewalls also play an important role in providing security [16]. The remainder of this paper is organized as follows. In the second section, the various literatures from the existing publication and their authors are presented. The third section presents different case studies which are being performed on Port Swigger labs and software used are BurpSuite (HTTP Proxy, Scanner, Intruder, Repeater), SQL Map, Metasploit, Nmap. The last section concludes the present work.

2 Literature Review Domain knowledge of attacks is needed to provide security [17]. Cooperation of stakeholders like developers, employers, and universities is important to improve awareness level [18]. Cross-Site Request Forgery (CSRF), Cross-Site Scripting (XSS) and SQL Injection are some of the most exploited vulnerabilities [19]. There are many tools to detect and exploit SQL injections or XSS, but there is no dedicated software that detects and exploits, command injection vulnerabilities [20]. 1. SQLi pose a serious threat to web apps [21], and even after many studies done on it, all SQLi issues have not been addressed [22]. SQL Injection and XPath are directly tied to the structure of the service code [23]. 2. Client-side XSS attacks attempt to steal cookies from the user’s browser [24] and allows attacker to take control of user’s browser [25]. 3. CSRF is called “sleeping giant” of web-based vulnerabilities, because many sites usually ignore them and fail to provide security against [26]. 4. In phishing, phony emails are sent that appear to come from reputable sources [27]. It is also believed that enforcement of security in the software development life cycle of the application applies decrement on high cost and efforts associated with implementing security at a later stage. It helps to ensure that developers are able to root out vulnerabilities in short term and also make sure that these defects are not

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introduced in the first place. Secure software is integrated with security operations that gather and refine the needs of the companies [28–30]. Web-based apps based on keywords have proven to be prevalent [31], and even, neural network approach helps to identify attacks that are not detected at the stage of signature analysis [32]. Data encryption and [33] XPath used to query XML database [34] are also used to provide security. Information security alters how we use computers, as well as how we access and store personal and business data, but reliance on DAC as the principal method of access control is not appropriate for commercial and government organizations [35, 36]. When performing security testing on a web application, a security tester should keep track of all concerns. The knowledge would also be useful in developing and modeling an effective test strategy and application [37, 38]. Other type of security concern with latest technology is also reported in [39–41]. Table 1 shows the literature from existing papers and their summary. From Table 1, it is concluded that the security of the web-based applications is an important task for nowadays and requires more attention of the clients as well as developers.

3 Case Study The tests are being performed on Port Swigger labs. Software used are BurpSuite (HTTP Proxy, Scanner, Intruder, Repeater), SQL Map, Metasploit, Nmap.

3.1 SQLi Attack The end goal is to perform SQLi attack and log in as the administrator user as shown in Fig. 1. When user will use a random username and password, the end user will receive an “invalid username or password” error. BurpSuite is used to proxy the traffic and to intercept and modify the login request. The result of BurpSuite is shown in Fig. 2. Modify the value of username to: administrator. Here, password is replaced by a SQLi to log in into the website with fake credentials. After forwarding the intercept, we find that we have successfully implemented the SQLi and have logged in by using the username: administrator—and any random password as shown in Fig. 3. Mitigation strategies for SQLi include secure coding and SDLC; input validation and sanitation; stored procedures and parameterization; prepared statements; program analysis techniques and proxies.

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Table 1 Literature from existing papers Author

Overview

Li et al. [1]

This paper studies about web The logic of application code is more application security with a goal to concerned on the client side, but the systemize the existing solutions that logic is getting sophisticated would encourage future study

Output

Ravichandra et al. [2]

A thorough review done on web application security, attack detection, and vulnerability identification

Penetration testing of the development project is essential as threats cannot be eliminated

Michele et al. [3]

Gather, classify, and evaluate existing approaches in JavaScript security

Formal approaches are already a reality, yet they still only account for a small portion of the whole web security literature

Ferrar et al. [4]

This article covers security This paper has given some preliminary guidelines for web and e-commerce guidance in security standards. There like access control, workflow are several prospects in this field security, XML security, and federated security

Velmurugan et al. [5]

Role of trust in businesses and things that can be used to build customer trust

Comprehensive development of several elements needed; government and businesses need to work together

Lomte et al. [6] This paper elaborates security concerns and widespread attacks in cloud computing

Applications are prone to social engineering attacks

Sharif et al. [7]

Specific solutions provided for top 16 vulnerabilities which are very beneficial to prevent attacks

Analysis of top sixteen web application vulnerabilities and their mitigation techniques

Meyer et al. [8] This paper helps to understand how Two basic attack detection methods to detect most critical web (rule based and anomaly based) application security flaws Kong et al. [9]

In this article, three aspects of web application security are studied: client side, server, and data transfer

To provide security, a full study of web application as well as development of an effective and reliable strategy to prevent assaults is needed

Black et al. [10] Creation of a test suite for The test suite is useful for identifying evaluating web application scanners tools depending on how well they thoroughly discover vulnerabilities Zeller et al. [11] CSRF protection frameworks; root cause of CSRF attacks and the best defense against them

Adobe’s cross-domain policy outside of Adobe’s own Flash program uses client-side browser plugins

Demchenko et al. [12]

Potential grid and web services’ attacks are classified

A security model is created that interacts with web services and grids

Fasolino et al. [13]

This paper presents main difference Testing aspects are directly related to between web-based and traditional implementation technologies applications (continued)

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Table 1 (continued) Author

Overview

Output

Christos et al. [14]

Cross-platform tool is discussed that detects and prevents web attacks based on input validation

Success of WebDefender and ModSecurity records high % compared to dotDefender

Bhatt et al. [15] This paper describes cybersecurity, online application security problems, top web application vulnerabilities, and ways and mentality for developers and security testers to understand

Modern web application cybersecurity risks need to be addressed on priority

Lorrien et al. [16]

Only is some scenarios, impact of enhanced security can be seen

This paper describes the security associated with firewalls and performance relationship for distributed system

Bama et al. [17] Along with the machine learning, many cybersecurity dangers are presented, and techniques that can be used to detect cyberattacks

Everyone should use technology only after weighing the benefits and drawbacks, as well as the security risks, and taking precautions to protect their data

Achmad et al. [18]

The study focuses on developers who are the key actors to provide security

Developers have medium level of awareness, and thus, they are most of the time unaware of the importance of application security and its implications on an organization

Mitchell et al. [19]

A new variant of CSRF is discussed Origin header is better than Referer header which allows sites some extra protection

Goertzel et al. [20]

Discussed how software’s have become high valued targets for hackers, terrorists, criminals, and also warfare opponents

Improvement models and life cycle methodologies are designed to assist developers to make significant increases in security of their processes and for also adding structure and repeatability to those processes

Orso et al. [21]

It provides description of many forms of SQL attacks and how can they be prevented

A broad differential in prevention capabilities based on the distinction between prevention-focused and general detection and prevention strategies

Mehrnoush et al. [22]

Review of relevant research in the Most of the papers focus on detection field of SQLi attacks to evaluate for and not on prevention of these attacks a better solution

Madeira et al. [23]

Evaluation done on vulnerabilities in 300 web services

Different vulnerabilities are detected by different scanners; number of false positives are generally high which reduces confidence on these vulnerabilities detected (continued)

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Table 1 (continued) Author

Overview

Output

Anjaneyulu et al. [24]

In-depth review of the present systems and methods used to prevent XSS attacks on the web applications

The proposed ideology helps decide whether it is made by the user or not. The inclusion of physical address of the device during connection to the server, helps reduce threat of hacking of active session

Subramaniam et al. [25]

The paper focuses on how XSS attacks are detected and prevented

A tool made using JAVA called, XSS Detection, and Prevention Tool is useful in detecting and preventing XSS attacks

Gaurav et al. [26]

We have attempted to identify numerous concerns and challenges related to web application security testing in this work

When performing security testing on a web application, a security tester should keep track of all concerns

Bhosle et al. [27]

This article focuses on data security Future of cybersecurity will be similar needed in corporate. Political, to present; endless and difficult to military, medical, and financial characterize

Jena et al. [28]

Examining current security patterns The pattern designers have been given and web application vulnerabilities an uniform template to work with. Three main categories have been established for the most frequent vulnerabilities

Anastasios et al. [29]

This paper studies about an open-source tool

Commix is able to perform attack vector generation and exploitation

Bhargava et al. [30]

Introduction of agile methodology to security applications

Agile software development does not guarantee security, but they are cheap to use

Otumu et al. [31]

The vulnerability of web applications to XSS attacks is illustrated and discussed

Various methods can be used to detect and prevent XSS attacks

Abbas et al. [32]

This papers focuses on building a web application firewall to detect new types of attacks

Neural network approach helps to identify attacks that are not detected in signature analysis stage

Geldiyev et al. [33]

Define terms like cyberspace and cybersecurity, as well as measure the level of danger posed by cyberattacks and cyber war

The developed program provides for the effective and speedy detection of Trojans and other malware cyberattacks and responds quickly

Aziah et al. [34] This paper proposes a an architecture that helps to prevent XPath injection attacks

The technique combines both static and dynamic analysis techniques to target XPath injections

Ankit et al. [35] This paper proposes an alternate method to verify information

In fog service, data access patterns are monitored by profiling user behavior; this determines if and when a malicious user has accessed documents (continued)

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Table 1 (continued) Author

Overview

David et al. [36]

This paper helps to understand how RBAC requirements and access control RBAC is more fundamental for rules can serve as the foundation of secure processing demands than access controls DAC

Nagendran et al. [37]

This paper provides a detailed knowledge about manual pen testing methods

Mohamed et al. Vulnerabilities specifically in web applications used by academic [38] institutions

Output

Steps used in manual penetration testing of a web application Some standard attacks made on education applications which can be mitigated with the help of suitable defend techniques

Fig. 1 Login page. Note This is the main login page where the user is taken to enter his credentials. After entering the credentials, user receives an “invalid username or password” message, and the user is not able to move any further

3.2 XSS Attack The end goal is to perform XSS attack that calls the alert function (Fig. 4). In the search box, write: as shown in Fig. 5. The user can see the alert being generated with the message: 1 as shown in Fig. 6. Mitigation strategies for XSS include filter input on arrival; encode data on output; use appropriate response headers; content security policy.

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Fig. 2 BurpSuite proxing the traffic. Note The tool, BurpSuite is used to proxy the website on which the user needs to login. While the user makes an attempt to login into the website, BurpSuite intercepts the traffic and shows the attempt made to log in with those very credentials

Fig. 3 Successfully logged in. Note After using these fake credentials, the user is able to log in into the account and perform operations in the website. This type of attack is known as SQLi attack, which helps users to log in with fake credentials as they exploit the SQL code

3.3 Clickjacking Attack The end goal is to craft HTML that frames the account page and fools the user into deleting their account. The credentials are: wiener: peter. The basic clickjacking attack is shown in Fig. 7. Figure 7 reflects that the main login page where the user is taken to enter his credentials. After entering the credentials, user is logged in the site successfully, but the main aim is to craft HTML.

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Fig. 4 Home page of the website. Note The home page of this lab consists of a search box. Aim of the attacker would be to write script in this search box to perform dynamic operation and gain information

Fig. 5 Adding script. Note The script is added in the search box to perform a dynamic operation. This script calls an alert box with message “1”

Fig. 6 Alert generated. Note In cross-site scripting, script is added to perform dynamic operation. In this case as well, the alert box is shown in the screen which was not initially a part of the code of the site

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Fig. 7 Login page of the website

Fig. 8 Making manipulations in the script. Note In the script, URL is changed with the target website user account page, so that the user is able to see sections which makes him perform operations other than the site intents to

Go to the exploit server to make the necessary changes. Replace the $url in the iframe src attribute with the URL of the target website as given in Fig. 8, user account page. After saving the changes as shown in Fig. 9, user can see the desired result. Mitigation strategies for Clickjacking using X-Frame-Options header; client side defenses; using Content Security Policy (CSP); using cookie’s SameSite origin.

3.4 IDOR Access Control Attack The end goal is to find the correct credentials. Select the live chat tab as shown in Fig. 10. Transcripts contain an incrementing number as their filename. Change the filename to 1.txt and review the text as given in Fig. 11. Notice a password within the chat transcript as shown in Fig. 12. Returning to the main lab page and log in using the stolen credentials is done. Mitigation strategies for IDOR access control include fuzz testing, model verification.

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Fig. 9 Clickjacking successfully implemented. Note When the attack is successfully implemented, the user sees HTML structure other than that already present. This makes him click on structure in the site which makes him delete his account

Fig. 10 Home page. Note The home page of this lab consists of live chat. The user can write a message, click on send to send the message, which is visible on the screen. The user can also view transcript of the chat

Fig. 11 Making changes in proxy. Note When you observe the proxy carefully, you can notice that file name of the transcript has a incrementing number. Thus, we can change the name to 1.txt to see what the text file contains

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Fig. 12 Got hold of the credentials. Note This file contains the password, and the user can use them to log in into the site. User supplied input is used to access objects directly

4 Conclusion In this work, a complexity of web-based application is explained with a case study on it. With the increasing complexity of the web structure, threats on web applications are also increasing at an alarming rate. There are various reasons that web is becoming complex like inbuilt complexity of internet applications like massive user base, great size of the web, various regulations in cyberspace, legal and policy frameworks, and lack of awareness. So, performing real-time tests is useful, but all security concerns should be considered. Also, knowledge gained should be used to build more tools and methodologies. Software testing is difficult, and testing web-based apps can be considerably more difficult due to their unique characteristics.

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Fault Tolerant Algorithm to Cope with Topology Changes Due to Postural Mobility Preeti Nehra and Sonali Goyal

Abstract Numerous data gathering devices and hundreds or thousands of nodes make up a mobile network. Localization of movable nodes is a critical issue in dynamic networks as many applications need the traveling node to know its location with exceptional correctness. To assist the mobile nodes in locating themselves, a variety of localization methods based on mobile anchor nodes have been proposed. However, none of these strategies tries to enhance the trajectory of the mobile anchor node. Most localization techniques use fixed anchors. By enhancing the trajectory of the movable anchor node, our study aims to provide a method that reduces localization mistake. Keywords Wireless sensor networks · Global positioning system · Stochastic gradient · Turbo product code descent

1 Introduction Wireless sensor technology is widely used in observing, investigation, and supplementary arenas. The majority of the equipment used in wireless sensor networks is pre-made and used for investigation and fitness observing. Each sensor node is tasked with recognizing changes in vital indicators. The variations are intermittently directed to the combination opinion or the dominant host and comprise distinct drive, changes in physique illness or blood weight, and others. With the aid of the position reference given by the sensor node, the dominant host or the combination host classifies the person. WSNs are predicted to be inexpensive, easily deployable, and self-adjustable [1]. So, these systems deal a variety of consumer requests alike target tracking and identification, also engineering submissions like scattered organizational wellbeing watching and conservational controller and army submissions like crisis liberate P. Nehra (B) · S. Goyal Department of Computer Science and Engineering, MMEC, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_56

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and misadventure respite, clever institutions, and tolerant observing. The creation of effective localization algorithms is crucial. Locating nodes in a network is referred to as localization. With the use of some infrastructure, a node may locate itself in the system by developing data from the arrangement; moreover, the set-up can govern the nodes’ positions via affecting them to direct indications on a consistent base. One typical localization scheme [2] is GPS [3]. At a height of 20,200 km, there are 24 satellites, divided over 6 detour airplanes. These settlements have extraordinary precision infinitesimal clocks and are aware of their precise coordinates. If the GPS receiver is not blocked from the line of sight, it can accept indications from at minimum 4 settlements. A headset may determine the interval change by comparing the signal’s code pattern and can determine the satellite’s distance from it by multiplying the time shift by the rapidity of light. Afterward that, using a localization process, the GPS receiver may determine it’s coordinate.

1.1 Parameters for Localization To identify the similarities and differences between various approaches, we must provide parameters names for the various methods of estimating location information. Here, we list the most common criteria used to categories various methodologies. • Accuracy: In WSNs, localization accuracy is crucial. In army systems, these sensor systems used for incursion detection, more accuracy is often required. The necessary precision might not be any lower for commercial networks, though, as they might employ geolocation to deliver adverts from nearby stores. • Static Nodes: The nature of all fixed sensor devices is similar. This indicates that the sensing, computing, and communication capabilities of every node are the same. We also presumptively deploy the nodes with identical starting battery volumes. • Movable Nodes: It is presumable that the WSN includes a trivial amount of GPS assisted movable devices. The nature of these devices is similar. However, it is anticipated that they need more battery lifespan related to fixed devices and don’t lose all of it during translation. It is anticipated that both over the whole duration of the localization procedure and also during the receiving of four beacon signals by a specific static node, the message variety of movable sensor devices would not amendment significantly [4–6].

1.2 Motivation Several procedures target to lesser these computational expenditures. If single node computes its position erroneously, the error broadcasts crossways the rest of the

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system and subsequent nodes, producing imprecise anchor node position information to be broadcasted. A method constructed on DV-hop is presented to progress localization accurateness.

2 Related Work Lazos et al. [1] SeRLoc, a unique range self-governing localization procedure that is finding suitable to a source self-conscious setting like a WSN, was proposed. Sensors may passively detect their location without interacting with other sensors according to the distributed algorithm SeRLoc, which is based on a two-tier network design. Stoleru et al. [2] it has been stated that each of the current localization algorithms performs healthy for particular groups of presumptions. These presumptions are not at all times true, especially in the circumstance of complicated out-of-doors surroundings. This issue is addressed with a framework that enables the use of various localization techniques. Due to its presumptions, this procedure’s “multi-functionality” offers sturdiness against any single procedure letdown. The framework’s design was given by the author, who demonstrated a 50% drop in localization inaccuracy when matched to cutting-edge node localization techniques. Yedavalli et al. [4] outlined a innovative sequence constructed localization technique for WSNs and confirmed in what way the localization planetary can be subdivided into dissimilar areas, each of which can be exclusively distinguishable through arrangements that designate the grade of spaces from the location nodes to that area. Owed to the multipath and investigation properties of wireless networks, the writer shaped a localization method by means of these position arrangements that is hardy to accidental errors. Pal et al. [5] examined various node location discovery methods in wireless sensor networks. A swift of the plans put out through several researchers to enhance localization in wireless sensor networks is also provided. Also highlighted are potential directions for future study and difficulties in enhancing node location in WSNs. Singh et al. [6] a summary of various node location discovery methods used in wireless sensor networks. An instant of the plans put out by means of several researchers to enhance localization in WSNs is also provided. Also highlighted are potential directions for future study and difficulties in enhancing node location in wireless sensor networks. Kuriakose et al. [3] reportedly has thousands of nodes, making it expensive to put GPS on individually sensor device. In addition, GPS might not transport precise localization findings in an inside setting. For a dense network, manually setting the position reference on each sensor node is also not possible. This creates a situation where the sensor nodes must locate themselves without the aid of somewhat dedicated hardware, such as a GPS, or physical arrangement. Luo et al. [7] with wireless sensor networks with chosen sensors, a novel energy-based target localization method was given. In this approach, sensors relay judgments to the fusion center using Turbo Product Code (TPC). If there are communication channel faults, TPC can lower the likelihood of bit errors. Additionally, this approach uses a heuristic way to establish edges for the energy centered object localization. For sensors that are evenly dispersed

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and targets that are typically distributed, this threshold design method is appropriate. Additionally, a sensor selection strategy is described in order to conserve sensor energy. According to the results of the simulation, localization performance may be enhanced if sensors transmitted decisions to the fusion center using TPC rather than Hamming coding. Additionally, the way we choose our sensors can significantly lower how much energy our target localization approach uses. Additionally, this target localization technique with chosen sensors offers acceptable localization performance. Lo et al. [8] offer a novel method for WBANs to automatically locate wearable device. Instantaneous measurements of atmospheric pressure are compared to actual map nodes in space. This improvement makes it possible to put sensor nodes independently, offering a workable way to determine and continually track node locations without anchor nodes or beacons. Movassaghi et al. [9] based on the most recent standards and publications; survey the present WBANs. Open problems and difficulties within each field are also investigated as a source of ideas for WBANs’ future advancements. Xu et al. [10] In order to talk the issue of inaccurate device localization in network, the author first examined the issues with the existing DV-HOP algorithm. The weighted centroid algorithm then uses the established indication RSS as a locus normal, efficiently dipping localization faults, and accepts an better-quality two-dimensional hyperbola procedure for distance approximation to recover the assessed space. The mockup outcomes demonstrated that the proposed technique has been greatly enhanced. Wahanea et al. [11] the suggested system enables the wireless integration of various medical sensors and the live wireless communication of measured vital signals to healthcare providers. The use of several kinds of devices to detect various parameters, including heats, sugar levels, heartbeats, ECG, and EEG, is discussed in this study in relation to various scenarios. Zhang et al. [12] proposed a classification of defect diagnosis methods (from 2013 to 2018) into three groups based on the key features of the utilized algorithms and the decision hubs. Noshad et al. [13] offered comparison analysis for the issue of failure detection. SVM, CNN, SGD, MLP, RF are the techniques that are evaluated in the study (PNN). Moridi et al. [14] summarized and analyzed numerous prior fault management frameworks created and intended for WSN. Liu et al. [15] give an overview of the topics, areas of application, conclusions, and performance difficulties in wireless body area networks today (WBAN). The survey covers a few WBAN signal processing topics, as well as network dependability, spectrum management, security, and WBAN integration with other technologies for highly effective future healthcare applications.

3 Proposed Work In this example, a typical network topology is shown with 15, 30 and 50 mobile nodes scattered randomly across a 100 * 100 m2 areas. The communication distances between each node are all set to 25 m. A1, A2, A3, and A4 are the four anchors, each

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of which is aware of where it is in relation to the other 15, 30 and 50 mobile nodes. The N1 through N50 movable nodes are unable to determine their exact position. Algorithm Step 1: The DV-Hop procedure is initially used in this step. All anchor computes and broadcasts the average hop distance over the whole system, and every unidentified node hits the anchor that is neighboring to it in direction to aid as a location node. Step 2: Every time, the unidentified node trilaterated with the location anchor node and selects any two anchors to detect where it is. We must thus record the outcomes of each combination. Imagine there are four anchor nodes and node A1 is the anchor that is closest to node N1. Hence, the different N1 combinations are (A1, A2, A3), (A1, A2, A4), and (A1, A3, A4). Step 3: Using Eq. (1), let’s calculate the N1 error at these spots and then select the position with the lowest error value. Errorn =

√

(X n' − X n )2 − (Yn' − Yn' )2

(1)

where (X n' , Yn' ) is the assessed match up of the node N, and (X n , Y n ) its real matches.

4 Results and Discussion To evaluate the effectiveness of the suggested method, we pretend a system scenario in Matlab and compute the localization results. The wireless array of the device nodes (R) is fixed at 25 m, and the experiment area is a square of 100 by 100 m2 . As shown in graphs from Figs. 1, 2, 3, 4 and 5, we change the system extent from 100 to 500, with the amount of anchor nodes and motion nodes fixed at 4 and 15, 30, and 50 respectively (Table 1). Figure 6 displays the typical approximation inaccuracy of planned procedure with 15 moveable nodes (Table 2). Figure 7 displays the typical approximation inaccuracy of planned procedure with 30 movable nodes (Table 3). Figure 8 displays the typical approximation inaccuracy of planned procedure with 50 movable nodes.

5 Conclusion and Future Scope An enhanced DV-Hop method based on the nearest anchor is the procedure suggested in this study. The average localization error is used as the assessment criterion for the localization issue. Without the need of extra equipment, the recommended procedure

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Fig. 1 Network extent 100 with 15 movable nodes

Fig. 2 Network extent 200 with 15 movable nodes

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Fig. 3 Network extent 300 with 15 movable nodes

Fig. 4 Network extent 400 with 15 movable nodes

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Fig. 5 Network extent 500 with 15 movable nodes Table 1 Estimation error (15 mobile nodes)

Network extent

Movable nodes

100

15

200

15

5.952

300

15

11.286

400

15

4.308

500

15

13.923

Approximation inaccuracy (m) 8.493

Estimation Error (Meters) 16

13.923

14 11.286

12

Estimation error (Meters)

10

8.493

8

Estimation Error (Meters)

5.952

6

4.308

4 2 0 100

200

Fig. 6 Estimation error (15 mobile nodes)

300

400

500

Fault Tolerant Algorithm to Cope with Topology Changes Due … Table 2 Estimation error (30 mobile nodes)

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Network extent

Movable nodes

100

30

200

30

4.193

300

30

14.700

400

30

12.716

500

30

5.418

Approximation inaccuracy (m) 2.699

Estimation Error (Meters) 16

14.7

14

12.716

12

Estimation error (Meters)

10

Estimation Error (Meters)

8 5.418

6 4

4.193 2.699

2 0 100

200

300

400

500

Fig. 7 Estimation error (30 mobile nodes)

Table 3 Estimation error (50 mobile nodes)

Network extent

Movable nodes

Approximation inaccuracy (m)

100

50

3.032

200

50

16.997

300

50

13.808

400

50

11.505

500

50

10.848

produces good results. Algorithms’ disadvantage is the added processing expense. The average estimation error of the proposed method is 13.923, which is smaller than the average estimation error of the traditional method, which is 21.38. This demonstrates the enhanced method’s superiority to the original DV-Hop algorithm. Soft computing will need to undergo further development if it is to be utilized to reduce computational expenses.

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Estimation Error (Meters) 16.997

18 16

13.808

14

11.505

12 Estimation 10 error 8 (Meters) 6 4

10.848 Estimation Error (Meters)

3.032

2 0 100

200

300

400

500

Fig. 8 Estimation error (50 mobile nodes)

References 1. Lazos L, Poovendran R (2005) SeRLoc: robust localization for wireless sensor networks. ACM Trans Sensor Netw 1(1):73–100 2. Stoleru R, Stankovic JA, Son S (2007) Robust node localization for wireless sensor networks. In: EmNets’07, June 25–26, 2007, Cork, Ireland. ACM. ISBN 978-1-59593-694-3/07/06 3. Kuriakose J, Joshi S, George VI (2013) Localization in wireless sensor networks: a survey. In: CSIR sponsored X control instrumentation system conference—CISCON-2013 4. Yedavalli K, Krishnamachari B (2008) Sequence-based localization in wireless sensor networks. IEEE Trans Mobile Comput 7(1) 5. Pal A (2010) Localization algorithms in wireless sensor networks: current approaches and future challenges. Netw Protocols Algorithms 2(1). ISSN 1943-3581 6. Singh PK, Tripathi B, Singh NP (2011) Node localization in wireless sensor networks. Int J Comput Sci Inf Technol 2(6):2568–2572 7. Luo Z, Min PS, Liu S-J (2013) Target localization in wireless sensor networks for industrial control with selected sensors. Int J Distrib Sens Netw 2013, Article ID 304631, 9 p. https:// doi.org/10.1155/2013/304631 8. Lo G, Gonzalez-Valenzuela S (2013) Wireless body area network node localization using small-scale spatial information. IEEE J Biomed Health Inform 17:715–726. https://doi.org/10. 1109/JBHI.2012.2237178 9. Movassaghi S, Abolhasan M, Lipman J, Smith D, Jamalipour A (2013) Wireless body area networks: a survey. IEEE Commun Surv Tutor 10. Xu C-X, Chen J-Y (2015) Research on the improved DV-HOP localization algorithm in WSN. Int J Smart Home 9(4):157–162. https://doi.org/10.14257/ijsh.2015.9.4.16 11. Wahanea V, Ingole PV (2017) A survey: wireless body area network for health monitoring. Am Sci Res J Eng Technol Sci (ASRJETS) 31(1):287–300 12. Zhang Z, Mehmood A, Shu L, Huo Z, Zhang Y, Mukherjee M (2018) A survey on fault diagnosis in wireless sensor networks. IEEE Access 6:11349–11364 13. Noshad Z, Javaid N, Saba T, Wadud Z, Saleem MQ, Alzahrani ME, Sheta OE (2019) Fault detection in wireless sensor networks through the random forest classifier. Sensors 19:1568 14. Moridi E, Haghparast M, Hosseinzadeh M, Jassbi SJ (2020) Fault management frameworks in wireless sensor networks: a survey. Comput Commun 155:205–226 15. Liu Q, Mkongwa KG, Zhang C (2021) Performance issues in wireless body area networks for the healthcare application: a survey and future prospects. SN Appl Sci 3:155. https://doi.org/ 10.1007/s42452-020-04058-2

Analysis on Speech Emotion Recognizer Yogesh Gupta

Abstract Speech emotion recognizer produces data that needs analysis on domain specific basis to help understand the data better as well to help in data augmentation. Data flows in through various data channels and must be characterized for convenient interpretation. Various modulations are done through masking on simple audio to turn it into audio of choice. The use of such augmentations is vast in the growing world with growing artificial intelligence. This paper provides in-depth analysis on speech emotion recognition using wave plots and spectrograms. This paper also explores the impact of various features such as noise, angry, and sad on emotion recognition. Keywords Speech emotion recognizer · Audio processing · Augmentation · Pitch modulation

1 Introduction Speech emotion recognition (SER) is the technique to take an audio sample which is live or prerecorded and analyze it to find the various emotions present. It has become one of the most challenging jobs in speech processing nowadays [1] and an important part of life as humans start interacting more and more with machines. Traditional systems such as humans on phone calls are being replaced by chatbots for support, and with the coming time, it will just become more and more popular. When people are introduced to the topic of SER, they usually tend to limit themselves to detection of lies or video games [2]; however, the possibilities grow exponentially with growing integrations with technology. Most of the research done toward SER usually focuses on a single phrase to understand the emotions [3], and hopefully in the future, larger speeches would be considered for such research. The advancements in artificial intelligence have still not shown significant advancements in the field of SER [4], as emotions are a concept which is very individualistic and can’t be Y. Gupta (B) School of Engineering and Technology, BML Munjal University, Gurugram, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_57

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discrete denoted for code understanding. However, deep learning is emerging to be very promising for this field. Speech emotion recognition systems are used in call centers or business processing organizations to categorize the calls according to several parameters such as emotions and then used to identify the unsatisfied customer using conversational analysis, which helps companies to improve their services and to plan accordingly for shortterm and long-term future. These systems may be used to detect the mental health of a driver in car board system, which may prevent him/her from accidents to happen. SER is also helpful in medical field to modulate the dosage according to the emotions shown by the patient while going through therapies such as chemotherapy. SER helps to monitor people with suicidal tendencies. Speech emotion recognition systems are also helpful in education system to identify bored students and to improve the teaching pedagogy. These systems can be used for the security purposes in public places by noticing exciting feelings like anxiety and fear, etc. This paper provides in-depth analysis on speech emotion recognition using waveplots and spectrograms. This paper also explores the impact of various features such as noise, angry, and sad on emotion recognition. The rest of the paper is structure as follows: Sect. 2 describes literature related to SER. Methodology and results are discussed in Sect. 3. Conclusion is drawn in Sect. 4 along with future work.

