Mobile Radio Communications and 5G Networks: Proceedings of Second MRCN 2021 (Lecture Notes in Networks and Systems, 339) 981167017X, 9789811670176

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Lecture Notes in Networks and Systems 339

Nikhil Marriwala C. C. Tripathi Shruti Jain Dinesh Kumar   Editors

Mobile Radio Communications and 5G Networks Proceedings of Second MRCN 2021

Lecture Notes in Networks and Systems Volume 339

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

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

More information about this series at https://link.springer.com/bookseries/15179

Nikhil Marriwala · C. C. Tripathi · Shruti Jain · Dinesh Kumar Editors

Mobile Radio Communications and 5G Networks Proceedings of Second MRCN 2021

Editors Nikhil Marriwala Department of Electronics and Communication Engineering University Institute of Engineering and Technology Kurukshetra, Haryana, India Shruti Jain Department of Electronics and Communication Engineering Jaypee University of Information Technology Solan, Himachal Pradesh, India

C. C. Tripathi University Institute of Engineering and Technology Kurukshetra, Haryana, India Dinesh Kumar Department of Electrical and Computer System Engineering RMIT University Melbourne, VIC, Australia

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-981-16-7017-6 ISBN 978-981-16-7018-3 (eBook) https://doi.org/10.1007/978-981-16-7018-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This conference provides a platform and aid to the researches involved in designing systems that permit the societal acceptance of ambient intelligence. The overall goal of this conference is to present the latest snapshot of the ongoing research as well as to shed further light on future directions in this space. This conference aims to serve for industry research professionals who are currently working in the field of academia research and research industry to improve the lifespan of the general public in the area of recent advances and upcoming technologies utilizing cellular systems, 2G/2.5G/3G/4G/5G and beyond, LTE, WiMAX, WMAN, and other emerging broadband wireless networks, WLAN, WPAN, other homes/personal networking technologies, pervasive and wearable computing and networking, small cells and femtocell networks, wireless mesh networks, vehicular wireless networks, cognitive radio networks and their applications, wireless multimedia networks, green wireless networks, standardization activities of emerging wireless technologies, power management, signal processing, and energy conservation techniques. This conference will provide support to the researchers involved in designing decision support systems that will permit the societal acceptance of ambient intelligence. It presents the latest research being conducted on diverse topics in intelligent technologies to advance knowledge and applications in this rapidly evolving field. The conference is seen as a turning point in developing the quality of human life and performance in the future; therefore, it has been identified as the theme of the conference. Authors were invited to submit papers presenting novel technical studies as well as position and vision papers comprising hypothetical/speculative scenarios. 5G technology is a truly revolutionary paradigm shift, enabling multimedia communications between people and devices from any location. It also underpins exciting applications such as sensor networks, smart homes, telemedicine, and automated highways. This book will provide a comprehensive introduction to the underlying theory, design techniques, and analytical tools of 5G and wireless communications, focusing primarily on the core principles of wireless system design. The book will begin with an overview of wireless systems and standards. The characteristics of the wireless channel are then described, including their fundamental capacity limits. Various modulation, coding, and signal processing schemes are then discussed in v

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detail, including the state-of-the-art adaptive modulation, multicarrier, spread spectrum, and multiple antenna techniques. The book will be a valuable reference for engineers in the wireless industry and will be extremely valuable not only to graduate students pursuing research in wireless systems but also to engineering professionals who have the task of designing and developing future 5G wireless technologies. For the proper review of each manuscript, every received manuscript was first checked for plagiarism and then the manuscript was sent to three reviewers. In this process, the committee members were involved and the whole process was monitored and coordinated by the general chair. The Technical Program Committee involved senior academicians and researchers from various reputed institutes. The members were from India as well as abroad. The technical program mainly involves the review of the paper. A total of 260 research papers were received, out of which 51 papers were accepted, registered, and presented during the three-day conference; acceptance ratio is 19.5%. An overwhelming response was received from the researchers, academicians, and industry from all over the globe. The papers were received from pan-India with places such as Guwahati, Kerala, Tamil Nadu, Raipur, Pune, Hyderabad, Rajasthan, Uttarakhand, Raipur, Ranchi, Uttar Pradesh, Punjab, Delhi, Himachal Pradesh, Andhra Pradesh, etc. The authors from premium institutes IITs, NITs, Central Universities, NSIT, PU, and many other reputed institutes participated in the conference. Organizers of MRCN-2021 are thankful to the University Institute of Engineering and Technology (UIET) which was established by Kurukshetra University in 2004 to develop as a “Centre of Excellence,” offer quality technical education, and undertake research in engineering and technology. The ultimate aim of the UIET is to become a role model for engineering and technology education not only for the state of Haryana but for the world over to meet the challenges of the twenty-first century. The editors would like to express their sincere gratitude to Patron of the Conference MRCN-2021 Prof. (Dr.) Som Nath Sachdeva, Honorable Vice-Chancellor, Kurukshetra University, Kurukshetra; Prof. Dinesh Kant Kumar, Professor at RMIT University, Melbourne; Prof. Schahram Dustdar, Professor of Computer Science and Head Research Division of Distributed Systems at the TU Wien, Austria; Dr. Utkarsh Srivastava, Western Michigan University, USA; general chairs; plenary speakers; invited speakers; reviewers; Technical Program Committee members; International Advisory Committee members; and Local Organizing Committee members of MRCN-2021, without whose support, the quality and standards of the conference could not be maintained. Special thanks to the Springer and its team for this valuable publication. Over and above, we would like to express our deepest sense

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of gratitude to UIET, Kurukshetra University, Kurukshetra, for hosting this conference. We are thankful to Technical Education Quality Improvement Program (TEQIP-III) for sponsoring the International Conference MRCN-2021 Event. Kurukshetra, India Kurukshetra, India Solan, India Melbourne, Australia

Nikhil Marriwala C. C. Tripathi Shruti Jain Dinesh Kumar

Contents

Manual and Automatic Control of Appliances Based on Integration of WSN and IOT Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Karthikeyan, Adusumalli Nishanth, Annaa Praveen, Vijayabaskar, and T. Ravi Face Recognition System Based on Convolutional Neural Network (CNN) for Criminal Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ankit Gupta, Deepika Punj, and Anuradha Pillai A Novel Support Vector Machine-Red Deer Optimization Algorithm for Enhancing Energy Efficiency of Spectrum Sensing in Cognitive Radio Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vikas Srivastava and Indu Bala

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Deceptive Product Review Identification Framework Using Opinion Mining and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minu Susan Jacob and P. Selvi Rajendran

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Detecting Six Different Types of Thyroid Diseases Using Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Mageshwari, G. Sri Kiruthika, T. Suwathi, and Tina Susan Thomas

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Random Forest Classifier Used for Modelling and Classification of Herbal Plants Considering Different Features Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priya Pinder Kaur and Sukhdev Singh EEG Based Emotion Classification Using Xception Architecture . . . . . . . Arpan Phukan and Deepak Gupta

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Automated Discovery and Patient Monitoring of nCOVID-19: A Multicentric In Silico Rapid Prototyping Approach . . . . . . . . . . . . . . . . . 109 Sharduli, Amit Batra, and Kulvinder Singh

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Estimation of Location and Fault Types Detection Using Sequence Current Components in Distribution Cable System Using ANN . . . . . . . . 119 Garima Tiwari and Sanju Saini LabVIEW Implemented Smart Security System Using National Instruments myRIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 S. Krishnaveni, M. Harsha Priya, and P. A. Harsha Vardhini Design and Analysis of Modified Sense Amplifier-Based 6/3T SRAM Using CMOS 45 nm Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Chilumula Manisha and Velguri Suresh Kumar Design and Implementation of Low Power GDI-LFSR at Low-Static Power Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Velguri Suresh Kumar and Tadisetty Adithya Venkatesh Lion Optimization Algorithm for Antenna Selection in Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Mehak Saini and Surender K. Grewal Sub-band Selection-Based Dimensionality Reduction Approach for Remote Sensing Hyperspectral Images . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 S. Manju and K. Helenprabha An Efficient Network Intrusion Detection System Based on Feature Selection Using Evolutionary Algorithm Over Balanced Dataset . . . . . . . 179 Manisha Rani and Gagandeep A Novel Hybrid Imputation Method to Predict Missing Values in Medical Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Pooja Rani, Rajneesh Kumar, and Anurag Jain Optimal LQR and Smith Predictor Based PID Controller Design for NMP System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Divya Pandey, Shekhar Yadav, and Satanand Mishra Detection of Cracks in Surfaces and Materials Using Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 R. Venkatesh, K. Vignesh Saravanan, V. R. Aswin, S. Balaji, K. Amudhan, and S. Rajakarunakaran Performance Analysis of TDM-PON and WDM-PON . . . . . . . . . . . . . . . . . 243 Raju Sharma, Monika Pathak, and Anuj Kumar Gupta Detection of Microcalcifications in Mammograms Using Edge Detection Operators and Clustering Algorithms . . . . . . . . . . . . . . . . . . . . . . 255 Navneet Kaur Mavi and Lakhwinder Kaur Analysis of the Proposed CNN Model for the Recognition of Gurmukhi Handwritten City Names of Punjab . . . . . . . . . . . . . . . . . . . . 267 Sandhya Sharma, Sheifali Gupta, Neeraj Kumar, and Himani Chugh

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Modeling and Analysis of Positive Feedback Adiabatic Logic CMOS-Based 2:1 Mux and Full Adder and Its Power Dissipation Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Manvinder Sharma, Pankaj Palta, Digvijay Pandey, Sumeet Goyal, Binay Kumar Pandey, and Vinay Kumar Nassa A Smart Healthcare System Based on Classifier DenseNet 121 Model to Detect Multiple Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Mohit Chhabra and Rajneesh Kumar An Effective Algorithm of Remote Sensing Image Fusion Based on Discrete Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Richa, Karamjit Kaur, and Priti singh QoS-Based Load Balancing in Fog Computing . . . . . . . . . . . . . . . . . . . . . . . 331 Shilpi Harnal, Gaurav Sharma, and Ravi Dutt Mishra DoMT: An Evaluation Framework for WLAN Mutual Authentication Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Pawan Kumar and Dinesh Kumar Multiword Expression Extraction Using Supervised ML for Dogri Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Shubhnandan S. Jamwal, Parul Gupta, and Vijay Singh Sen Handwritten Text to Braille for Deaf-Blinded People Using Deep Neural Networks and Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 T. Parthiban, D. Reshmika, N. Lakshmi, and A. Ponraj Two Element Annular Ring MIMO Antenna for 2.4/5.8 GHz Frequency Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Kritika Singh, Shadab Azam Siddique, Sanjay Kumar Soni, Akhilesh Kumar, and Brijesh Mishra A Secure Hybrid and Robust Zone Based Routing Protocol for Mobile Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Vishal Gupta, Hemant Sethi, and Surinder Pal Singh Multimedia Content Mining Based on Web Categorization (MCMWC) Using AlexNet and Ensemble Net . . . . . . . . . . . . . . . . . . . . . . . . 415 Bhavana and Neeraj Raheja DDSS: An AI Powered System for Driver Safety . . . . . . . . . . . . . . . . . . . . . . 429 Neera Batra and Sonali Goyal Improving Performance and Quality of Service in 5G Technology . . . . . . 439 Stuti Jain, Ritika Jain, Jyotika Sharma, and Ashish Sharma