2 Literature Review The existing work reported in literature is described in this section. Speech emotion recognizer is a part of audio processing and has been a challenging task for researchers. In literature, few works have been reported related to SER. Dave et al. [5] presented an efficient Mel frequency cepstral coefficient for SER based on various features like loudness, format, etc. Liu et al. [6] developed a model for SER using a speech dataset based on Chinese content [7] (CASIA). Fahad et al. [8] presented a technique to select the features using glottal and DNN-based models for SER. Wei et al. [9] implemented a method for SER based on autoencoder and sparse classifier. They obtained some improvement in results. Zhang et al. [10] proposed a technique using convolutional neural network (CNN). George et al. [11] used CNN and LSTM and developed a model technique for spontaneous SER. Hamid [12] presented a methodology for Egyptian Arabic speech emotion recognition based on prosodic, spectral, and wavelet features. He did feature ranking and anger detection also and found improved emotion recognition rates. Akcay et al. [13] explored and discussed various areas of SER. They presented an exhausted survey on SER along with current challenges of it. Yoon et al. [14] analyzed the impact of visual modality on speech and text for improving the accuracy of the emotion detection system. Noroozi et al. [15] analyzed audio and video signals in the proposed emotion recognition system. Saxena et al. [16] used FFT with machine learning techniques to for detecting the emotions. Pandey and Seeja [17] used deep learning method to implement subject independent emotion detection system.

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3 Methodology and Result Discussion This work is centered around the analysis of various types of emotions to understand the results generated by the SER. The main analysis done is on the various emotions present and the count of each emotion. Python and its various libraries oriented to audio such as librosa and IPython have used in this work. To visualize the results Matplotlib and Seaborn are used. To perform all the experiments, four different data frames that are Ravess, Creta-D, Tess, and Savee are used in this work. The data is then sorted based on the emotions and labeled accordingly. The results are shown as a bar chart to help with visualization in Fig. 1. This figure presents the comparison to characterize the data based on emotions. The datasets have more fear, sad, angry, happy, and disgust with neutral taking the second highest occurrences, surprise appears almost have the times as neutral and calm occurs the least. Another important analysis technique offered is of wave plots and spectrograms as shown in Figs. 2, 3, 4, and 5. These figures show the variations of different emotions like fear, angry, sad, and happy. Waveplots represent the loudness of the audio sample into consideration, and spectrograms represent the frequency of the audio with variation of time. The comparison between different emotions and their waveplots and spectrograms aids in understanding the correlation between these. Lastly, they play an important role to see the difference after data augmentation. Data augmentation is the ability to synthesize new audio samples by adding permutations or if compared to image processing it can be considered as a type of masking to vary the original audio samples. The sample audio and its augmentations are presented in Figs. 6, 7, 8, 9 and 10.

Fig. 1 Count of emotions

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Fig. 2 Audio with fear emotion: a waveplot and b spectrogram

Fig. 3 Audio with angry emotion: a waveplot and b spectrogram

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Fig. 4 Audio with sad emotion: a waveplot and b spectrogram

Fig. 5 Audio with happy emotion: a waveplot and b spectrogram

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Fig. 6 Waveplot of sample audio (original)

Fig. 7 Waveplot after noise injection

Fig. 8 Waveplot after stretching

Fig. 9 Waveplot after shifting

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Fig. 10 Waveplot after pitch modulation

4 Conclusion As per literature, there are many challenges in SER to improve accuracy in recognition of speech emotions as well as to decrease the computational complexity of the overall model. Therefore, this paper provides an analysis for speech emotion recognition. It also explores the impact of various features on audio signals. For future, other types of features may be included to improve the performance of SER. The applications of SER will be extended to mental health industry and the incorporation of Internet of Things may improve the whole model by providing solutions for people in distress or having suicidal tendencies.

References 1. Schuller B, Rigoll G, Lang M (2003) Hidden Markov model-based speech emotion recognition. In: Proceedings of IEEE International conference on acoustics, speech, and signal processing, pp 11–17 2. Cowie R, Cowie ED, Tsapatsoulis N, Votsis G, Kollias S, Fellenz W, Taylor JG (2001) Emotion recognition in human-computer interaction. IEEE Signal Process Mag 18(1):32–80 3. Kun H, Yu D, Tashev I (2014) Speech emotion recognition using deep neural network and extreme learning machine. In: Proceedings of fifteenth annual conference of the International Speech Communication Association 4. Amir N (2001) Classifying emotions in speech: a comparison of methods. In: Proceedings of Eurospeech. pp 127–130 5. Dave N (2013) Feature extraction methods LPC, PLP and MFCC in speech recognition. Int J Adv Res Eng Technol 1:1–4 6. Liu ZT, Wu M, Cao WH, Mao JW, Xu JP, Tan GZ (2018) Speech emotion recognition based on feature selection and extreme learning machine decision tree. Neurocomputing 273:271–280 7. Liu CL, Yin F, Wang DH, Wang QF (2011) CASIA online and offline Chinese handwriting databases. In: Proceedings of international conference on document analysis and recognition, pp 37–41 8. Fahad M, Yadav J, Pradhan G, Deepak A (2020) DNN-HMM based speaker adaptive emotion recognition using proposed epoch and MFCC features. Circuits Syst Signal Process 9. Wei B, Hu W, Yang M, Chou CT (2019) From real to complex: enhancing radio-based activity recognition using complex-valued CSI. ACM Trans Sens Netw 15(3):1–32

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10. Zhang S, Zhang S, Huang T, Gao W (2017) Speech emotion recognition using deep convolutional neural network and discriminant temporal pyramid matching. IEEE Trans Multimed 20:1576–1590 11. Trigeorgis G, Ringeval F, Brueckner R, Marchi E, Nicolaou MA, Schuller B, Zafeiriou S (2016) Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 5200–5204 12. Hamid LA (2020) Egyptian Arabic speech emotion recognition using prosodic, spectral and wavelet features. J Speech Commun 122:19–30 13. Akcay MB, Oguz K (2020) Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. J Speech Commun 116:56–76 14. Yoon S, Dey S, Lee H, Jung K (2020) Attentive modality hopping mechanism for speech emotion recognition. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 3362–3366 15. Noroozi F, Marjanovic M, Njegus A, Escalera S, Anbarjafari G (2017) Audio-visual emotion recognition in video clips. IEEE Trans Affective Comput 16. Saxena A, Tripathi K, Khanna A, Gupta D, Sundaram S (2020) Emotion detection through EEG signals using FFT and machine learning techniques. In: Proceeding of international conference on innovative computing and communications. Advances in intelligent systems and computing, vol 1087. Springer 17. Pandey P, Seeja KR (2018) Subject-independent emotion detection from EEG signals using deep neural network. In: Proceeding of international conference on innovative computing and communications. Lecture notes in networks and systems, vol 56. Springer

Summarization of Research Paper into a Presentation Neha Badiani, Smit Vekaria, Santosh Kumar Bharti, and Rajeev Kumar Gupta

Abstract With the increase in the use of the internet over the globe, the exchange and storage of information have seen an exponential surge as well. While writing and reading, people prefer shorter versions of content to save time and derive only significant insights. This has promoted the use of summarization in the past few decades. Since human-generated summaries are resource and time-intensive, automatic text summarization was developed. Inspired by these problems and by observing the issue of time management among our peers in the research community, we have tried to form a system that converts a research paper into a presentation. This system takes the research paper as the input document, summarizes the content section-wise and adds it to slides which are combined into a presentation. The techniques used to summarize are Text Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis, and Text Rank Algorithm. The model is tested on four different papers and evaluated on their ROUGE scores to compute their utility.

1 Introduction Text summarization is the technique of condensing a piece of data into a fluent and coherent format. Automatic text summarization is the process of generating a summary computationally using an algorithm to decipher the significant parts of the text and bring them together to form a cogent gist. Goldstein et al. [1] say that the N. Badiani (B) · S. Vekaria · S. K. Bharti · R. K. Gupta Pandit Deendayal Energy University, Gandhinagar, Gujarat 382421, India e-mail: [email protected] S. Vekaria e-mail: [email protected] S. K. Bharti e-mail: [email protected] R. K. Gupta e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_58

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techniques face the challenges of redundancy, temporal dimension, sentence order, etc., and thus make the task complex to navigate. With the increase in contributions to the research community, the challenges faced by the people involved are coming to light. One of the problems they face is that even after toiling for months or years to research their domain and come up with solutions, they need to summarize their findings in a way that is well portrayed in a PowerPoint presentation and manage their time while working in a team with other researchers. The two main types of summarizers are extractive and abstractive [2]. When compared to extractive summarization, abstractive summarization is a more effective method of summarization because it pulls information from several texts to construct an accurate summary of the material. The “extractive summary” is a method that involves picking important passages from the original material, such as phrases, paragraphs, and other sections, and then compiling them into a condensed version. There is a substantial correlation between the statistical and linguistic properties of sentences and their importance of sentences. In this paper, we will discuss how we developed the system to generate a PowerPoint presentation from a paper by employing extractive summarization techniques. Our proposed system uses three techniques to summarize the content: Text Frequency Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), and Text Rank. The system analyses the document to find headings and splits the whole paper based on which heading it lies under. The split parts are then summarised individually and added to a presentation.

2 Literature Survey See Table 1.

3 Proposed Work The goal of this paper is to suggest a system that turns a research paper into a presentation. The content of the document would be summarized based on its relevance and added to the presentation. The proposed approach is shown in Fig. 1. As can be seen from the flow, the research paper (the input document) is passed into the system. The system then identifies section headings and uses them to split them into parts. Each part is passed through the summarization models, and a summary is generated. This output is added to slides, which are finally merged into the presentation to achieve the goal of the system. The implementation of the system is explained in the further section of this paper. The introduction to summarization techniques is given below.

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Table 1 Literature review Paper

Description

Klyuev and Oleshchuk [3]

The review focuses on how content-aware summaries perform in comparison to keyword-based gist. It shows how the former works better due to the mechanism it is based on

Elena and Manuel This study gives an exhaustive review of the text summarization field as a whole. It evaluates recently proposed techniques against metrics and draws [4] conclusions based on the results Bittencourt and Ramalho [5]

This paper talks about challenges faced by automatic summarization techniques. It points out that not enough algorithms exist that can prioritize topics and words by importance and then summarize them. This is achieved by testing well-known datasets against baseline summarization techniques

Christian et al. [6] This study introduces the TF-IDF technique by explaining its working. The technique is proven to have an accuracy of 67% when tested on 6 documents and compared against 1 human-generated summary, 2 online text summarizers and 1 TF-IDF summary Kaiz and Yash [7] This study proposes to use the LSA method to create a system for people involved in legal work such as lawyers. The aim of the system is to save the time lawyers spend in going through cases by summarizing judgements from the documents. It was approved by real lawyers and had a ROUGE-1 score of 0.58 Muhamad Fahmi et al. [8]

This paper develops a system to help understand the Qur’an better. It uses Text Rank method to summarize the various meanings of a word in different contexts to give the reader an overall view. The system has a F-score of 0.6173 and the summary is considered human-like in most scenarios

Fig. 1 Proposed approach diagram

3.1 Text Frequency Inverse Document Frequency (TF-IDF) This method, which compares the frequency of words in one document with the inverse proportional frequency of that word in other documents, is referred to as the “term frequency-inverse document frequency method”. [9] It is a statistical extraction method that works by comparing the frequency of words in one document with the

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frequency of that word in other documents. On the other hand, common terms like articles and prepositions tend to have lower TF-IDF scores than words that are used often inside a single text or within a small group of papers. The workings of TF-IDF are described below. We calculate, given a document collection called D, a word called w, and an individual document called d that belongs to D: Wd = fw,d

  |D| ∗ log fw,d

(1)

where f w,d equals the number of times w appears in d, |D| is the size of the corpus, and f w,D equals the number of documents in which W appears in D.

3.2 Latent Semantic Analysis (LSA) Latent Semantic Analysis is a method that combines mathematics and statistics to uncover the underlying semantic structures that are buried within words and phrases. It is a method that does not require any supervision and does not call for any training or outside information. LSA uses the relevance of the input sequence to extract information, such as which phrases are used in conjunction with one another and which words are used in a variety of phrases. There is a strong correlation between the phrases’ meanings if there are a significant number of words that are shared between them [10]. It is common for LSA-based summarization algorithms to consist of three distinct steps. Step-1: Input Matrix Selection The input document must be written in a way that a computer can understand and use to do calculations. This is usually shown as a matrix, where each column is a sentence and each row is a word or phrase. The cells show how important certain words are in a sentence. There are many different ways to fill in the cell values. Step-2: Singular Value Decomposition (SVD) SVD is an algebraic technique for modelling the relationships between words/phrases and sentences. In this approach, the input matrix A is decomposed into the following three new matrices: A = UWVT

(2)

where A is the input matrix (m × n), U is the extracted words (m × n), W is a diagonal matrix (m × n), and V is the extracted sentences (n × n). Step-3: Sentence Selection SVD’s results are used in a number of ways to choose the most important sentences.

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3.3 Text Rank Based on Page Rank The PageRank technique is utilised to determine the relative significance of interconnected websites. It does this by giving every node on the web page a specific numerical weight, with the intention of determining the relative “importance” of the collection of connected connections. The rank value provides an indication of the significance of a specific page. In this case, nodes are built out of sentences, and edges are determined by the similarity score between different keywords, which can be either words or sentences [11]. Within the framework of the strategy that we have developed, we have assumed that individual phrases inside the document are analogous to individual web pages within the PageRank system. The degree to which two sentences are the same determines how likely it is that one will transition from sentence A to sentence B. With the help of the modified TextRank, we are able to choose the most significant sentences from the text document that we are given by ranking the sentences using the understanding that is behind the PageRank algorithm. Important pages are connected to other significant web pages as part of the PageRank algorithm. In the same vein, our method operates under the assumption that the significant sentences of the given document are connected to, or comparable to, the important phrases of other important documents [12].

4 Implementation Details The implemented system is divided into four parts. First, process the input document by splitting it into multiple sections based on the headings present. We are doing so because processing the document as a whole would be tedious. Second, the most crucial part, developing the model using Natural Language Processing (NLP) techniques that can convert the input text into a summarised form, representing the gist of the text. Third, convert the summary text into a presentation slide. Fourth, add the images present in the document to the end of the presentation, so that the final presentation can be made.

4.1 Splitting the Input Document For this section, we will first convert the input document from other file formats into a .docx file. We are determining if the encountered line is a heading using its styling. If it’s a heading, then we add that line and the next lines in an array until we encounter another line that is a heading. Those stored lines form a paragraph when joined together, and the same process is repeated until the end of the file. Based on this process, the document gets split into multiple parts.

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4.2 Generating Summary Now that the document has been split into multiple parts, we can move on to the next part. Here, the text is being passed to a model that is developed based on the NLP techniques. To limit the length of the generated summary, we decided to select the top five sentences based on the rankings.

4.3 Making a Slide Based on the generated summary, the content is transferred to a slide. We used the python-pptx library to accomplish this. Using that library, we are creating slides with the bulleted list layout. A heading and its respective content are added to the slide, and then each slide is added to the presentation to form a complete presentation.

4.4 Handling Media Elements Media elements, such as tables and images, are crucial information resources and necessary to convey complex insights in a presentable form. The obstacle was to recognize media elements in the document as they were encountered and insert them in the final output to create a comprehensive presentation. We partially accomplished this by adding the images to a separate folder, and while the presentation is being compiled, the images from the folder are added to the slides for the user to use as needed.

5 Results and Analysis For summarization, we have employed three techniques: TF-IDF, LSA, and TextRank. We used the ROUGE scores to judge these methods. The F1 scores of ROUGE-1, ROUGE-2, and ROUGE-L are calculated, and an average score is presented for simpler analysis and understanding. We have tested four papers through all three methods and our system and have gotten various results. They are shown as follows: Tables 2, 3, and 4. The score presented here is the F1 score, which is derived from two factors: precision and recall. These two factors depend on the N value in N-grams. This means that it is harder to pick just one concrete factor that affects the good or poor performance of a technique. Even though we can’t say for sure why these results came out the way they did, we have seen some things that affect the results.

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Table 2 ROUGE 1 score Techniques

Paper 1

Paper 2

Paper 3

Paper 4

TF-IDF

0.809

0.804

0.498

0.768

LSA

0.782

0.754

0.496

0.713

TextRank

0.777

0.749

0.441

0.712

Table 3 ROUGE 2 score Techniques

Paper 1

Paper 2

Paper 3

Paper 4

TF-IDF

0.762

0.755

0.408

0.708

LSA

0.725

0.704

0.386

0.642

TextRank

0.732

0.708

0.358

0.654

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Paper 3

Paper 4

Table 4 ROUGE L score Techniques

Paper 1

TF-IDF

0.809

0.804

0.498

0.768

LSA

0.782

0.754

0.496

0.713

TextRank

0.777

0.749

0.441

0.712

Firstly, Text Rank works based on the similarity between sentences. It checks one word of one sentence against one word of another and calculates the similarity index accordingly. This means that if the overall similarity between sentences is lower in a document, the comprehensive similarity indexes will also be lower. Second, it was discovered that papers with the same number of words under each section performed better than sections with varying lengths. For example, in the tables above, Paper 3 has the least accuracy among all because of the same reason, while the other papers have an overall balanced content distribution. Moreover, even though the output accuracy is higher in TF-IDF than in LSA, on evaluating the summaries, it was found that the LSA-generated gist is more coherent than TF-IDF summaries. A comprehensive judgement needs to be made to choose just one technique.

5.1 Obtained System Output For a better understanding of the system, we have added the following figures to explain what output is generated from the input given and how document elements such as images are handled.

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Here, Fig. 2 displays the input paper and its section ‘Introduction’. When the document is passed through the system, this heading is recognized and a summary is generated. This output added to a slide and presentation is shown in Fig. 3.

Fig. 2 The input document

Fig. 3 The output slide for the input in Fig. 2

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6 Conclusion We have implemented a system that takes a research paper document as input (any file type), divides it into sections and summarises each section. After this, the summarized text is added to slides with the section heading, and the slides are combined orderly into a presentation. While conducting the research and looking for related works, we didn’t find any system or proposal that provided the functionality our system provides. By developing such a system, we have tried to open the doors for a potential new product. Consequently, such a system would help students, researchers, and professors by reducing the time spent on creating a presentation for their research paper.

7 Future Scope Currently, the proposed system is not mature and requires work to fully provide the functionality it is supposed to. Here are some of the future directions one could incorporate into their system to make it better: Reduce text format sensitivity: As a future direction, one can develop a system that uses a different splitting factor (not heading formats) that would not result in a sensitive model. Incorporating tables and images: The implemented system fails to include images and tables in their respective sections of the presentation. By adding media and relevant text about the media, the presentation that was made would be much easier to use. Use hybrid summarization techniques: For generating the summary in our system, we have used conventional techniques, which do a good enough job of generating a summary. Different summarization techniques can be utilised to further increase the semantics and coherence of the generated summary.

References 1. Goldstein J, Mittal V, Carbonelll J, Kantrowitz M (2000) Multi-document summarization by sentence extraction. In: NAACL-ANLP 2000 workshop on automatic summarization, pp 40–48 2. Moratanch N, Chitrakala S (2016) [IEEE 2016 international conference on circuit, power and computing technologies (ICCPCT)—Nagercoil, India (18 March 2016–19 March 2016)] 2016 international conference on circuit, power and computing technologies (ICCPCT)—a survey on abstractive text summarization, pp 1–7. https://doi.org/10.1109/ICCPCT.2016.7530193 3. Klyuev V, Oleshchuk V (2011) Semantic retrieval: an approach to representing, searching and summarising text documents. Int J Inf Technol Commun Converg 1(2):221. https://doi.org/10. 1504/ijitcc.2011.039287

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4. Lloret E, Palomar M (2012) Text summarisation in progress: a literature review. Artif Intell Rev 37(1):1–41. https://doi.org/10.1007/s10462-011-9216-z 5. Bittencourt G, Ramalho GL (2002) Automatic text summarization using a machine learning approach. In: Advances in artificial intelligence. Lecture notes in computer science, vol 2507, pp 205–215. https://doi.org/10.1007/3-540-36127-8_20 6. Christian H, Agus MP, Suhartono D (2016) Single document automatic text summarization using term frequency-inverse document frequency (TF-IDF). ComTech: Comput Math Eng Appl 7(4):285–294. https://doi.org/10.21512/comtech.v7i4.3746 7. Merchant K, Pande Y (2018) [IEEE 2018 international conference on advances in computing, communications and informatics (ICACCI)—Bangalore, India (19 Sept 2018–22 Sept 2018)] 2018 international conference on advances in computing, communications and informatics (ICACCI)—NLP based latent semantic analysis for legal text summarization, pp 1803–1807. https://doi.org/10.1109/ICACCI.2018.8554831 8. Fakhrezi MF, Bijaksana MA, Huda AF (2021) Implementation of automatic text summarization with TextRank method in the development of Al-Qur’an vocabulary encyclopedia. Procedia Comput Sci. https://doi.org/10.1016/j.procs.2021.01.021 9. Rani U, Bidhan K (2021) Comparative assessment of extractive summarization: TextRank TF-IDF and LDA. J Sci Res 65(1):304–311 10. Tapas G, Mehala N (2021) Latent semantic analysis in automatic text summarisation: a stateof-the-art analysis. Int J Intell Sustain Comput 1(2):128–137 11. Jiang H, Nazar N, Zhang J, Zhang T, Ren Z (2017) PRST: a PageRank-based summarization technique for summarizing bug reports with duplicates. Int J Software Eng Knowl Eng 27(6):869–896. https://doi.org/10.1142/s0218194017500322 12. Nayak J, Abraham A, Krishna BM, Chandra Sekhar GT, Das AK (2019) Graph-based text summarization using modified TextRank. In: Soft computing in data analytics. Advances in intelligent systems and computing, vol 758 (Proceedings of international conference on SCDA 2018), pp 137–146. https://doi.org/10.1007/978-981-13-0514-6_14

Novel Design of Conformal Patch Excited Four Element Biodegradable Substrate Integrated MIMO DRA Rasika Verma and Rohit Sharma

Abstract In this study, a conformally supplied MIMO antenna that was electrified by a trapezoidal-shaped patch mounted in conformal position on the side of a DRA opposite to its radiating face is shown. The design utilizes a ‘+’ shaped cavity to provide connection to the antenna feed, as well as reduce the mutual coupling between adjacent antennas. The objective of this suggested design is to provide a wider range of coverage without compromising coverage area. In order to be environmentally friendly and contribute to solving the e-waste problem, the proposed MIMO antenna was constructed using the biodegradable filament known as polylactic acid (PLA). All RDRAs are integrated with the substrate and printed as a single piece in order to improve mechanical resilience and reduce the most frequent DRA issue, namely demounting and mis-mounting of DRA on substrate. This proposed design with good MIMO characteristics provides a bandwidth (|S11 | ≤ −10 dB) of 2.06 GHz, radiation gain of minimum 5.8 dBi and HPBW on elevation plane of 170°. Keywords Dielectric Resonator Antenna (DRA) · Multiple Input Multiple Output (MIMO)

1 Introduction An antenna is of utmost importance in the field of wireless communication. With a rapid growth in multifunctional antenna with easy fabrication and low cost [1]. An antenna capable of transmitting and receiving a number of information through a single or multiple devices on a single channel with the help of multiple antennas can be termed as MIMO antenna. Also, by generating numerous variants of the same signal, more possibilities are given for the data to reach the receiving antenna unaffected by fading, which raises the Signal-to-Noise Ratio (SNR) and error rate. R. Verma (B) · R. Sharma Department of Electronics and Communication, SRM Institute of Science and Technology, Ghaziabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_59

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MIMO produces a more reliable connection and reduced congestion by increasing the capacity of radio frequency networks [2]. One of the most researched materials in antenna building are dielectrics, these are less lossy materials with dielectric constant (εr ) > 2, and a DRA is a resonant antenna, fabricated from low-loss microwave dielectric material, the resonant frequency of which is predominantly a function of its size, shape, and material permittivity [3]. The dielectrics also have a lower conduction loss and have radiation efficiency > 98% [4]. Though a disadvantages such as higher volume and mounting error are apparent but advantages such as higher degree of freedom in designing, high radiation efficiency, wide bandwidth, and 3D printability of several dielectric materials make the disadvantages lacklustre. Being able to design a radiator in 3D environment gives the researchers a better perspective into the antenna designing and opens door to researching new parameters. The problem with hazardous e-waste is becoming worse each year as a result of a rise in electronic production. The e-waste problem might be solved without impeding market growth by using a biodegradable polymer. A biodegradable material does not decay during its lifetime. It only degrades under specific conditions and using specific approaches. When microorganisms like bacteria, fungus, and algae come into contact with bio-based polymers generated from renewable resources including corn, potato, rice, soy, sugarcane, wheat, and vegetable oil, they decompose into inorganic chemicals, biomass, methane, carbon dioxide, and water (H2 O). PLA is a common 3D printer material that is easily obtainable as 3D printed filament. One of the most potential replacements for conventional petroleum-based plastics, which have various negative environmental effects, including air, water, and land pollution as well as global warming, is biodegradable plastics. One of the biodegradable polymers, polylactic acid (PLA), is not only commercially available, but it can also be used safely and disposed of without creating damage to the ecosystem. In terms of mechanical, physical, electrical, biocompatibility, and processability, PLA is equivalent to other common polymers. PLA has become the most frequently utilised biopolymer in a variety of markets, including electronics, packaging, agriculture, and the automotive and medical sectors, as a result of its distinct features. The suggested design makes use of PLA as the dielectric material for the antenna due to its appealing properties. Using 3D printing, it is also possible to create complex antenna shapes. Because PLA, the dielectric substance used in the antenna, is a widely used printing filament, 3D printing was able to create the object. A new revolution in many applications has been sparked by the ability to produce functioning prototypes or components using 3D printing technology at a lower cost and with shorter fabrication timeframes [5–7]. Low-loss dielectric filaments have also been made available [8] and high-conductive filaments [9] has made it possible to manufacture sophisticated microwave topologies at a reasonable cost using additive manufacturing. Current research has focused on high-frequency characterization of 3D printing materials [10–12] as well as the creation of high-frequency structures such as metamaterials [13–15], antennas [16–19], dielectric lenses [20–24], amongst other implementations [25]. Dielectric resonator antennas are one type of structure that might benefit directly from the advent of 3D printing (DRA). This well-known topology consists of

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an electrically stimulable dielectric slab that has the ability to radiate. It is a changeable topology with higher radiation efficiency than a typical microstrip implementation because its radiation characteristics and frequency depend on its size, shape, and relative permittivity of the dielectric slab [26]. Two of this technology’s key drawbacks are that the permittivity values of popular dielectric materials are confined and that the form’s complexity either enhances the cost of production or is constrained by present manufacturing procedures. This shape problem is readily handled by 3D printing, which permits the fabrication of varied shapes based solely on the printer’s precision, but for permittivity values, we may acquire multiple dielectric constants simply by adjusting the infill % of the 3D printed dielectric [20, 21, 27]. In this research we present a 3D printed MIMO antenna with integrated substrate that reduces the mounting error and gives us a sturdy antenna as shown in Fig. 1.

2 Design Parameters The presented antenna comprises 4 directional RDRA radiators of height (H D ) = 28 mm, and width (W D ) = length = 20 mm [28], placed off centred to reduce the interference between the adjacent and orthogonal antennas. These radiators are fed through a trapezoid shaped patch antenna placed in a conformal fashion on the face opposite to the radiating face, these trapezoid feeds are all identical with the smaller parallel side of width = Microstrip feed width (W F ) = 5.5 mm, and larger parallel side of width (W LT ) = 14 mm, and distance between the two parallel sides (H TP ) = 13 mm. The arrangement of feed in this manner also reduces the interference from orthogonal antennas during radiation. The 4 RDRAs are united with a substrate of thickness (H S ) = 2 mm, width (W S ) = length (L S ) = 100 mm, this complete assembly is placed on a ground plane of width = length = WS = 100 mm. The ground plane and feed of the antenna are made of copper whereas the DRA and substrate are made up of a biodegradable plastic called polylactic acid (PLA) with dielectric constant (εr ) = 2.65, and dielectric loss tangent (tan δ) = 0.003. A ‘+’ shaped cavity of length (L CAV ) = 50 mm, and width (W CAV ) = 10 mm, can be observed on the substrate and ground plane, this cavity provides the location to feed the antenna and also increases the interference between the adjacent antennas. This completes the complete antenna design and its parameters as observed for the final results of the antenna. The defined parameters can be observed in Fig. 2.

3 Results and Comparison The presented antenna takes its base DRA radiator from, further these DRAs are placed to make a four port MIMO antenna and the following iterations of parametric are made to arrive at the desired results.

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Fig. 1 Proposed antenna: a side view and b top view

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Fig. 2 Antenna parameters and Trapezoid feed Parameters

3.1 DRA Position The radiators are initially placed off-centre to reduce the cavity space needed to feed the antenna. The radiators are then moved laterally outwards. The observed results can be seen in Fig. 3. With increase in the distance between the two DRA it can be observed that |S21 | and |S31 | slowly move to a more negative thus confirming the fact that by increasing the distance interference between the antennas can be reduced. To keep the size of antenna compact while obtaining the optimal MIMO results we kept the DRA at 20 mm from the origin-axis.

3.2 Cavity Length Initial cavity length was kept to a point where both feeds can be connected to the microstrips, and a parametric was made with increasing length of this cavity to reduce the interference between the adjacent antenna, and the results of this parametric can be observed in Fig. 4 where we can see |S21 | gradually improves and goes further negative and we get the most optimal results at the cavity length of 50 mm.

3.3 Cavity Width After completing the cavity length parametric, a cavity width parametric was done as well to observe further enhancement in isolation between the radiators, and minute changes were observed as well, these changes can be observed when we take width

770 Fig. 3 S parameters on various values of Xpos (lateral position of DRA), a |S11 |, b |S21 |, and c |S31 |

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Novel Design of Conformal Patch Excited Four Element Biodegradable … Fig. 4 S parameter values with change in cavity length (L cav ), a |S11 |, b |S21 |, and c |S31 |

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of the cavity from 2 to 12 mm. The value of S parameters at 10 mm were deemed perfect for the antenna and the parametric results can be observed in the Fig. 5. The far field results of the proposed antenna are presented below. The presented antenna has a very stable radiation efficiency that stays above 93% in the entire radiation band as show in Fig. 6, also within the band a constant rise in gain can be observed with rise in frequency that can be observed in Fig. 7. It can be observed that the MIMO antenna radiates in all 4 major directions with gain of 4.5 dBi, in elevation plane and has a 5.2 dBi gain in Azimuthal plane at 3.7 GHz and a peak gain of 5.8 dBi at 4.25 GHz which has been depicted in Fig. 8. The correlation coefficient (ρ) and Diversity gain (Gapp ) are the two important parameters, and to validate the appropriate MIMO operability, these parameters need to be calculated for the antenna system. As correlation and diversity gain are negatively correlated, lowering the correlation value will improve the performance of the system’s diversity. To quantitatively represent their relationship, apply the given formula: √ G app = 10 ∗ 1 − |ρ|2 (1) The value of ρ is less than 0.002 and Gapp is ≈ 10 within the operating band, which reflect good MIMO characteristic of the proposed design (Fig. 9).

4 Conclusion From the observation of simulated and measured results we can confer that the desired MIMO antenna with 2.06GHz bandwidth and 5.8 dBi peak gain has been obtained. Isolation of the antenna is observed by two graphs namely |S21 | and |S31 | these two parameters define all the possible interactions between different radiators as they represent the adjacent and opposite configuration of antennas, and it is considered ideal for the antenna if the isolation between the two antennas is under −20 dBi within the band, which is already been observed in the proposed antenna. Observed difference between the simulated and measured results is minimal thus concluding the research. In future, bandwidth can further be enhanced by introducing air gaps in the DRA and changing the effective dielectric constant of antenna system so that most of the ISM band can be covered with ultra-wideband antenna.

Novel Design of Conformal Patch Excited Four Element Biodegradable … Fig. 5 S parameter values with change in cavity width (W cav or G): a |S11 |, b |S21 |, and c |S31 |

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Fig. 6 Simulated Radiation efficiency of proposed design

Fig. 7 Simulated Peak Gain of proposed design

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(a) 3.3GHz

(b) 3.7GHz

(c) 4GHz

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Fig. 9 Co-relation coefficient and diversity gain of proposed design

References 1. Aishwaryaa Devi G, Aarthi J, Bhargav P, Pandeeswari R, Ananda Reddy M, Samson Dalniel R (2017) UWB frequency reconfigurable patch antenna for cognitive radio applications. In: IEEE international conference on antenna innovation & modern technologies for ground, aircraft and satellite applications (iAIM) 2. https://www.techtarget.com/searchmobilecomputing/definition/MIMO 3. Petosa A, Ittipiboon A, Antar YMM, Roscoe D, Cuhaci M (1998) Recent advances in dielectricresonator antenna. IEEE Antennas Propag Mag 40(3):35–48 4. Mongia RK, Ittipiboon A, Cuhaci M (1994) Measurements of radiation efficiency of dielectric resonator antennas. Microwave Guided Wave Lett 4(3):80–82 5. Macdonald E, Salas R, Espalin D, Perez M, Aguilera E, Muse D, Wicker RB (2014) 3D printing for the rapid prototyping of structural electronics. IEEE Access 2:234–242 6. Kim C, Espalin D, Liang M, Xin H, Cuaron A, Varela I, Macdonald E, Wicker RB (2017) 3D printed electronics with high performance, multilayered electrical interconnect. IEEE Access 5:25286–25294 7. Willis S (2018) The maker revolution. Computer 51(3):62–65 8. Premix Preperm website, https://www.preperm.com. Accessed on: 3 March 2021 9. Multi3D website, https://www.multi3dllc.com/. Accessed on: 3 March 2021 10. Pizarro F, Salazar R, Rajo-Iglesias E, Rodríguez M, Fingerhuth S, Hermosilla G (2019) Parametric study of 3D additive printing parameters using conductive filaments on microwave topologies. IEEE Access 7:106814–106823 11. Kim MJ, Cruz MA, Ye S, Gray AL, Smith GL, Lazarus N, Walker CJ, Sigmarsson HH, Wiley BJ (2019) One-step electrodeposition of copper on conductive 3D printed objects. Addit Manuf 27:318–326 12. Roy S, Qureshi MB, Asif S, Braaten BD (2017) A model for 3D printed microstrip transmission lines using conductive electrifi filament. In: 2017 IEEE international symposium on antennas and propagation USNC/URSI national radio science meeting, pp 1099–1100 13. Stuardo P, Pizarro F, Rajo-Iglesias E (2020) 3D-printed sievenpiper metasurface using conductive filaments. Materials 13(11)

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14. Tasolamprou AC, Mentzaki D, Viskadourakis Z, Economou EN, Kafesaki M, Kenanakis G (2020) Flexible 3D printed conductive metamaterial units for electromagnetic applications in microwaves. Materials 13(17) 15. Xie Y, Ye S, Reyes C, Sithikong P, Popa B-I, Wiley BJ, Cummer SA (2017) Microwave metamaterials made by fused deposition 3D printing of a highly conductive copper-based filament. Appl Phys Lett 110(18):181903 16. Li Y, Ge L, Wang J, Da S, Cao D, Wang J, Liu Y (2019) 3D printed high-gain wideband waveguide fed horn antenna arrays for millimeterwave applications. IEEE Trans Antennas Propag 17. Moscato S, Bahr R, Le T, Pasian M, Bozzi M, Perregrini L, Tentzeris MM (2016) Infilldependent 3-D-printed material based on ninjaflex filament for antenna applications. IEEE Antennas Wirel Propag Lett 15:1506–1509 18. Li Y, Wang C, Yuan H, Liu N, Zhao H, Li X (2017) A 5G MIMO antenna manufactured by 3-D printing method. IEEE Antennas Wirel Propag Lett 16:657–660 19. Tawk Y, Chahoud M, Fadous M, Costantine J, Christodoulou CG (2017) The miniaturization of a partially 3-D printed quadrifilar helix antenna. IEEE Trans Antennas Propag 65(10):5043– 5051 20. Bjorkqvist O, Zetterstrom O, Quevedo-Teruel O (2019) Additive manufactured dielectric Gutman lens. Electron Lett 21. Poyanco J-M, Pizarro F, Rajo-Iglesias E (2020) 3D-printing for transformation optics in electromagnetic high-frequency lens applications. Materials 13(12) 22. Belen A, Günes F, Mahouti P, Palandöken M (2020) A novel design of high performance multilayered cylindrical dielectric lens antenna using 3D printing technology. Int J RF and Microw Comput-Aided Eng 30(1):e21988 23. Belen MA, Mahouti P (2020) Design of nonuniform substrate dielectric lens antennas using 3D printing technology. Microw Opt Technol Lett 62(2):756–762 24. Mahouti P, Belen MA, Günes F, Yurt R (2019) Design and realization of multilayered cylindrical dielectric lens antenna using 3D printing technology. Microw Opt Technol Lett 61(5):1400– 1403 25. Shin S-H, Shang X, Ridler NM, Lucyszyn S (2021) Polymer-based 3-D printed 140–220 GHz low-cost quasi-optical components and integrated subsystem assembly. IEEE Access 9:28020–28038 26. Lai Q, Almpanis G, Fumeaux C, Benedickter H, Vahldieck R (2008) Comparison of the radiation efficiency for the dielectric resonator antenna and the microstrip antenna at ka band. IEEE Trans Antennas Propag 56(11):3589–3592 27. Xia Z, Leung KW, Lu K (2019) 3-D-printed wideband multi-ring dielectric resonator antenna. IEEE Antennas Wirel Propag Lett 18(10):2110–2114

Early Kidney Stone Detection Among Patients Using a Deep Learning Model on an Image Dataset Sharwan Buri and Vishal Shrivastava

Abstract Kidney stones, also known as renal calculi, are solid masses formed by the crystallization of urine. To perform surgical procedures on the urinary calculus, it is essential to determine the specific and correct location of the calculus. Consequently, since ultrasound pictures include speckle noise, it is challenging to visually identify urinary calculi in ultrasound images, and it’s, therefore, necessary to use automated algorithms for the identification of kidney stones in computed tomography. Renal abnormalities such as changes in kidney size and location, leg swelling, and the production of stones as well as cystic variations can be diagnosed using ultrasound imaging methods. This report summarizes the results of an audit on the detection and acceptance of renal abnormalities in most populations. Significant progress has been achieved in the field of artificial intelligence with the help of deep learning (DL) method, which proposes the automatic detection of kidney stones (whether they contain stones or not) using coronal computed tomography (CT) images, which can be used to recommend an automated diagnosis of kidney stones. We discovered that our model is able to reliably detect kidney stones of any size, including stones that are very small. It has been shown in this work that the newly popular DL approaches may be used to handle additional difficult issues in urology. According to this study, we can detect even very small stones in the early stages of stone formation and it has obtained new results by improving the algorithm used based on the results of previous studies, which are discussed in the paper. The methodology used in this paper can achieve the accuracy of 100% on the kidney stone dataset. Keywords Kidney abnormalities · Renal calculi · Kidney stones · VGGNET-19 · Kidney dataset · Deep learning (DL) · Computed tomography (CT)

S. Buri · V. Shrivastava (B) Computer Science & Engineering, Arya College of Engineering & I.T., Jaipur 302028, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_60

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1 Introduction Kidney stone illness has been thoroughly documented over the past several decades and is now recognized as a worldwide health concern that harms human health. Kidney stone illness is quite frequent in the populations of industrialized nations, particularly among the elderly. The condition is furthermore classified as an important socio-medical concern [1]. Kidney stones are the result of a pathological biomineralization process in the urinary system [2], and they are often composed of a combination of two, three, or more different constituents. Stone development in the urinary system is a multifactorial problem that is affected by the physical and chemical characteristics of the urinary system [3]. Although major components of the process of stone formation have been examined over the years, there is no full and satisfying explanation for the pathophysiology of stone formation. To determine the pathophysiology of kidney stones and formulate future therapy and preventive methods for these stones, it is necessary to examine kidney stones at several levels, including their elemental and molecular compositions. Despite the inherent benefits and promise of artificial intelligence-based automated and objective kidney stone identification systems, only a small number of papers have been published in this field. It was studied if a conventional strategy [4] or a deep learning method [5] might be used; however, the findings were only somewhat successful. The authors in [6] demonstrated that the ResNet-101 design considerably enhanced the classification performance for five kidney stone types (the leave-one-out cross-validation led to recall values from 71% up to 94% according to the class). The primary shortcoming of these earlier studies is that the approaches were only evaluated on ex-vivo pictures collected under very controlled acquisition settings and without the use of endoscopes, which represents a significant constraint. The pictures obtained from ureteroscopy in vivo data are influenced by blur, significant light fluctuations among collections, and also reflections, and the views are difficult to optimize. At present, preventing the formation and recurrence of kidney stones still remains a serious problem for human health. Due to the formation of this, there is a decrease in kidney function. Therefore, early diagnosis of kidney stones is important. In recent years, advances in technology have led to the widespread adoption of ML and DL techniques for the diagnosis of diseases. With the help of these methods, a very reliable tool is available for definitive clinical decision, which requires long and complicated procedures, as these tools reduce the time of diagnosis and the accuracy of the diagnostic result. Let’s increase so far, many studies have been done in which the diagnosis of diseases has been done with the help of deep learning methods. Earlier studies have used deep learning methods in the diagnosis of diseases such as the detection and classification of brain tumors from medical images [7]. Similarly for recognizing pathological features of diabetic patients [8], and additionally, to diagnose thyroid nodules from ultrasound images [9]. This research paper ensues as follows. Section 2 provides a literature review of our relevant topic. Section 3 defines the research methodology that provides overall

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research methods techniques to implement the proposed model, in Sect. 4 experimental findings are described, and then in the final section conclusion of the study with possible directions for future work is offered. Advanced techniques have been used in the algorithm used under this research, so that kidney stones can be detected in the early stage itself, which has yielded better results than the results of previous research.

2 Literature Review There have been considerable investigations undertaken in the domains of this deep learning techniques for kidney stone detection. The study utilized different kinds of techniques to locate the stone in a kidney. Rahman et al. [9] Gabor filter has been used to decrease speckle noise, and the picture improvement has been accomplished by the use of histogram equalization. Two segmentation strategies have been used to recognize the renal areas: cell segmentation and region-based segmentation [10]. Cell segmentation was used to recognize the renal areas. A dataset focused on the qualities generated from renal ultrasound images was produced by Hafizah, Wan, Supriyanto, Eko, Yunus, Jasmy, as well as Wan (2012). They split renal ultrasound images into separate classes as well as built a database of different attributes obtained from the photographs. They published their findings in the journal Radiology. As shown in this work, level set segmentation makes use of two-phase to identify the stone section of the level set: momentum as well as resilient propagation (Rprop). Using Symlets, Biorthogonal (bio3.9, bio4.4) wavelet sub-bands, plus Daubechies lifting method wavelet sub-bands, the energy levels of the kidney stone may be extracted. It is clear by comparing these levels of energy to the average power levels in the region under inquiry that stone has already been found there to be. With an accuracy rate of 98.8%, they are trained to utilize multilayer perceptron’s as well as backpropagation neural networks (ANNs). Finally, they are put through their paces as well as put through their paces again. Xilinx System Generator (XSG) Verilog, as well as MATLAB 2012a, are used to implement the proposed work in real-time on both Vertex-2Pro FPGAs and Field Programmable Gate Array Vertex-2Pro FPGAs. [11]. The level set segmentation technique, used in the detection of kidney stones, is used in proposed research to identify kidney stones. First, the images are preprocessed as well as the region of interest is determined and segmented. An efficient method for overcoming the problem of segmentation is the level set segmentation approach. Medical operations and diagnosis may be carried out using computed tomography scans, which are diagnostic devices. To begin, a CT scan transmits an X-ray across the body in minute slices, which are subsequently saved as images on a computer. Before being displayed on the computer, the CT images are preprocessed to remove unnecessary information. To use the level set segmentation method, the input image is

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segmented just after preprocessing step. When all of the images have been analyzed, the stone’s dimensions and location may be determined [12]. The purpose of this research [13] is to categorize healthy or sick persons according to the presence or absence of KS from medical photographs using different ML approaches and CNN [14]. Some of the most popular ML methods were examined and rated. These included decision trees as well as random forests as well as SVM including NNs using CNNs and also KNN or NB. As shown by the studies, the DTC achieves the best classification results. With an 85.3% success rate utilizing the S + U sampling approach, this technique has the highest F1 score rate of any method tested. Experiment findings demonstrate that the DTC is a viable tool for discriminating between different types of renal X-ray pictures. There is a lot of speckle noise in the ultrasound pictures because of the poor contrast. As a consequence, the diagnosis of kidney abnormalities is a problematic process to do. As a result, speckle noise is removed from ultrasound pictures by the use of preprocessing. During preprocessing, picture restoration is performed first to eliminate speckle noise, and then the restored image is used to the GF for smoothening. Then, using HE, the resulting picture is improved even more. Level set segmentation is done twice: first to segment the kidney component, and then its output is used as input for the second segmentation to separate the stone portion since it produces superior outcomes. In their research on level set segmentation, we make use of two terminologies. The first is based on the concept of momentum, while the second is based on the concept of resilient propagation (Rprop). There are three ways in which one may extract energy levels from a kidney stone: Symlets or Biorthogonal (bio3.7, bio3.9, as well as bio4.4), respectively, and also the Daubechies lifting scheme wavelet sub-bands (bio4.4). These energy levels provide an indicator of the existence of a stone since they differ greatly from the typical energy levels in the area. Using MLP and BP ANN, these energy levels are developed to spot the kind of stone with an accuracy of 98.8%, while holding that the agreement is accomplished using Verilog on a Vertex-2Pro FPGA [14].

3 Research Methodology Here, in this section, we provide a research methodology with each step that helps to deploy our proposed model. The problem statement, proposed methodology, data preprocessing, data augmentation, and proposed CNN-based VGG-19 model.

3.1 Problem Statement Having a problem with the kidneys may be life-threatening. As a consequence, it’s critical to detect a kidney stone early. The outcome of a kidney stone surgical procedure depends on accurate identification. Because of the poor contrast as well as

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speckle noise in renal ultrasound pictures, it is difficult to detect kidney anomalies. As a way, clinicians may have difficulty determining the kind and size of tiny stones in the kidneys, making diagnosis more difficult. The success of a kidney stone surgery is directly related to the accuracy of the first diagnosis. Poor contrast and speckle noise in renal ultrasound images make it difficult to detect kidney disease. As a result, doctors may have difficulty correctly identifying small kidney stones and their nature. Aside from this problem of overfitting, it must also address.

3.2 Proposed Methodology To solve this problem, a deep learning model for classifying the position of stones is presented. The model is implemented in Python and ran using a Jupyter notebook. First and foremost, we want a dataset, and thus we get the kidney stone dataset (GitHub, 2021). First, since the ultrasound kidney picture includes speckle noise and has poor contrast, one of the image pre-processing techniques is employed to reduce the speckle noise. This procedure is repeated for each kidney image. The preprocessing step comprises the operations of picture resizing and scaling. Then we used data augmentation to improve our results. To produce additional data and tackle the overfitting issue, data augmentation has been undertaken in this study. Among the photo-editing methods used in data, augmentation is shifting, flipping, zooming, resizing, and other similar operations. Following that, we implemented the deep CNN-based VGG-19 model that was presented. “VGG16” was chosen as the pre-trained network for our proposed learning model since it is well-known and frequently utilized in the industry. The features of the VGG19 model were fine-tuned using either the Adam optimization approach or a loss function. Finally, we compute the performance matrix for the actual outcomes obtained by the predictions.

3.2.1

Preprocessing

The input test image is preprocessed. Data preprocessing is a technique that is being used to transform raw data into a clean dataset that is involved in data cleaning. To look at it another way, whenever data is received from numerous sources, it is obtained in raw format, which makes it hard to undertake an interpretation of the data. The collected images resize and scale with the help of preprocessing.

3.2.2

Data Augmentation

Data augmentation is a collection of strategies that are used to artificially expand the quantity of data available by producing additional data points from the previously collected information. This might involve making minor adjustments to existing

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data or using deep learning techniques to produce whole new data points. Cropping, padding, as well as horizontal flipping, are examples of data augmentation methods that are routinely employed to train massive neural networks in a variety of applications. As part of this research, those with kidney stones are referred to as the “patients” and those without stones as the “control mechanisms.” In this investigation, a total of 433 patients were used, 278 of whom were stone positive and 165 of whom were normal. CT pictures acquired from diverse areas of these individuals’ bodies were utilized in the research. Approximately 790 pictures of individuals with kidney stones and 1009 photographs of normal people were acquired throughout the study. It was decided not to utilize subjects who had participated in the training and validation phases for the testing phase to avoid bias in the findings. To put it another way, the subjects employed in the test and train phases are entirely distinct from one another. Figures 1 and 2 illustrate typical instances of normal and kidney stone CT images acquired utilizing different augmentation procedures, as well as the results of the augmentation approaches.

Fig. 1 Image visualization with stone in kidney

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Fig. 2 Image visualization with stone in kidney

In this study, no personal information about the patients was utilized, while radiology, as well as a urology expert, separately evaluated the coronal CT sections (CT protocol: 120 kV; auto tube current: 100–200 mA; 128-mm detector; milliamps: 203; slice thickness: 5 mm). When the professionals (radiologists and urologists) performed the labeling procedure, they did so without completing some segmentation on CT images. They just said whether or not there were stones. Patients ranging in age from 18 to 80 years were comprised in this research project. The research examined 67 individuals with double-J (pigtail) ureteral catheters, aged less than 18 and greater than 80 years, who had a single kidney, kidney with abnormalities, or atrophic kidney.

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Fig. 3 VGG-19 architecture

3.3 Proposed Model Simonyan and Zisserman introduced a pre-trained CNN model at the University of Oxford in the UK in early 2014, as well as the VGG network, which is the tradename for this model. By using the ImageNet ILSVRC dataset, the Visual Geometry Group (VGG) was trained using 100,000 photographs for training and 50,000 images for validation. The dataset comprises of 1.3 million photographs separated into 1000 classes, with 100,000 images used for training, and 50,000 images only for validation. To facilitate analysis, the dataset was separated into two parts: training as well as validation. The VGG-19 architectural (Fig. 3), a variation of the VGG architectures, contains 19 strongly connected layers and, while contrasted to other state-of-the-art models, has repeatedly shown outstanding quality. As a result, the model consists of highly interconnected convolutional layers, as opposed to fully connected layers, which allows for enhanced feature extraction. Additionally, the model employs Maxpooling (rather than average pooling) for downsampling before classification to use the SoftMax activation function, which enhances the overall classification results. Additionally, the Adam optimizer is employed as an optimizer. It is the loss function binary cross-entropy that is employed in this technique. When it comes to form, color, and structure, VGG19 is the most sophisticated CNN available. It contains layers that have been pre-trained, and it also has a good understanding of what defines an image. In addition to forming, color, as well as architecture, this is a very deep neural network that has been trained on numerous different photographs with tough classification tasks to become extremely effective. It is called the VGG19 network. This VGG model version contains 19 layers (16 convolution layers, 3 fully connected layers, 5 MaxPool layers, and 1 SoftMax layer), and it is a variant of the VGG model with 19 layers. It is a variant of the VGG model with 19 layers.

3.4 Proposed Flowchart Figure 4 shows the proposed flowchart of the overall proposed methodology. It is composed of the following components: renal image database, data preparation,

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Fig. 4 Proposed flowchart

Start

Collect the input dataset

Data pre-processing (Resizing and Scaling) ) Data Augmentation (Zoom range, Rotate)

Data Set Split Training

Dataset

ValidationDataset (20%)

Deployed CNN based VGG-19 Model

Calculate performance matrix

Predicted Results

End

data preprocessing, data augmentation, data splitting, proposed VGG-19 model, and performance Metrix.

4 Results Analysis Here this section provides an experimental result and their description. Python and also its libraries were used to conduct this research. The below section gave the dataset description, and performance evaluation also described the proposed model results and comparison of base and proposed model.

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4.1 Dataset Description This research was carried out after receiving consent from the ethical committee at Frat University in Turkey. We have gathered 500 NCCT pictures from patients who were hospitalized at Elazig Fethi Sekin City Hospital in Turkey for urinary system stone illness and had their photos analyzed. Unless otherwise stated, all pictures were taken in supine position applying a single scanner, Philips Healthcare Ingenuity Elite (Netherlands), without administration of contrast material. The dataset, as well as the code, may be found at https://github.com/yildirimozal/Kidney_stone_detection.

4.2 Dataset Splitting The input dataset is separated into two phases: training and validation. The training used 80% of data and validation used 20% of data. Eighty percent of the photographs in the training dataset are normal and abnormal kidneys, with the remaining 20% being aberrant. In image processing, training is a process of learning that is often used to detect features and forms, as well as patterns and other patterns. The training procedure is identical to the processing of the test picture, which includes pre-processing, data augmentation, and post-processing.

4.3 Experimented Results and Discussion A total of 1453 CT pictures were utilized for training and validation purposes throughout the model’s training phase. Eighty percent of images were utilized for training and twenty percent for validation. To get test performance results after the end of model training, we utilized 346 photographs that had not been used throughout the deep learning model’s training process. Figures 5 and 6 show a graph of the loss values and accuracy rate for every epoch during the training of the DL model, respectively, throughout the training process. For a total of 30 epochs, the model continued to learn from the training data. Figure 7 depicts the confusion matrix that was created using the test data. Based on the confusion matrix, it can be shown that the model accurately predicted more than 161 kidney stone pictures overall (true positive, TP). Aside from that, the model successfully categorized over 809 pictures (true negative, TN) as belonging to the usual class. “Precision (TP/(TP + FP)), recall (TP/(TP + FN)), F1 score (2 precision-recall/(precision + recall)), and accuracy ((TP + TN)/ (FP + FN))” are the performance measures used to assess the model. To assess the overall efficiency of the system, it is necessary to evaluate all of these indicators at the same time. While precision reflects the accuracy with which kidney stone cases are properly predicted, recall reveals how well the instances that must be anticipated as positive are predicted as positive when they are not. F1 score is the harmonic average

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of accuracy and recall values calculated by the algorithm. Using 146 test instances, the model achieved a perfect accuracy rate of 100%. Our suggested model’s performance metrics are summarized in Table 1, which includes a summary of metrics acquired using our proposed model. The suggested DL model effectively diagnosed instances of kidney stones of small size, with accuracy and recall rates of 98% and 97%, respectively, in the research design.

Fig. 5 Proposed model training and testing accuracy graphs for several epochs of training

Fig. 6 Proposed model training and validation loss graphs for several epochs of training

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Fig. 7 Confusion matrix obtained by VGGNET-19

Table 1 Comparison of performance parameters of base and proposed methodologies Model

Training acc

Validation acc

Training loss

Validation loss

XResnet-50

0.95

0.96

0.045

0.129

VGG-19

0.99

0.95

0.005

0.176

Figure 5 shows the accuracy graph of the proposed model. There are x-axis illustrations no. of epochs and y-axis shows the accuracy of the model. The training accuracy is 95.53% representing the blue line in the above figure, and orange line shows the validation accuracy is 98%, respectively. Figure 6 shows the loss graph of the proposed model. There are x-axis illustrations of the no. of epochs and y-axis illustrations of loss of a model. The training loss of 0.0032 is represented by the blue line in the above figure, and orange line shows the validation loss is 0.1672, respectively. The confusion matrix of the suggested model is seen in the preceding Fig. 7. The x-axis signifies the predicted label, while the y-axis signifies the actual label for a supplied dataset. To compute recall, accuracy, true positive and true positive rates, and other statistics, a confusion matrix is employed. The kidney stone dataset is used to demonstrate the confusion matrices of the suggested technique. Figure 8 shows the classification report of the VGG-19 proposed model. In deep learning, a classification report is a metric for evaluating the performance of the system. It is utilized to demonstrate the accuracy, recall, F1 score, as well as support of our previously trained classification model, among other things. Precision 99% for the kidney stone and 97% for normal, recall 94% for the kidney stone, and 100% for the normal category. F1 score is 97% for kidney stones or 98% for a normal category, respectively.

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Fig. 8 Classification report of the proposed model

Table 1 shows the training accuracy/loss and validation accuracy/loss of the base XResnet-50 and proposed VGG-19 model. Following training on the training dataset, a model can distinguish between the two photographs. As well as classifying photographs from a validation dataset. In contrast to training loss, validation loss is a measure of how well an exact solution details in new ways. Figure 9 shows the training and validation accuracy performance of the base and proposed model. The proposed VGG-19 achieved 99% accuracy while XResnet-50 achieved only 95% which is lower than our proposed VGG-19 model. The x-axis shows parameters, and y-axis shows the accuracy of the deployed models. Figure 10 shows the training and validation losses of the base model XResnet-50 and the proposed model VGG-19. The dark blue line in the graph represents the base model XResnet-50, while the dark red line represents the proposed model VGG-19. The graph clearly shows the training loss is only 0.005 while the training loss is 0.045, respectively.

Accurcay Performance

Fig. 9 Accuracy performance of base and propose a model

1 0.99 0.99 0.98

in%

0.97 0.96

XResnet-50

0.96 0.95

0.95 0.95 0.94 0.93 Training acc

Validation acc

Parameters

VGG-19

792 Fig. 10 Loss performance of base and proposed model

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Loss Performance 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0

0.176

0.129

XResnet-50

0.045

VGG-19 0.005

Training loss

Validation loss

parameters

5 Conclusion and Future Work Based on our findings, it is possible to train machine learning models to predict kidney stones from digital images. These findings suggest that artificial intelligence technology may be included in the urologist’s workflow to determine the reasons (lithogenesis) of kidney stone development. Because precious morphological information used for diagnosis can be extracted before proceeding to pulverize the stone, speeding up preventive diagnosis measures. Furthermore, in this work, a DL model for the identification of kidney stone patients using CT images is provided. Using data from 433 subjects, the suggested deep model achieved a perfect accuracy rate of 100%. On a diagnostic basis, the locations shown by our model were consistent with the areas identified by our medical experts in most of the photographs. Because of this, we believe that the DL model we suggested is accurate and using this model can assist radiologists to accurately diagnose kidney stone patients. Work in the future, the suggested method will most likely be developed for realtime application by connecting it to scanning equipment. There should be less focus on complex structures in future DL research, especially in medical imaging. In the future, work on lowering the amount of time required to train deep learning models should be prioritized. It is necessary to simplify and reduce the complexity of systems that may be applied to models to develop more effective models that can be used across disciplines. Further improvements can be made in this technique in the future, after which kidney stones can be detected in a 100% accurate way, and after being detected in the early stage, they can be treated in a better way.