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Machine Learning-Based Ensemble Classifier Using Naïve Bayesian Tree with Logit Regression for the Prediction of Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Arunraj Gopalsamy and B. Radha Analysis of Radiation Parameters of Kalasam-Shaped Antenna . . . . . . . . 471 Samyuktha Saravanan and K. Gunavathi Political Sentiment Analysis: Case Study of Haryana Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 Dharminder Yadav, Avinash Sharma, Sharik Ahmad, and Umesh Chandra Efficient Key Management for Secure Communication Within Tree and Mesh-Based Multicast Routing Protocols . . . . . . . . . . . . . . . . . . . . 501 Bhawna Sharma and Rohit Vaid Detection of Signature-Based Attacks in Cloud Infrastructure Using Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 B. Radha and D. Sakthivel A Modified Weighed Histogram Approach for Image Enhancement Using Optimized Alpha Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521 Vishal Gupta, Monish Gupta, and Nikhil Marriwala Spectrum Relay Performance of Cognitive Radio between Users with Random Arrivals and Departures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 V. Sreelatha, E. Mamatha, C. S. Reddy, and P. S. Rajdurai Energy Efficient Void Avoidance Routing for Reduced Latency in Underwater WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Swati Gupta and N. P. Singh Sentiment Analysis Using Learning Techniques . . . . . . . . . . . . . . . . . . . . . . 559 A. Kathuria and A. Sharma Design of Novel Compact UWB Monopole Antenna on a Low-Cost Substrate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 P. Dalal and S. K. Dhull Multi-Objective Genetic Algorithm for Job Planning in Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 Neha Dutta and Pardeep Cheema Facial Expression Recognition Using Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605 Vandana and Nikhil Marriwala FPGA Implementation of Elliptic Curve Point Multiplication Over Galois Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619 S. Hemambujavalli, P. Nirmal Kumar, Deepa Jose, and S. Anthoniraj

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Blockchain Usage in Theoretical Structure for Enlarging the Protection of Computerised Scholarly Data . . . . . . . . . . . . . . . . . . . . . . . 635 Preeti Sharma and V. K. Srivastava A Framework of a Filtering and Classification Techniques for Enhancing the Accuracy of a Hybrid CBIR System . . . . . . . . . . . . . . . . 645 Bhawna Narwal, Shikha Bhardwaj, and Shefali Dhingra Microgrid Architecture with Hybrid Storage Elements and Its Control Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 Burri Ankaiah, Sujo Oommen, and P. A. Harsha Vardhini A Robust System for Detection of Pneumonia Using Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Apoorv Vats, Rashi Singh, Ramneek Kaur Khurana, and Shruti Jain Reduce Overall Execution Cost (OEC) of Computation Offloading in Mobile Cloud Computing for 5G Network Using Nature Inspired Computing (NIC) Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679 Voore Subba Rao and K. Srinivas Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691

Editors and Contributors

About the Editors Dr. Nikhil Marriwala did his Ph.D. from NIT, Kurukshetra in Electronics and Communication Department. He did his post graduation in Electronics and Communication Engineering from IASE University, Sardarshahar, Rajasthan and did his B.Tech. Electronics and Instrumentation from MMEC, Mullana affiliated to Kurukshetra University, Kurukshetra. He is having additional charge of Training and Placement Office, UIET, Kurukshetra University, Kurukshetra and heading the T&P cell for more than 7 years now. He was also the M.Tech. Coordinator of ECE in U.I.E.T, KUK for more than 3 years. He has more than 16 years of experience of teaching graduate and post graduate students. More than 31 students have completed their M.Tech. dissertation under his guidance presently 2 students are working along with him. He has more than 30 publications to his credit in National and International Journals with 5 publications in reputed SCI and Scopus International Journals. He also has one patent published to his credit. He has also been Chairman of Special Sessions in more than 5 International/National Conferences. His areas of interests are Software Defied Radios, Wireless Communication, Fuzzy system design, and Microprocessors. Prof. C. C. Tripathi did his in Ph.D. (Electronics) from Kurukshetra University, Kurukshetra. Since 2016, he is working as a Director, University Institute of Engineering Technology (an autonomous institute), Kurukshetra University, Kurukshetra. The institute is having more than 75 faculty member, above 150 non-teaching technical, laboratory and administrative staff and above 1600 students. As a Director, he is also heading institute academic bodies like board of studies, academic council with four UG, 8 PG programs and spearheading research in various engineering and applied sciences departments in the institute. Microelectronics, RF MEMS for Communication, Industrial Consultancy are his specialization areas. He has developed Micro-fabrication R&D Lab and RF MEMS R&D lab. He is a member of more than 14 Professional Bodies. He has published more than 80 papers in conferences

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and journals. Also filled one patent. He has implemented TEQIP-II grants of Rs.10.00 Crores by preparing Institution Development Plan (IDP). Shruti Jain is an Associate Professor in the Department of Electronics and Communication Engineering at Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India and has received her Doctor of Science (D.Sc.) in Electronics and Communication Engineering. She has a teaching experience of around 15 years. She has filed three patents out of which one patent is granted and one is published. She has published more than 09 book chapters, and 100 research papers in reputed indexed journals and in international conferences. She has also published six books. She has completed two government-sponsored projects. She has guided 06 Ph.D. students and now has 02 registered students. She has also guided 11 M.Tech. scholars and more than 90 B. Tech. undergrads. Her research interests are Image and Signal Processing, Soft Computing, Bio-inspired Computing and Computer-Aided Design of FPGA and VLSI circuits. She is a senior member of IEEE, life member and Editor in Chief of Biomedical Engineering Society of India and a member of IAENG. She is a member of the Editorial Board of many reputed journals. She is also a reviewer of many journals and a member of TPC of different conferences. She was awarded by Nation Builder Award in 2018–19. Prof. Dinesh Kumar, B.Tech. from IIT Madras, and Ph.D. from IIT Delhi, is a Professor at RMIT University, Melbourne, Australia. He has published over 400 papers, authored 5 books and is on a range of Australian and international committees for Biomedical Engineering. His passion is for affordable-diagnostics and making a difference for his students. His work has been cited over 5600 times and he has also had multiple successes with technology translation. He is the member of Therapeutics Goods Administration (TGA), Ministry of Health (Australia) for medical devices. He is also on the editorial boards for IEEE Transactions of Neural Systems and Rehabilitation Engineering and Biomedical Signals and Controls. He has been the chair of large number of conferences and given over 50 key-notes speeches.

Contributors Sharik Ahmad Computer Science Department, Glocal University, Saharanpur, UP, India K. Amudhan Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, India Burri Ankaiah REVA University, Bengaluru, Karnataka, India S. Anthoniraj Department of ECE, MVJ College of Engineering, Bangalore, India V. R. Aswin Department of Mechanical Engineering, Ramco Institute of Technology, Rajapalayam, India

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Indu Bala Lovely Professional University, Jalandhar, India S. Balaji Department of Mechanical Engineering, Ramco Institute of Technology, Rajapalayam, India Amit Batra Department of CSE, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India Neera Batra Department of CSE, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed To Be University) Mullana, Ambala, Haryana, India Shikha Bhardwaj Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India Bhavana CSE Department, Maharishi Markandeshwar (Deemed To Be University), Mullana, India Umesh Chandra Computer Science, Banda University of Agriculture and Technology, Banda, UP, India Pardeep Cheema Acet Eternal University Baru Sahib, Baru Sahib, Himachal Pradesh, India Mohit Chhabra CSE Department, MMEC, Maharishi Markandeshwar Deemed to be University, Mullana, Ambala, Haryana, India Himani Chugh Chandigarh Group of Colleges, Ajitgarh, India P. Dalal Guru Jambheshwar University of Science and Technology, Hisar, India Shefali Dhingra Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India S. K. Dhull Guru Jambheshwar University of Science and Technology, Hisar, India Neha Dutta Acet Eternal University Baru Sahib, Baru Sahib, Himachal Pradesh, India Gagandeep Department of Computer Science, Punjabi University, Patiala, India Arunraj Gopalsamy Sree Saraswathi Thyagaraja College, Pollachi, Tamil Nadu, India; IBM, Bangalore, Karnataka, India Sonali Goyal Department of CSE, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed To Be University) Mullana, Ambala, Haryana, India Sumeet Goyal Department of Applied Science, Chandigarh Group of Colleges, Landran, Punjab, India

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Surender K. Grewal D. C. R. University of Science and Technology, Murthal, India K. Gunavathi PSG College of Technology, Coimbatore, India Ankit Gupta Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, Haryana, India Anuj Kumar Gupta Chandigarh Group of Colleges, Mohali, Punjab, India Deepak Gupta Department of Computer Science and Engineering, National Institute of Technology, Jote, Arunachal Pradesh, India Monish Gupta Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India Parul Gupta PGDCSIT, University of Jammu, Jammu, Jammu & Kashmir, India Sheifali Gupta Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India Swati Gupta Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, India; Panipat Institute of Engineering and Technology, Panipat, India Vishal Gupta Department of Computer Science and Engineering, MMEC, MM(DU), Mullana, Ambala, India; University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India Shilpi Harnal Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India M. Harsha Priya Department of ECE, CMR College of Engineering and Technology, Hyderabad, Telangana, India P. A. Harsha Vardhini Department of ECE, Vignan Institute of Technology and Science, Deshmukhi, Telangana, India K. Helenprabha Department of Electronics and Communication Engineering, R.M.D. Engineering College, Kavaraipettai, India S. Hemambujavalli Department of ECE, CEG, Anna University, Chennai, Tamil Nadu, India Minu Susan Jacob Department of Computer Science and Engineering, KCG College of Technology, Chennai, India Anurag Jain School of Computer Sciences, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

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Ritika Jain Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India Shruti Jain Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India Stuti Jain Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India Shubhnandan S. Jamwal PGDCSIT, University of Jammu, Jammu, Jammu & Kashmir, India Deepa Jose Department of ECE, KCG College of Technology, Chennai, Tamil Nadu, India S. Karthikeyan Department of ECE, Sathyabama Institute of Science and Technology, Chennai, India A. Kathuria Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed Too Bee) University, Haryana, India Karamjit Kaur Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University, Gurugram, India Lakhwinder Kaur Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab, India Priya Pinder Kaur Punjabi University, Patiala, Punjab, India Ramneek Kaur Khurana Department of Biotechnology, Jaypee University of Information Technology, Solan, Himachal Pradesh, India S. Krishnaveni Department of ECE, CMR College of Engineering and Technology, Hyderabad, Telangana, India Akhilesh Kumar Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India Dinesh Kumar Department of Information Technology, D.A.V. Institute of Engineering and Technology, Jalandhar, India Neeraj Kumar Chitkara University Institute of Engineering and Technology, Chitkara University, Solan, Himachal Pradesh, India Pawan Kumar Department of Computer Science, DAV College, Bathinda, India Rajneesh Kumar Department of Computer Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India N. Lakshmi Department of Electronics and Communication Engineering, Easwari Engineering of College, Chennai, India

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R. Mageshwari Department of Information Technology, KCG College of Technology, Chennai, India E. Mamatha School of Science, GITAM University, Bangalore, India Chilumula Manisha Department of ECE, Maturi Venkata Subba Rao Engineering College, Telangana State, Hyderabad, India S. Manju Department of Medical Electronics, Saveetha Engineering College, Thandalam, Chennai, India Nikhil Marriwala Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India Navneet Kaur Mavi Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab, India Brijesh Mishra Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India; Shambhunath Institute of Engineering and Technology, Prayagraj, India Ravi Dutt Mishra Seth Jai Parkash Mukand Lal Institute of Engineering and Technology, Radaur, India Satanand Mishra AcSIR & CSIR—Advanced Materials and Processes Research Institute (AMPRI), Bhopal, India Bhawna Narwal Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India Vinay Kumar Nassa Department of Computer Science Engineering, South Point Group of Institutions, Sonepat, Haryana, India P. Nirmal Kumar Department of ECE, CEG, Anna University, Chennai, Tamil Nadu, India Adusumalli Nishanth Department of ECE, Sathyabama Institute of Science and Technology, Chennai, India Sujo Oommen REVA University, Bengaluru, Karnataka, India Pankaj Palta Department of ECE, Chandigarh Group of Colleges, Landran, Punjab, India Binay Kumar Pandey Department of IT, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Digvijay Pandey Department of Technical Education, IET, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India