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References 1. Aune D, Mahamat-Saleh Y, Norat T, Riboli E (2018) Body fatness, diabetes, physical activity and risk of kidney stones: a systematic review and meta-analysis of cohort studies. Eur J Epidemiol 33(11):1033–1047 2. Ari A, Hanbay D (2018) Deep learning based brain tumor classification and detection system. Turk J Electr Eng Comput Sci 26(5):2275–2286 3. Gulshan V et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410 4. Serrat J, Lumbreras F, Blanco F, Valiente M, Lopez-Mesas M (2017) mystone: a system for automatic kidney stone classification. Expert Syst Appl 89:41–51 5. Amado AT (2018) Metric learning for kidney stone classification 6. Black KM, Law H, Aldouhki A, Deng J, Ghani KR (2020) Deep learning computer vision algorithm for detecting kidney stone composition. BJU International 7. Kumar K, Abhishek B (2012) Artificial neural networks for diagnosis of kidney stones disease. Int J Inf Technol Comput Sci 4(7):20–25 8. Sorensen MD, Harper JD, Hsi RS, Shah AR (2013) B-mode ultrasound versus color Doppler twinkling artifact in detecting kidney stones. J Endourol 27(2):149–153 9. Rahman T, Uddin MS (2013) Speckle noise reduction and segmentation of kidney regions from ultrasound image. In: International conference on informatics, electronics and vision (ICIEV). IEEE, pp 0–4 10. Tamilselvi PR (2013) Detection of a Renal calculi using a semi automatic segmentation approach. Int J Eng Sci Innov Technol (IJESIT) 2(3) 11. Viswanath K, Gunasundari R (2015) Analysis and implementation of kidney stone detection by reaction diffusion level set segmentation using Xilinx system generator on FPGA. VLSI Design 12. Akkasaligar PT, Biradar S, Kumbar V (2017) Kidney stone detection in computed tomography images. In: 2017 international conference on smart technologies for smart nation (SmartTechCon) 13. Aksakallı IK, Kaçdıo˘glu S, Hanay YS (2021) Kidney X-ray images classification using machine learning and deep learning methods. Balkan J Electr Comput Eng 9(2) 14. Viswanath K, Gunasundari R, AathifHussan S (2015) VLSI implementation and analysis of kidney stone detection by level set segmentation and ANN classification. Procedia Comput Sci 48:612–622

Determination of License Plate Using Deep Learning and Image Processing Shiva Tyagi, Riti Rathore, Vaibhav Rathore, Shruti Rai, and Shruti Rohila

Abstract Automatic license plate recognition (ALPR) provides a way to extract the vehicle’s number plates using the computer vision technology. To achieve higher accuracy rates, ALPR is widely used across the world in traffic controlling, automated toll collected system and in society parking, etc. Any ALPR system consists of following processes, viz. video recording as an input, identifying that object is moving or not, differentiating between vehicles and non-vehicles, localization of license plate of vehicles, extractions of characters of license plate, and recognition of segmented characters. In the real world, due to changing weather conditions, polluted environments, different intensities of headlight, etc., it is very difficult to capture an input image. The ALPR processes become more difficult if the license plates of each vehicle do not follow the rules and protocols set by the corresponding Motor Vehicles Department. If the license plate is broken, tilted, dirty, or missing some characters, then all these difficulties add up to generate an efficient ALPR system. Traditional LPR methods extract characters by one-by-one characters segmentation which is very time consuming, but our method provides image preprocessing, machine learning, and neural network techniques which makes it time saving and achieves greater accuracy rates. Neural network technique consists of image preprocessing, contour extraction, rotation, distortion, and character segmentation processes. Experimentally, we can verify our proposed method on a public dataset, and the results state that our method overcomes the drawbacks of previous methods and it achieves accuracies of more than 97% for different types of license plate datasets. Keywords Automatic license plate recognition (ALPR) · Image preprocessing · Convolutional neural networks · Deep learning The original version of this chapter was revised: The author name “S.S.Tyagi” has been changed to “Shiva Tyagi”. The correction to this chapter is available at https://doi.org/10.1007/978-981-99-3315-0_70 S. Tyagi (B) · R. Rathore · V. Rathore · S. Rai · S. Rohila Ajay Kumar Garg Engineering College, Ghaziabad, India e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_61

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1 Introduction License plate is used to identify the owner’s name or address of the vehicle. ALPR system is used in traffic controlling, automated toll collected system and in society parking, etc. Its main purpose is to extract and recognize each character of the license plate of the vehicle which is given as the input [1]. In the real world, due to changing weather condition, polluted environments, different intensities of headlight, etc., it is very difficult to capture an image of a license plate number. If license plate is broken, tilted, dirty, or missing some characters which adds up the difficulties in developing an ALPR system, the current system works upon OCR [2]. Basically, OCR is known as a process that is used to convert the input images into the format of text that is readable to machines. OCR is used for searching and editing in a scanned document. We are proposing the method that can conduct license plate detection and recognition. Initially, we obtain edges and remove the noise by preprocessing. In the given image, wavelet edge detection and close operations are performed for locating the license plate and horizontal and vertical projections are applied for doing character segmentation. In this, we describe our method ALPR system, where it is used to extract and recognize the segmented characters of the license plate of the vehicle. It requires computer vision technology which provides higher accuracy rates. It takes less human effort and also it consumes less time.

2 Literature Review When deep learning was not in use, almost all the conventional LPR methods adopted a widely known procedure, i.e., detection of characters on the license plate and then separately recognizing each of the characters. These methods can be well distinguished by the ways they are implemented. Here we purely assess the research related to detection of characters and their recognition. A bounding box is used to analyze each character in the image of the license plate as part of the character detection process. The majority of conventional character detection algorithms may be easily divided into two categories. The first technique is based on CCA, and the second one is based on projection techniques [3]. In the first technique, the binarized image of the license plate is taken to perform connected component analysis to assemble all the connected areas, and it is referred to as a character, while the second method is based on template matching of the characters on the binary picture by extracting the top and bottom boundaries horizontally and then dividing each character vertically [4]. The above methods are relatively expensive plus prone to failure. Methods based on features use more differentiated features. Handcrafted features and specific classifiers, PNN, HMM, and MLP neural networks extract LBP type features and use

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the process of linear discriminant analysis abbreviated as LDA as a classifier. After the arrival of deep learning, LPR category character recognition is executed by CNN networks [5]. In this paper, we propose the use of image preprocessing and deep learning methods to detect the characters of license plates. Our proposed framework ignores sensitive character recognition and uses global information from license plate images. Additionally, our process is highly efficient even without the sliding window profile [6].

3 Overview With the use of the combination of machine learning techniques like CNN and deep learning and image processing, our aim is to extract and detect the license plate numbers. The process involved in identifying the license plate numbers if the input to our suggested system is movement monitoring, classification determining if a moving object is a vehicle or not, and placement of the plate using neural networks for character recognition. Additionally, we may move straight to the second phase if the input to our system is an image. Once the object has been recognized as a vehicle, we can extract the license plate numbers. To reduce the unwanted distortions or to enhance specific visual properties, preprocessing is used to improve the image which is provided as input, and it is important for subsequent processing and analysis tasks. For image preprocessing, there are certain steps to make it easier to deal with the license plate’s location on the image. First step is to use Gaussian blur on the image as it is a non-uniform low-pass filter, and it will help to reduce the noise in the image. Second step is to use grayscale transformation on the image as it carries only the intensity information. Third step is to detect the edges of objects in the image. It is done by checking the change in the brightness in the image. Fourth step is to locate the license plate in the image. There are numerous ways to locate, retrieve, and identify the license plate numbers on an object. The archaic and conventional approach is based on grayscale images. This records the beginning and ending points of the potential region. For CNN and deep learning, there are also certain steps which are to be taken to identify the license plate, segmentation of characters into individual symbols, feature extraction, and then training the features for the further datasets.

4 Working Methodology Automation of license plate recognition plays an important and crucial role in daily life with the growth of technology, and it can help in reducing the manpower resource. Development in the technology has led us to this way that we can inspect, localize,

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and recognize the license plate number with no manpower. But there are various challenges in the detection of license plates such as size, font style and color, location of the license plate on vehicle (non-uniform position), multiple license plates, damaged license plates, and plates can have different intensities due to environment conditions. In this paper, our goal is to detect, extract, and recognize the license plate numbers using the combination of image processing and machine learning methods like CNN and deep learning. If the input to our proposed system is a video, then the steps involved to recognize the license plate numbers are as follows: 1. 2. 3. 4.

Movement detection Classification—whether the moving object is vehicle or non-vehicle Localization of the plate Character recognition—using neural networks.

And if the input to our system is an image, then we can directly skip to the second step. Data preprocessing is the most crucial step before building the model as it helps to boost the accuracy of result and saves time by removing the unwanted data and providing certain methods to missing values. Image preprocessing is used at the lowest level of abstraction to perform certain operations on images. These operations do not increase the information about the image but can decrease the unwanted information or needless data in the image. In this case, to reduce the noise, image preprocessing is used in the image and to extract the license plate easily (Fig. 1). Step 1—Gaussian blur Gaussian blur can also be termed as the two-dimensional Weierstrass transform. To reduce the noise level in the image, the Gaussian function is used on the image and the obtained result is known as Gaussian blur. There are always some noises in the image. Convolving the image with Gaussian function gives the Gaussian blur to the image. Gaussian blur can remove the noise from the license plate [7]. For one dimension, the formula of Gaussian function is 1 2 2 G(x) = √ ∏ e−x /2σ . 2 2 σ

(1)

To obtain the formula for Gaussian function in two dimensions, it is taken as the multiplication of two such Gaussian functions: G(x) =

1 2 2 2 e−x +y /2σ . 2∏σ 2

In the horizontal axis, the distance from the origin O is taken as x. In the vertical axis, it is taken as y. And for the Gaussian distribution, the standard deviation is taken as σ.

(2)

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Fig. 1 Working methodology

Step 2—Grayscale Transformation A grayscale image can be defined as black and white or gray monochrome (composition of shades of gray). In this, the value of each pixel represents the amount of the light as it carries only the information of the intensity. When the intensity is low, it is black, and when it is high, it is white. Thus, the contrast ranges from black (where intensity is weak) to white (where intensity is high). A color image consists of three colors—red, blue, and green. Grayscale transformation transforms images from color to gray, and the weight of the three colors is equal. The formula for calculating every pixel’s final gray value is

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G = (R ∗ 299 + G ∗ 587 + B ∗ 114 + 500)/1000.

(3)

In the above formula, R, G, and B are taken as the symbolic representation for the red, green, and blue component value of the pixel. Step 3—Edge Detection The method of edge detection comes into play when boundaries of the objects within the images are to be found. The process of edge detection works by detecting the discontinuities in the brightness of the image elements. Image segmentation and data extraction are also based on the method of edge detection. There are various edge detection algorithms which are used according to their requisite purpose. Some of them are Sobel, Canny, Prewitt, Roberts Cross, Laplacian, and other logic methods. Out of these, Sobel, Robert Cross, and Laplacian are the most used ones and give better results after applying these edge detection operators. But the question still is which of these would be the best one for license plate recognition. Sobel operator works if the input is a grayscale image. At each point, the Sobel operator finds the absolute gradient magnitude approximately. It measures the spatial gradient of an image in two dimensions, and hence, it emphasizes the regions which have spatial frequency of high magnitude to each edge (Fig. 2). The approximate magnitude after the use of Sobel operator is given by |G| = |(P1 + 2 ∗ P2 + P3) − (P7 + 2 ∗ P8 + P9)| + |(P3 + 2 ∗ P6 + P9) − (P1 + 2 ∗ P4 + P7)|.

(4)

Roberts cross operator is used for highlighting the regions of those spatial frequencies which are high corresponding to each edge. The most common way to use this filter is that it takes a grayscale image as the input and the same is the output. It measures the spatial gradient of an image in two dimensions, and it is simple and easy to compute (Fig. 3). The approximate magnitude after the use of Roberts cross operator is given by

Fig. 2 Sobel convolution kernels

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Fig. 3 Roberts cross-convolution kernels

|G| = |P1 − P4| + |P2 − P3|.

(5)

Laplacian operator works if its input is a single image which is gray level, and it will produce another image which is also a gray level image as the output. The Laplacian operator gives the isotropic measurement of the second spatial derivative of the image in two dimensions. The Laplacian of an image is good at highlighting the portions of rapid change in intensities, and therefore, it is good to use for edge detection of objects in the image (Fig. 4). The approximate magnitude is given by LoG(x, y) =

[ ] −1 1 − x 2 + y 2 −x 2 +y 2 /2σ 2 e . ∏σ 4 2σ 2

(6)

There is no sensitivity to noise if we use Roberts cross operator and it is quite good at locating. The Laplace operator is also not sensitive to noise, and it is quite good at detecting edges. So in this paper, we choose the Sobel operator as it is good for denoising. Step 4—Plate Location There are many methods to localize the license plate of the object to extract and recognize the license plate numbers. The primitive and traditional method is based

Fig. 4 Laplacian convolution kernels

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on gray level images. In this, the start point and end point of the possible region are recorded. And then scanning of the possible region is done and then it is compared with the real license plate. Mathematical method to localize the license plate—Morphological Image Processing The basic morphological mathematical operations in image processing are erosion, dilation, and open and closed operations. Erosion—Output pixel has the least possible value from all the values of the pixels present in the neighborhood. While performing the process of erosion in the binary image, the neighboring pixels are checked, and if any of them is 0, then that pixel is set to 0. Dilation—Output pixel has the greatest possible value from all the values of the pixels in the neighborhood. While performing the process of dilation in the binary image, the neighboring pixels are checked, and if any of them is 1, then that pixel is set to 1. Open—Opening is just another name of erosion followed by dilation. It is effective to remove salt noise. Close—Closing is just another name of dilation followed by erosion. It is effective to remove pepper noise. The process of dilation and erosion will make the image size diminish or enlarge it. But the difference between the primary image and the changed image will give us the image edge. Step 5—Neural network method to recognize the license plate A convolutional neural network can also be written as ConvNet/CNN, and it is a deep learning algorithm. In this, it takes input images, assigns importance according to certain features such as informed weights or biases to different objects in the image, and is able to separate one from the other [8]. In this paper, we propose the use of ConvNet for license plate recognition. Step 6— Character Segmentation Character segmentation is a widely used process which comes with the objective of decomposition of the image (sequence of characters) into the sub-images, and these sub-images are termed as individual symbols [9]. It consists of three steps which are as follows: a. Detection of the initials of the character b. Test for the end of the character c. Detection of the end of the character. Step 7—Feature extraction The process of changing raw data into arithmetical features on which some operations and changes can be performed while not modifying the information given in the original dataset is called feature extraction [10]. Better results are expected from this

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process; it improves accuracy by modifying the input data so that it takes minimum time and effort. This is better than applying machine learning directly. The image should be put into an artificial neural network model for training. Before that, features should be extracted for training. The extracted features are brought for training. Step 8—Training of the features The extracted features are brought to OpenCV for training. In this paper, we need to find the best match and easiest way to our solution. In this, we have applied an artificial neural network model to be trained by characters and predict characters later.

5 Experiment In our proposed method, we are going to test almost 300 images for the required accuracy. The formulae which we use to calculate the accuracy of the model are calculated by dividing the number of recognized characters and numbers to the total number of characters and numbers present on the vehicle plate. To test our method and experiment, we have taken the images from Google manually. Some images are collected from the license plate database that is also one place to collect these sets of images for testing our method. Now, the next thing we do is change the name of the file of every image as license plate number manually so that we can check the accuracy of the proposed model easily. Normally, the accuracy for license plate recognition is 100%, but our total accuracy that can be achieved is 97.05%. The restriction in accuracy is due to the distortion recovery limitations. If the license plate is found to be in extremely distorted condition, it is impossible to recover the original plate of the vehicle. The accuracy of our model is also affected badly when there is presence of additional marks between the characters of the license plate. These marks influence the accuracy of our model. The improvement in recognition accuracy can be achieved by changing our neural network models to better accuracy, the slower models like R-CNN are taken into consideration for changing the accuracy in a better improvised way. After testing the model for performance for around or approximately 10 times, we reach at a conclusion that CNN model performs well in the given circumstances. We train our model well, and with the accuracy of the model and the given acceptable losses, our proposed model reaches the accuracy almost 100% (Table 1).

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Table 1 Comparison of three algorithms on the basis of computational time, recognition rate, and detection rate S. No.

Methods

Detection rate (%)

Recognition rate (%)

Computational time (s)

1

Morphological method

95.5

90.75

1.15

2

Histogram

96.75

3

Proposed method

97.85

– 92.15

2.78 3.18

6 Conclusion Our proposed method conducts license plate detection and recognition. Firstly, we preprocess the license plate for obtaining edges. We also do the noise removal by preprocessing. For locating the plate in the given image, wavelet edge detection and close operations are performed. Then for doing character segmentation, horizontal and vertical projections are applied. We are also using optical character recognition in our methodology. From the tested 300 images, we get an accuracy of about 97.05%. A scope of improvement is always there, so by adjusting parameters of neural networks and distortion recovery, we can get better results. For the future scope of this project, we aspire to propose a full-fledged automatic toll tax collection system with accurate tracking using GPS technology. The application would facilitate the registration of the vehicle, viewing transaction history and automatic payment of toll after the detection of the pre-registered recognized number plate. The system can be used to eliminate the manual registration process used to register incoming vehicles in society. The automated system would save time and will be more reliable for the aspired safety of the people living in the society.

References 1. Saraswathi S, Subban R, Shanmugasundari T, Manogari S (2017) Research on license plate recognition using digital image processing. In: 2017 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–6. https://doi.org/10.1109/ICCIC. 2017.8524147 2. Memon J, Sami M, Khan RA, Uddin M (2020) Handwritten optical character recognition (OCR): a comprehensive systematic literature review (SLR). IEEE Access 8:142642–142668. https://doi.org/10.1109/ACCESS.2020.3012542 3. Bailey DG, Johnston CT, Ma N (2008) Connected components analysis of streamed images. In: 2008 International conference on field programmable logic and applications, pp 679–682. https://doi.org/10.1109/FPL.2008.4630038 4. Liang X, Huang Y, Huang M (2021) Research and application of improved probabilistic neural network algorithm in dynamics of flexible job-shop under the situation of arrival of new workpiece. In: 2021 IEEE 9th international conference on computer science and network technology (ICCSNT), pp 8–11. https://doi.org/10.1109/ICCSNT53786.2021.9615396

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5. Markopoulos PP (2017) Linear discriminant analysis with few training data. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4626–4630. https://doi.org/10.1109/ICASSP.2017.7953033 6. Sevak JS, Kapadia AD, Chavda JB, Shah A, Rahevar M (2017) Survey on semantic image segmentation techniques. In: 2017 International conference on intelligent sustainable systems (ICISS), pp 306–313. https://doi.org/10.1109/ISS1.2017.8389420 7. Gedraite ES, Hadad M (2011) Investigation on the effect of a Gaussian Blur in image filtering and segmentation. In: Proceedings ELMAR-2011, pp 393–396 8. Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 International conference on engineering and technology (ICET), pp 1–6. https://doi. org/10.1109/ICEngTechnol.2017.8308186 9. Manikandan V, Venkatachalam V, Kirthiga M, Harini K, Devarajan N (2010) An enhanced algorithm for Character Segmentation in document image processing. In: 2010 IEEE international conference on computational intelligence and computing research, pp 1–5. https://doi. org/10.1109/ICCIC.2010.5705728 10. Kumar G, Bhatia PK (2014) A detailed review of feature extraction in image processing systems. In: 2014 Fourth international conference on advanced computing & communication technologies, pp 5–12. https://doi.org/10.1109/ACCT.2014.74

Critique of Non-fungible Token (NFT): Innovation, Analysis and Security Issues Prayash Limbu and Rohan Gupta

Abstract The market for non-fungible tokens (NFT) has been brewing for some time, but NFT is still a minefield of unknowns because we have yet to fully recognize its infinite potential. The notion for NFT stemmed from that of an Ethereum token benchmark which sought to distinguish the difference of one token from all the others. This instance of a token could be used to connect with digital properties as unique exotic identifiers. Now, it can be unanimously traded and it will stimulate the prosperity of decentralized currency market. NFT sales increased by more than 200 times to $17.7 billion in 2021, up from $82.5 million in 2020. Total NFT profits from auctioning or purchasing drastically increased from USD 12 mil throughout 2020 to USD 5.4 billion throughout 2021. The NFT entire ecosystem, on the other hand, is still very much in relative infancy, and so are NFT innovations. Research of state-of-the-art NFT standards, protocols and security evaluations will be carried out.

1 Introduction NFT also known as non-fungible token is another cryptocurrency [1] type that arose as a result of Ethereum smart contracts [2]. The evolution of NFT was initially shown in Ethereum Improvement Proposals (EIP)-721 [3], but it was then significantly improved in EIP-1155 [4]. NFT are fundamentally different from other traditional cryptocurrencies [5] such as Bitcoin [6]. Bitcoin is a standard coinage under which all bitcoins remain identical P. Limbu Department of Computer Science Engineering, University Institute of Engineering, Chandigarh University, Gharuan, Mohali, Punjab 140413, India e-mail: [email protected] R. Gupta (B) Department of Electronics and Communication Engineering, University Institute of Engineering, Chandigarh University, Gharuan, Mohali, Punjab 140413, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_62

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and unrecognizable from each other. Meanwhile, NFT was exceptional in that it cannot be transferred in the very same mannerism (it is non-fungible), rendering it helpful for concisely distinguishing that something. Utilizing NFTs as well as smart contracts, an originator can seamlessly validate ownership as well as transaction rights of digital products such as videos, event tickets [5], images, arts and so forth. Not to mention that the creator continues to earn royalties each time his or her NFT is successfully traded. NFT’s full-history credibility, broad fluidity, as well as simple compatibility make it a promising Internet protocol protection option. Because even though, in reality, NFTs were nothing but code, asset access codes will always show who owns the sole rights. NFTs have gotten a tremendous amount of attention in recent years both from the industrial and scientific communities. The “most expensive” NFT was purchased for $532 million. A crypto punk sold for $532 million in October 2021, but this is still a highly speculative transaction because he/she sold it to himself/herself. This is known as a wash trade more technical term. The genuine greatest valuable NFT, however, was purchased for roughly $92 million. [7]. There is still disagreement over whether this is an artwork or, rather, a series of artworks than CryptoPunk#9998, though the sale was not as suspicious as CryptoPunk#9998. Rather than distributing it to a sole bidder, many bidders were given the opportunity to purchase any amount of coins. The average cost per unit began at $575 and gradually escalated up $25 every few hours. It must have been ended up selling to over 30,000 people for a combined amount of $91.8 million. During the third quarter of 2021, NFT trading reached a record high. During this time period, the trading volume reached $10.67 billion, according to the analytical DappRadar [8], an analytics platform. This is indeed a 700% improvement over through the corresponding period. Axle Infinity[9], a Vietnamese computer game which has become the world’s most valuable NFT collection, will also become perhaps the highest expensive NFT catalogue internationally on August 2021, according to Statista. Trading volumes increased by more than $5 billion in August alone, making it a career high month. Despite not trying to reach the same volume, September taken into account for some more than $4 billion [7]. As according to Google trend searches [10], Chinese and Singapore citizens seem to be presently by far the most interested in NFTs and Venezuela took the third place. Remarkably, here The Us is not even being listed among the top 10 countries. The above information was retrieved by having reviewed Google.com over through the prior period. Despite having a substantial potential influence on subsequent decentralized marketplaces and potential financial opportunities, NFTs are still in their early life. Some potential barricades must be addressed, while good potential prospects must be emphasized. Furthermore, despite the fact that a large amount of publications on NFTs has been publicly obtainable through all of blog posts, weblogs, Internet forums, standards, as well as other means, comprehensive research is lacking. The above paper would then encapsulate existing NFT remedies while also bring awareness to such concerns as those who emerge.

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As according to NonFungible.com [11] statistical information, NFT revenues could indeed presently vary from 15 to 50 thousand a week every week. Until about the massive spike in the second quarter of 2017, it has all been more than 100 revenues a week every week. One such current estimate by itself demonstrates how much rapidly the NFT enterprise has evolved in recent decades. NonFungible.com [11] statistics show that NFT sales average between $10 million and $20 million a week each. However, there had already been several weeks throughout 2021 when periodical weekly sales managed to cross USD 170 million. For instance, around the end of April as well as the end of May, the regular volume traded multiplied from 50 million USD to by over 200 million USD. The latter translates towards a 300 per cent growth! DappRadar data [8] indicates that sales within the first half of 2021 amounted to approximately $2.47 billion. NonFungible.com, meanwhile, puts the figure at $1.3 billion. Regardless of the estimates, it is already in the billion US dollars and constitutes the substantial growth over the previous financial session. NFT revenues for the very same time frame in 2020 totalled “just” about $250 million, as per Cloud wards [12]. Non-fungible token market capitalization nearly tenfold risen among both 2018 and 2020, as according to Statista. Furthermore, even though gathering statistics on such a fresh as well as unexpected sector is difficult, these figures are still regarded uncertain. Yet another thing is undoubtedly true: the NFT economy is progressively spreading. When NFTs were first developed, they faced a unique challenge: photographs could not be saved in the block-chain due to storage constraints. Instead, it was recommended that an image identifier (such as the picture’s web address or hash be stored in the block-chain and used in a third-party platform to visualize the NFT. That means that when someone purchases certain NFTs, they are purchasing an identifier that could direct to a URL on the web or the Interplanetary File System (IPFS) [13], rather than the actual picture. In many cases, the IPFS node is operated by the same company that provided you the NFT, calling legitimate possession into conundrum. If the platform out of which you acquired the NFT vanishes, the NFT may have become non-operational and end up losing all value.

2 Literature Review A crucial literature review is carried out, with such a focus on the diverse components of the NFT and block-chain. (a) Regner, Ferdinand & Schweizer, André & Urbach, Nils. (2019). NFTs in Practice—NFTs as Core Component of a Block-chain-based Event Ticket application [14] We investigated into NFTs as a new phenomenon and assessed them as a crucial component of a decentralized ledger-based event ticketing infrastructure. We successively crafted a prototype using a conceptual framework methodology based on Hevner (2004) suggestions. Through the design process, creating and evaluating the

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NFT-based prototype, we were able to generate some meaningful results about the merits and drawbacks of the unique token type. We discovered that non-block-chain event ticketing systems have several vulnerabilities, namely fraud vulnerability, loss of control over secondary trading activity and ownership authentication. Furthermore, our study reveals that utilizing NFTs now coincides with a multitude of limitations, the overwhelming of which are derived from the block-chain technology inherently. We encourage more research in the coming years to re-evaluate the situation of these said vulnerabilities, as we have established that research on approach to overcome these constraints is already underway. (b) Mazur, Mieszko. (2021). Non-Fungible Tokens (NFT). The Analysis of Risk and Return. NFT-based enterprises’ risk and return characteristics whose valuations are established on a cryptocurrency exchange are investigated in this study. The use of NFTs has increased in recent months, with primary NFT offerings and frenetic NFT trading on secondary markets. In addition to fundraising, remittance, store of value, borrowing and lending, the NFT industry represents another key use case for blockchains. Our dataset yields a variety of intriguing outcomes in terms of NFT risk and return features. Firstly, we discover that NFTs have a high first-day return of 130 per cent on average. This is a tenfold increase above the yields on the IPOs that seem to be start-ups which go mainstream on a conventional share market. The very first NFT volume is indeed exceptionally large, implying that what a substantial quantity of commodities is progressing up for auction throughout the 1st day of this same offering. Furthermore, we illustrate that NFTs produce higher long-term returns on both a raw and risk-adjusted basis. In terms of long-term yields, NFT yields are significantly higher than both IPO and VC investment returns (about 23 per cent NFTs in our sample give a return greater than 1,000 per cent). Additionally, NFT fluctuation continues extraordinarily high, indicating that NFTs are really only appropriate for risk-averse stockholders. NFTs can reduce portfolio variability while sustaining expected returns because their connection with the S& amp; P500 seems to be almost zero. [15]. (c) Vidhi Shah (Mar 2022). NFT: An Overview, Investment Perception and Its Sustainability. Investing in NFTs carries a lot of risks and hurdles, as does investing in any new and unproven financial industry. Putting money into a finite resource isn’t a wise long-term plan. A thing’s worth is determined by its scarcity, yet scarcity is not the only factor. NFTs may lose public interest in the long run, especially if NFT owners’ controversial concepts of value and scarcity are challenged too loudly, and if a wider sequence of hacks and sabotage activities by hostile actors occurs too regularly to preserve any confidence in NFTs as a store of value. An NFT’s worth is exclusively established with how much someone else is willing and able to reimburse for it. As the direct consequence, instead of foundational, technological, as well as economic considerations, that have conventionally influenced share prices or, in the very bare minimum, constituted the foundation for corporate earnings, clamour would then

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force the cost. As a result, you might be able to resale an NFT for less than you paid for it. You might not be able to resell it at all if no one wants it. One of the major risks associated with investing in NFTs is the lack of regulatory frameworks that control the industry. Because no government authority oversees NFTs, investors risk losing all of their money if, for example, a rogue player on the block-chain tries to defraud the block-chain with a fake transaction. Due to a lack of regulatory frameworks, it is difficult for investors to sell their shares [16]. (d) Doctor Garrick Hileman and Michel Rauchs (2017) Global cryptocurrency benchmarking study BITCOIN [6], ALTCOINS AND INNOVATION. During January 2009, Bitcoin [6] would become the first decentralized cryptocurrency, but Name coin decided not to follow suit until April 2011, and over two years late. Hundreds of cryptocurrencies have such a market price nowadays, in addition to thousands of coinage that goes all the way back. In several cryptocurrency systems, natural tokens are broadly applied as a mechanism to incentivize network individuals to administrate the network in the lack of a centralized power. The level of creativity exhibited by the other cryptocurrencies, but at the other hand, differs significantly. The significant proportion of digital currencies are replicas of cryptocurrencies like bitcoin with minimal characteristic variations [17]. (e) Yukun Liu and Aleh Tsyvinski (June 2021) Risks and Returns of Cryptocurrency Cryptocurrency is a relatively new phenomenon that has piqued people’s interest. On the one side, it is constructed on cutting-edge innovation which has yet to realize its full potential. On the other hand, in its current state, it serves a comparable purpose to other, more traditional assets. Academics have already been paying close emphasis on the development of theoretical frameworks supporting cryptocurrencies. A multitude of factors have indeed been highlighted as potentially important in determining bitcoin valuation in the theoretical and empirical literature on cryptocurrencies. The first set of studies create models that emphasize the network effect of bitcoin adoption and the market fluctuations caused by the positive externality of the networking impact The second set of research examines the experience and operational of the coins—the miners’ dilemma and concludes that cryptocurrency values were linked to the cost of production [18]. (f) Izwan Amsyar, Amar Najiv Khan, Sabda Maulana, Ethan Christopher, Arusyi Dithi (September 2020) The Challenge of Cryptocurrency in the Era of the Digital Revolution: A Review of Systematic Literature Money is undoubtedly a basic human necessity that cannot be avoided, and it can be used to satisfy human desires. Given the scarcity of comprehensive literature review papers on bitcoin, this is both an issue and a significant goal of this study. Cryptocurrency, a block-chain-based technology, has grown in tandem with the advancement of modernization and globalization, ushering in the Industrial Revolution 4.0. Cryptocurrency is an extensively utilized block-chain-based decentralized cryptocurrency.

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The terminology “cryptocurrency” pertains to both a digital money with no physical existence and a trusted payment currency. Examples include Bitcoin [6], Ethereum [2], Litecoin, Monero and a range of other digital currencies. Despite the fact that it has no physical form, this currency has an exchange rate and functions similarly to traditional currencies. Because cryptocurrency exchange prices fluctuate, traders may profit from this. There is no need for a third party in cryptocurrency transactions because funds are sent from one person to another over the Internet. Every technology has benefits and drawbacks in addition to efficiency and ease. Money laundering is widespread, and cryptocurrency has the disadvantage of having no central power to govern all concerns that emerge during exchanges [19]. (g) Rui Zhang, Rui Xue and Ling Liu (May 2020) Security and Privacy on Block-chain Based on a variety of responses, we’ve pulled together a survey on block-chain confidentiality and protection. To commence, we separated block-chain’s privacy protection qualities into two categories: intrinsic and additional characteristics in the area of internet transactions. Second, we began to look at how reflective consensus algorithms, blending, pseudonymous electronic signature, cryptographic, safeguard multi-party arithmetic operations, non-interactive zero-knowledge evidence and secure smart contract confirmation are being used to achieve such privacy and security attributes in block-chain-based systems and applications. Regardless of the fact that only a small fraction of block-chain platforms can genuinely accomplish the above-mentioned security protocols, the privacy and security of block-chain technology have aroused people’s interest with the rising popularity in block-chain in both academia research and industrial applications. We assume that such a thorough understanding of block-chain’s privacy and security characteristics is required to enhance block-chain’s degree of trust and drive technical development in defence methods and responses. We believe that developing compact cryptographic algorithms, as well as many other pragmatic privacy and security solutions, will be a powerful catalyst for the growth prospects of block-chain and its implementations [20]. (h) Iuon-Chang Lin and Tzu-Chun Liao (Sept. 2017) A Survey of Block-chain Security Issues and Challenges Bitcoin [6] was really the first block-chain application implementation; it’s a decentralized cryptocurrency based on the block-chain which can be used to exchange goods like money even over the entire internet. Consumers can now leverage blockchain technologies in a range of domains and businesses, including those of financial markets, IoT, supply networks, elections, medical treatment and archiving, thanks to the achievement of Bitcoin. Since we use these items and/or services on a regular basis, cybercriminals also have access to them. In Bitcoin, for example, 51 per cent cyberattacks are indeed a frequent security threat, wherein hackers deploy the same strategy to seize control of the system’s machinery. There’s really no debating that block-chain has become a prominent topic in recent years; however, there are some

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challenges to be aware of, others have been addressed as technological innovations improve and become even more robust on the application level. For avoiding having a massive influence on the present structure, the authorities must enact legislation concerning block-chain technology, while businesses would have to be prepared to accept it. While we highlight the value of block-chain technology, we must simultaneously be aware of the risks involved of influence and safety concerns [21]. (i) Qin Wang, Rujia Li, Qi Wang, Shiping Chen (25 Oct 2021): Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges Our task took a lot less time than we expected because of the amount of study and data provided by our predecessors, but finding the truth was still difficult. It was more difficult than we thought to consider and reflect on our peers’ reviews. We still learned a lot, not just about NFT but also about many aspects of the NFT craze. A lot of research and learning about NFT as a whole and how to put it into practice has been done. We have kept looking for any backdoors that could be exploited, but we’ve also learned how dependable block-chain technology can be.

3 Technical Components In this section, we showcase technology aspects relating to the NFT’s operations. These aspects make up a framework of a perfectly functioning NFT algorithm. Block-chain. To achieve specific objectives just on block-chain, Bitcoin leverages the proof-of-work (PoW) mechanism, which has since been proposed by Satoshi Nakamoto [6]. In a decentralized network, transaction data is stored. A distributed and attached-only database, block-chain is described as a set of data records linked together. To preserve the data, cryptographic policies and procedures are utilized. The traditional Byzantine problem is handled utilizing block-chain, which contains a large amount of dishonest individuals. Since any modifications to the information stored nullify all subsequent data, the block-chain becomes irreversible when the majority of decentralized nodes authenticated the shared information. Another very extensively utilized block-chain framework throughout NFT techniques seems to be Ethereum, which provides an encrypted atmosphere for execution of the smart contract. Flow [22], EOS [1], Hyperledger and Fast Box are really just a few of the solutions that already have been forsaken their proprietary large-chain engines or block-chain portals in order to serve their own specific uses. Smart Contract [23]. Smart contracts had first been proposed by Nick Szabo [24] as a mechanism to expedite, verify and execute digital agreements. In addition, Ethereum [3] and Smart contracts were established upon this block-chain technology. Blockchain-based Smart contracts leverage Turing-complete programming language that can provide complex functionalities with complete state transitions duplication across

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consensual process to ensure definitive integrity. Using smart contracts, a reliable third party and a standardized methodology for designing applications along a broad spectrum of businesses, unidentified parties and decentralized individuals can execute fair trades. The applications that function on smart gadgets. Negotiated settlement use of state-transition mechanisms. Address and Transaction. The block-chain identity and transactions are indeed the two essential fundamental elements in cryptocurrencies. A block-chain address seems to be a distinctive form of identification for depositing and acquiring assets, analogous to a bank account after investing money in banks. It’s composed of a sequence of numbers and letters generated from a combination of cryptographic keys. To exchange NFTs, the proprietor must show if he or she holds the corresponding private key and use a legitimate unique identifier to transfer the properties to some other addresses. This basic process is typically conducted with the aid of a cryptocurrency wallet and is indicated by the ERC-777 standard as completing a transaction.

4 Covenants and Pertinent Prospects The Covenants, token standards and important aspects are the fundamental NFT algorithms which are described in this section.

4.1 Covenants Regarding peer group trading, NFT requires the construction of an underpinning distributed network ledger for recordkeeping as well as interchangeable transactions. In this research, the distributed ledger is primarily handled like a sort of database that contains NFT data. We presume that now the ledger has essential security integrity, validity and accessibility, among several other things. Depending on this, we propose two methods again for NFT framework. The old procedure follows a very basic yet typical method from top to bottom: construct NFTs from the originator and afterwards try and sell them to the consumer. The latter method, on the other hand, reverses this path: after creating an NFT template, users generate their own NFTs. We provide thorough protocols for these two design patterns individually, as seen below. It’s worth mentioning that when both of them have been run on block-chain systems, they still maintain a very comparable procedure (see Fig. 1), indicating that various designs did not impact the fundamental working mechanism.

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Fig. 1 NFT systems’ workflow

Top to Bottom. For the first implementation, an NFT procedure provides two additional specific roles that are an NFT holder and an NFT bidder. • NFT Digitize. The content, caption and synopsis are now all double-checked by an NFT proprietor. Afterwards when, all raw data is transcribed into a digitized format. • NFT Store. The NFT proprietor retains the raw metadata in an external database outside of the block-chain system. Note that, regardless of the fact that somehow this approach is gas-intensive, he/she also was allowed to save the relevant data on the inside of a block-chain. • NFT Sign. A transaction was authorized by that of the NFT proprietor and that is transmitted to a smart contract [23], which includes the hash of NFT data. • NFT Mint & Trade. So, after the smart contract acknowledges the transactions including the NFT metadata, the minting and trading processes commences. The key technique underpinning NFTs is indeed the validity of the token standards (Fig. 2). • NFT Confirm. Once the deal is completed, the minting phase is finished. Using this strategy, NFTs will indeed be eternally associated to a distinctive block-chain address as permanence evidence. Bottom to Top. This concept’s methodology has two distinct positions: NFT creator and NFT consumer. Since an NFT product is manufactured utilizing randomized seeds when a consumer bids upon that, a consumer also can act as just a creator

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Fig. 2 Token standards

in most cases. This widens the functionality in terms of user personalization. The superscript is being used to highlight differences from the preceding paragraph. • Template Create ∗ . The project’s creator uses a smart contract to construct a framework that incorporates fundamental rules of the game including avatar styling, armament and accoutrements. • NFT Randomize ∗ After casting a bidding on such an NFT, the bidder can customize it just by incorporating a set of enhanced features to the standard lines. These additional characteristics are picked at random from just a database that has been generated prior to the beginning of the game. • NFT Mint & Trade. The minting and trading procedure commences as once necessary smart contract is implemented. • NFT Confirm. Every one of the operations is performed out by using smart contracts. Whenever the consensual process has been completed, the generated NFT would be retained on-chain indefinitely. In such a block-chain system, every block seems to have a limited capacity. Additional transactions will indeed be redirected to a subsequent block associated towards the source block of data whenever a block’s capacity is surpassed. Eventually, all interconnected blocks having established a long-term record which will continue to exist indefinitely. The NFT system is fundamentally a block-chain based service. So, every moment an NFT is created or exchanged, a fresh transaction must always be made to trigger the smart contract [2]. The NFT data and property details are appended to a new block after the transaction is authenticated, ensuring that the NFT’s existence and possession are retained.

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4.2 NFTs Pertinent Properties NFT algorithms are fundamentally decentralized services and therefore also leverage from the entitlements of the public ledgers that underpin them. The list consists of the most key traits. • Verifiability. The NFT’s ownership and token information may be confirmed publicly. • Transparent Execution. The activities of minting, distributing and acquiring NFTs are also all available to the general public. • Accessibility. This NFT network really never goes down. Anyone could also sell and buy every one of the tokens and NFTs that have already been generated. • Tamper-proofing. Here the NFT information and transaction histories are consistently retained and cannot be altered once trades are proclaimed approved. • Convenience. Each NFT has by far the most up-to-date transaction data in some kind of a user-friendly yet in a relevant data format. • Atomicity. NFTs can also be exchanged using only a simple atomic, consistent, isolated and durable (ACID) procedure. These NFTs can operate together in such a common state. • Tradability. NFTs and even their own related products can also be sold and transferred at any time.

5 Security Evaluation (Methodology) A block-chain, metadata and web-based application all are part of such an NFT system. The confidentiality of the NFT system is difficult to evaluate since every component could potentially operate as an exploiting gateway, leaving the main system susceptible to any attacker. As a solution, we implement the STRIDE hazard and risk evaluation [25] that takes into account all components of a system’s security, such as identification, integrity, non-reputability, adaptability and access control. We examine potential security risks and suggest countermeasures to alleviate them (Refer to Table 1). Denial of Service: The denial-of-service (DoS) attempt is just a private network attack wherein a malicious perpetrator manages to make a server unusable to its intended recipient by interfering with its own normal operating condition. The functionality of the NFT system is jeopardized by DoS attacks, permitting unauthorized users to obtain access. In contrast, the block-chain guarantees that Internet activity is readily accessible. Approved consumers will indeed be able to access this information they require if they need it, and shared resources would not be lost caused by human error. However, outside of the block-chain, DoS could be used to attack centralized web apps or hard data, culminating in a services (dos) attack on the NFT system.

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Table 1 Stride Stride

Issues on security

Solutions

Spoofing

* Authentication flaws might be exploited by an attacker *.A user’s private key might be stolen by an attacker

* The smart contract’s formal verification * To avoid the leakage of private keys, use a cold wallet

Tampering

* Data held outside of the block-chain * Provide both the source and hashed can be tampered with metadata to the NFT customer upon transferring NFTs

Repudiation

* Metadata could be manipulated anywhere outside of the block-chain

* A multi-signature contract could be used partially

Information disclosure

* An assailant could simply trace a specific NFT purchaser or vendor using the hashes and transactions

* Rather than using smart contracts to safeguard the user’s confidentiality, we could use privacy-preserving smart contracts

Denial of service Elevation of privilege

* If somehow the commodity is maintained beyond the block-chain, then NFT metadata can also become inaccessible * NFTs may lose these features if a smart contract is improperly structured

* Using a poor unanimity method and a composite block-chain structure * A conventional confirmation on the shrewd agreements

Tampering: Tampering is defined as the unauthorized manipulation of NFT information in attempt to jeopardize its validity. Assuming the block-chain technology is indeed a credible public transactional database using preimage and secondary preimage resistant hash functions. The information and possession rights of NFTs cannot be altered fraudulently after a transaction has been authenticated. Information stored outside the block-chain, on the other hand, can be manipulated with. We suggest that clients send both the hashed data and the raw data to the NFT purchaser when transferring NFT-related items. Repudiation: Repudiation is really defined as the condition wherein the source of the declaration seems unable to contradict it, and this is linked to the safety mechanism of non-reputability. It’s indeed impossible to deny that a user transmits NFT to yet another client in specifically. This is guaranteed by the original encryption of a verification mechanism as well as the confidentiality of the block-chain. A malicious assailant, on the other hand, could manipulate with the hash value, or the hash info could’ve been linked to the assailant’s IP. We believe that implementing a multisignature convention can significantly address this difficulty because each tie must be authenticated by more than one party. Spoofing: Spoofing would be the ability to impersonate other network components (e.g. a human or a computer), and this is linked to authenticity. Whenever a person comes into contact with the system to tokenize or trade NFTs, a potential hacker could take advantage of a vulnerability hole or acquire the user’s cryptographic keys

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to fraudulently transfer NFT ownership and control. As a consequence, we propose standardizing the NFT smart contract by using a hardware wallet to eliminate private key leakage. Information Disclosure: When confidential information is made susceptible to illegal disclosure, a security flaw occurs. Throughout the NFT system, data transmission and programming codes in smart contracts are completely upfront, and also any condition and its changes are transparent to everyone. Even though the user just transfers the NFT hashes through into block-chain, malevolent hackers can easily leverage the hash’s and proposed transaction’s approachability. We encourage that the NFT creator uses privacy-preserving smart contracts instead of traditional smart contracts to safeguard the confidentiality.

6 Results NFTs were truly cutting-edge technology, unrivalled in many areas against all crypto currencies, but it still has lower percentages due to up-and-coming competitors and future security concerns. We continue to believe that NFT is a fantastic innovation that has yet to be thoroughly debunked. Stats continue to show growth, and all we have to do now is wait. NFT has already been sold for more than $500,000.00. As a result, NFT has a good chance of becoming the next big thing. As shown in the figure above, we believe in all likelihood that NFT is slowly but surely rising up to its competitors and taking big steps in people’s mind. Judging from this data alone we can summarize the whole upcoming of the said NFTs. Since, it is new, it might have many flaws and may need iterations and patches to become better but still its market base and its popularity can be seen. It had quite a great head start in the beginning of 2021 but it kind of fizzled out till the autumn of 2021. It started its gradual increase again till 2021s end. We can still see the signs of how NFTs changed market base in 2021, and it can be compared to bitcoin, one of the well-known of cryptocurrency and see how it fares against one of the best. Like every other advancing technology, a number of modifications must always be surmounted to facilitate the progression of the above-mentioned NFT implementations. We address both of the foundation layer concerns created with block-chainbased phases and human factors including such governance, legislation and community in our discussion of a few general problems from the standpoint of comfort, confidentiality, management and flexibility. In the end, its human’s task to improve and be a better version of themselves (Fig. 3). Due to thorough analysis of the said technology, we are quite inclined to say that NFT might not be the greatest innovation but it’s still groundbreaking for the people who want to at least have a physical/visual representation of their said assets. That in itself will be able to pave in more utility of NFTs (Fig. 4).

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Fig. 3 NFT versus crypto (month/date/year-thousand dollars)

Fig. 4 NFT versus bitcoin (month/date/year-thousand dollars)

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7 Conclusion The objective of this research paper would be to investigate NFTs, block-chain technologies and innovative financial representations in depth. An enormous amount of information from a multitude of resources, including NFTs sites have been gathered. NFT’s planning defects, potential threats, methodological features, anticipated qualities along with obstacles and challenges are done. Research paper contains several important analyses, details of currently existing approved solutions and endeavours, rendering it straightforward for novices to keep themselves updated in the aforementioned field. This study will provide every reader at least the basic information to help them understand the concept and seeing the challenges of crypto, and especially NFT.

References 1. Fairfield JAT (2021) Tokenized: the law of non-fungible tokens and unique digital property. Indiana Law J 97(4):1–5 2. Wood G (2014) Ethereum: A secure decentralized generalized transaction ledger. Ethereum Project Yellow Paper 151:1–32 3. Entriken W, Shirley D, Evans J, Sachs N (2018) EIP-721: non-fungible token standard. In: Ethereum improvement proposals, no 721 4. Radomski W, Cooke A, Castonguay P, Therien J, Binet E, Sandford R (2018) EIP-1155: Multi token standard. In: Ethereum improvement proposals, 1155 5. Shirole M, Darisi M, Bhirud S (2020) Cryptocurrency token: an overview. IC-BCT 2019. Acad/ Scholarly J, pp 133–140 6. Nakamoto S, Bitcoin A (2008) A peer-to-peer electronic cash system. Tech Rep Manubot J, pp 1–5 7. Influencer Marketing Hub. https://influencermarketinghub.com/nfts-statistics/. Last accessed 20 Jan 2022 8. Dappradar Homepage (2021) https://dappradar.com/ 9. Axie Infinity Homepage (2021) https://axieinfinity.com/ 10. Google Trends Homepage (2021) https://trends.google.com/trends/?geo=IN 11. Non-fungible.com Homepage (2021) https://nonfungible.com/ 12. Cloudwards Homepage (2021) https://www.cloudwards.net/ 13. Daniel E, Tschorsch F (2022) IPFS and friends: a qualitative comparison of next generation peer-to-peer data networks. IEEE Commun Surv Tutor 24(1):1–52 14. Regner F, Urbach N, Schweizer A (2019) NFTs in practice—NFTs as core component of a blockchain-based event ticket application In: Proceedings of the 40th international conference on information systems; ICIS, Munich, Germany, pp 1–17 15. Mazur M (2021) Non-fungible tokens (NFT): the analysis of risk and return. Acad/Scholarly J, pp 1–3 16. Shah V (2022) NFT: an overview, investment perception and its sustainability. Int J Res Appl Sci Eng Technol 10(3):1–4 17. Hileman G, Rauchs M (2017) Global cryptocurrency benchmarking study. Trade J, pp 4–5 18. Liu Y, Tsyvinski A (2021) Risks and returns of cryptocurrency. Rev Fin Stud 34(6):2689–2727

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19. Amsyar I, Christopher E, Dithi A, Khan AN, Maulana S (2020) The challenge of cryptocurrency in the era of the digital revolution: a review of systematic literature. Aptisi Trans Technopreneurship (ATT) 2(2):153–159 20. Zhang R, Xue R, Liu L (2019) Security and privacy on blockchain. ACM Comput Surv 52(3):1– 2 21. Lin IC, Liao TC (2017) A survey of blockchain security issues and challenges. Int J Netw Secur 19(5):653–659 22. Flow (2020) Project accessible. https://www.onflow.org/ 23. Kushwaha SS, Joshi S, Singh D, Kaur M, Lee H-N (2022) Systematic review of security vulnerabilities in ethereum blockchain smart contract. IEEE Access 10:6605–6621 24. Szabo N (1996) Smart contracts: building blocks for digital markets. EXTROPY. J Transhumanist Thought 18(2) 25. AbuEmera EA, ElZouka HA, Saad AA (2022) Security framework for identifying threats in smart manufacturing systems using STRIDE approach. In: International conference on consumer electronics and computer engineering (ICCECE), pp 605–612

Face Mask Detection Using Transfer Learning and TensorRT Optimization Paleti Nikhil Chowdary, Pranav Unnikrishnan, Rohan Sanjeev, Mekapati Spandana Reddy, KSR Logesh, Neethu Mohan, and K. P. Soman

Abstract TensorRT is a high-performance deep learning inference optimizer and runtime that can be used to speed up the deployment of deep learning models. In this paper, the performance of different neural network architectures when using TensorRT is compared and showed that TensorRT can significantly reduce the inference time of deep learning models on embedded systems. The SARS-CoV-2 virus, which causes the infectious disease COVID-19, has had a significant impact on global health and the economy. Non-pharmaceutical interventions such as wearing face masks are an effective way to reduce the spread of the virus, and automatic detection systems based on CNN’s can help to detect mask-wearing without requiring human intervention which saves resources or manpower deployed. The results demonstrate that TensorRT is a valuable tool for deploying deep learning applications in resourceconstrained environments and can help to improve the performance of a wide range of neural network architectures. Keywords Real-time face mask detection · TensorRT · Embedded systems · Computer vision · Jetson nano · Transfer learning

P. N. Chowdary · P. Unnikrishnan · R. Sanjeev · M. S. Reddy · K. Logesh · N. Mohan (B) · K. P. Soman Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India e-mail: [email protected] P. N. Chowdary e-mail: [email protected] K. Logesh e-mail: [email protected] K. P. Soman e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_63

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1 Introduction An effective way to detect face masks in real time is by using a combination of TensorRT and embedded systems. TensorRT is a high-performance deep learning inference platform that can be used to optimize trained models and deploy them on platforms with limited resources such as embedded devices. By leveraging the capabilities of TensorRT, it is possible to develop a real-time face mask detection system that can run efficiently on embedded devices such as drones, surveillance cameras, or mobile phones. This system can be used in a variety of applications, including public health, security, and surveillance. The SARS-CoV-2 virus causes the infectious disease known as coronavirus (COVID-19). Up to Jan 2022, 346 million COVID-19 confirmed cases have been reported in more than 200 countries and territories, eventuating approximately 5.5 million deaths [1]. Due to this, the need for face masks has increased due to the ongoing COVID-19 pandemic. We present the implementation of a face mask detection system using TensorRT, client-server models, and embedded systems like the Nvidia Jetson Nano. The proposed model aims a good efficiency, scalability, and easy deployment. We will provide a detailed description of the system’s design and implementation, as well as an evaluation of its performance in the sections that follow.

2 Related Works Machine learning algorithms have been proposed to address this problem. Loey et al. [2] proposed a model with an initial feature extraction component using the ResNet50 architecture, and a second detection component using various machine learning algorithms. The SVM classifier has achieved the highest testing accuracy for all data sets considered in this study. A serverless edge-computing-based methodology to identify face masks is discussed in [3]. This method helps to keep additional hardware costs to a minimum. It may be run locally on a variety of edge devices with no danger of privacy, low network bandwidth requirements, and quick response times. After 120 epochs, the model attained an average accuracy of 89% with batch size 16. Research in [4] presented a methodology for determining whether a person is wearing a face mask or not using a Raspberry Pi-based system. The gathered data set contains 25,000 photos, and the trained model performed with a pixel resolution of 224 × 224, resulting in 96% accuracy rate. In [5], deep learning was used to monitor the proper use of face masks. This model was built using a data set that has been manually categorized. MobileNetV1 model was found to have the greatest performance with an accuracy of 79% and a processing speed of 3.25 fps in this study. In general, the majority of the published material focuses on binary classification with two classes: mask and no mask. In this study, we are taking this study further and exploring the possibility of detecting incorrect mask and also the absence of a face in front of the camera.

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3 Methodology This section describes the outline of the methodology starting from data set collection to training models, evaluating, optimizing using TensorRT, and finally deploying the models.

3.1 Data Set Collection The data set for the approach mentioned in this study requires four classes namely “Mask”, “Incorrect-Mask”, “No-Mask”, and “No-Face”. For face mask detection, there are many large data sets available for research use like: • “MaskedFace-Net (MFN)” [6] is a database of human faces that have a mask on them, either appropriately or erroneously (133,783 images total). This is currently the largest data set accessible, with 67,049 photos in the Correctly Masked Face data set (CMFD) and 66,734 images in the Incorrectly Masked Face data set (IMFD). • “Labeled Faces in the Wild (LFW)” [7] is a web-based collection of more than 13,000 photos of faces. The data set is intended for research into the problem of face recognition with no constraints. • There are 67 Indoor categories in the “MIT Indoor Scenes” [8] collection, with a total of 15620 photos. The quantity of photographs varies by category; however, each category has at least 100 images. At present, most of the data sets available are with binary classes like “Mask or No-Mask” and “Mask or Incorrect-Mask”. A data set with the classes mentioned in our approach could not be found. Thus, images from the above-mentioned data sets were combined to form a larger data set for the required classes. The “MFN” was selected to represent the “Mask” and “Incorrect-Mask” images as it is the only data set with proper representation of “Incorrect-Mask” images. The “LFW” and “MIT Indoor Scenes” data sets were selected to represent the “No-Mask” and “No-Face” classes, respectively. Using all the images from the data set may lead to overfitting hence, only 1500 images are randomly selected from the above-mentioned data sets for each class, and those images are split into 80:20 training testing ratio.

3.2 Transfer Learning Transfer learning (Fig. 1) is a machine learning (ML) research subject that involves storing knowledge obtained from one problem and applying it to a different but related problem. To determine the best architecture in terms of accuracy, transfer learning

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Fig. 1 Transfer learning overview

was used on pre-trained neural networks such as MobileNetV2, MobileNetV3, EfficientNetB0, and ResNet50. MobileNet architectures are selected because they give the best performance for low-end devices. Efficientnet and ResNet architectures are well known for their accuracy. MobileNetV2: Models like ResNet have a lot of parameters and more computation is required per each inference. MobileNet architectures have less parameters and are specifically made for resource-constrained devices like mobile phones and embedded devices. MobileNetV2 filters features using lightweight depth-wise convolutions in the intermediate expansion layer. MobileNetV3: Mainly two MobileNetV3 models are defined: MobileNetV3Small and MobileNetV3-Large [9]. When compared to MobileNetV2, MobileNetV3Large is 3.2% more accurate on ImageNet classification while lowering latency by 15% and MobileNetV3-Small is 4.6% more accurate while lowering latency by 5% [9]. The MobileNetV3-Small architecture is selected in this study as it showed better performance compared to the MobileNetV3-Large variant. EfficientNet: These networks use a baseline model and scale the baseline model to get the family of EfficientNets. The networks provide more accuracy and improve the efficiency compared with other ConvNets. In this study, the base architecture EfficientNetB0 is selected as it has the fewest parameters and performs better on embedded devices. ResNet: The ResNet architecture was developed to address the disappearing or growing gradient problem by adding a notion called Residual Network. The ResNetV2 architecture uses pre-activation which strengthens the regularization of the model, and the convergence speed is faster when the model depth does not change and its has advantage over the ResNetV1. In this study, ResNet50V2 architecture is used.

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Table 1 Parameters used for data augmentation Augmentation Value Augmentation Value Rotation range 20 Zoom range

0.2

Width shift range Hight shift range

Augmentation Value

0.2

Shear range

0.2

0.2

Horizontal flip True

Fig. 2 Optimization overview

3.3 Training TensorFlow 2.5.0 + Cuda 11.3 with Keras backend on an ubuntu machine with Intel i7-10875h CPU and Nvidia Quadro P620 GPU with 32 GB ram is used to train the networks. The training data set comprising of 4800 total images is first divided into batches of size 32. These batches of images are then augmented using the values given in Table 1. Back propagation is done through Adam optimizer and categorical cross-entropy is the loss function. The networks are trained for 10 epochs with 0.001 learning rate and later for fine-tuning, the last 10 layers of the network were unlocked and retrained on the same data set for 10 more epochs with 0.0001 learning rate.

3.4 Model Optimization NVIDIA® TensorRT™ is an software development kit which provides a deep learning inference optimizer and run time for deep learning inference applications with low latency and high throughput. The SDK is built on the NVIDIA’s parallel programming model (CUDA® ). ONNX is a machine learning framework that acts as a translator between other machine learning frameworks. The overview of optimization is given in Fig. 2. The inputs and outputs of the engine are “fp16:chw” format, and the conversion is done in 128 MB workspace. The TensorFlow saved model with best validation accuracy is converted to ONNX format using the “tf2onnx” package in python. Using the trtexec executable, the ONNX model is converted to TensorRT engine with batch size as 1 and “fp16” precision.

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(a)

(b)

Fig. 3 a Hardware configuration and b The actual hardware setup

Fig. 4 Screenshots of the real-time inference

3.5 Simulation The models are implemented on the NVIDIA®Jetson Nano with a webcam and a screen connected to it. Figure 3a shows the hardware configuration, and Fig. 3b shows the actual hardware setup. The implementation of the models is done on Jetson Nano using two python scripts with two shared memory segments similar to the clientserver paradigm (Fig. 4). Multiprocessing was utilized here as the Jetson Nano had 4 cores. This technique showed a significant increase in the performance of the program as there is no overhead. A visual perspective regarding the working of the client-server model is provided in Fig. 5. Screenshots of the real-time working are presented in Fig. 4.

4 Results The major finding of this study is presented here. Four classes are considered for experimentation, and each class has 1200 train images and 300 test images. After training each of the five models with 4800 total train images, the models were evaluated on the following metrics and the results are presented in Table 2.

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Fig. 5 Proposed client-server model Table 2 Accuracy, precision, and recall for five different models Network (Name-Size) Accuracy (%) Precision (Average) MobileNetV3-300 MobileNetV3-224 MobileNetV2-224 ResNet50V2-224 EfficientB0-224

99.83 99.42 99.33 99.83 99.50

99.83 99.42 99.33 99.83 99.50

Recall (Average) 99.83 99.42 99.33 99.83 99.50

Classification Evaluation Metrics • Accuracy: Percentage of the images that were classified correctly. • Precision: Accuracy of the positive predictions. • Recall: Fraction of the positives that were correctly identified. MobileNetV3 (input size 300) and ResNet50V2 (input size 224) showed the best results among the five evaluated models. They had an accuracy of 99.83%. The average precision and recall were 99.83.

4.1 Runtime Metrics TensorFlow runtime and TensorRT runtime were selected to benchmark the models on the runtime. The TensorRT runtime was evaluated with and without the clientserver model. The benchmark metrics are:

830 Table 3 Benchmarks Network Name-Size MobileNetV3 300 MobileNetV3 224 MobileNetV2 224 EfficientNetB0 224 ResNet50V2 224

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TensorFlow

FPS TensorRT

4.5 4.5 4.6 4.3 3.8

15.2 21.4 21.6 19.2 17.2

Table 4 Comparison to other works Ref. & Publish date Hardware November 2021, [5] September 2022, [10] Our implementation

Raspberry PI Nvidia Jetson Nano Nvidia Jetson Nano

TensorRT client-server 58 85 60 36 29

Throughput TensorRT (qps) 113.50 150.76 80.19 41.13 33.82

FPS 3.25 22 85

• Frames per second (FPS): It is the number of frame processed by the network every 1 s. • Throughput (qps): Dividing the number of queries by the Total Host Walltime gives us the observed throughput. The benchmarks are presented in Table 3, and the benefits of optimization can be observed from the table. On average, the TensorFlow runtime gave an average fps of 4.34 , while the TensorRT runtime had an average of 19 fps. It can be observed that the client-server approach with TensorRT runtime gave a significant boost to the performance compared to the other runtimes.

5 Discussion The newer MobileNetV3 architecture outperformed the ResNet, EfficientNet, and the MobileNetV2 architectures as the architecture is lightweight and well-optimized for low power mobile devices. From the evaluation metrics and benchmarks, it was observed that the size of the image affects the performance of the model. The MobileNetV3 architecture with input size 224px gave better fps and lower accuracy compared to the architecture with image size 300px showing the trade-off between speed and accuracy. The larger ResNet and EfficientNet models were slow as expected and had similar accuracy to the lighter and faster MobileNet Models. Thus, we choose MobileNetV3 for our application since it gave the best results in all aspects. In Table 4, we compare the performance (fps) of our work with other similar works.

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6 Conclusion and Future Scope We have proposed a new face mask detection system using TensorRT and demonstrated its effectiveness in detecting masks on faces with high accuracy. We also introduced an “incorrect mask” class and optimized the system for use on Linux-based embedded devices, improving its performance compared to existing approaches. There are several areas for future work that could further improve the system. These include using a larger and more diverse data set to improve generalization, implementing the system in a faster programming language to reduce latency, developing a mechanism for storing the faces of people not wearing masks, and enabling remote monitoring through an online database. These improvements could make the system more accurate, efficient, and useful in real-world applications.