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Divya Pandey Madan Mohan Malaviya University of Technology, Gorakhpur, U.P., India T. Parthiban Department of Electronics and Communication Engineering, Easwari Engineering of College, Chennai, India Monika Pathak Multani Mal Modi College, Patiala, Punjab, India Arpan Phukan Department of Computer Science and Engineering, National Institute of Technology, Jote, Arunachal Pradesh, India Anuradha Pillai Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, Haryana, India A. Ponraj Department of Electronics and Communication Engineering, Easwari Engineering of College, Chennai, India Annaa Praveen Department of ECE, Sathyabama Institute of Science and Technology, Chennai, India Deepika Punj Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, Haryana, India B. Radha Department of Information Technology, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India Neeraj Raheja CSE Department, Maharishi Markandeshwar (Deemed To Be University), Mullana, India S. Rajakarunakaran Department of Mechanical Engineering, Ramco Institute of Technology, Rajapalayam, India P. S. Rajdurai Department of Mathematics, Srinivasa Ramanujan Center, SASTRA University, Kumbakonam, India Manisha Rani Department of Computer Science, Punjabi University, Patiala, India Pooja Rani MMICT&BM (A.P.), MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India Voore Subba Rao Department of Physics and Computer Science, Dayalbagh Educational Institute (DEI), Agra, India T. Ravi Department of ECE, Sathyabama Institute of Science and Technology, Chennai, India C. S. Reddy Department of Mathematics, Cambridge Institute Technology - NC, Bangalore, India D. Reshmika Department of Electronics and Communication Engineering, Easwari Engineering of College, Chennai, India Richa Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University, Gurugram, India

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Mehak Saini D. C. R. University of Science and Technology, Murthal, India Sanju Saini Department of Electrical Engineering, DCRUST Murthal, Sonipat, Haryana, India D. Sakthivel Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, Tamil Nadu, India Samyuktha Saravanan PSG College of Technology, Coimbatore, India P. Selvi Rajendran Department of Computer Science and Engineering, KCG College of Technology, Chennai, India Vijay Singh Sen PGDCSIT, University of Jammu, Jammu, Jammu & Kashmir, India Hemant Sethi Department of Computer Science and Engineering, MMU, Sadopur, Ambala, India Sharduli Department of Biotechnology, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India A. Sharma Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed Too Bee) University, Haryana, India Ashish Sharma Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India Avinash Sharma Computer Science, Maharishi Markandeshwar Deemed To Be University, Ambala, Haryana, India Bhawna Sharma Department of Computer Science and Engineering, MMEC, MM (Deemed To Be University), Mullana, Ambala, India Gaurav Sharma Seth Jai Parkash Mukand Lal Institute of Engineering and Technology, Radaur, India Jyotika Sharma Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India Manvinder Sharma Department of ECE, Chandigarh Group of Colleges, Landran, Punjab, India Preeti Sharma Department of Computer Science, Baba Mastnath University, Rohtak, Haryana, India Raju Sharma Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India Sandhya Sharma Chitkara University Institute of Engineering and Technology, Chitkara University, Solan, Himachal Pradesh, India

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Shadab Azam Siddique Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India Kritika Singh Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India Kulvinder Singh Faculty of CSE, Department of CSE, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India N. P. Singh Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, India Rashi Singh Department of Computer Science Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India Sukhdev Singh M.M. Modi College, Patiala, Punjab, India Surinder Pal Singh Department of Computer Science and Engineering, MMEC, MM(DU), Mullana, Ambala, India Priti singh Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University, Gurugram, India Sanjay Kumar Soni Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India V. Sreelatha School of Science, GITAM University, Bangalore, India G. Sri Kiruthika Department of Information Technology, KCG College of Technology, Chennai, India K. Srinivas Department of Electrical Engineering, Dayalbagh Educational Institute (DEI), Agra, India V. K. Srivastava Faculty of Engineering, Baba Mastnath University, Rohtak, Haryana, India Vikas Srivastava PSIT, Kanpur, India; Lovely Professional University, Jalandhar, India Velguri Suresh Kumar Department of ECE, Maturi Venkata Subba Rao Engineering College, Hyderabad, Telangana, India T. Suwathi Department of Information Technology, KCG College of Technology, Chennai, India Tina Susan Thomas Department of Information Technology, KCG College of Technology, Chennai, India Garima Tiwari Department of Electrical Engineering, DCRUST Murthal, Sonipat, Haryana, India Rohit Vaid Department of Computer Science and Engineering, MMEC, MM (Deemed To Be University), Mullana, Ambala, India

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Vandana University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India Apoorv Vats Department of Computer Science Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India R. Venkatesh Department of Mechanical Engineering, Ramco Institute of Technology, Rajapalayam, India Tadisetty Adithya Venkatesh Department of ECE, Maturi Venkata Subba Rao Engineering College, Hyderabad, Telangana, India K. Vignesh Saravanan Department of Computer Science and Engineering, Ramco Institute of Technology, Rajapalayam, India Vijayabaskar Department of ECE, Sathyabama Institute of Science and Technology, Chennai, India Dharminder Yadav Computer Science Department, Glocal University, Saharanpur, UP, India Shekhar Yadav Madan Mohan Malaviya University of Technology, Gorakhpur, U.P., India

Manual and Automatic Control of Appliances Based on Integration of WSN and IOT Technology S. Karthikeyan, Adusumalli Nishanth, Annaa Praveen, Vijayabaskar, and T. Ravi

Abstract As indicated by the Internet of Things, the future home the purported Smart Home, will be a consistent mix of actual savvy objects, interfacing, among them and with the general climate. Furthermore, since handsets are often used to manage certain aspects of people’s lives, the capability to power and monitor Smart Households from they will become a requirement. The justification for the home automation system is to monitor the limits such as voltage, current, and temperature using remote network architecture that operate on a screen. The main aim is to reduce a smart condo’s excessive energy consumption. It aids with in improvement of controlling organisation introduction. The aim of its project is to design a wellthought-out intra smart home system that allows the consumer to monitor all of their electric and electronic devices from every other Mobile. This project’s use includes features such as screen surveillance, light and fan power, fire warning, and greenhouse service. The detectors are linked to the Pic microcontroller, which sends the sensor’ position to the email address. The Arduino is used to interfere with device, and Wlan is also connected to the Arduino to have a Domain name from either an adapter. With the use of WSN, this research framework provides a solution for providing an extremely accurate monitoring and scheduling position of current state of gear. Keywords Home Automation · Arduino · Bluetooth · WSN · Smartphone

1 Introduction A Wireless Sensor Network (WSN) is a centralised organisation made up of dispersed and automatic modules that monitor material or chemical concentrations using sensors. The WSN arrangement is created by combining hubs or self-administering with a gateway and toggle. Household computerization or flat designs, according to some reports, may use resources more effectively than conventional systems. As a result, a few researchers have advocated for the use of remote monitoring to reduce S. Karthikeyan (B) · A. Nishanth · A. Praveen · Vijayabaskar · T. Ravi Department of ECE, Sathyabama Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_1

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energy consumption. In the drafting, also suggested clear housing assemblages get the WSN (Wireless Sensor Network) [1] as a manifestation of ineffable creation Because of its flexibility and long battery life, the WSN has indeed been widely seen in controller and inspecting implementations that instead Wi-Fi. Humans differ from most other animals in that they have special supervisory powers, which necessitate proper details and knowledge of history or rather its general situation. Monitoring systems are used to obtain data through structures and their average soil characteristics in a motorised or dynamic device to reduce the energy acquiring [2]. Sensor is a sensor which can augment or replace a person’s Sight, hearing, taste, smell, and interaction are the 5 students. For use of a master device network is shown to be versatile and effective inside a variety of situations. even a variety of settings, including domestic digitalization, manufacturing engineer, and control threats, among others. A far-flung mobile device [3] uses spatiotemporal confiscated free devices to measure and regulate body temperature, voltage, and budget constraints. Because with the abundance of high straightforwardness and ease by laptop connectivity and internet, the remained of domestic digitalization has been rising amazingly since later last year. A mobile electric drivetrains system connects electronic components in a residence. The methods used in household distributed computing are similar to those used in housing industrialization and urbanization and management of local tasks such as smart tv setup, pot plant including yard planting, and pet care, among other things. Devices can be connected by a personal organise and licenced for web connection from web to experiences related from a PC. The use of technology advancements and management structures to reduce human labour is referred to as a product advancement method. The rapid advancement in technology encourages one to use cells to power home equipment in an indirect manner. Computerized devices have the potential to operate with agility, dedication, and a low rate of errors. Home scientists and home computer partnerships, the bolster method is a huge problem. If the client lies and outside the house, a consumer pneumatic tires culture is built on automation operating intelligent home gadgets. Home computer technology allows a person to monitor various aspects of their home usually. A personal computer is a system or tool designed to perform a particular function, usually a mechanical unit for home use, such as with a refrigerator.House digitization combines the monitoring of lighting, temperature, equipment, and different models to provide offers a feature, functionality, efficiency, and protection. Household mechanisation may be a replacement for formal thinking for hampered and elderly people. Household digitalisation or construction urbanisation, combined with the fuel concept, makes living in today’s world incredibly easy. It includes personalised control over all electrical and electronic devices in the household, as well as disorders can lead via long-distance messaging. This arrangement allows for unified monitoring of lighting, water heating, sound/video facilities, surveillance mechanisms, kitchen machinery, and all other remaining devices used in homes frameworks. Canny home can be defined as a “propelled bedroom” because it combines power of devices with vision. Home People with advancement deficiency, the elderly, having poor vision, sensory people who are blind, and mentally impaired human beings all need an electronic device. a large number of individuals One of it’s primary goals of sharper

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households is to reduce energy consumption. Smart systems can be used in a controller to accomplish this impartiality. Furthermore, led lighting management systems should take signature light into account (daylight). As a result, a few studies have shown that in industry or administrative structures, daytime will stand in for mainly lighting control. Scanners and fast controls use light to reduce the amount of energy used to operate external light [4], ensuring that a room is adequately illuminated. Despite numerous suggestions for intelligent street lights for fuel savings in beautiful houses, a precise electric lighting framework of strong fearlessness and assist in ensuring is yet to be uncovered. The current arrangement’s main goals are to broaden the scope of automatic monitoring considerations and reduce the effect of a remote barrier for connected home structure [5] on the WSN collecting data interface. A large number of narrow reach peer far away sensor line segments make up a dispersed sensor. By merging two different forms of advancement, such as converter and long-distance connectivity. Before submitting results to the device, WSN [6] notices, receives, and manages paper material in its concealing region. The WSN is commonly used in manager and tracking apps due to its flexibility and light weight. WSN in a genius home cooperative [3] consists of confiscated free gadget with extensive applications, such as surveillance, military tracks, and intelligent motorised vehicles.Every unit with in organization in WSN for connected home [7] is freed from various places; they are power energised, small in scale, and have good indicator. Via a visual functional tissue, a brilliant home current controller that used a far-off network system was already developed for healthy products in this article. It will, in turn, be realised without the involvement of customers in the future. As a result, it is simple to securely monitor and manage the insightful household using web-based technologies.