References 1. Organization WH et al (2022) Covid-19 weekly epidemiological update, Ed 76, 25 Jan 2022 (2022) 2. Loey M, Manogaran G, Taha MHN, Khalifa NEM (2021) A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemic. Measurement 167:108288 3. Wang Z, Wang P, Louis PC, Wheless LE, Huo Y (2021) Wearmask: fast in-browser face mask detection with serverless edge computing for covid-19. arXiv preprint arXiv:2101.00784 4. Militante SV, Dionisio NV (2020) Real-time facemask recognition with alarm system using deep learning. In: 2020 11th IEEE control and system graduate research colloquium (ICSGRC). IEEE, pp 106–110 5. Lopez VWM, Abu PAR, Estuar MRJE (2021) Real-time face mask detection using deep learning on embedded systems. In: 2021 3rd International conference on electrical, control and instrumentation engineering (ICECIE). IEEE, pp 1–7 6. Cabani A, Hammoudi K, Benhabiles H, Melkemi M (2021) Maskedface-net-a dataset of correctly/incorrectly masked face images in the context of covid-19. Smart Health 19:100144 7. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Tech. Rep. 07-49, University of Massachusetts, Amherst 8. Quattoni A, Torralba A (2009) Recognizing indoor scenes. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 413–420 9. Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 1314–1324 10. Sertic P, Alahmar A, Akilan T, Javorac M, Gupta Y (2022) Intelligent real-time face-mask detection system with hardware acceleration for covid-19 mitigation. In: Healthcare, vol 10. MDPI, p 873

Skin Disease Recognition by VGG-16 Model Ankit Yadav, Vinay Sharma, and Jyotsna Seth

Abstract Skin conditions such as herpes, atopic dermatitis, and acne are a serious threat. They can be treated if discovered in time. The majority of the early symptoms of skin disease are visible. To differentiate between them, various methods have been used. Through dermoscopic analysis and clinical skin screening, early skin disease detection is possible. The automatic detection of skin diseases is a typical task. In this study, we suggest a system for categorizing three types of skin conditions: dermatitis, herpes, and acne. A skin condition has been identified by CNN. This CNN classifier uses VGG-16, which involved resizing the image, adding weights, and enabling data augmentation. Keywords Convolutional neural network · VGG-16 model · Acne · Herpes · Dermatitis

1 Introduction The epidermis, dermis, and subcutaneous tissues make up the skin, the largest organ in the human body. It has nerves, muscle, blood vessels, and lymphatic vessels that may perspire, feel the weather outside, and protect the body. The skin, which covers the entire body, may defend several human structures and organs against intrusions from the outside, including artificial skin damage, chemical damage, accidental viruses, and people’s immune systems. Additionally, skin can stabilize the performance of

A. Yadav (B) · V. Sharma · J. Seth Department of Computer Science and Engineering, Sharda University, Greater Noida, India e-mail: [email protected] V. Sharma e-mail: [email protected] J. Seth e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_64

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the skin barrier by avoiding the loss of oils, fats, and water from the epidermis and dermis. According to a survey conducted in 2010, three of the most common illness worldwide were skin disorders, making them the fourth-highest burden of non-fatal disease [1]. Both high-income and low-income nations have experienced significant economic costs as a result of skin disorders. Skin issues can negatively impact a person’s connections with others, career, social life, physical activity, and mental health, among other elements of life. Despite the fact that skin illnesses are quite prevalent among the populaces of many developing nations, public health measures have not been considered to be a substantial solution. In fact, in the same countries, some less widespread health issues frequently receive greater attention. This mindset results from the belief that skin conditions are innocuous minor annoyances that do not pose a threat to life and do not require interventions that may seem excessive given their low importance. However, it appears that there is a considerable desire from patients and healthcare professionals for more attention to be paid to skin problems, at least in some nations [2]. Numerous factors, including UV rays, alcohol consumption, smoking, physical activity, and infections, and the workplace all have an effect on someone’s skin health [3]. These substances are harmful to human health, the integrity of skin function, as well as causing smoke skin damage and, in some dire situations, posing a threat to life. These days, one of the more common health problems affecting people is skin conditions. Skin illness affects people of all ages and crosses all cultural boundaries. A third to seventy percent of people are in high-risk groups [4]. Approximately, 60% of British citizens have a skin problem, in accordance with a British Skin Foundation report from 2018 [5]. Every year, in the US, around 5.4 million new cases of skin cancer are reported annually, and one-fifth of people will be diagnosed with cutaneous malignancy at some point in their lifetime [6]. Skin conditions have a serious negative impact on people’s daily lives, interpersonal relationships, internal organs, and even their ability to survive. This condition may also qualify as mental illness, which could result in loneliness, depression, or even suicide [7]. Figure 1 shows skin and subcutaneous diseases: regional burden [8]. Skin disease can be caused by chemicals, diseases, diet, and stress. Skin disease diagnosis necessitates more money and time. People are generally unaware of the type and stage of skin illness due to a lack of medical understanding. Medical tests and diagnosis for skin diseases are prohibitively expensive. In today’s world, early detection of skin disease is critical. This proposed method detects three kinds of skin diseases: acne, dermatitis, and herpes. This concept is very accurate. As a result, a method based on VGG-16 model is proposed in this paper to distinguish between the three different skin diseases of dermatitis, herpes, and acne. Convolutional neural networks (CNNs) and other deep learning algorithms are now widely used in machine learning. Convolutional (“conv”) and pooling (“pool”) layer parameter tuning allow CNN or deep CNN (DCNN) for automatic image feature extraction. The competence of the renowned pre-trained CNN model VGG-16 to automatically

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Fig. 1 Skin and subcutaneous diseases: regional burden [9]

Fig. 2 Flow of research work 1

identify skin diseases such as herpes, dermatitis, and acne has been examined in the present paper. Here is the flow of the proposed research work (Fig. 2).

2 Related works

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Authors

Data source

Xiaoyu Fan et al. [10]

Edinburgh Transfer learning Innovations Ltd.’s Dermofit Image Library is located at the University of Edinburgh

Algorithm/method

Features

Results

Impact of picture Inception-v3 DCNN noise on how architecture skin lesions are classified

HM10000 Amirreza Rezvantalab dataset and PH2 et al. [11] dataset

Four DCNN with TensorFlow which is pre-trained on ImageNet

Convolutional neural networks’ efficiency and potential have been investigated in the classification of eight skin disorders

The performance of highly skilled dermatologists is compared to deep learning’s capacity

Ma. Christina R. Navarro et al. [12]

Derma clinic, medical Websites, and image taken with an Android mobile device

Bag of features algorithm for classification and combined speed-up robust features (SURF) algorithm for extraction of features

The type of skin condition that the person is expected to have is the study’s anticipated outcome

This study can accurately detect the kind of skin condition thanks to the use of an upgraded BOF algorithm

Alaa Haddad et al. [13]

The dataset consists of 100 images which contains both healthy and diseased skin images, both mild and severe

Two segmentation algorithms

Identify skin diseases from skin images, analyze those images by using filters to remove noise or undesired elements, and then change the images to grayscale to aid in processing and obtain usable information

Assist the doctor identify the ailment and aid in the initial diagnosis

Detect various skin conditions by extracting some features

This technique accurately diagnoses eight unique skin conditions with a percentage accuracy rating of 94.79%

Anika A picture of the Color segmentation Nawar et al. skin taken with a technique + SVM [14] mobile phone’s classifier camera or another device’s camera, and a plenty of dataset for training

(continued)

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(continued) Algorithm/method

Features

Results

Four Ling-Fang Li et al. [15] self-collected datasets and fourteen public datasets

Utilize hierarchical search techniques to find and gather pertinent literature for each database; study and analyze the gathered literature in-depth; and statistically analyze pertinent data

Assess 45 research projects and analyze them in terms of disease kind, dataset, data augmenting technology, data processing technology, model for detecting skin diseases, deep evaluation metrics, learning framework, and performance of model

In terms of diagnosing skin diseases, the deep learning-based skin disease picture identification approach outperforms dermatologists and other computer-aided therapeutic techniques. The multi-deep learning model fusion method in particular has the best identification performance

Zhe Wu et al. [16]

Xiangya–DermThe biggest clinical image database of skin diseases in China

Five standard CNN algorithms which had previously been trained on ImageNet. ResNet-50, DenseNet121, Inception-v3, Xception, and Inception-ResNet-v2 are the five structures

To categorize these disorders in the dataset, tests were conducted using five widely used network methods and compared the outcomes

Best model in the test dataset, which had 388 facial photos, reached recall rates of 92.9%, 89.2%, and 84.3% for the LE, BCC, and SK, respectively. Recall and precision averaged 77.0% and 70.8%, respectively

Wan-Jung Chan [17]

COCO dataset

Faster R-CNN + Inception ResNet_ v2_Atrous model

Identifies and treats four typical scalp hair symptoms

A mobile application ScalpEye is an intelligent scalp identification and diagnosis technology that uses deep learning

Authors

Data source

(continued)

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(continued) Authors

Data source

Algorithm/method

Ahmed A. Around 3000 CNN + SVM Elngar et al. photos were classifier [18] acquired for this dataset from numerous sources

Masum Shah Junayed et al. [19]

5-class acne scar Deep convolutional neural network (CNN) model with deep feature extraction

Features

Results

How many images of skin illnesses have been found in the skin disease dataset which enable accurate diagnosis of skin diseases and delivery of treatment-related prescriptions to the user

(CNN-SVM–MAA) CNN + SVM Mobile Android Application

The suggested architecture’s filter and kernel sizes, activation functions, loss function, optimizer, regularization techniques, and batch size are optimized to improve classification performance while minimizing computing cost

ScarNet: an automated system for classifying acne scars

3 Preliminaries A significant of the quantity of suspicious cases must be screened, followed by appropriate treatment and, if necessary, quarantine, in order to stop the spread of infectious skin diseases like leprosy. The gold standard of testing, screening by a dermatologist, has a significant amount of false negative results. In order to combat the disease, effective and quick analytical techniques are eagerly anticipated. Using industry-accepted standards, the same disease can be examined from a variety of manifestations, risk factors, and a skinned biopsy highlighting important features of a medical condition. Skin examination by hand disease scans radiologists must spend a lot of time identifying numerous patients. Therefore, we need an automated system that can conserve the radiologists’ valuable time.

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3.1 Proposed System To categorize the data, a convolutional neural network (CNN) is used. A CNN model called VGG-16 is suggested for the dataset’s classification. Training data, validation data, and test data make up the dataset. The training dataset is used to train the model. The training images are used by the model to hone itself. The training procedure is validated using validation data, which is also used to assess the validity of the data. The test data, which is unidentified data, is used to test the model to see how accurate it is. A CNN model is initially created, and a dataset is imported. Next, we take into account the VGG-16 model, and since the VGG-16 is a pre-trained model, we incorporate its weights into the CNN model.

3.2 Dataset The Kaggle, which is where the dataset is found, is used. It includes 19,500 images of dermnet [20]. 3468 pictures of dermatitis, herpes, and acne were chosen from among them for the model. The images have been reduced in size to (256 × 256x3) RGB. This model categorizes diseases according to whether they are dermatitis, acne, or herpes. The images have been downsized to 256 × 256, and their RGB values are used to load them into a NumPy array. Labeling images and adding data to the training set should be done (Fig. 3).

Fig. 3 Graphical representation of the count of the various skin diseases [20]

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Fig. 4 VGG-16 model architecture [22]

3.3 VGG-16 Model Architecture The architecture of this network consists of several convolutional layers and three fully connected layers. Dense layers are used to represent the FC layers. Training Images are provided as the input to the model for training of the model. We take patches from these images and feed them to the network during training. K. Simonan and A. Zisserman from the University of Oxford present “very deep convolutional networks for large-scale image recognition” in their paper. Propose the CNN model known as VGG-16. The model’s accuracy is 92.7%. The ImageNet dataset, which contains over 14 million images and is organized into more than 1000 classes and 22,000 categories, was used in this model. The illustration below depicts the architecture of VGG-16 (Fig. 4). VGG-16’s 16th digit denotes its 16 weighted layers. VGG-16 has 21 layers altogether—13 convolutional layers, 5 max pooling layers, 3 dense layers—but only 16 of them are weight layers, also referred to as learnable parameters layers. Here is the VGG-16 model summary (Fig. 5).

3.4 Processing of the Model Images are reduced in size to 256*256 and provided to the model as input. In order to process the data, the image dataset is divided into train and test data and stored in folders. The required packages and libraries are imported before importing the model for processing. 50–60 epochs can be tested for better outcomes in order to achieve 85% accuracy on testing data (Fig. 6).

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Fig. 5 VGG-16 model summary [21]

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Fig. 6 Images extracted from dataset by the model [21]

For identification, specific labels are provided for each image in the train data. These are a few results of the plotGridImages() function’s displayed images. Once the model has been trained, you can see accuracy and loss of validation and training.

4 Results and Analysis Accuracy and epochs for training and testing images are plotted in this graph. Our accuracy yielded better outcomes. Testing results can be displayed as an orange line and training data as a blue line. Epochs are 50 in the graph, and accuracy for testing images is 83.99% before becoming constant (Fig. 7). The relationship between loss and epochs for training and testing images is plotted in this graph. We obtained better results despite the data loss. Testing results can be displayed as an orange line and training data as a blue line (Fig. 8). Figures 9, 10, and 11 display the trained model’s predicted outcomes.

Fig. 7 Graphical representation of the model accuracy

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Fig. 8 Graphical representation of model loss

Fig. 9 Predicted outcomes showing herpes classification by proposed model [20]

Confusion matrix A table called a confusion matrix is frequently utilized to assess the effectiveness of a classification model. The test data are the ones used to create this matrix. Confusion matrix is a tool for visualizing algorithm performance (Fig. 12).

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Fig. 10 Predicted outcomes showing atopic classification by proposed model [20] Fig. 11 Predicted outcomes showing acne classification by proposed model [20]

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Fig. 12 This figure represents the confusion matrix

5 Conclusions and Future Scope 5.1 Conclusions In this study, a deep learning model using CNN as the classifier is proposed for categorizing three different types of skin disease. Based on our findings, accuracy of more than 83.99% was attained. Given that VGG-16 is a pre-trained model, the CNN architecture benefited greatly from using its architecture. It was very beneficial to use VGG-16 as a pre-trained model because of its strong performance in differentiating between three different types of skin diseases, if a large dataset is fed into the model, the accuracy of the model can be enhanced. In future, it is also possible to conduct research into various skin conditions.

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5.2 Future Scope For greater accuracy, the VGG-16 model requires a larger number of parameters. There is insufficient randomness in the taken into account input image and the model’s resulting outputs to fully explore all potential patterns during the assessment process. By including the model’s capacity for self-learning and knowledge acquisition from its prior experiences, the model can be further enhanced. The model’s training efforts can be drastically cut back. In future, this model will be more accurate at predicting various skin diseases.

References 1. Hay RJ, Johns NE, Williams HC, Bolliger IW, Dellavalle RP, Margolis DJ, Marks R, Naldi L, Weinstock MA, Wulf SK, Michaud C, Murray CJL, Naghavi M (2014) The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions. J Invest Dermatol 134:1527–1534 2. Epidemiology and management of common skin diseases in children in developing countries [WWW Document] (n.d.) [WWW Document]. World Health Organization. URL https://www. who.int/publications-detail-redirect/WHO-FCH-CAH-05.12. Accessed 6 Nov 2022 3. Hameed N, Hameed F, Shabut A, Khan S, Cirstea S, Hossain A (2019) An intelligent computeraided scheme for classifying multiple skin lesions. Computers 8:62 4. Johnson MT, Roberts J (1978) Skin conditions and related need for medical care among persons 1–74 years. United States 1971–1974. Vital Health Stat 11(212):i–v 5. British Skin Foundation (2018). [online] Available: http://www.britishskinfoundation.org.uk 6. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologistlevel classification of skin cancer with deep neural networks. Nature 542:115–118 7. Picardi A, Lega I, Tarolla E (2013) Suicide risk in skin disorders. Clin Dermatol 31:47–56 8. Karimkhani Aksut C, Dellavalle RP, Naghavi M (2017) 181 global skin disease morbidity and mortality: an update from the global burden of disease study 2013. J Invest Dermatol 137 9. Karimkhani C, Dellavalle RP, Coffeng LE, Flohr C, Hay RJ, Langan SM, Nsoesie EO, Ferrari AJ, Erskine HE, Silverberg JI, Vos T, Naghavi M (2017) Global skin disease morbidity and mortality. JAMA Dermatol 153:406 10. Fan X, Dai M, Liu C, Wu F, Yan X, Feng Y, Feng Y, Su B (2020) Effect of image noise on the classification of skin lesions using deep convolutional neural networks. Tsinghua Sci Technol 25:425–434 11. Rezvantalab A, Safigholi H, Karimijeshni S (2018) Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural network algorithms. arXiv: 1810.10348 [cs, stat]. Available: https://arxiv.org/abs/1810.10348 12. Navarro MC. Yousefian Barfeh DP (2019) Skin disease detection using improved bag of features algorithm. In: 2019 5th Iranian conference on signal processing and intelligent systems (ICSPIS) 13. Haddad A, Hameed SA (2018) Image analysis model for skin disease detection: framework. In: 2018 7th International conference on computer and communication engineering (ICCCE) 14. Nawar A, Sabuz NK, Siddiquee SM, Rabbani M, Biswas AA, Majumder A (2021) Skin disease recognition: A machine vision-based approach. In: 2021 7th International conference on advanced computing and communication systems (ICACCS) 15. Li L-F, Wang X, Hu W-J, Xiong NN, Du Y-X, Li B-S (2020) Deep learning in Skin disease image recognition: a review. IEEE Access 8:208264–208280

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16. Wu Z, Zhao S, Peng Y, He X, Zhao X, Huang K, Wu X, Fan W, Li F, Chen M, Li J, Huang W, Chen X, Li Y (2019) Studies on different CNN algorithms for face skin disease classification based on clinical images. IEEE Access 7:66505–66511 17. Chang W-J, Chen L-B, Chen M-C, Chiu Y-C, Lin J-Y (2020) ScalpEye: A deep learning-based scalp hair inspection and diagnosis system for scalp health. IEEE Access 8:134826–134837 18. Elngar AA, Kumar R, Hayat A, Churi P (2021) Intelligent system for skin disease prediction using machine learning. J Phys: Conf Ser 1998:012037 19. Junayed MS, Islam MB, Jeny AA, Sadeghzadeh A, Biswas T, Shah AF (2022) ScarNet: development and validation of a novel deep CNN model for acne scar classification with a new dataset. IEEE Access 10:1245–1258 20. Goel S (2020) Dermnet [WWW Document]. Kaggle. URL https://www.kaggle.com/datasets/ shubhamgoel27/dermnet 21. karma12 (2022) Skin disease classification VGG16 [WWW Document]. Kaggle. URL https:/ /www.kaggle.com/code/karma12/skin-disease-classification-vgg16 22. Khaleghian S, Ullah H, Kræmer T, Hughes N, Eltoft T, Marinoni A (2021) Sea ice classification of SAR imagery based on convolution neural networks. Rem Sens 13:1734

Machine Learning Approach for Securing of IoT Environment Amit Sagu, Nasib Singh Gill, Preeti Gulia, and Deepti Rani

Abstract The term “Internet of Things” (IoT) is used to describe the global network of billions of devices, buildings, cars, and other physical things that are interconnected and exchange information. IoT security is necessary for the secure connection of devices and the components of such devices, as well as for the protection of those devices from cyberattacks which includes DoS, Botnet, brute force and SQL injection. In order to mitigate these potential attacks, several security methods and algorithms are introduced. However, the emergence and advancement of machine learning provide new options to address privacy and security concerns. Machine learning predicts the pattern of attacks that would occur in a network by analyzing past data that was generated in an IoT environment. Consequently, in this paper a number of machine learning models are employed to categorize the various types of IoT security assaults. Moreover, comparisons have demonstrated that machine learning models produce promising results. Keywords IoT · Machine learning · Security attacks classification

A. Sagu (B) · N. S. Gill · P. Gulia · D. Rani Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India e-mail: [email protected] N. S. Gill e-mail: [email protected] P. Gulia e-mail: [email protected] D. Rani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_65

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1 Introduction IoT is a foremost mechanism through which numerous advantageous Internet applications can be created. It lets devices control remotely through the current network structure. IoT is a decent and intellectual system that reduces human struggle along with relaxed access to physical devices. Things in IoT, refer to the combination of hardware, software, information, and services. These things gather valuable statistics and share the data between other things. But security remains a primary concern for IoT infrastructure. Vulnerable devices are generally connected straight to a mobile network and are an easy target for DoS, Botnet, and other rising threats [1]. Figure 1 illustrates the intrusion detection system (IDS) that can be an important a component in IoT infrastructure by monitoring network traffic flow for malicious movement and can raise alarm when such susceptibility is exposed. Traditional IDS is generally incapable to deliver security to IoT system, as novel threats are gradually increasing and lacking a regular pattern [2]. The combination of IoT and machine learning leads to a noble IDS, where machine learning will act as an engine that helps to decide against the threat. In this paper, we use supervised machine learning methods to forecast the flow of malicious traffic in IoT environment precisely. The following are the main contributions to the paper: • • • • •

Understand the different models of machine learning. Explore the IoT dataset as well as its numerous different features. Explaining the threats to the IoT environment. Use machine learning technique to detect the threats. Evaluate the performance of a number of different models.

The remaining of the paper is structured as follows. Following a discussion of similar related works in Sects. 2 and 3 delves into the specifics of the dataset, including the preprocessing of the data, optimal feature selection, and balanced splitting. The Fig. 1 Intrusion detection system framework

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findings of the machine learning classifier that was used, are presented in Sect. 4. The final observations of the paper are presented in Sect. 5.

2 Related Work A comprehensive analysis of a broad variety of low-rate distributed denial of service (LDOS) detection methods which are actively being deployed for software defined networks was presented by Ahmed et al. in [3]. The authors provide evidence to support their claim that strategies employing deep learning in conjunction with a hybrid model can result in improved outcomes. Abbas et al. [4] in 2022 demonstrated that traditional tools and techniques were inadequate to handle emerging security threats and challenges, and they detailed how machine learning paves the way for an array of novel approaches that could improve the safety of the IoT. Using a recurrent neural network that has long short-term memory (LSTM), Gopali et al. in [5] were able to identify abnormalities in the surroundings of the IoT. In addition to this, the authors evaluate and contrast a number of different deep learning methods, including convolutional neural network (CNN). The LSTM model that was found to have the quickest learning rate was also found to provide the most accurate predictions. In [6], Syed et al. propose a machine learning-based model for the prediction of Zika virus. The suggested model was tested on a sample dataset, and the findings obtained from that evaluation were encouraging. In [7] the author suggested machine learning approach in order to predict the T-cell epitopes of the SAR-CoV-2 virus. The ensemble machine learning techniques is used, the fact that the ensemble method is less susceptible to being thrown off by outliers and has a higher probability of being able to generalize to new data is the primary justification for utilizing it.

3 Dataset 3.1 Dataset Collection Picking up a dataset is not obvious and might be full of challenge. CIC-IDS17 [6] dataset is used for evaluation of the machine learning models. The dataset was given in separated files, for Monday, Friday, Wednesday and Thursday. We combine those files into one and use 3000 instances out of them. Total number of features are 77 and one column for class which has different class including benign network. In our project we used “label” attribute as target for classification. Some of the features have negative values, floating and even infinity values, thus dataset must be preprocessed.

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3.2 Dataset Classification The dataset contains 32% normal data, while the remaining data represents attack patterns, as seen in Figs. 2 and 3. The followings attacks are included in the dataset.

Normal 32% Attack 68%

Fig. 2 Dataset distribution

1200 1000 800 600 400 200 0

Fig. 3 Attack classification of CIC-IDS17 dataset

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Bot Attack: or botnet is a group of inter-connected device terminals which attacker has compromised. A distinctive efficient behaviors of botnets make them thriving suitable for longstanding intrusion. Moreover, bots can exploit supplementary infected devices on the network as communication medium [8]. DoS: Denial of service (DoS) attack occurs after attacker effort to make service undelivered by sending the malicious data and requests. Generally, DoS attack originates from device network more likely to be botnet network. This possibly will be referring a web server with countless requests resulting it crashes, or it can be a database being smash with a volume of requests. The consequence is existing Internet bandwidth, CPU resources and memory volume becomes overwhelmed [9]. DoS Golden Eye: This attack is identical to Hypertext Transfer Protocol (HTTP) flood plus a DoS attack which intended to crash web servers and resources through endlessly requesting from various terminals. The attack continuously modifies its created requests and tries to retain connection active and also supplements a suffix to URL which lets the request to sidestep various systems. DoS Hulk: It is a DoS tool which is generally used for practicing stress testing of servers. But attacker is using it for malicious purpose as it can bypass the cache engine and can get into resources pool of server. Hulk tool can produce immense bulk of complicated unique network traffic. This tool is written in Python language and can be employed in any operating system. FTP-Patator: It is a multipurpose brute-force attack and contains a well quantity of components and facility to completely flexible settings. Infiltration: It is single of many types of cyber-based attacks. Attacker attempts to bargain some sort of security gap to seize the network, so that they can compromise network security. Brute Force: However brute force may be the oldest and slightest sophisticated attack, yet, it is very popular and also an operative method. This is because, maximum IoT devices having default password and username along with user don’t have any clue about security. SQL Injection: Generally, attacker intends to tap into databases that have confidential data like passwords, username, and permissions. In these phenomena, attacker gives lawful request along with blends, new and innovative commands which also get executed. XSS: Cross-site scripting (XSS) is a web-based susceptibility that an invader may get advantage to augment malicious code which can be appearance benign. In IoT infrastructure, network attached storage (NAS), digital video recorder (DVR), and cameras are some of the devices where XSS blemishes might hide.

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4 Machine Learning Approach for Securing IoT Environment A brief discussion on detecting security attacks in IoT networks is presented in this section. Supervised machine learning methods are selected for use in the detecting system, which are useful not just for classification but also for generating regression values. In this paper, we use classification, which classifies the label into different categories. On the other hand, regression algorithm deals with continuous numbers.

4.1 Supervised Machine Learning Overview Supervised machine learning is defined by the utilization of labeled datasets for the purpose of training algorithms that properly categorize data or forecast outcomes [10]. The training dataset consists of inputs as well as the appropriate outputs, which enables the model to acquire knowledge over the period of time. Following are some of the most popular supervised learning algorithms that have been used in this paper. • • • • •

Random Forest. Artificial Neural Network (ANN). K-NN (k-nearest neighbor). Naïve Bayes. Support Vector Machine (SVM).

Random Forest: Frequently employed in both classification and regression situations, random forest has become a widely used machine learning method. It’s a type of ensemble learning in which numerous decision trees are constructed and their predictions are averaged to arrive at a single result. In addition to being a helpful tool for feature selection and data analysis, random forest can also be important in the significance of selected features. Its ability to handle large datasets while maintaining accuracy has made it a preferred among machine learning models, and it has found widespread application in areas as diverse as finance, healthcare, and marketing [11]. ANN: It is the most popular machine learning algorithm. The phrase “artificial neural network” is developed from the word “biological neural network,” which describes the networks in the human brain that are responsible for developing the structure of the human brain. There are three key layers in ANN, and they are known as input layer, hidden layer, and final is output layer. The first layer of an ANN is the input layer, which takes in data from many sources including textual, speech, numbers, picture, etc. The ANN model contains hidden layers in the middle of the structure. These layers perform a number of mathematical operations on input data, in order to identify the patterns that are present in the data. Consequently, the result obtained by the hidden layer’s computations is obtained in the output layer [12].

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K-NN: It is also a supervised learning algorithm, which makes use of proximity in order to classify or make predictions regarding the grouping of each individual data point [9]. Following are the steps to guide how algorithm works: • In the first stage, the training and testing datasets are loaded. • Pick the K value, according to the closest data points. The value of K might be any integer. • Using any method, determine the distance between every row of the test data and every row of the training data. The Euclidean method is the one that is utilized most frequently to calculate distance. • Arrange these items in descending order according to the distance value. • We choose the K neighbors with the shortest Euclidean distance and select them as our closest neighbors • Count the number of data points in each category surrounded by the k neighbors. • Give the new data point to the class that has the most neighbors already. Naïve Bayes: The Bayes theorem acts as the basis for this probabilistic approach to machine learning, which finds application in a broad variety of classification challenges. The Bayes theorem is a fundamental mathematical formula, given in Eq. (1) that can be used for the purpose of determining conditional probabilities. P( A|B) =

P(B|A).P( A) P(B)

(1)

SVM: The purpose of the support vector machine, or SVM method is to generate the optimal line or decision boundary that can separate space into classes in order to conveniently place fresh data points in the appropriate category, which is known as hyperplane [13]. A linear SVM and a nonlinear SVM are the two possible types of SVM, and they are used, respectively, for linearly and nonlinearly separated data. SVMs are popular because it can discover complicated correlations in data without requiring us to manually perform several changes. Figure 4 shows block diagram of supervised machine learning framework in which it contains various submodules, i.e., data preprocessing, splitting of dataset, supervised machine learning models, ensemble, and model fitting. In data preprocessing, operation have done like handle with missing values, class balancing which is ensuring that weightage of all labels are equals else it may affect the model accuracy and precision. Feature extraction is another important step in preprocessing, only features that assist in prediction should be included in the final model, some inactive features might add extra costs and delay. This step resulting us in final dataset which further can be split into training dataset and test dataset. Splitting of dataset can be done mainly by two methods, first is K-Fold cross-validation and second is holdout method. In k-fold cross-validation, we split the dataset into k subsets of data, also known as folds. For every fold in our dataset, we build model on k-onefold, then, we test the model using kth fold for evaluating. We iterate this till every fold has traversed. Accuracy of model is derived from the average of all k dataset subset.

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But the holdout approach is a simple cross-validation technique. High variance is a problem, though, because the technique can’t predict which individual data points will be included in the validation set and the results may look very different across different sets of data. The last crucial step in the framework is hyper-parameter optimization. Hyperparameters describe the model architecture and a process of probing for the ideal model. Parameter tuning addresses the question like what would be the maximum depth for decision tree, how many trees would be comprised in random forest model or how many hidden layers should be in neural network, etc. Fig. 4 Supervision of machine learning framework

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Table 1 Model results S. No.

Model

Accuracy

F1

Precision

1

ANN

0.976

0.955

0.975

2

SVM

0.832

0.820

0.853

3

K-NN

0.904

0.948

0.929

4

Random forest

0.956

0.956

0.951

5

Naïve Bayes

0.834

0.849

0.929

5 Comparative Analysis While experimenting, the k-fold dataset validation with k set to 5 is used. For categorization of IoT security threats, five different machine learning models were used that includes ANN, random forest, SVM, Naive Bayes, and kNN. Among all the machine learning models, ANN outperformed the other machine learning models in classification accuracy, with a score of 0.976, F1 of 0.955, and precision of 0.975. It has total of five layers, including three hidden layers and ReLu is used as activation function. Radius basis function, also known as RBF, is put to use by the SVM kernel. A classification accuracy of 0.832 is attained by the SVM. Concerning the KNN, we have attempted both expanding and contracting the number of neighbors through a series of experiments. There has been no noticeable change in the model’s accuracy as a result of this. Ultimately, we opted on to make the neighbor size 10. The tuning procedure for the random forest classification method begins with a seed value of 0, and it is later set to 12, since the rate of development in classification accuracy reached a plateau after the seed value of 12, which remained constant throughout. In Table 1, the results of the model are summarized in terms of their accuracy, F1, and precision.