2 Related Work Pawar et al. [1] have designed a Home Automation System concept that includes the Arduino ATmega328 microchip, a Wi-Fi device, and motion sensors. The CPU is the core computer, which communicates the with Wi-Fi unit and takes requests to view monitor computers.The steelworker manages the communication between programme and the chip, as well as the buyers and the creations. The platform for the items conversation system is an Android device that establishes a connection for the client to communicate only with processor. The proposed setup involves a staff, a client, several contact networks, that are all managed by connection tools. Choudhary et al.[2] have suggested a flexible and wireless home mechanisation device that utilises Wi-Fi technology to link its dispersed instruments to a homes framework labourer. In this article, an Arduino Uno R3 microcontroller and a connecting wires Voltage regulator are being used to power a variety of equipment and programming and other devices with a large amount of energy. PIR meter, when PIR-based technology locaters are used to discern enhancement of humans, pets, or different posts, thermocouple, etc. for testing heat and moisture content, design

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identifiable evidence, video seeing, etc. are among the different sensors used during device structure. (5). Sagar et al. [3] Assign probabilities to build a ringed home coordinations device using an Arduino Microcontroller pic microcontroller with a simple Wi-Fi card slot, in addition to the serial communication sensors (sensors) that is used. As a result, the Galileo serious progress functions as a network specialist, allowing the development layout to be passed from web programmes on any main Computer in a comparable LAN using journeyman carpenter IP and from any PC or network globally using Network IP WiFi technology is the organisation organisation used in this article. Thermocouple and moisture, construction transparency, fire and smoke field, light scale, and on/off function for multiple devices are all included in the proposal components. [8] Pooja et al. [4] The aim of the External Keyword Powered Consumer Products Monitor Progress Program was to analyse data from Micro controller, initialise the Hdtv and Arduino show, and display the circumstance the with physical pressures on the LCD. The machine is familiar with the divider’s wiring clicks. And using direct current devices, the danger of dangerous electrocution may be held to a minimum. The design employs two user interfaces, one for the PC the other for the Device. This GUI may be used to determine the status of the creations, such as whether they are on or off. Any time the story with the hardware shifts, a quick suggestion appears here on GUI. Only after Device’s Wireless is connected to the PC’s Hdmi, the screen GUI will most likely act as a professional to advance or transfer any info to both the Mobile and the main power pad. If the Network card between both the PC or PC and the power fence fails, connection may be s right by connecting via USB. The person can control and operate the devices using IOT of any mobile location on the network. Dhakad Kunal et al. [5]. A system’s components are divided into three types: PCB, toughness processor, and Microcontrollers controller. On the PCB, the LPT port, transistors, and diode shunt resistor are all connected. They connected two gadgets to the PCB, such as a machine and a lamp. The Arduino and the dampness transmitter are connected. It can also tell the difference between temperature and moisture. PC is connected to Arduino and the PCB Arduino and PCB can connect along others through the Window.They measured the mass and also the amount of dust in the atmosphere. They have just a fixed time when temperatures but moisture are accurately resourced. It consistently services temperature and tensed muscles after at regular intervals throughout the screengrab. Predilections (a) Increases safety by controlling appliance (b) Obtains the home by using web access Increment usability by regulating the temperature (c) loses time (d) reduces costs and adds relaxation (e) allows computers to be regulated while the user is away [8]. Santhi et al. [6] captures how the innovations and correspondence production are used to achieve a company’s home medicine expenses or information technology. Rate focused on strewn data registration assist in connecting with matters encompassing all because one can know that it is simple to get to something at any moment and in any location in a simplistic manner with beautifully portrayed line stuffAs

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a result, cloud will most likely serve as a gateway to IoT. The inspiring opportunities for constructing the organisation and connection of mobile gadgets for homes advancement reasons to the internet that are still accessible. Jain.Stevan Maineka et al. [7] depicts The Internet of Things (IoT) is a case inside which objects, species, entities were granted specific identity as well as the ability to send data over the network without the need for a person link.The term ‘Web of Things’ refers to a variety of capabilities and investigative tools that allow the Net to delve into the current reality of actual items. Summary the furthest contacts, RFID, unreferenced and far smart cities (WSNs), that are all a component of the Internet of Things (IoT). The use IoT technology and then a large network to digitalize the writing activities greatby integrating basic digital units to it, and the devices are operated sufficiently indirect via the site. This report covers the potential of WSN, IoT, and connected home architecture.

2.1 Existing System The Bluetooth, ZigBee, and Snapdragon software are currently used for home automation. The system necessitates a time-consuming and expensive powered installation as well as the use of an end PC. Gestures are used to organise a home control system. The controller transmits hand gestures to the system using a glove. A cell phone remote controller for office automation. The system differs now that all communications are conducted over a specific phone system rather than over the Network. Any mobile that supports double tone different re - occurrence can be used to access the system (DTMF). Our project’s current setup does not access the internet of Things concept to power home appliances. This is fitting for modern technologies that allow you to monitor devices anywhere on the globe. As a result, we’ve decided to use IoT engineering as a basis for our connected home project.

3 Proposed System The suggested plan inside this paper is not identical to the one of regular WSNbased superb residences [10], that are using WSNs to pass controlling order for homes working frameworks. The main motivations for the designed antenna are to raise awareness of a clear home remote controller and to reduce the effect of faroff resistance on the WSN [11] innovation for a sharp layout and data collection device. The proposed platform, which is intended for protection and to screen the, consists primarily of an Arduino software and a meter. To monitor the hardware, Microcontroller board is connected to the internet through the Modulated signal Killing (MODEM) gui. When an individual enters the house and the PIR sensor detects them, it sends out a different task to regulate, such as turning on the light and fan. We may use the Wearable technology to control domestic electrical equipment

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Fig. 1 Overview of the Proposed System

such as light bulbs, fans, and engines (IOT). For identifying metal for identifies cheat, the product tracking device is used. If some unknown party comes into another phone app zone, it would be personal to the property owners. Following that, a water reservoir is used to assess the total available in the drain pipe. LDR is used to naturally switch on the lighting in the evenings and turn off lights all through the day (Fig. 1). The A PIR sensor can also be placed above the hospital’s head so that when he wakes up, the switch in the police station turns on, alerting the hospital personnel. The LDR sensors measure illumination from inside hospital and controls the lighting in the corridors and other areas. The metal detector sensor can be placed in the doctors office so that if someone approaches the room playing metal ions, the bomb sounds off and the doctors and nurses is notified.

4 Hardware Implementation 4.1 Aurdino UNO Arduino Uno Arduino is a device that allows yourself to appear well-intentioned when controlling a larger portion of the adult situation beyond your computer. It’s an active real-time computing platform based on a simple microcontroller module, as well as a development environment for writing programs for the system. The ATmega328 is used in the Arduino, which is a microcontroller. A USB connection, a force jack, a Honest view, and a 16 MHz clay transformer light switch are among the 14 computerised feedback pins (of which 6 can be used as PWM returns). It includes anything needed to assist the processor; basically, attach it to a PC through USB or compel that with an Ond cable or battery to get started.“ Arduino is a real-time recording level that is open-source. The Buying framework is built on a basic so I also

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row and an enrichment layout.Microcontroller can be used to make self-contained connected devices. Otherwise, it could be linked to the computer’s coding.“ A real Input/Output (I/O) board with such a configurable Embedded System (IC) (Figs. 2 and 3).

Fig. 2 Aurdino UNO

Fig. 3 Hardware Aurdino UNO

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Fig. 4 Bluetooth Hardware

4.2 Bluetooth Bluetooth HC-05 module The HC-05 The Bluetooth module is also used to connect between Arduino Ide and a device over a distance.The HC-05 is a slaves device that runs on 3.6 to 6 V of fuel. State, RXD, TXD, GND, VCC, and EN are the six pins. Assign the TXD pin of the Bluetooth enabled device HC-06 to RX (pin 0) of the Arduino Board and the RXD pin to TX (pin 1) of the Arduino Uno for equipment. Adriano’s connection with the Bluetooth (BT) unit is sketched forth (Fig. 4).

4.3 PIR A PIR tracker senses activity by detecting the heating emitted by a living body. This are often mounted on security cameras so that they can switch on as soon as an individual approaches. They are incredibly convincing when it comes to upgrading home surveillance systems. The sensor is turned off because, but instead of sending lighting or metastasis, it really should be obstructed. by a passing individual to “sense” The person, the PIR, is sensitive to the cosmic rays emitted by all living things (Figs. 5 and 6). At an intruder walks into field of view of the monitor, the tracker “sees” a sudden increase in thermal energy. A PIR sensor light is expected to turn on as someone

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Fig. 5 PIR

Fig. 6 Hardware PIR

approaches, but it won’t react to someone passing. The lighting is set up in this manner. The spatial production of the PIR sensor is obtained in Fig. 7, and also the operational amplifier of the PIR sensor is obtained in Fig. 7. The PIR sensor is primarily used only for action track and to reduce power usage. When an individual walks into the room, the color will change on, and the transfer function will be produced and in PC, as seen in Fig. 6.

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Fig. 7 PIR Sensor output

4.4 Metal Detector Metal A locator is an automated visual depiction of metal in the immediate vicinity. Metal detectors are useful for locating metal speculations hidden inside objects or metallic articles deep underground.They routinely involve a handheld unit with a sensor This is a test that can then be run on the land or on different posts. A shifting sound in ear buds or a needle proceeding forward a pointer appear if the indicator steers against a hunk of iron. The device frequently provides a sign of location; the closest the metal is to the headphones, the louder the sound or the further the device goes. Set “walk around” iron reminders used for security bug at ways in confinement workplaces, civic centres, and airports to detect rotating steel arms on a part of the body are also another basic kind. As the LC circuit, which really is L1 and C1, receives a dissonant repeat on any material nearby, an electromagnetic field is generated, and causes a stream to flow up and down and alters the sign direction through twist. In comparison to the Circuit, a reference voltage is often used to adjust the togetherness indicator. It is far more smart to verify the distance as there is a circle and no strategy for dealing the with material. Right LC circuit may have altered sign as the metallic is seen. The modified sign is sent to the proximity indicator (TDA 0161), which will detect the shift in the flag and react accordingly. The attract system yields 1 mA when no object is observed, and around 10 mA once the circle is near to the earth. Once the express trigger is high, the resistor R3 can give a reference voltage to silicon Q1. Q1 will indeed be

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switched on, and the driven will shine, as well as the doorbell. The regulator r2 limits the current channel (Figs. 8, 9 and 10). The The real out of the metal detector sensor is seen in Fig. 11 above, while the voltage signal of the metal detector sensor is shown in Fig. 11. The metal detector is mostly used to locate metal and for safety purpose. When object is detected and also an alarm sounds, the display can be seen in Fig. 10 and also the sensor signal received in a PC is seen in Fig. 12.

Fig. 8 Digital sensor PIR output

Fig. 9 Digital sensor PIR

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Fig. 10 Hardware of Metal Detector

Fig. 11 Output of Metal Detector

4.5 LDR Sensor A A heat variable variable, also known as a photographic diode, colour resistor (LDR), or photoconductor. A picture resistor’s opposing decreases as the event illumination voltage increase, indicating photoelectric effect. In light-sensitive identifier

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Fig. 12 Digital Output of Metal Detector

circuitry and glow dim activated sharing protocols, an image regulator may be used. A image resistor is produced of a transistor with a high resistance. A image resistor can get an obstruction as high as some few mega ohm (M) in the dark, in the sun, it can have a distortion as low as very few hundred ohms. If the incidence light on a photo sensor exceeds a predetermined occurance„ photons consumed by the semiconductor provide enough energy for bound electrodes to jump into the conduction band Following that, the charge carriers (and their opening companions) guide force, lowering conflict. The distortion scope and assorted variety of a picture blocker can vary greatly between different devices. In fact, fascinating photo transformers can react differently to photons within particular frequency categories (Figs. 13 and 14).

Fig. 13 LDR Sensor

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Fig. 14 Hardware of LDR Sensor

The visible out of the LDR sensor is shown in Fig. 15, or the operational amplifier of the LDR sensor is shown in Fig. 16. The LDR sensor senses light output and turns on the lighting so when intensity of light in it is extremely low, as seen in Fig. 15, which is great for students who could really toggle on the lights at a certain time. Figure 16 depicts the display screen achieved in a Device.