6 Conclusion To ensure that IoT devices are secure to the required levels, many technologies, such as mobile and cloud infrastructures, physical devices, and wireless communication must be safeguarded. The advancement of machine learning has enabled the development of a wide range of sophisticated approaches that may be used to strengthen the security of the IoT environment. In this paper, a wide range of IoT security concerns and potential attacks are discussed, along with the approaches of machine learning that may potentially be utilized to safeguard the environment of the IoT are also highlighted. Finally, these approaches are appraised and contrasted in the result section according to the classification accuracy, F1, and precision metrics. The ANN machine learning model stands out as the most promising compared with other methods. This paper aims to give insights that will encourage future researchers to employ machine learning to make IoT systems safer.

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References 1. Sagu A, Gill NS, Gulia P, Chatterjee JM, Priyadarshini I (2022) A hybrid deep learning model with self-improved optimization algorithm for detection of security attacks in IoT environment. Future Internet 14(10):301. https://doi.org/10.3390/fi14100301 2. Sagu A, Gill NS, Gulia P (2022) Hybrid deep neural network model for detection of security attacks in IoT enabled environment. Int J Adv Comput Sci Appl (IJACSA) 13(1):2022. Accessed 03 Feb 2022 [online]. Available www.ijacsa.thesai.org 3. Alashhab AA, Zahid MSM, Azim MA, Daha MY, Isyaku B, Ali S (2022) A survey of low rate DDoS detection techniques based on machine learning in software-defined networks. Symmetry 14(8):1563. https://doi.org/10.3390/SYM14081563 4. Abbas G, Mehmood A, Carsten M, Epiphaniou G, Lloret J (2022) Safety, security and privacy in machine learning based internet of things. J Sensor Actuat Netw 11(3):38. https://doi.org/ 10.3390/JSAN11030038 5. Gopali S, Namin AS (2022) Deep learning-based time-series analysis for detecting anomalies in internet of things. Electronics 11(19):3205. https://doi.org/10.3390/ELECTRONICS1119 3205 6. Bukhari SNH, Webber J, Mehbodniya A (2022) Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates. Sci Rep 12(1):1–11. https://doi.org/10.1038/s41598-022-11731-6 7. Bukhari SNH, Jain A, Haq E, Mehbodniya A, Webber J (2021) Ensemble machine learning model to predict SARS-CoV-2 T-cell epitopes as potential vaccine targets. Diagnostics 11(11):1990. https://doi.org/10.3390/DIAGNOSTICS11111990 8. Alkahtani H, Aldhyani THH (2021) Botnet attack detection by using CNN-LSTM model for internet of things applications. Secur Commun Netw 2021. https://doi.org/10.1155/2021/380 6459 9. Dong S, Sarem M (2020) DDoS attack detection method based on improved KNN with the degree of DDoS attack in software-defined networks. IEEE Access 8:5039–5048. https://doi. org/10.1109/ACCESS.2019.2963077 10. Sagu A, Gill NS (2020) Machine learning techniques for securing IoT environment. Int J Innov Technol Explor Eng (IJITEE) 9:2278–3075. https://doi.org/10.35940/ijitee.D1209.029420 11. Lan T, Hu H, Jiang C, Yang G, Zhao Z (2020) A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification. Adv Space Res 65(8):2052–2061. https://doi.org/10.1016/j.asr.2020.01.036 12. Farivar F, Haghighi MS, Jolfaei A, Alazab M (2020) Artificial intelligence for detection, estimation, and compensation of malicious attacks in nonlinear cyber-physical systems and industrial IoT. IEEE Trans Industr Inform 16(4):2716–2725. https://doi.org/10.1109/TII.2019.2956474 13. Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A (2020) A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408:189–215. https://doi.org/10.1016/J.NEUCOM.2019.10.118

DDOS Attack in WSN Using Machine Learning Manu Devi, P. Nandal, and Harkesh Sehrawat

Abstract The wireless sensor networks (WSNs) are susceptible to various sorts of security risks and attacks. To identify attacks, a powerful intrusion detection system (IDS) must be employed. Identifying attacks, particularly distributive denial of service (DDoS) attack, can be a challenging task. As DDoS attacks can readily change the port/protocol in use or the operating mechanism, it is important to study these attacks. In the proposed work, DDoS attack detection has been achieved using machine learning techniques. The efficiency of five machine learning techniques for identifying DDoS attack in WSNs was evaluated in this paper. The assessment is based on the CIC-IDS2017 dataset. The random forest classifier exceeds the similar classifiers having an accuracy of 99.98%, according to the data. Keywords Wireless sensor networks (WSNs) · Machine learning · DDoS attack

1 Introduction Wireless sensor networks (WSNs) are a category of wireless networks that comprise the collection of small nodes which are utilized to assimilate the data from the environment by identifying the component. Battery-operated sensor nodes use radio links to communicate with one another. All the sensor nodes or source nodes available in the sensor network send the collected information to the base station also known as sink node or destination node. Wireless sensor node communicates via

M. Devi (B) · H. Sehrawat Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Haryana, India e-mail: [email protected] P. Nandal Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, GGSIP University, Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_66

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two modes, namely direct and indirect. In direct mode, source node directly communicates with destination node, and in indirect node, source node communicates with destination node via some intermediate nodes. Limited processing power, computational resources, communication capacity, and bandwidth storage are all intrinsic characteristics of WSNs. Many routing algorithms, power management, and other procedures have been explicitly proposed where energy conservation is an important aim. Sensor nodes may detect physical or environmental factors like motion, vibration, pressure, sound, temperature, and so on and collaboratively convey the data to the sink node or primary node, from where it can be gathered and analyzed. WSNs are employed in multiplicity of areas, including habitat monitoring, military surveillance, industrial performance monitoring, landslide detection, earthquake, and safety monitoring to name a few [1]. WSNs applications are constantly evolving with advancements in artificial intelligence and data mining. Outdoor deployments of WSNs are common, as are deployments in hostile areas. As a result, these deployments may be more susceptible to attacks and security threats. Additionally, WSNs are vulnerable to attacks by the virtue of their broadcasting nature and ad hoc network structure. Active and passive attacks are the two main forms of attacks. The attacker watches the conversation but does not alter the data or resources in passive attacks. Active attacks, on the other hand, alter resources of the system and render the system inaccessible to anticipated users. Additional types of attacks include internal attacks, which are initiated by nodes that are already members of the network and external attacks, which are initiated by nodes outside the network. One of the most systematic techniques of analyzing security attacks is layer-wise classification. Jamming attack [2] and node tampering [3] are the most notable common attacks identified in the physical layer. Jamming attacks cause the entire network to be disrupted by interfering with radio waves from the benign nodes. Replay attacks, collision attacks, and synchronization attacks are all common attacks at the data link layer [2]. A notable dangerous attack at the data link layer is the denial of sleep attack which is a sort of denial of service (DoS) attack. Sybil attacks, black hole attacks, gray hole attacks, wormhole attacks [4], sinkhole attacks [4], and hello flooding attacks are some of the network layer attacks. Boubiche et al. [2] and Pathan et al. [4] discussed the methods of these attacks as well as available responses. UDP and TCP flooding attacks are meant to take advantage of flaws in transport layer protocols [5]. The security challenges of transport layer protocols with respect to WSNs were investigated by Dvir et al. [6]. A detailed taxonomy of security attacks is provided in literature [2]. The detailed security threats that WSNs face, as well as the numerous types of attacks, are also explained in [7]. Traditional security solutions may not be suited for usage in WSNs owing to the inherent restrictions of sensor networks. It is a complicated task to develop the security algorithms for WSNs. This is because the sensors have limited energy, but most of the security algorithms are based on difficult computation. Complex computation of security algorithms drains the energy of the sensors, and hence, the security algorithms should rely on reasonable level of computation. However, the sensor nodes must be provided with good security. Machine learning has emerged as the most important techniques for enhancing, complementing, and supplementing

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the traditional security experience. The technique of data acquisition, training, and output is at the heart of machine learning. Determining the key features and the technique which will be most appropriate for attack detection is a topic that remains unanswered. In the suggested research, the authors have utilized various machine learning methods to analyze the distributed denial of service (DDoS) attack in WSNs. The rest of the paper is structured as follows: Sect. 2 explains the DDoS attacks in WSNs. Section 3 reviews the related work in this area. Section 4 explains the experiment and evaluation pursued in the suggested work including the results of the study. Lastly, the conclusion and future work is presented in Sect. 5.

2 Distributed Denial of Service Attacks in WSNs One of the most harmful cybersecurity threats are DoS attacks that are traditional network bandwidth-based attacks. There are two methods that DoS attacks can be executed, either by flooding the system or crashing the system. In the first case, such attacks send an enormous amount of malicious traffic to a server, app, or service platform in order to overload its computational or network resources, causing malfunctions and/or congestion, therefore rendering the machine or network, unreachable to its intended users. On the contrary, DoS attacks are carried out in a way such that the target is flooded with traffic or transmitted information which crashes the system. In both the above cited types of the DoS attack, the legitimate users are denied from the service or resource they expected [8–12]. The DDoS attack is the most challenging sort of DoS attack. A combined attack action is carried out by a group of many attackers with diverse sources and, typically, dynamic/spoofed IP addresses. Although normal traffic prefers to retreat to avoid more congestion, the attack traffic does not. As a result, the links are overburdened and legitimate traffic is also affected [13]. Attackers, on the other side, are employing further upgraded methods to enhance attacks and overwhelm the victim. SYN flood, ICMP flood, and UDP flood are the three types of DDoS attacks [14]. The most apparent sign of a DDoS attack is when a website or service all of a sudden becomes slow or unavailable. On the one hand, the significant rate of success, for the aforementioned category of attack, is due to the fact that maximum considerable Internet routers use the DROP-TAIL and First-In-First-Out (FIFO) queuing methods that do not delineate among distinct type of traffic and enforce equal loss rates for both legitimate traffic and attacks. Blocking DDoS attack is challenging since traditional IP address blacklist defense method established on fixed laws is ineffective. However, further investigation is typically necessary because a variety of factors, such as a real traffic surge, might result in the same performance concerns. The majority of DoS attacks can now be defended against by modern security measures, but DDoS is yet seen as a high-risk threat and is of greater concern to businesses that fear becoming the target of one.

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Machine learning technologies, which have been shown to be useful in cybersecurity [15], are utilized to handle DDoS attacks among many other protection mechanisms offered for identifying DDoS attacks. There are three categories of supervised, unsupervised, and hybrid methods for machine learning-based DDoS attack detection. Naïve Bayes, random forest, logistic regression, decision tree, and linear discriminant analysis are some of the machine learning algorithms we employed.

3 Related Work Different approaches based on machine learning and deep learning have been proposed by researchers to help secure WSNs from DoS attacks. Some reviews on WSNs security and DoS attacks have been published. Earlier, in 2002, Wood et al. [16] conducted a review of DoS attacks and defense methods. To find DoS vulnerabilities, they looked at two effective sensor network protocols. Raymond et al. [17] released a survey of WSNs with updated information about DoS attack and related prevention strategies. They looked at the denial of sleep attack, which is specific to sensor network deployments and targeted energy-efficient protocols. They examined several security measures to consider for the denial of sleep attack that tend to keep the sensor nodes’ radio links on to deplete the batteries quickly, in addition to analyzing the characteristics of such networks. DoS attacks and potential countermeasures were addressed by Buch and Jinwala [18]. Shahzad et al. [7] published another study employing machine learning techniques, in which they evaluated the efficiency of the J48 decision tree and the support vector machine using the WSN-DS dataset, which is a customized dataset for WSNs. The algorithms were evaluated on the complete dataset as well as a subset of it that included only floods, gray hole, and typical attacks. On the basis of their findings, it was inferred that J48 decision tree outperformed the support vector machine. The authors intended to expand their work in the future by integrating additional categories of attacks and experimenting with other machine learning methodologies. To build trust-based energy usage, a hybrid DoS attack prevention strategy was proposed [19]. On the WSN-DS dataset, Quincozes and Kazienko [20] conducted a comparison research employing Naive Bayes, random tree, J48 decision tree, reduced error pruning (REP) tree, and random forest. The authors employed five of the 18 features in the dataset for their research. The performance metric they utilized was accuracy. According to their study, J48 decision tree and REP tree outclassed other algorithms for identifying gray hole and black hole attacks, while random forest is the outstanding method for identifying flooding attacks. One of the key constraints of the analysis was that it used only one assessment matrix. Using the WSN-DS dataset, Jiang et al. [21] developed SLGBM on the basis of sequence backward selection (SBS) for feature extraction and LightGBM for identifying attacks employing machine learning. The SLGBM algorithm was evaluated against random forest, Naive Bayes, decision tree, k-nearest neighbor, support vector machine, logistic regression, CatBoost, XGBoost, and LGBM. Further analysis using feature extraction was

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conducted; SBS was evaluated against correlation feature selection, chi-square, and information gain for this purpose. Using feature extraction, the best ten features were chosen from the feature set. The suggested SLGBM outperformed previous models and feature extraction strategies in terms of model performance. Alsulaiman and Ahmadi [22] tested the machine learning classification approaches for DoS detection with respect to WSNs in their study. The efficiency of machine learning approaches was calculated using WSN-DS dataset. Four forms of DoS attacks were classified using the dataset: flooding, gray hole, black hole, and TDMA. On the WSN-DS dataset, WEKA was utilized to test the efficiency of DoS detection. With a detection accuracy of 99.72%, the authors discovered that random forest outperformed other approaches in categorization. The authors wish to expand the research in future to include various types of classifiers and machine learning approaches. Deep learning models have been utilized to detect DoS attacks, similar to machine learning models. Ahmad et al. [23] presented one such study. An artificial neural network was developed for DoS attack classification. The neural network was trained using the WSN-DS dataset. The neural network model consisted of five layers: an input layer, three hidden levels each consisting of four neurons, and a five-neuron output layer. They accurately identified 98.57 percent of occurrences using this model, exhibiting a mean absolute error of 0.0073. With the help of tenfold crossvalidation, the neuron was trained. The authors wanted to increase more hidden layers and attacks such as wormhole and Sybil. In the literature, a variety of machine learning-based DDoS attack detection algorithms have been developed, focusing on machine learning’s proficiency to systematically deduce anomalous patterns in a sequence of data packets [24]. To detect DDoS attacks in WSNs, a quick authentication method was established [25]. Smart detection, a unique defense method to counter DDoS attacks, is proposed in the article by Filho et al. [26]. High-volume as well as low-volume DDoS attacks were addressed for the network traffic by the authors. DDoS attacks detection (DAD) automatically using machine learning methods was achieved which focused on TCP SYN flood attacks [27]. Several research have been proposed for DDoS attack employing support vector machine (SVM) classifier [28–36]. Additional machine learning methods have been employed to study DDoS attack [37–42]. In a recent article [43], reinforcement learning was also used to do DDoS mitigation in the network. A lightweight but effective DDoS attack detection method for software-defined wireless sensor networks was proposed by Segura et al. [44]. Both supervised and unsupervised algorithms were combined by the authors to detect unprecedented DDoS attacks employing a hybrid machine learning method [45]. The literature review reveals that machine learning is being used to detect security attacks.

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4 Experiment and Evaluation Five various classification methods are evaluated in this paper. The specific CICIDS2017 dataset, which is described below, was then subjected to these algorithms.

4.1 Dataset Description The CIC-IDS2017 dataset is produced from the July 2017 Canadian Institute for Cybersecurity (CIC) cyber-data collection. The CIC-IDS2017 is proposed by Sharafaldin et al. [46]. The CIC-IDS2017 dataset is among the most recent intrusion detection datasets containing benign and current prevalent attacks, and it nearly reflects real-world data. The dataset consists of 2,830,743 records organized into eight files, with every record comprising more than 80 distinct attribute feature dimensions and their associated tags [46]. It also provides the outcomes of a network traffic analysis using CIC Flow Meter that is freely available on the Canadian Institute for Cybersecurity website. The results comprise labeled data on the basis of time stamp, protocols, attack vectors, source and destination IP addresses, and source and destination ports (CSV files). The attributes of the CIC-IDS2017 dataset are divided into three groups. The first section consists of space features; the second section consists of time features; and the third section consists of content features. CIC-IDS2017 covers all eleven criteria, as well as typical updated attacks comprising Port scan, Botnet, Infiltration, SQL Injection, XSS, Brute Force, DoS, and DDoS.

4.2 Evaluation Metrics An evaluation metric is applied to measure the efficiency of a predictive model. A model must first be trained on a dataset, after which it must be used to predict values on a holdout dataset that was not utilized during training. The predicted values in the holdout dataset must then be compared to the predictions. In machine learning, accuracy is the main focus area. For correct classification, confusion matrix is used to serve this purpose. By counting the number of binary classifier results, a two-by-two table known as a binary classification confusion matrix is produced. Table 1 is used to introduce the confusion matrix. The work presented here is assessed on the basis of following performance metrics: Accuracy Accuracy yields the fraction of the total number of instances that are accurately classified. Accuracy (ACC) is determined by dividing the total number of correct predictions by the total number of observations in the dataset.

DDOS Attack in WSN Using Machine Learning Table 1 Explanation of confusion matrix

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Explanation

Symbol

True positive (TP) The identified amount of normal data is indeed normal data False positive (FP)

The attack data that is mistaken for normal data

True negative (TN)

The identified amount of attack data is indeed attack data

False negative (FN)

The normal data that is mistaken for attack data

Accuracy =

TP + TN . TP + TN + FN + FP

(1)

Sensitivity Sensitivity gives details regarding the ratio of occurrences that were correctly identified as normal to all of the instances, known as a true positive rate (TPR), recall rate (REC), or detection rate (DR). Sensitivity (SN) is estimated as the number of correct positive predictions divided by the total number of positives. Senstivity =

TP . TP + FN

(2)

Specificity The specificity (SP) is computed by dividing the total number of valid negative predictions by the total number of negatives. A true negative rate (TNR) is another name for it. Specificity =

TN . TN + FP

(3)

4.3 Experimental Results and Discussion The dataset was split into the training and testing dataset. The dataset was split such as that the testing set comprises additional attacks that are not in the training dataset. To analyze the efficacy in the event of a DDoS attack, five machine learning algorithms to execute classification for DDoS were used. To carry out the experiment and train the classifiers, the authors employed tenfold cross-validation. Approximately, 240 epochs were performed with a batch size of 64. We concentrated on algorithms, namely logistic regression, linear discriminant analysis, Naïve Bayes, decision tree,

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and random forest. The confusion matrix for all the five algorithms is shown in Figs. 1, 2, 3, 4, and 5, respectively. Comparison of the algorithms used, showing accuracy, sensitivity, and specificity, is presented in graph of Fig. 6. Logistic regression had the least accuracy at 43.33%, as it incorrectly classified 38,380 instances, although random forest had the utmost detection accuracy at 99.98%. Decision tree appeared second at 99.97%, and the linear discriminant analysis attained 98.03% detection accuracy. The Naïve Bayes had an accuracy rate of 56.68%. Next, we discuss the sensitivity results for all considered data instances. Naïve Bayes has the highest sensitivity of 99.99%, whereas the lowest sensitivity is shown nil by logistic regression. The sensitivity of random forest and decision tree is 99.97%. Linear discriminant analysis achieved 99.96% sensitivity. Regarding the Fig. 1 Results of logistic regression

Fig. 2 Results of linear discriminant analysis

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Fig. 3 Results of Naive Bayes

Fig. 4 Results of random forest

specificity result of each classifier, the classifier with the least precision was Naïve Bayes, whereas random forest and logistic regression had the highest specificity of 100%. The above data is summarized in Table 2. By type of attack, random forest performed best in detecting DDoS attack, while on the other hand, the Naïve Bayes performed least for detecting DDoS attacks. Table 3 displays a comparison between this work and a few other existing study. To do this, we examined various machine learning classifiers based on their accuracy, etc. Table 3 compares the accuracy, sensitivity, and specificity of our model’s findings to those of the model put forward by Ahmad et al. [47], Wani et al. [48]. It can be noted that the random forest and decision tree models outperformed the other machine learning models in terms of accuracy. Random forest, Naïve Bayes, and decision tree models show better results for sensitivity. Random forest, logistic regression, and

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Fig. 5 Results of decision tree 120 100 80 60

Accuracy Sensitivity

40

Specificity

20 0 Logistic Linear Naive Bayes Regression Discriminant Analysis

Random Forest

Decision Tree

Fig. 6 Graph representing comparison of the algorithms Table 2 Comparison of classification models used in this study

Algorithm

Accuracy

Sensitivity

Specificity

Logistic regression

43.33

0.0

100.0

Linear discriminant analysis

98.03

99.96

95.5

Naïve Bayes

56.68

99.99

Radom forest

99.98

99.97

Decision tree

99.97

99.97

0.03 100.0 99.98

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Table 3 Comparison of classification models with existing work Model Proposed model

Model proposed in Ahmad et al. [47], Wani et al. [48]

Parameter

Logistic regression

Naïve Bayes

Random forest

Decision tree

Accuracy

43.33

56.68

99.98

99.97

Sensitivity

0.0

99.99

99.97

99.97

Specificity

100.0

0.03

100.0

99.98

Accuracy

89.98

98.0

97.6

96.7

Sensitivity

91.62

86.0

99.3

97.11

Specificity

0.83

0.83

0.85

0.85

decision tree outclassed for the specificity parameter. For the publications evaluated in Table 3, the parameters accuracy, specificity, and sensitivity for the model, linear discriminant analysis, were not available for comparison. It should be noted that despite this, the suggested work has helped produce high performance results.

5 Conclusion Sensor networks are susceptible to a number of attacks owing to their communication patterns and deployment mode. The security and privacy of data packets are the most important considerations. In WSNs, DDoS attacks have a negative impact on the efficiency of the system. We examined machine learning assisted DDoS attack detection systems for use in a WSN environment in this paper. The performance including the accuracy of the selected methods was evaluated. Furthermore, the performance was compared to that of contemporary and comparable approaches. The comparative analysis reveals a considerable improvement in performance. With the increase in the number of samples, the performance metrics remain same, and the model building time does not significantly rise. We plan to examine different types of attacks and datasets in the future and apply it in a real-world network. We also intend to research attack-type identification in the future.

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Prediction of Thorax Disease in Chest X-ray Images Using Deep Learning Methods Saranya Bommareddy, B. V. Kiranmayee, and Chalumuru Suresh

Abstract Thorax disease is said to be a chronic chest disease that leads to chronic cough through which pressure is built in the intra-abdominal region resulting in increased strain on the pelvic floor of the body. Hence on time diagnosis and early prediction of thorax are very crucial and primary to take care of one’s health. The specific segmentation and classification of chest abnormalities from radiographic images are useful for clinical analysis and therapeutic approaches in predicting diseases like thorax, pneumonia, etc., beforehand. However, the multi-label classification for interpreting medical radiographic images to indicate multiple requested or suspected medical conditions has empirical limitations. Creating a very accurate classification model often uses manual labelling of many images to find the mask, which is really expensive to obtain. To identify a supervised learning problem of this nature, a combination of pre-trained deep CNN models with different features is proposed. This architecture consists of two main components. One is adaptive focus segmentation on pathologically abnormal regions using pre-trained DenseNet, The other is convolution feature extraction. This approach efficiently exploits the dependency between target annotations to determine future outcomes for various chest diseases compared with the current gold standard in published chest radiography datasets. The fundamental aspect of the paper is to look at the disease affecting important areas of the patient and find chest pathologies and try to bypass healthy areas, helping to target areas efficiently.

S. Bommareddy (B) · B. V. Kiranmayee · C. Suresh VNR VJIET, Hyderabad 500072, India e-mail: [email protected] B. V. Kiranmayee e-mail: [email protected] C. Suresh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_67

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1 Introduction The most common cause of death globally is lung disease. The risk of developing lung cancer from common thorax diseases makes screening, early detection, and individualized therapy crucial. A computer-aided system that satisfies the above purposes should have a major goal of concentrating on lesion areas and reduce the noise provided by the unrelated lesion areas in the classification of the disease. The system’s performance can also be improved by investigating inherent correlations among various pathologies. Chest X-ray images broadly known as CXR have become a popular method because it is affordable and accessible. It accounts for roughly 45% radiological investigations and is used to diagnose a wide variety of disorders. Globally, a huge amount of chest radiological scans have been created, and they are currently being examined visually. Each X-ray scan, however, may contain dozens of patterns that represent hundreds of possible lung diseases, making interpretation challenging, increasing the likelihood of radiologists disagreeing, and leading to unnecessary follow-up treatments. It takes a great level of expertise and focus to handle huge amount of data which is heterogeneous in nature and is a slow process, costly, and susceptible to human errors. The creation of adaptable algorithms is crucial for the diagnosis of thorax diseases using computer system. Certain X-ray images might have lesion regions which are quite small in comparison with the overall ones. Also, there can be a patient having much pathology in one image, which indicates that they have experienced a variety of diseases over time. The detection and classification of abnormalities are two areas where CAD is limited because of the complexity and heterogeneity among various diseases that can be confirmed through X-rays and also the poor magnification factor in CXR pictures. In general, an expert must put in significantly more effort to manually indicate locations that are pathologically abnormal than to identify them. This has led to the publication of chest radiography datasets which include disease labels in addition to small portion of region-level labelling of suspicious regions. This labelling technique causes a weakly supervised challenges during CAD. A specific disease class is much less informative than bounding boxes for clustering tasks, which greatly enhances model performance. But it can be expensive and challenging to get thorough disease localization markers. Therefore, creating models that work well with a limited number of annotated masks is essential for the success of clinical applications. Even though they were trained using simply class labels, a number of attention-based models have recently been developed and shown to improve the localization and recognition of multiple objects. Deep learning-based algorithms have achieved the accurate performances in a variety of domains, especially image classification, in the domain of computer vision. Consequently, the basis for image representation is primarily comprised of features.

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Regardless of the spatial information, an image can be viewed as a collection of local patches that can be easily produced by grouping shallow artificial features like scale-invariant feature transform. In conclusion, the following are the study’s significant contributions. • In order to see the pathological abnormalities out from ChestX-ray14 dataset’s class activation map (CAM), pre-trained deep DenseNet-121 is being employed. • Comparison between the supervised classification outcomes of various classifiers.

2 Literature Survey Information bottleneck (IB) is a parameter for the perspective of deep networks which was employed for learning encoding technique by maximizing correlation among the variable X’s latent space Z and indeed Y, the class [1]. For directing the concentration of the network towards extracting the features that are disease specific and thereby obtaining accurate classification, a unique conditional variable called selective information bottleneck which is variational (VSIB) is introduced. The proposed new InfoMask pneumonia localization approach [2] is connected to some of the current work. In contrast to [2], which focuses on localizing the lesion region, the major objective behind the work in a multi-label learning model is disease classification. Contrarily, VSIB adds a specific mechanism to standard information bottleneck principle in order to understand the disease-specific characteristics. There are various difficulties with X-ray imaging, such as complex backgrounds and the existence of numerous potential anomalies. This makes clinical interpretation of radiographs extremely difficult [3]. These scans look for abnormalities in the chest and compare the results by disease. It is therefore necessary to manually annotate the images using a radiologist. Automated X-ray image analysis is becoming a crucial clinical tool. Neural networks are employed to complete the task that categorizes X-ray images due to their recent success in this area. Chest radiography allows for the classification of numerous disorders, including difficult lung infections [3]. CoroNet was introduced, a deep network model that assists in the detection of diseases in the body [4]. In order to find chest anomalies on scans and during testing, a deep neural network learning framework has been developed [5]. Using artificial data based on different methodologies, it was able to attain an accuracy of 87° [6]. The most crucial job is dividing the thorax in the diagnosis of CXR illnesses. This helps prepare TB analyses in general [7]. DeTraC network design was suggested [8]. ConvNets are also useful for extracting attributes [9]. In order to identify pneumonia, it used feature selection, processing, and collation based on the effectiveness of older ML approaches. The lack of a significant amount of annotated data and effective ML algorithms to comprehend its properties makes automated illness division in radiographs challenging. We integrated textual information from diagnostic radiographs with annotated picture information to train a classifier for lung diseases. Deeply

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resolved genetic models, as opposed to the traditional direct illness classification method, can be helpful to produce remained maps of various pathogens along with essential files [9]. Utilizing this method, out of the given chest X-ray image, diseases can be identified and also one can get that pattern of irregularities present. Because of the deficiency of huge collection of annotated data and effective ML algorithms to extract the biased features from them, automatic classification of diseases in the radiographic pictures is difficult [10]. For increasing the factor named precision for disease prediction, many data streams and formats can be used. The thorax disease morpheme was trained using the text data that is being extracted out of the X-ray images in conjunction with composite data of the image [11]. Using a deep extracted generative model, residual maps for deviating disorders can be produced alongside normal images, in contrast to the conventional method of directly splitting diseases. This method aids in distinguishing between the aberrant and ordinary regions of an X-ray image. To effectively change CXR disease, semigenerative models can be used. Numerous healthcare applications, including the classification of diseases, have exploited edge computing [12]. Without involving the experienced radiologists, using DenseNet121 which is a deep CNN with 121 layers that can accurately identify 14 thorax diseases and pneumonia from chest X-rays [12]. Similar to the previous work, another work involved ResNet-50 model to identify the spatially locate the eight prevalent thorax diseases [13]. DenseNet161 was used to develop computer-aided diagnosis (CAD) systems; resulting technique outperformed the BR with PWE loss single classifier. Considering the fact that the classifier and localization performance has to be improved [14]. Rule-based model is employed as a self-training approach along with a three-class classifier and Adam optimizer approach which was able to create a labeller that could quickly and accurately identify 14 different diseases. A higher F1-score is obtained using the labelling algorithm [15]. For obtaining the image and extracting the text of chest X-rays, CNN-RNN and LSTM models are used on the dataset which is ChestX-ray14. Considering normality and pathology, LBP and GDAV models were used to classify chest radiographs automatically [16]. In order to improve the accuracy of CAD systems for identifying thorax disorders, DenseNet-121 is used for multi-label classification.