Fig. 15 Output of LDR Sensor

Title Suppressed Due to Excessive Length

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Fig. 16 Digital output of LDR Sensor

4.6 Water Level Sensor level Neurotransmitters discern various oils and fluidized bed particles such as blends, ridged materials, and powders with a higher solid body. Level sensors can detect the level of free-flowing liquids, as their name suggests. Fluids include water, grease, mixtures, and other emulsions, as well as solid particles of cement matrix, are included in those materials (solids which can stream). This compounds can, in general, Because the force, they become at balance or in storage facilities, maintaining their amount in the closed condition. The level of a sensor is measured against a standard index (Figs. 17 and 18). The visible output of the water level sensor is obtained in Fig. 19, and the voltage signal of the water level sensor is obtained in Fig. 20. A water level sensor is primarily used it for detecting water level and motor switch, as seen in Fig. 19, and indeed the voltage signal obtained is observed in Fig. 20.

4.7 Relay Module A leg is an electrically driven mechanism that could be turned down the brightness. This device is used in the field of force coverage. This is applicable to all AC and DC systems. This device is small, consistent, and secure. It regulates an input torque. The turn component is depicted in Fig. 22. The first section is the genius transfer apparatus, which is connected to the existing wiring of a machine tools in the home, such as a roof cooler and lights to obtain electricity. This unit will significant contributions

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Fig. 17 Water Level Sensor

Fig. 18 Hardware of Water Level Sensor

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Title Suppressed Due to Excessive Length

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Fig. 19 Water Level Sensor Output

Fig. 20 Digital output of water level sensor

from the lifestyles and unrelated home stocks that are linked to the action module. With a 5 V amplifier style Dc power Wi-Fi connection, it is 240 VAC to turn from (AC) to (DC). The hand-off component’s potential as an usual switch “ON” and “OFF” will toggle a light on and off. An ambient recognizing system includes an ultrasonic camera as well as a switch module and an Arduino Wi-Fi connection. Wi-Fi is an RF module with a lot of features that can be used on a central network

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Fig. 21 Relay Module

system. The IEEE 802.15.4 standard greatly reduces the amount of code required to ensure knowledge exchanges. Aside from its data warehousing capabilities, Wi-Fi has a slew of other advantages for use with a WSN. (#13) (Fig. 21).

4.8 GSM For The GSM protection platform sends immediate messages based on the sensors used in this module. If the PIR sensor detects something out of the norm, the server will be notified immediately. This indicates that there is someone close to the house who is not the customer. With the aid of a fire detection, the GSM also plays an important role in the fire alarm system. When smoke is detected, the guide dog a sms alert via GSM.

Title Suppressed Due to Excessive Length

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Fig. 22 Relay Driver Hardware

5 Conclusion Proposed kit is a practical method for partnering with your house. It also has a management structure that is used for interaction. It improves the intelligence and efficiency of household partnerships. An structure and production concept for a fantastic home advancement device based on the Arduino microcontroller board is published in this research schemes. The microcontroller board is connected to the sensing. Due to any signals received from similar sensors, the residential mechanically installations can be tested, managed, and accessed normally. The device is reliant on electricity. The partnership will be terminated and SMS ready restrictions will be lifted if the power source falls short of expectations. For defence productivity, every power centrepiece energises the security mechanism. It is not able to achieve the framework without a security framework. Any base cost may even be induced in the conceptual proposal for dream home or buildings. It alters the problem of establishing trading center in WSNs and expands the effect of far-flung blocks. The suggested system was said to be a foundational, monetarily informative, and adaptable system.

References 1. Pawar, Nisha P.Singh, , “A home automation system using Internet of Things”, International Journal of Innovative Research in Computer and Communication Engineering, Vol.4, Issue 4, (2016).

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2. Vinod Choudhary, Aniket Parab, Satyajeet Bhapkar, Neetesh Jha, Ms. Medha Kulkarni, “Design and Implementation of Wi-Fi based Smart Home System”, International Journal Of Engineering And Computer Science, Volume 5, Issue No.2, (2016). 3. Vinay Sagar K N, Kusuma S M, “Home Automation using Internet of things”, International Research Journal of Engineering and Technology (IRJET), Vol. 2, Issue 3, (2015). 4. Pooja N.Pawar, Shruti Ramachandran, P., Varsha V.Wagh4, “A Survey on Internet of Things Based Home Automation System”,International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 1, January (2016). 5. Dhakad Kunal1, Dhake Tushar2, Undegaonkar Pooja3, Zope Vaibhav4, Vinay Lodha5,” Smart Home Automation using IOT”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 2, (2016). 6. H. Santhi, Gayathri.P , “A Review of Home Automation using IoT Applications”, International Journal of Computer Science & Engineering Technology, ISSN : 2229–3345 Vol. 7 No. 07, (2016). 7. Prof S A Jain.Stevan Maineka, Pranali Nimgade, “Application Of IoT-WSN in Home Automation System: A Literature Survey”, Multidisciplinary Journal of Research in Engineering and Technology, Volume 3, Issue 1, Pg.916–922, (2017). 8. Gwang jun kim,Chang soo jang,Chan ho yoon,Seung jin janz and jin woo lee, “The implementation of smart home system based on 3G and zigbee in wireless network system” International Journal Of Smart Home,vol.7no.3, pp311–320, (2013). 9. Prof Andrea goldsmith “Wireless sensor networks technology for smart buildings” for Funded Project Final Survey Report In Precourt Energy Efficiency center, pp1–5, (2017). 10. Jayashri bangali and Arvind shaligram (2013) Design and implementation of security systems for smart home based on GSM technology. International Journal Of Smart Home 7(6):201–208 11. Anuja P and Murugeswari T “A novel approach towards building automation through DALIWSN integration”, International Journal Of Scientific & Research Publication, volume 3, Issue 4, , pp1–5, (2013). 12. Firdous kausar, Eisa al eisa and Imam Bakhsh, “Intelligent home monitoring using RSSI in wireless sensor networks”, International Journal of Computer Networks and Communications, vol.4.no.6, pp33–46, (2011).

Face Recognition System Based on Convolutional Neural Network (CNN) for Criminal Identification Ankit Gupta , Deepika Punj, and Anuradha Pillai

Abstract In this research paper, we give emphasis on the face recognition system (FRS) and the evolution of the various approaches adopted for FRS by making use of the CNN model. From recent study, we can conclude that recognized face recognition systems (FRS) were easily attacked and tricked by using faked images of the person whom they are targeting obtained from various social networks like Facebook, Instagram, Twitter, etc. The main task which is involved in image classification is of extracting important features. By using the important features of image processing methods like scaling, thresholding and contrast enhancement which is based on deep neural networks, the face recognition system can classify the results more efficiently and achieve high accuracy. This paper gives emphasis on extracting various important features required for face recognition using a CNN model and gives a brief overview of various techniques employed by researchers in this area. In this research, we have used Flickr-Faces-HQ Dataset (FFHQ) and test images mixed with some real time images captured from camera devices like CCTV, mobile camera, and when appropriate match is found, it gives information according to the matched faces. Accuracy obtained by using this CNN model using Google Colab is 96%. Keywords Artificial intelligence · Cloud computing · Convolutional neural network · Criminal identification · Deep learning · Face recognition

1 Introduction There is a significant need for high security due to accumulation of data and information in surplus amount. Face recognition system intent to recognize people in videos or images captured by surveillance cameras using pattern recognition methods and techniques. In recent times, face recognition system has been flourishing, demanding and fascinating area in various instantaneous applications happening throughout the A. Gupta (B) · D. Punj · A. Pillai Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, Haryana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_2

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world. In last decade, enormous number of face recognition algorithms have been developed for recognizing faces. The FRS is an immensely difficult task because of various aspect varying within the personalized to natural circumstances. An individual can formulate contrasting aspect of face by making adjustments to the expression, facial hair, cap, haircut, spectacles, etc. However, due to various obstacles such as bad lighting condition, illumination, occlusions, falsification of faces, difficult pose, the capturing devices such as CCTV camera and mobile camera are not able to click or capture good quality face images. In spite of this fact, the face recognition system has an immense prospective to automate various applications in real life scenario. During current scenario, the government agencies of various countries such as USA, China, India and Singapore are functioning on the expansion of FRS to counter terrorism. Authentication systems of the majority of various government agencies and offices are constructed on soundness of the information that are established on the physical and behavioral surroundings known as biometrics. This system extracts invaluable features by processing the data which is not treated such as fingerprint, eyes, face, etc. The attribute characterizes the data essence which is allocated to this system and forms a conclusion relative to it. Automated face recognition systems are extensively used in applications varying from social media platforms to futuristic authentication systems. Due to gap between the subjective emotions and the hand-crafted features, it is very difficult task to recognize facial expressions or impressions in video sequences. In this paper, we emphasize on various algorithm needed for face recognition and detection problems. Face recognition modus operandi amidst all biometric techniques have one significant advantage, which is user-friendliness and adaptable nature. The computer algorithm which is related to the facial recognition system is alike human visual recognition. But if persons accumulate visual data in the brain and instinctively recollects visual data once required, computers should also call for data from a database stored in the system and match that data so as to recognize a human face. In a concise manner, an automated computer system furnished by a capturing device like camera or CCTV recognizes and detects a person face, draw out facial attributes such as the nose length, distance amidst both eyes, shape of the forehead and cheekbones. The most significant assessment for face recognition systems programs is the width of nostrils, the distance amidst both the eyes, the length of nose, the shape and height of the cheekbones, the height of the forehead, the width of the chin and other parameters. The individual is picked out if the parameters coincide of the data obtained from the capturing devices compared with those accessible in the database stored in the cloud. CNN could precisely put in the authentic image, such a way that the preprocessing of image turns out to be easy. CNN employs the approach of confined sensing field, pooling and sharing of weights to immensely minimize the parameters required for training. Simultaneously, CNN also has translation of image-to-image, translation

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Fig. 1 Face recognition system

and rotational invariance. In deep learning, the CNN is exclusively implemented toward computer vision which includes both object recognition and image classification. Before CNN was broadly accepted, most of the pattern recognition jobs were accomplished by virtue of the inceptive phase of manually extracting of the features and classifier. Nevertheless, the development of CNN entirely brings changes to pattern recognition. In place of making use of a conventional methods such as feature extraction, the CNN makes use of input image—unprocessed pixel strength similar to flat vector. Predominantly, CNN has outstanding transforming capability for 2D data, for instance image, voice. The CNN basically contains a stack of modules, all of them are performing three functions: convolution, ReLU and pooling (Fig. 1).

1.1 Convolution This draw out tiles of the featured input map, where as to evaluate features—filters are used, by using this convolution layer output feature maps are produced. This produced output is also known as convolved feature (that might possess a contrary depth, size in comparison to input feature map). Following are the parameters of convolutions (Figs. 2 and 3): • Tile size (5 * 5 or 3 * 3 pixels). • Output feature map depth.

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Fig. 2 3 × 3 convolution containing depth 1 is implemented across a 5 × 5 input map with depth 1. This convolution results in 3 * 3 output feature map

Fig. 3 It shows how value is calculated on output feature map over 5 × 5 input feature map

1.2 ReLU After convolution comes a ReLU step, which is used to transform the convolved features, which is used to present the nonlinearity within the model. The rectified linear activation function which is F(x) = max (0, x) gives result x for every values of x greater than 0 as well as it returns zero for every values of x less than or equal to 0.

1.3 Pooling This step comes after ReLU step; in this, the convolved features downsampling takes place (minimizing time required for processing), which in turn lessen the feature map dimensions amount. Max pooling algorithm is used for this process.

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Fig. 4 Max pooling operation

Max pooling is managed likewise convolution. In this, we move on the feature maps along with extracting tiles of the given size. Therefore, the maximal value results in the new feature maps for every tile; after that, every values other than that are rejected. The working of max pooling utilizes two parameters which are as follows: • First parameter of pooling is max pooling filter sizes. • There is also second parameter in pooling known as stride which is the distance between each tile extracted in pixels. In contrast to convolution at which filters move pixelswise on the feature maps, the location at which every tile will be extracted in max pooling will be decided by stride. Stride of 2 decides the work that will be performed by the max pooling that is it will extract every nonoverlapping 2 * 2 tiles from feature map for a 2 * 2 filter (Fig. 4).

2 Literature Survey 2.1 Uchôa, Britto, Veras, Aires and Paiva [1] They proposed a method by applying data augmentation throughout which we can recognize the face and transfer the learning to the pretrained convolutional neural networks (CNNs). From the images, we can extract the features and with the use of VGG-face CNN trained a KNN. The test shows that with the dataset saturation, the classifier trained and the accuracy of 98.43% was obtained with the supreme results. For the propriety dataset, the accuracy obtained was 95.41%, with the contrast, brightness and saturation combined version.

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2.2 Srivastava, Dubey and Murali [2] They proposed a PSNet CNN model with the use of PSN layer. According to this paper, they resolve the problems which occurs in the existing layers of CNN which normally generate inferior class scores alongside including a PSN—parametric sigmoid norm layer prior to the ultimate fully connected layer. The datasets which are used as testing are as follows: YouTube Faces (YTF) datasets and Labeled Faces in the Wild (LFW) datasets. Various experiments performed by it, and with the use of these Angular-Softmax as well as ArcFace Loss Function across PSNet and ResNet CNN models, we can easily compare the results. In the PSNet, the hyperparameter settings are retained as a α equal to 1 along with β, γ which are also known as network trainable. At ResNet18, the best performance of 97.79% is achieved, and by using PSNet50 using ArcFace Loss Functions on LFW dataset, 99.26% performance is achieved, and on YTF dataset, it attains the best performance of 94.65% at PSNet18 and 97.10% at PSNet50 using ArcFace Loss Functions.

2.3 Mr. Deepak B. Desai, Prof. Kavitha S. N. [3] They used the NUAA database for distinguishing between the real and fake face images. Totally, 1300 face images were selected for this model. The imperative features were drawn out by using the CNN model along with a SVM classifier which was used to differentiate between the spoofed and un-spoofed face images. The model built by the authors scored 95% accuracy.

2.4 Radhika C. and Bazeshree V. [4] They proposed the system by using machine learning approach along with computer vision for FRS. With the use of vigorous DNN-based detector, we can detect face. For face recognition evaluation, following classifier are utilized such as CNN, MLP and SVM. On self-generated database, the best testing accuracy of 98% is achieved by CNN and 89% accuracy is achieved on real time input via camera.

2.5 C. Nagpal and S. R. Dubey [5] They conducted an accomplishment with the usage related to CNN models to compare the face anti-spoofing. According to this paper, various architectures of CNN are utilized, for instance, ResNet152, ResNet50 and Inception-v3. The dataset

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which is used to recognize the face in this model is mobile face spoofing database (MFSD). The bottommost learning rate for ResNet152 is effective, whereas more advanced learning rate is effective for ResNet50. The experiments were concluded by examining the different aspects, for instance, the depth of the model, random initializing of weights versus transferring of weight, training vs tune up through dissimilar learning rate along with scratch. The model built by the authors scored 99.59% for ResNet152 in conjunction with 10−3 learning rate and 94.26% accuracy for ResNet50 alongside learning rate of 10−5 throughout transfer learning.

2.6 E. Winarno, H. Februariyanti, Anwar and I. H. Al Amin [6] They introduced an attendance model which is based on face recognition. The model used is this research is CNN-PCA model for extracting the features of face images also known as object which is taken by the camera. The objects in this model is used as an identity and is stored in the database for further uses. This model gave high accuracy and provides more accurate results of feature extraction. This model obtained 98% of accuracy.

2.7 Yinfa Yan, Cheng Li, Yuanyuan Lu [7] They implemented the improved CNN model trained by adding LBP feature information in the dataset, and it shows high recognition accuracy and robustness. From the facial expression image, a local binary pattern (LBP) image is extracted, combining original face image and LBP image as a training dataset. FER2013 expression dataset is used. With the combination of LBP feature map, the results show the excellent recognition effect and improved CNN is better than other methods. We can effectively avoid illumination versus expression with the full use of the characteristics of rotation invariance and gray invariance of local binary mode. We can achieve 95% of the accuracy by using this method.

2.8 Anugrah Bintang Perdana, Adhi Prahara [8] They proposed a method to recognize the face with the minimum dataset by using CNN light-build which is improved with VGG16 model. Here, 120 × 120 pixels image sizes are used in the proposed architecture and contains convolutional layers

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of two different types succeed alongside max pooling. According to its study, every layer of convolutional is accompanied alongside rectified linear activation function (ReLU). 512 neurons are present in the two different connected layers, and in final layer of classification, 30 neurons are present. The model built by the authors scored 94.4% accuracy; it shows exceptional results in the finite dataset alongside with little quantity of labels (Table 1).

3 Implementation and Result Analysis In this research, we have used CNN model and enhanced its accuracy by using GPU—“Tesla V100-16 GB RAM” of Google Colab. We get 27.4 GB of available RAM. In this we, will firstly use 2D polling, and then we will use rectified linear activation function (ReLU) in it. Then we will use Softmax activation with dense of ReLU activation function, and then the result will be evaluated through epoch and by using this we will calculate accuracy and loss. This will be evaluated layer by layer, and we have use binary cross entropy for evaluating classification report. Then we will summarize history for accuracy and loss using graph. Then we will plot confusion matrix. The dataset we used in this research is Flickr-Faces-HQ Dataset (FFHQ). In the project, we have used Google Colab which results in following advantages: • Faster computation than your typical 8–16 GB RAM • It results in better accuracy then the normally calculated accuracy • Less cost involved during running of project as we do not have to buy good quality PC with GPU in it. The accuracy we obtained by using this method is 96%. However, in future, we can further improve the accuracy by combining CNN with SVM or by further scaling the model by using cloud services like Amazon Web Services, Microsoft Azure, Google Cloud, etc. Following are the output of the research conducted using CNN model in the Google Colab (Figs. 5, 6, 7 and 8).

4 Conclusion The main emphasis of this research article was to study the facial expression recognition research area extensively. In this study, we exclusively studied techniques for FER, which employs CNN as a deep learning perspective. The recent research was scrutinized in detail and concluded with the problems faced by researchers while executing the models for FER. The conclusion that can be drawn from this research using the various type of convolutional neural network method is that it is able to recognize faces very efficiently and effectively.

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Table 1 Comparison of various CNN techniques in face recognition S. No.

Researcher/Year

Technique used

Dataset used

Accuracy

1

Valeska Uchoa, Rodrigo Veras, Kelson Aires, Anselmo Paiva, Laurindo Britto (2020)

KNN classifier by implementing VGG-face CNN

LFW dataset

98.43% for saturation dataset and 95.41% with proprietary dataset

2

Yash Srivastava, PSNet CNN model Shiv Ram Dubey by making use of the and Vaishnav PSN layer Murali (2019)

Datasets used in this research are Labeled Faces in the Wild (LFW) and YouTube Faces (YTF)

97.79% at ResNet18 and 99.26% at PSNet50 on LFW dataset and 94.65% at PSNet18, 97.10% at PSNet50 on YTF dataset

3

Mr. Deepak B Desai, Prof. Kavitha S. N. (2019)

FRM using CNN for NUAA database feature extraction and for classification SVM is used

95% accuracy is achieved

4

Radhika C. Damale, Prof. Bageshree V. Pathak (2018)

SVM, MLP and CNN are utilized as a classifier for evaluation

Self-generated database

98% accuracy is achieved on self-generated database and 89% is obtained on real time input via camera

5

Chaitanya Nagpal, Shiv Ram Dubey (2019)

CNN architectures for instance Inception-v3, ResNet152 and ResNet50 are used

MSU Mobile Face Spoofing Database (MFSD) dataset

99.59% accuracy for ResNet152 is achieved and 94.26% for ResNet50 is obtained

6

Edy Winarno, Imam Husni Al Amin, Herny Februariyanti (2019)

CNN-PCA (Convolutional neural network—principal component analysis)

2D-3D image reconstruction process is used for storing the database

98% accuracy is obtained

7

Yinfa Yan, Cheng Improved CNN Li, Yuanyuan Lu model trained by (2019) adding LBP feature information

8

Anugrah Bintang Light-CNN formed Limited dataset Perdana, Adhi on modified VGG16 (ROSE-Youtu Face Prahara (2019) model Liveness Detection Database while few images are taken from camera)

FER2013 expression This research dataset produces an accuracy of 95% Light-CNN method produces high accuracy of 94.4%

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Fig. 5 Confusion matrix

This paper contains an interpretation to various CNN techniques which can be used to solve the problems like illuminations, inadequate resolution problems, falsification of faces, similar faces, position of faces, occlusions etc. while recognizing faces. Accuracy obtained by using CNN model on Flickr-Faces-HQ Dataset (FFHQ) dataset is 96%. Therefore, we should use more images for training data in deep learning and better cloud computing resources like Aws, Azure, or Google Cloud for faster computing, so that better results can be achieved.

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Fig. 6 Training of our deep learning model (CNN)

Fig. 7 Classification report

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Fig. 8 Summarize history for accuracy and loss using graph

References 1. Uchôa V, Aires K, Veras R, Paiva A, Britto L (2020) Data augmentation for face recognition with CNN transfer learning. In: 2020 International conference on systems, signals and image processing (IWSSIP). IEEE, pp 143–148 2. Srivastava Y, Murali V, Dubey SR (2019) PSNet: parametric sigmoid norm based CNN for face recognition. In: 2019 IEEE Conference on information and communication technology. IEEE, pp 1–4 3. Desai DB, Kavitha SN (2019) Face anti-spoofing technique using CNN and SVM. In: 2019 International conference on intelligent computing and control systems (ICCS). IEEE, pp 37–41 4. Damale RC, Pathak BV (2018) Face recognition based attendance system using machine learning algorithms. In: 2018 Second international conference on intelligent computing and control systems (ICICCS). IEEE, pp 414–419 5. Nagpal C, Dubey SR (2019) A performance evaluation of convolutional neural networks for face anti spoofing. In: 2019 International joint conference on neural networks (IJCNN). IEEE, pp 1–8 6. Winarno E, Al Amin IH, Februariyanti H, Adi PW, Hadikurniawati W, Anwar MT (2019) Attendance system based on face recognition system using CNN-PCA method and real-time

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camera. In: 2019 International seminar on research of information technology and intelligent systems (ISRITI). IEEE, pp 301–304 7. Yan Y, Li C, Lu Y, Zhou F, Fan Y, Liu M (2019) Design and experiment of facial expression recognition method based on LBP and CNN. In: 2019 14th IEEE Conference on industrial electronics and applications (ICIEA). IEEE, pp 602–607 8. Perdana AB, Prahara A (2019) Face recognition using light-convolutional neural networks based on modified Vgg16 model. In: 2019 International conference of computer science and information technology (ICoSNIKOM). IEEE, pp 1–4