3 Proposed System According to the previous literature work, many researches and implementations have took place in analysing the X-ray images using various algorithms that resulted in considerable range of accuracy. Few models of CNN were being used like ResNet, etc., but have considered a limited number of images in the dataset. To achieve good range of accuracy and deal with larger datasets, we propose a system that is designed

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Fig. 1 System architecture of proposed methodology

by using deep learning concepts to automatically detect and classify thorax diseases. This makes use of deep CNN, a classifier which helps to extract useful features present in the CXRs and classify according to the corresponding labels. DenseNet and MobileNet are the two deep learning algorithms that are used in the model and evaluated on the basis of metrics like accuracy and loss. Proposed system does the prediction of the thorax disease in the given patient’s chest X-ray image. This is done in the process which is split into phases like collection of data, data pre-processing, splitting of the data and training followed by building the model. Figure 1 depicts the proposed system architecture that is being considered and that explains the flow of the system including all the phases.

3.1 Dataset The dataset used in the system is downloaded from open Kaggle datasets which is “chest X-ray” dataset with 12,835 images. This dataset is divided into two folders which are normal and thorax (Table 1). Table 1 Split of images into folders normal and thorax

Folder

Total images

Normal

6770

Thorax

6065

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This dataset is loaded into the system and further split into train and test datasets. And then training process takes place and a model will be built.

3.2 Data Pre-processing After the data is being loaded from the dataset consisting of two folders, namely normal and thorax, next predominant step in any deep learning process is data preprocessing. Loaded data consists of noisy and heterogeneous data. In order to standardize, the dataset is pre-processed first and then sent to next phases like training, building model and prediction. Firstly, images undergo resizing process. Not all the images present in the dataset will be of same size. Hence, all the images are resized into a standard dimension of 128 * 128. And then these images undergo rescaling. All the images will be converted into standard greyscale format. Image data generator is a class in Python used for pre-processing methods.

3.3 Splitting of Data into Train and Test After the data is being loaded and underwent pre-processing, the data will be split into two major categories, namely train and test. More the images are split into train, more will be the training process and prediction will be accurate. This is why it is being said that deep learning runs good with more amount of data and higher amount of data should be split into train. Considering this case, a reliable split which does not end up into the cases of overfitting and underfitting, a 80–20 split, is done on the pre-processed dataset.

3.4 Modelling This is the major part of the deep learning process, which is model training. After the completion of the phases, pre-processing and splitting of data, now training process will be started. In this case, we use two deep learning models that are DenseNet and MobileNet for the goal of classifying a chest X-ray image into normal or thorax. Both the algorithms are applied on the data, and model is built. These are compared for efficiency through measures like accuracy and loss.

3.4.1

DenseNet

A DenseNet is a sort of convolutional neural network that makes use of dense connections between layers by connecting all layers (with matching feature map sizes)

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Fig. 2 Architecture of DenseNet algorithm

directly with one another using dense blocks. Each layer receives extra inputs from all earlier layers and transmits its own feature maps to all later layers in order to maintain the feed-forward character of the system (Fig. 2). Each layer in DenseNet receives extra inputs from all levels that came before it and transmits its own feature maps to all layers that came after it. You utilize concatenation. Each layer receives “collective knowledge” from the levels that came before it. Each layer receives feature maps from all layers that came before it, allowing for a more compact and thin network with fewer channels. Consequently, it has improved memory and processing efficiency. In a DenseNet design, the transition layers between two adjacent dense blocks are 1 × 1 Conv and 2 × 2 average pooling. Within the dense block, feature map sizes are uniform, making it simple to concatenate them. A softmax classifier with a ReLU activation function is applied after a global average pooling is completed at the conclusion of the final dense block. In neural networks, the process continues by feeding the results of one layer into the next. Convolution, also known as pooling layers, coupled with batch normalization and an activation function in this instance, ReLU, are used together in a composite operation to achieve this. Generalized equation for this process is xl = hl(xl − 1).

(1)

Later ResNet has further modified this equation by including skip connections xl = hl(xl − 1) + (xl − 1 + 1).

(2)

Contrary to ResNet, DenseNets concatenate rather than sum the layer’s output feature maps and incoming feature maps xl = hl([x0, x1, x2, . . . , xl − 1]).

(3)

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Fig. 3 Architecture of MobileNet algorithm

3.4.2

MobileNet

In order to train our incredibly small and incredibly quick classifiers, we use MobileNet, a class of CNN that Google open sourced. Convolutions that can be separated based on depth are used by MobileNet. When compared to a network with conventional convolutions of the same depth in the nets, it dramatically lowers the number of parameters. Deep neural networks that are lightweight are the outcome of this (Fig. 3). Two processes result in a depth-wise separable convolution. Convolutions are depth-wise and point-wise.

4 Experimental Analysis The model is designed to classify X-ray images whether the patient condition is normal or affected with thorax disease. Implementation is done using Python programming language, in PyCharm Application. For training, first step is treating the data through data pre-processing which is followed by data splitting and model training. The steps involved in the implementation process are mentioned in Fig. 4. Collected dataset is pre-processed through a sequence of processes which are loading the dataset, resizing each image into a standard size of 128 * 128 dimension, and then rescaling is applied on the images. In the next step, pre-processed data is split into train and test datasets, which will now undergo training process and build a model using DenseNet and MobileNet algorithms. The model is then deployed into an application. For evaluating the built model, two parameters are considered which are accuracy and loss.

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Fig. 4 Flow of implementation

Accuracy is the measure that tells how many correct predictions were made by the model out of total number of predictions. Loss is also a measure that represents the count of incorrectly classified images in the training and testing phase. Acuuarcy =

True Positive + True Negative . True positive + True Negative + False Positive + False Negative (4)

The mathematical equation for metric accuracy is represented by Eq. (4). As mentioned above, the accuracy and loss of the two algorithms, DenseNet and MobileNet, are compared. Two models accuracy and loss values for a total of 20 epochs are displayed in Table 2. The above table can be represented in the form of graphs as shown in Figs. 5 and 6. Table 2 Comparison between DenseNet and MobileNet in terms of accuracy and loss

Model

Epochs

Accuracy

Loss

MobileNet

05

0.901

0.211

DenseNet

10

0.886

0.435

15

0.849

0.532

20

0.931

0.182

05

0.806

2.495

10

0.911

1.235

15

0.901

2.765

20

0.895

1.311

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Fig. 5 a Accuracy of DenseNet; b loss of DenseNet

Fig. 6 a Accuracy of MobileNet; b loss of MobileNet

Classification is done using two models for 20 epochs each. Table 2 represents the values of accuracy and loss for every 5 epochs. And Figs. 5 and 6 represent the graph of accuracy and loss measures for respective 20 epochs. It is observed that for 5 epochs, DenseNet has got an accuracy of 80.6% and MobileNet model got an accuracy of 90.1%. And for 10 epochs, DenseNet model has got an accuracy of 91.11% and MobileNet model got an accuracy of 88.6%. Similarly for 15 epochs, DenseNet model has got an accuracy of 90.1% and MobileNet model got an accuracy of 84.9%. And for 20 epochs, models have reached a standard accuracy range where DenseNet model has got an accuracy of 89.5% and MobileNet model got an accuracy of 93.1%. From the above observed data, MobileNet can be concluded as a better model with an accuracy of 93.1% when compared to that of DenseNet model with an accuracy of 89.5%.

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5 Conclusion and Future Scope There is currently ongoing research on using deep learning algorithms to identify thorax illnesses from chest x-rays. Thorax diseases are the severe and persistent hazard to the global population’s health. An accurate diagnosis is essential. Deep learning could speed up the diagnosis of thorax disorders, hence saving time. For the purpose of early thorax disease prediction, a powerful model is created utilizing deep learning techniques. Testing the model with several X-ray images from each class and receiving the results are further tasks.

References 1. Tishby N, Pereira FC, Bialek W (2000) The information bottleneck method. https://arxiv.org/ abs/physics/0004057 2. Taghanaki SA et al (2019) Infomask: masked variational latent representation to localize chest disease. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham 3. Ismael AM, Sengür ¸ A (2021) Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst Appl 164:114054 4. Bhandary A et al (2020) Deep-learning framework to detect lung abnormality—a study with chest X-Ray and lung CT scan images. Pattern Recogn Lett 129:271–278 5. Albahli S (2021) A deep neural network to distinguish covid-19 from other chest diseases using x-ray images. Curr Med Imag 17(1):109–119 6. Souza JC et al (2019) An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. Comput Methods Progr Biomed 177:285–296 7. Abbas A, Abdelsamea MM, Gaber MM (2021) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 51(2):854–864 8. Pereira RM et al (2020) COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Progr Biomed 194:105532 9. Khatri A et al (2020) Pneumonia identification in chest X-ray images using EMD. Trends Commun Cloud Big Data: 87–98 10. Teixeira LO et al (2021) Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images. Sensors 21(21):7116 11. Wang X et al (2018) Tienet: text-image embedding network for common thorax disease classification and reporting in chest x-rays. In: Proceedings of the IEEE conference on computer vision and pattern recognition 12. Rajpurkar P et al (2017) Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225 13. Wang X et al (2017) Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE conference on computer vision and pattern recognition 14. Kumar P, Grewal M, Srivastava MM (2018) Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs. International conference image analysis and recognition. Springer, Cham 15. Palani S et al (2020) Detection of thoracic diseases using deep learning. ITM Web Conf 32 16. Carrillo-de-Gea JM et al (2016) A computer-aided detection system for digital chest radiographs. J Healthcare Eng 2016

Comparative Analysis of Machine Learning Algorithms for Medical Insurance Cost Prediction Mahadasa Praveen, Gundu Sri Manikanta, Gella Gayathri, and Shashi Mehrotra

Abstract In the health industry, predicting medical insurance costs remains an issue. These days medical prices are very high, making it very difficult to pay for good treatment. The medical insurance cost amount must be included in financial budgets. Experts use various approaches to anticipate annual medical premium costs in the insurance industry. Usually, the erroneous prediction has a negative impact on a business’s success as a whole. The paper presents a comparative analysis of machine learning techniques, multiple linear regression, random forest, and XGBoost for predicting health insurance prices. Keywords Regression · Insurance · Machine learning · XGBoost · Random forest

1 Introduction There are various risks to people, families, businesses, assets, and properties. These dangers comprise the possibility of one’s health, wealth, possessions, or even life. The most crucial is a person’s life and health. As it is not always possible to avoid risks, the financial industry has incorporated a wide range of products that protect people and businesses from such losses by reimbursing them with money. Insurance is an instrument that diminishes or utterly nullifies the rate of damage triggered by various risks [1, 2]. The purpose of insurance, in the world of finance, is to provide financial protection and eliminate the cost of losses. Several variables that affect a person’s health impact insurance premiums. These factors are taken into account when formulating insurance policies. We may use machine learning to create a model M. Praveen · G. S. Manikanta · G. Gayathri Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, Andhra Pradesh, India S. Mehrotra (B) Faculty of Engineering, College of Computing Science and IT, Teerthankar Mahaveer University, Moradabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_68

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that can predict insurance prices considering the required factors. Recognizing the lifestyles of individuals for insurance, it is pivotal for providers of insurance plans to estimate or compute the amounts guaranteed by insurers and the amounts that consumers are required to pay. Some parameters are used to estimate these values. The overall policy changes if any factor is overlooked when calculating the amounts. Therefore, it is crucial to complete these tasks to a high standard. Machine learning can be helpful in such a situation. The machine learning model may be developed and supplied with the factors needed to calculate the premium charges. The model can accurately predict the insurance costs for the policy. As a result, labour costs and operating time can be saved, and the industry’s efficiency rises. Supervised learning is used in machine learning to make predictions depending on the information as inputs. The labels or target classes are predetermined in supervised learning. The output of a dependent variable can be anticipated using one supervised learning technique called regression, in which the values of one or more independent variables are used. Regression only has a class, target class, or dependent variable. In this study, we perform a performance evaluation of three machine learning algorithms, random forest, multiple linear regression, and XGBoost to predict the insurance premier amount. The price of insurance charges depends on no. of variables. Regression techniques include linear regression, multiple linear regression, and nonlinear regression. We utilize multiple linear regression in this study. The study makes use of the medical insurance data set [3]. The rest of the paper is organized as follows: Sect. 2 discusses related research, and Sect. 3 describes machine learning algorithms used in the study. Section 4 presents the design and methodology. Section 5 presents the experiment and result analysis, and Sect. 6 is the conclusion and future work.

2 Related Work The healthcare sector is still researching and designing machine learning (ML) models for forecasting health insurance prices [4]. A computational intelligence technique for computing the cost of health insurance uses a diverse range of machine learning methodologies, and one of them was presented in work. One essay [5] started by considering the possible effects of employing predictive algorithms to determine insurance rates. The author in [6] aimed to determine customers’ thoughts about using Twitter on medical insurance. Sentiment analysis was used to determine what people think of doctors and health insurance. The writers included API to assemble Twitter messages that contained the terms “healthcare plan” or “medical insurance” during the twentieth century of 16–17 health insurance and the American enrolment timeframe. An article by Nidhi et al. [7] presents insurance premiums depending on the health conditions of individuals. Regression is utilized to examine and equate the effectiveness of the various algorithms. The model was then scrutinized and validated against the real data to distinguish the anticipated quantity. The accuracy of these

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models was compared. The outcomes demonstrate that the techniques for gradient boosting and multiple linear regression outperform linear regression and decision trees. Gradient boosting is appropriate because, even though it has performance comparable to multiple regression, it requires much fewer calculations to reach the same performance index. Goundar et al. [8] present an ANN-based forecasting annual medical insurance system. By adapting conceptual aspects such as epoch, learning rate, and neurons in distinct layers after the ANN model was assembled, the mission is to lower the mean absolute percentage error. The annual claim amounts were forecasted using feed-forward and recurrent neural networks. Goundar et al. [8] present an ANNbased forecasting medical yearly insurance system. By adapting conceptual aspects such as epoch, learning rate, and neurons in distinct layers after the ANN model was assembled, the mission is to lower the mean absolute percentage error. The annual claim amounts were forecasted using feed-forward and recurrent neural networks. Joseph Ejiyi et al. [9] looked into a piece of insurance information using the Zindi Africa website that has been claimed via Lagos, Nigeria-based Olusola Insurance Company. The findings revealed that the collected data from Zindi revealed that insurance enterprises, owners, organizations, and each client expressed concern regarding the insolvency of insurance companies. The above problem resulted in a perceivable need to lessen management and auditing obligations while protecting the consequences of insurer insolvencies on the broader public. In this article, the author accounts for a plan to avoid insurance companies’ insolvency [10]. For predicting insolvency existed, multiple regression, logistic analysis, and recursive partitioning algorithms are used. Kumar Sharma et al. [11] aimed to create a mathematical representation to forecast upcoming prices and verify the outcomes using regression modelling. The author employed the random forest method to predict whether customers would decide to cancel their life insurance policies.

3 Machine Learning Algorithms This section describes machine learning algorithms used in the study for the experiment.

3.1 Random Forest A random forest is a collection of trees which take a numeric value. It consists of n numbers of trees where n is a number. The random forest tree grows to a maximum depth to the data. The random forest model is formed by calculating the average error of the n trees [12].

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3.2 Regression A statistical tool called regression is utilized to examine a dependent variable’s link to multiple changing variables, often named independent variables. Regression is incorporated in many diverse sectors, including finance and investing. The following are categories of regression: linear regression, multiple linear regression, and nonlinear regression. To forecast the dependent variable Z, linear regression uses one independent variable. Equation 1 provides the formula for linear regression [13]. Z = m X + c + B,

(1)

where Z X m c B

dependent variable. independent variable. slope. intercept. regression residual.

3.3 Multiple Linear Regression Multiple linear regression models use many independent variables to examine the link between independent variables and the dependent variable Y. Each independent variable is given a weight based on how well it predicts the dependent variable. These weights are also known as regression coefficients. The dependent variable Y changes accordingly, where one independent variable transfers by one while the other independent variables stay the same. Using a regression coefficient, this is quantified [14]. Equation 2 can be used to represent a multiple linear regression model with k independent variables (p1 , p2 , p3 , …, pk ) and the outcome [5] Mi = G 0 + G 1 pi1 + G 2 pi2 + · · · + G n pin + u, where i Mi G0 Pi u

1 to n observations. dependent variable. Constant value. explanatory variable. regression residual(model error).

(2)

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3.4 XGBoost XGBoost is an open-source package. It is a machine learning scalable tree boosting system [15].

4 Design and Methodology The data is first pre-processed, and then the machine learning algorithms are used to train the model. To show the effectiveness of each of the ML algorithms, we used multiple linear regression (MLR), random forest regression, and XGBoost for the experiment. Data Description The Insurance .csv [3] file from Kaggle.com is incorporated for the research. The CSV file includes seven related features (age of the person, BMI, no. of children, region, gender, smoker, charges). Every attribute helps to anticipate the insurance premiums, which are the desired variable costs. The data set is described in Table 1 (Fig. 1). The Insurance data set from Kaggle.com is utilized for the experiment in the research. The study’s goal is to estimate insurance costs based on features of the data set. The mentioned data set in Sect. 4 contains the following categorical columns, the sex, smoker, and area columns. Label encoding is used to reshape the categorical columns to numerical values since regression models only accept numerical data. Table 1 Data description S. No.

Feature name

Data description

Value

1

Age

One of the most pivotal aspects of health management is age

Int measure

2

Sex

Gender

(Male = one, female = Zero)

3

Body mass index

Recognizing the human species: unusually high or low weights with respect to height

Height to weight ratio-based objective body weight index (kg/ m2 ), ideal 18.5–25

4

Children

No. of child

Int measure

5

Smoker

Smoking condition

(Smoker = one, nonsmoker = Zero)

6

Region

Locality where he/she lives

(Northeast = Zero, northwest = one, southeast = two, southwest = three)

7

Charges

Health insurance covers medical costs

Int measure

890

M. Praveen et al.

Fig. 1 Graphical representation of study workflow

After that, testing and training data sets were generated. The data set for this study is split into 20% of testing data and 80% of training data. Additionally, the training data set is adopted to train the model and test data to evaluate the model. Every model is tested on test data after being trained on training data. We are endeavouring to foresee the variable insurance premiums affected by these elements.

5 Experiment and Results Analysis Google Collaboratory and Python 3.10 are used for the experiment. We used the R-squared measure for the performance evaluation of the model. The R-squared equation is described in Eq. (3). R - squared =

explained variance Total variance

(3)

A higher R-squared value indicates that the model’s value prediction is closer to the actual value. If the R-squared value is 1, it fits perfectly. The models utilized in this paper are multiple linear regression, random forest regression, and XGBoost regression. After being trained the model using training data, such models are then tested using test data. In terms of R-squared, Table 2 displays the models’ level of prediction accuracy. A higher value indicates that the model’s prediction is closer to the actual value. It is observed from Table 2 that the XGBoost model achieved the highest R-squared value while the multiple linear regression model achieved the least R-squared value.

Comparative Analysis of Machine Learning Algorithms for Medical … Table 2 R-squared value of the models

891

S. No.

Algorithm

R-squared value

1

Multiple linear regression

74.47

2

Random forest regression

84.1

3

XGBoost

84.2

6 Conclusions The paper presents the performance evaluation of machine learning algorithms, multiple linear regression, random forest regression, and XGBoost for medical insurance price prediction. Experimental results demonstrate XGBoost model outperformed multiple linear regression, and random forest regression. AI and machine learning can analyse and assess enormous amounts of data to speed up and optimize medical insurance procedures. Businesses can incorporate these models to quickly and accurately calculate charges, saving time, and money for the organization.

7 Future Work As the amount of digital data increases, we will integrate these models with toptier computing resources in our future work. They might be deployed onto cloud platforms later in their life cycles to handle real-time data more rapidly [16].

References 1. HDFC. https://www.hdfcergo.com/blogs/general-insurance/importance-of-insurance.html 2. Gupta S, Tripathi P (2016) An emerging trend of big data analytics with health insurance in India. In: 2016 international conference on innovation and challenges in cyber security (ICICCS-INBUSH). IEEE, pp 64–69 3. Kaggle medical insurance datasets. https://www.kaggle.com/datasets/awaiskaggler/insurancecsv 4. Kaushik K, Bhardwaj A, Dwivedi AD, Singh R (2022) Machine learning-based regression framework to predict health insurance premiums. Int J Environ Res Publ Health 19(13):7898 5. Cevolini A, Esposito E (2020) From pool to profile: social consequences of algorithmic prediction in insurance. Big Data Soc 7(2):2053951720939228 6. van den Broek-Altenburg EM, Atherly AJ (2019) Using social media to identify consumers’ sentiments towards attributes of health insurance during enrollment season. Appl Sci 9(10):2035 7. Bhardwaj N, Anand R (2020) Health insurance amount prediction. Int J Eng Res 9:1008–1011 8. Goundar S, Prakash S, Sadal P, Bhardwaj A (2020) Health insurance claim prediction using artificial neural networks. Int J Syst Dyn Appl (IJSDA) 9(3):40–57 9. Ejiyi CJ, Qin Z, Salako AA, Happy MN, Nneji GU, Ukwuoma CC, Chikwendu IA, Gen J (2022) Comparative analysis of building insurance prediction using some machine learning algorithms

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10. Rustam Z, Yaurita F (2018) Insolvency prediction in insurance companies using support vector machines and fuzzy kernel c-means. J Phys Conf Ser 1028(1):012118 11. Kumar Sharma D, Sharma A (2020) Prediction of health insurance emergency using multiple linear regression technique. Eur J Mol Clin Med 7:98–105 12. Oshiro TM, Perez PS, Baranauskas JA (2012) How many trees in a random forest? In: Machine learning and data mining in pattern recognition: 8th international conference, MLDM 2012, Berlin, Germany, 13–20 July 2012. Proceedings 8. Springer Berlin Heidelberg, pp 154–168 13. Shinde A, Raut P (2018) Comparative study of regression models and deep learning models for insurance cost prediction. In: International conference on intelligent systems design and applications. Springer, Cham, pp 1102–1111 14. Kayri M, Kayri I, Gencoglu MT (2017) The performance comparison of multiple linear regression, random forest and artificial neural network by using photovoltaic and atmospheric data. In: 2017 14th international conference on engineering of modern electric systems (EMES). IEEE, pp 1–4 15. Torlay L, Perrone-Bertolotti M, Thomas E, Baciu M (2017) Machine learning–XGBoost analysis of language networks to classify patients with epilepsy. Brain Inform 4(3):159–169 16. Iqbal J, Hussain S, AlSalman H, Mosleh MAA, Ullah SS (2021) A computational intelligence approach for predicting medical insurance cost. Math Probl Eng 2021

Correction to: IoT-Based Home Automation System Using ESP8266 Jyoti Rawat, Indrajeet Kumar, Noor Mohd, Kartik Krishnan Singh Rana, Nitish Pathak, and Rajeev Kumar Gupta

Correction to: Chapter “IoT-Based Home Automation System Using ESP8266” in: A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_53 In the original version of the chapter, the following correction has been incorporated: In chapter “IoT-Based Home Automation System Using ESP8266”, the affiliation “MAIT, GGSIPU, New Delhi, India” of author “Nitish Pathak” was changed to “BPIT, GGSIPU, New Delhi, India”. The correction chapter and the book have been updated with the changes.

The updated version of this chapter can be found at https://doi.org/https://doi.org/10.1007/978-981-99-3315-0_53

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_69

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Correction to: Determination of License Plate Using Deep Learning and Image Processing Shiva Tyagi, Riti Rathore, Vaibhav Rathore, Shruti Rai, and Shruti Rohila

Correction to: Chapter “Determination of License Plate Using Deep Learning and Image Processing” in: A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_61 In the original version of this chapter, the following correction have been incorporated: The author name “S.S.Tyagi” has been changed to “Shiva Tyagi” in Frontmatter, Backmatter and Chapter 61.

The updated version of this chapter can be found at https://doi.org/10.1007/978-981-99-3315-0_61

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0_70

C3

Author Index

A Abha Rawat, 539 Abulhail, Saif F., 45 Adarsh Srinivas, V., 589 Aditya Padir, 627 Alaa Sabree Awad, 171 Albuquerque De, Victor Hugo C., 33 Al-Faiz, Mohammed Z., 45 Aloísio Vieira Lira Neto, 33 Amit Sagu, 849 Anandkumar, K. M., 589 Anjali Singh, 539 Ankita Sawant, 677 Ankit Yadav, 833 Ankur Gupta, 33 Anurag Rana, 355 Aref Billaha, Md., 653 Arja Greeshma, 207 Arti Chauhan, 455 Aruna Varanasi, 665 Ashish Khanna, 23 Ashok Kumar Yadav, 83 Ashwani Sethi, 467 Astha Tripathi, 347 Ayush Maheshwari, 723

B Batra Neera, 147, 391 Bhaskar Roy, 653 Bholeshwar, 577

C Celina, A., 265

Chalumuru Suresh, 873 Chandan Singh, 567 Charan Abburi, 483 Chimata Meghana, 483 Choubey Dilip Kumar, 295, 641

D Debasis Mukherjee, 653 Deepak Dharrao, 627 Deepak Parashar, 369 Deepti Rani, 849 Devansh Verma, 327 Devipriya, K., 401 Devi Usha, 147, 391 Dharmesh Dhabliya, 23 Dhumane, Amol V., 677 Dinh Dien La, 253

E Eeshita Gupta, 327 Eko Hari Rachmawanto, 1

G Gayathri, V. M., 379 Gella Gayathri, 885 Gelli Sai Sudheshna, 183 Gobi, M., 95 Gonge Sudhanshu, 369, 627 Gottala Surendra Kumar, 443 Govinda Giri, 369 Gundu Sri Manikanta, 885 Gurujukota Ramesh Babu, 443

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 703, https://doi.org/10.1007/978-981-99-3315-0

893

894 H Hanna Paulose, 467 Harkesh Sehrawat, 859 Hasan, Ekram H., 171 Heem Amin, 531 Hemalatha, R., 401 Hussein, Noor A., 13

I Iqbal Singh Saini, 279 Islam, Sardar M. N., 327

J Jananee, M., 69 Jayasurya, J., 589 Joshi Rahul, 369, 627 Jyotsna Seth, 833

K Kalpna Sagar, 455 Kalyan Rayapureddy, 183 Kamaldeep Joshi, 505 Karthikeyan, P., 513 Karthikeyan, V., 513 Kartik Krishnan Singh Rana, 695 Kavitha, K., 265 Ketan Kotecha, 369 Kiranmayee, B. V., 873 Kommu Sujith Kumar, 207 KSR Logesh, 823 Kumar Indrajeet, 695, 723 Kunal Chakate, 369

L Lahib Nidhal Dawd, 1 Lakshman, K. R., 589 Le Thanh Trung, 253 Liubov Oleshchenko, 553

M Mahadasa Praveen, 885 Mamidipaka Ramya Satyasri Prasanna, 207 Manikandan Parasuraman, 117 Manu Devi, 859 Mekapati Spandana Reddy, 823 Mitali Laroia, 539 Mohammed Ahmed Jubair, 1 Mohd Noor, 695, 723 Monika, 243

Author Index Mostafa, Salama A., 1 Muhammad Noman Shafique, 497 Mukesh Kumar Ojha, 295 Mukkera Pushpa, 197 Munnangi Ashok Kumar, 117, 133 Mustafa Amer Obaid, 171

N Nallamotu Haritha, 197 Naman Pandya, 627 Nandal, P., 859 Nandana, G. M., 83 NandhaKishore, R., 225 Nashreen Begum Jikkiriya, 133 Nasib Singh Gill, 849 Navneet Kaur, 279 Neelam Sharma, 723 Neethu Mohan, 823 Neha Badiani, 755 Nihar Ranjan Roy, 455 Nikita Garg, 601 Nirmal Dagdee, 311 Nitish Pathak, 695

O Om Mishra, 369

P Paleti Nikhil Chowdary, 823 Pankaj Vaidya, 355 Patibandla Yugala, 197 Pereira, Elisabeth T., 497 Phaneendra Varma Chintalapati, 443 Pokkuluri Kiran Sree, 443 Prajwal Kadam, 677 Pramanik Sabyasachi, 23, 33 Pranav Unnikrishnan, 823 Pratyush Vats, 627 Praveenkumar, S., 33 Prayash Limbu, 807 Preeti Gulia, 849 Preeti Mulay, 369 Preeti Nehra, 737 Priyanka Kaldate, 677 Priyanka Sharma, 419 Priyesh Kanungo, 311

Q Quoc Hung Nguyen, 253

Author Index R Rabei Raad Ali, 1 Rainu Nandal, 505 Rajasekhar Kommaraju, 197 Rajeev Kumar Gupta, 531, 695, 755 Rajeyyagari Sivaram, 117, 133 Rajveer Singh, 627 Ramachandran Manikandan, 117, 133 Rani Poonam, 243, 347 Rasika Verma, 765 Ravi Saini, 505 Ravula Vaishnavi, 183 Rawat Jyoti, 695, 347 Reddy, Ananapareddy V. N., 207 Reetu Gupta, 311 Rehana Begum, 183 Rekha Gupta, 709 Ridhima, 601 Rishabh Hanselia, 641 Ritam Dutta, 653 Riti Rathore, 795 Robin Newton, 295 Rohan Gupta, 807 Rohan Sanjeev, 823 Rohit Kumar, 355 Rohit Sharma, 765 S Sandhiya, V., 379 Sanikommu Yaswanth Reddy, 197 Santosh Kumar, 295 Santosh Kumar Bharti, 531, 755 Sanyam Raina, 531 Saranya Bommareddy, 873 Saranyaraj, D., 225 Saravana Kumar, P., 513 Saroj Kumar Biswas, 665 Sasmitha, 157 Satish Kumar Kode, 443 Seeja, K. R., 539, 601 Sekaran Ramesh, 117, 133 Shaik Mahaboob Basha, 33 Sharwan Buri, 779 Shashi Mehrotra, 885 Shivam Bhardwaj, 327 Shobhit Mudkhedkar, 627

895 Shrey Sanghvi, 531 Shruthi Rao, 601 Shruti Rai, 795 Shruti Rohila, 795 Sireesha Moturi, 611 Siva Brindha, G., 95 Smit Vekaria, 755 Sneha Ananya Mallipeddi, 611 Soman, K. P., 823 Sonali Goyal, 737 Srikanth Vemuru, 611 Srishti Negi, 601 Suresh, A., 57, 69, 157 Suvarna Vani, K., 483

T Tapas Kumar, 419 Tariq Hussain Sheikh, 23 Thi Thuy Kieu Phan, 253 Thi Xuan Dao Nguyen, 253 Tirumala Rao, S. N., 611 Tyagi, S. S., 419 Tyagi, Shiva, 795

V Vadhera Shelly, 567, 577 Vaibhav Rathore, 795 Vaisshale, R., 225 Vanjipriya, V., 57 Veeraiah Vivek, 23, 33 Vidya Chellam, V., 23 Vijaya Lakshmi, K. S., 483 Vinay Chopade, 677 Vinay Sharma, 833 Vishal Shrivastava, 779

X Xuan Nam, 253

Y Yaganteeswarudu Akkem, 665 Yogesh Gupta, 747