A Novel Support Vector Machine-Red Deer Optimization Algorithm for Enhancing Energy Efficiency of Spectrum Sensing in Cognitive Radio Network Vikas Srivastava and Indu Bala Abstract 5G Cognitive Radio Network (CRN) has a drawback in energy efficiency and spectrum sensing trade-off. When it comes to building the cognitive radio, it is essential to get the most energy-efficient system. Spectrum gaps are identified with a hybrid Support Vector Machine (SVM) and Red deer algorithm (RDA). Using the values for transmitting power, sensing bandwidth, and probability of detection helps remove the spectrum gaps. RF energy harvesting (RH) is seen as highly effective and realistic in next-generation wireless technology, especially for things that don’t need a lot of energy, such as the Internet of Things (IoT). It is believed that all secondary users in a network system can harvest several bands of RF from each Primary User location. The proposed algorithm can identify the spectrum holes through sensing bandwidth, transmitting power, and the probability of detection. This led to an improvement in energy efficiency (EE). The simulation result shows that energy efficiency increase with the help of SVM-RDA concerning sensing bandwidth, transmission power, and probability of detection compared with existing Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Hybrid PSO-GSA (Particle Swarm Optimization -Gravitational Search algorithm). Keywords Energy efficiency · Spectrum sensing · Support vector machine-Red Deer algorithm · Cognitive radio

1 Introduction The widespread use of wireless networking has certainly revolutionized the way people live. In 1901, after the successful demonstration of wireless communication by Marconi, a new era of communication was born. After evolving over a century, these wireless applications are an essential part of the daily lifestyle. As a result, electronic V. Srivastava (B) · I. Bala Lovely Professional University, Jalandhar, India V. Srivastava PSIT, Kanpur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_3

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technologies and applications, such as mobile and satellite communications and mobile networks, as well as privately used Wi-Fi networks, have seen significant growth in the twenty-first century. In contrast to the issue of spectrum scarcity, a recent Federal Communication Commission (FCC) study is an eye-opener to the global telecommunication market. It has been realized most bandwidth is idle much of the time. Because of strict spectrum distribution rules, the spectra have been ignored entirely [1]. In the year 2000, J. Mitola proposed cognitive radio (CR) as a viable alternative to the spectrum shortage due to its ability to interact with the RF environment and to allocate radio resources as per the end-user quality of service (QoS) requirements [2]. In 2002 FCC Spectrum Working Group study, cognitive radios seem to be the most important way to achieve a virtual spectrum scarcity by learning [3]. One of the spectrum management technologies that may help address the scarcity problem is cognitive radio technology. The basic idea of increasing spectrum utilization through cognitive radio technology was introduced by J. Mitola III’s in his Ph.D. thesis. Cognitive radio’s main characteristics that make it different from other radios are cognitive capability and reconfigurability [3, 4]. The three main stages in the cognitive process are sensing the spectrum, analyzing the spectrum, and deciding on the spectrum. Spectrum sensing helps cognitive radio to obtain information from available bands, identify spectrum opportunities, and explore spectrum variants. A core element of the dynamic spectrum access requirement is dynamic spectrum allocation/scheduling, which ensures affordable and effective use of the spectrum. Along with spectrum shortage, one critical parameter energy efficiency (EE) will exist. Deficient EE will impact the wireless technological environment. So it is important to enhance energy efficiency. Since cognitive radio devices are battery-powered, a concentrated study is needed to increase the Energy Efficiency of Cognitive Radio devices. Spectrum sensing accuracy can be enhanced by the optimization of the spectrum sensing parameter. Here, energy harvesting is one of the common successful ways to reduce energy costs for mobile networks (of which 30%) comes from networking [5]. Previously, several circuit designs have been introduced in the literature which uses multiple antennas [6, 7] or a single antenna [8, 9] to collect from multiple bands, where the multi-band principle increase energy to the area ratio. [10, 11] proposed ABC (Artificial Bee Colony algorithm), PSO (Particle Swarm Optimization) and, metaheuristic related algorithm for efficient spectrum sensing. The author in [12] proposed a Fish school algorithm to improve spectrum sensing efficiency. For optimizing resource allocation, the author in [13] proposed a novel grey wolf optimization algorithm for spectrum sensing in CRN. To improve spectrum sensing in CRN, a bio-inspired algorithm can be used. An author in [14] discussed a hybrid PSO-GSA algorithm to improve EE in the context of spectrum sensing parameters. So, Support Vector Machine (SVM) with Red Deer Optimization (RDO) algorithm has optimized spectrum sensing in multi-band CRN. The SVM algorithm is utilized to sense the spectrum in CRN, while The RDO algorithm is applied to optimize SVM parameters to enhance the sensing accuracy. Prime focus of the paper is evaluation of energy efficiency, sensing bandwidth, and probability of detection through Red Deer Optimization algorithm with SVM and

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also compared with PSO, ABC, and PSO-GSA optimization algorithm. According to the plan, this paper is to be structured as follows: Related works are found in Sect. 2. Section 3 discusses an energy-efficient optimization factor. Section 4 contains a discussion of the existing methodology. Section 5 describes the proposed algorithm. Details of simulation results are described in Sect. 6, and Sect. 7 contains the conclusion.

2 Related Works Energy efficiency is an essential parameter of 5G technology. In future wireless technology, we consider energy harvesting from radiofrequency. In [15], multi-band energy harvesting schemes under the cognitive radio inter-band approach have been analyzed. All nodes in the secondary are allowed to harvest energy from different RF sources. In this work, a mutually beneficial arrangement where SUs can detect the range to decide the correspondence between geological areas has been proposed. In this way, they can choose to collect or communicate information. Additionally, we mutually enhanced the number of detecting tests. Energy consumption depends on the data rate addressed in [16] with the suggestion of a wireless power transfer scheme. Throughput and power consumption of IoT devices will be improved by multichannel IOT with dynamic spectrum sharing [16]. Wideband spectrum sensing is discussed in [17, 18]. In traditional radio, power optimization parameters are crucial for the system. In [16, 19–23] focused on improve the EE of spectrum sensing. For efficient spectrum prediction, the multilayer perceptron and hidden Markov model are used in [24]. The energy efficiency and spectrum efficiency have improved, but total computational complexity and prediction energy have increased [24]. The researchers presented the iterative algorithm of convex transformations in [25] as a spectrum sharing scheme for improving energy efficiency. The non-linear programming issue is used to formulate the problem. In [16], power allocation using channel state information is implemented for CRN. Sudhamani and Satya Sai Ram [21] uses cooperative spectrum sensing of fusion rules (AND and OR) with EE enhancement depending on the number of users. Ghosh et al. [22] designed a game theory method to improve spectrum efficiency and EE for massive Multi-input Multi-Output Cognitive radio networks based on 5G networks. The disadvantage of this game theory method is that it converges to a suboptimal solution rather than a globally optimal solution [26]. As per the research paper, many studies have focused on improving spectrum sensing as a convex optimization issue. To the best knowledge of the authors awareness, energy efficiency optimization is now only developed as a non-convex function [23]. ABC algorithm was being used to improve a CRN’s EE in recent literature [27]. GSA is incorporated to improve PSO’s exploitation ability, as Particle Swarm Optimization is recognized for lags in exploitation but improved exploration [28].

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3 Energy-Efficient Optimization Factor This paper aims to enhance energy efficiency when addressing various spectrum sensing theories. Furthermore, EE is enhanced by using a hybrid SVM-RDA to improve the EE metrics. Scanning spectrum for spectrum holes is the initial step in the traditional spectrum sensing method. For smooth communication, periodic sensing is needed. If a vacant channel is observed during spectrum sensing, data transfer is completed, and the secondary user repeats the next sensing time procedure. SU’s bandwidth ‘B’ is divided into two sections for this purpose: Bs for spectrum sensing and B–Bs for data transmission with marginal energies beyond spectrum [29]. If the PU is not observed, SU conducts data transfer. The data transfer is completely disconnected if new spectrum sensing senses Primary User. Depending on the existence of PU, data transfer through secondary users can turn off and on for bandwidth B–Bs in this scheme. However, the spectrum sensing carried out in this manner over the bandwidth ‘B’ is constant. As a result, the consistent spectrum sensing method is referred to as an efficient spectrum sensing scheme.

3.1 The EE Through Spectrum Sensing and Data Transfer in Terms of Overall Opportunistic Throughput and Energy Utilization Signal sampling is a significant contributor to energy utilization during spectrum sensing. Because the sampling process accounts for a large portion of the energy used during spectrum sensing, it is interesting to think about how much energy is used per sampling time. Let E s denote the energy used by the secondary user during the sensing phase per sampling time, and Gt denote the power spectral density (PSD) of the secondary user signal used during data transfer throughout channel. SU’s maximal transmitting power is Qt,max , which is spread uniformly around the range of B–Bs . In this CR system layout, the two hypotheses for energy detection-based spectrum sensing are represented by Eq. (1) [30, 31] 

H0 : x(n) = w(n) H1 : x(n) = hs(n) + w(n)

(1)

where n = 1, 2, …, M; M denotes the number of samples. W (n) is AWGN noise with zero mean and variance E[|w(n)|]2 = σw2 . s(n) is the PU signal with zero mean and variance σs2 [32], h is the AWGN block faded channel gain. The p on and p off value shows whether or not the PU currently fills the channel. Since p on + p off = 1, the probability of false alarm and probability of detection can be expressed as Eqs. (2) and (3).

A Novel Support Vector Machine-Red Deer Optimization Algorithm …

39

P f = q f p off

(2)

Pd = qd p on

(3)

where qd = probability of detection and q f = probability of false alarm by energy detector. The normalized energy detection technique used for sensing spectrum can be written as Eq. (4) TH =

2T p Bs 1  |xn |2 σw2 n=1

(4)

T p = frame period and 2T p Bs = number of samples. Due to the large value of 2T p Bs .  TH ∼

  H0 : N 2T   p Bs , 2T p Bs  H1 : N (2 1 + SNRpr T p Bs , 2 1 + SNRpr T p Bs

(5)

SNRpr is the Signal to Noise Ratio of Primary User measured at secondary user,   Probability of detection qd = probability TH > γ  |H1 

γ  − 2T p Bs =Q  1 + SNRpr 2T p Bs   Probability of false alarm q f = probability TH > γ  |H0 

γ =Q − 2T p Bs 2T p Bs 1 ∞ −t 2 /2 ∫e dt Q function is Q(x) = √ 2π x

(6)

(7) (8)

If qd increases, then q f will decreases. Threshold probability of detection qdth < qd . So the probability of false alarm    q f = Q(Q −1 qdth 1 + SNRpr + SNRpr 2T p Bs

(9)

Since spectrum sensing energy efficiency depends on spectrum sensing variables, the below assumptions could be found depending on distinct spectrum sensing contexts (Table 1). Total energy consumption

Useful detection

False alarm

Missed detection

Spectrum is busy

Contexts e1 =

c1 = 0

c4 =

  poff 1 − q f

e4 =    p off 1 − q f 2T p Bs E s + G t (B − Bs )T p

e3 = 2T p Bs E s p off q f

c3 = 0

q f p off



   G t (B−Bs ) T p poff 1 − q f (B − Bs ) log2 1 + |H |2 G 0 (B−Bs )

e2 =    p on 1 − qdth 2T p Bs E s + G t (B − Bs )T p

c2 = 0

2T p Bs E s p on qdth

Energy consumption

Throughput

  pon 1 − qdth

qd

p on

Probability

Table 1 Value of probability, throughput, and energy consumption

The vacant channel is properly found by SU

when the channel does not have a PU, SU does not transmit

For data transmission, PU and SU will concurrently occupy the channel

Spectrum is occupied by the PU, and it has been correctly detected by the SU

Remarks

40 V. Srivastava and I. Bala

A Novel Support Vector Machine-Red Deer Optimization Algorithm …

ET =

4 

  ek = 2T p Bs E s + G t (B − Bs )T p 1 − p on qdth − p off q f

41

(10)

k=1

Throughput      2 G t (B − Bs ) (11) CT = cr = T p poff 1 − q f (B − Bs ) log2 1 + |H | G 0 (B − Bs ) r =1     2 G t (B−Bs ) off |h| p 1 + 1 − q − B × log (B ) f s 2 G 0 (B−Bs) CT   Energy Efficiency = =ε= ET 2Bs E s + G t (B − Bs ) 1 − pon qdth − poff q f (12) 4 

3.2 System Model for Sensing the Spectrum IEEE 802.22 is related to the wireless network, where we concentrate mostly on Cognitive Radio Network with one Primary User, one fusion center, and i secondary user [33]. Each secondary user calculates energy and sends it to fusion center, which therefore calculates the PU state. For time duration τ 0 , each SU calculates the energy. The sampling rate is f s , so the energy value of SUi is given by-

yi =

⎧ f s τ0 ⎪ 2  ⎪ ⎪ [n i ( j)]2 2 ⎨ σi ⎪ 2 ⎪ ⎪ ⎩ σi2

j=1 f s τ0 

H0 (13)

[h i S( j) + n i ( j)] H1 2

j=1

h i = channel gain from Primary User  to ith secondary user, S( j) = PU signal and n(j) = noise. Noise power σi2 = E |n i ( j)|2 .

4 Description of Conventional Red Deer Algorithm and Support Vector Machine 4.1 Red Deer Algorithm The red deer algorithm is a population-based metaheuristic algorithm [34]. The optimization technique can be divided into two groups: (i) mathematical programming (ii) metaheuristic algorithm. The existing metaheuristic algorithm may be divided into two main categories (i) evolutionary algorithm (ii) swarm algorithm.

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The RDA, like some other meta-heuristics, begins with a random population that is the contrary of RDs. The group’s strongest RDs are identified and called the “male RD,” while the others are called the “hinds.” However, first, the male RD will roar. They are divided into two categories depending on the strength of the roaring process (i.e., stags and commanders). Implementing that, commanders and stag of each harem compete for control of their harem. Commanders always develop harems. The volume of hinds in harems is proportional to the commanders’ roaring and battle skills. As a result, commanders in harems pair with a significant number of hinds. Generate an initial red deer Definition of array related to red deer is: Red Deer = [X 1 ; X 2 ; X 3 ; . . . ; X N var ]

(14)

A feasible method X refers to a red deer. Also, the feature value for each RD can be measured: Value = f (Red Deer) = f (X 1 ; X 2 ; X 3 ; . . . ; X N var )

(15)

The initial population size is N pop and N hind = N pop − N male , N male is no. of male RD. N hind is no. of female RD. Roar male RDs Male RDs are increasing their grace by roaring in this phase. As a result, just like in reality, the roaring task will succeed or fail. In consideration of the best solution, we find the male RD’s neighbors and swap them with the prior ones if their objective functions are larger than the male RD’s. In reality, we allow any male RD to change positions. The following equation is suggested to change the position of males:  malenew =

maleold + a1 ((UB − LB)a2 + LB); if a3 ≥ 0.5 maleold − a1 ((UB − LB)a2 + LB); if a3 < 0.5

(16)

LB and UB are lower and upper bound of search space. maleold is the present position of male RD, and malenew is updated position of male RD. a1 , a2 , and a3 are lies between 0 and 1. Select γ % of the best male Red Deer as male commanders Male RD is two types, first is commander, and second one is stags. The following formula is used to calculate the number of male commanders: Ncom = round{γ .Nmale } Ncom = no. males who are harem’s commander, 0 < γ < 1. At last, stags number is determined using the following formula:

(17)

A Novel Support Vector Machine-Red Deer Optimization Algorithm …

Nstag = Nmale − Ncom

43

(18)

Fight between stags and male commanders Assigned stags to every commander at random. Two mathematical formulas are given for the fighting process: New1 =

Com + Stag + b1 ((UB − LB)b2 + LB) 2

(19)

New2 =

Com + Stag − b1 ((UB − LB)b2 + LB) 2

(20)

New1 and New2 are the two new solutions generated by the fighting process. Com = commander, stag = Stag. b1 and b2 are lies between 0 and 1. Form harems We separate hinds among commanders to form harems in proportion to: Vn = vn − max{vi }

(21)

vn = power of nth commander V n = normalized value. The following equation can be used to measure commanders’ normalized strength    V n   Pn =   Ncom   i=1 Vi 

(22)

No. of hinds contain each harem is N .haremn = round{Pn .Nhind }

(23)

Mate commander of a harem with α percent of hinds in his harem A commander carries out this operation; the percentage of the hinds in his harem is parents. N .haremmale = round{α.N .Haremn } n

(24)

N .haremmale = the number of hinds from the nth harem who mate with their n commander. α is a starting parameter value. In general, mating process is defined as follows: Offs = Offs is a new solution.

Com + Hind + (UB − LB)c 2

(25)

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Mate commander of a harem with β percent of hinds in another harem The percentage of hinds in the harem who mate with the commander is calculated using the following formula: = round{β.N .Haremn } N .haremmale k

(26)

The number of hinds in the kth harem that mate with the commander is N.haremk male . Mate stag with the closest hind Locating closest hind, the distance between all hinds in J-dimension and a stag is ⎛ ⎞2  di = ⎝ (stag j − hindij )2 ⎠

(27)

j∈J

Specify the next generation We choose the next generation of males based on fitness and use an evolutionary process to choose a solution. Convergence It may be the case that the stopping condition is the number of iterations.

4.2 Support Vector Machine (SVM) Among classifiers that have a linear decision boundary, the support vector machine is the most powerful one [35]. If there are non-linear functions, it is called kernel function. Support vector machines can work with a relatively larger number of features without requiring too much computation. In the SVM, we need a classifier to maximize the separation between distance surface and point. Distance between closest points of negative surface to decision plane is the margin. So margin is denoted by the minimum distance of a training instance from the decision surface. This is an assumption that positive and negative points are linearly separable by a linear decision point. The points nearest to decision surface are called support vectors. Functional margin Let take point (x i , yi ) point concerning a decision surface. The decision surface equation is wx + b = 0

A Novel Support Vector Machine-Red Deer Optimization Algorithm …

45

So equation of line is w1 x 1 + w2 x 2 + b = 0. Function margin is the distance between x i , yi to the decision boundary.   γi = yi w T xi + b

(28)

More certainty about the classifiers translates into a greater functional margin. Functional margin of set of points so there are set of point x S = {(x1 , y1 ), (x2 , y2 )} m

γ  = min γi

(29)

i=1

The best dividing hyperplane will be the one with the greatest margin, and all training samples are assumed to satisfy the limitation, yi (w T xi + b) > 1

(30)

To separate non-linear results, one must look for an optimal classification plane, so as to keep the misclassification rate as low as possible. The ideal hyperplane is analogous to a quadratic function optimization problem in which the Lagrange function is used to calculate the global limit [36]. Training samples are (PU(k), Z i (k)), where class label is PU(k) and feature vector is Z i (k). Classifier is described by the following definition: wi .Z i (k) + bi > 1 if PU(k) = 1

(31a)

wi .Z i (k) + bi ≤ −1 if PU(k) = 0

(31b)

wi = weighting vector and bi = bias. Hence optimization problem is written as  1

wi 2 + C ξi , 2 i=1 N

min (wi , ζ ) =

N is number of samples, ξi is slack variable, and C is set by user to limit the misclassification rate.

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5 Proposed Methodology: Support Vector Machine-Red Deer Algorithm (SVM-RDA) Pseudocode for spectrum sensing using Support Vector Machine (SVM) based Red Deer Optimization (RDO) Algorithm and flowchart is given in Fig. 1. Pseudocode for spectrum sensing using Support Vector Machine (SVM) based Red Deer Optimization (RDO) Algorithm Create system model using Primary Users (PUs), Secondary Users (SUs), and Fusion center (FC). Generate full duplex multi-band signal. Separate multi-band into several subbands. Initialize energy for SUs, PUs, and subband. Placement PUs in a subband. Check the availability of PUs in spectrums and collects FC the energy value submitted by SUs in each round. Sense the availability of spectrum using SVM algorithm. Begin Initialize the parameters of SVM such as bias and weight vector (training).

Start

Develop a SVM model

Start

Take input values as energy level of SU and availability of PU

Initialize RDs (weight parameter)

Roar male RDs

Train the network with initial weight

Select percent of best male RDs as male commanders

Yes Is correctly sensed?

End

Fight between male commanders and stags as well as form harems

No Update the weight of SVM using RDO algorithm

Mate commander with and percent of hinds in another harem

Spectrum unavailability

SVM classification based on optimal weight value (testing set)

Mate stage with nearest hind and select new weight parameters

Yes

Spectrum availability Assign SUs to available spectrums

Stop

Fig. 1 Algorithm of SVM-RDA

End criteria satisfied?

Stop

No

A Novel Support Vector Machine-Red Deer Optimization Algorithm …

47

for each SUi Take input as energy level and availability of PU(k). Calculate weight and bias vector by wi .Z i (k) + bi > 1, if PU(k) = 1 wi Z i (k) + bi ≤ 1 if PU(k) = 0 Here, wi is weight vector, bi is a bias vector, and Z i (k) is an energy value submitted by SUi in each kth round. end for Weight vector is randomly updated and it can’t able to sense the spectrum availability accurately. N  ξi , N is number of samples, ξi is Optimize min (wi , ζ ) = 21 wi 2 + C i=1

slack variable, and C is set by user to limit the misclassification rate. Initialize Red Deer population (weight vectors) Calculate fitness and sort them according to fitness and form hinds and male Red Deers. X ∗ is the best solution (weight vector) While (t < max _iter) for each male RD Roar male based on equation malenew = maleold + a1 x((UB−LB) ∗ a2 + LB); if a3 ≥ 0.5 malenew = maleold − a1 x((UB−LB) ∗ a2 + LB); if a3 < 0.5 Update the position if better than previous one. end for Sort the males and form the commanders and stags by NCom = round{γ .Nmale } and Nstag = Nmale − NCom for every male commander Fight among stags and male commanders by Com + Stag + b1 x((UB−LB) ∗ b2 + LB); 2 Com + Stag − b1 x((UB−LB) ∗ b2 + LB); New2 = 2 New1 =

Update the male commanders’ position and stage position end for for each male commander N .haremmate = round{α.N .haremn } n

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Mate a male commander with selected hinds of his harems randomly Offs =

Com + Hind + (UB − LB)xc 2

= round{β.N .haremk } Select a harem randomly and name it k N .haremmale k Mate a male commander with some selected hinds of harem by Offs =

Com + Hind + (UB − LB)xc 2

end for for each stag Calculate distance between stag and all hinds and select nearest hind by ⎛ ⎞2  di = ⎝ (stag j − hindij )2 ⎠ j∈J

Mate stag with selected hind by Offs = Com+Hind + (UB − LB)xc. 2 end for Select next generation Update X ∗ , if there is a better solution. end while Return X ∗ (optimal weight value) With trained energy vector, test energy vector in classification module Classify the CRN with two classes like spectrum availability and spectrum unavailability class. SUs access available spectrums. End whole process.

6 Simulation Results The Energy Efficiency of Cognitive Radio Network is based on sensing bandwidth, transmitted power, and probability of detection using MATLAB simulation data. The optimal solution in each search space of the corresponding parameters was obtained by segmenting each vector’s search space (parameter) and then plotting the simulation result. The metrics of EE obtain from SVM-RDA, PSO-GSA, PSO, and ABC algorithms were simulated for 50 runs, with each run having 200 iterations for each algorithm. The bits/Joule unit of EE feature is used in this case. Collection of Qt and its Bs can be achieved using the optimization process. Simulation Parameters:

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49

The following conditions are taken into account when calculating numerical results: T p = 0.01 s, B = 1 × 106 Hz, qon = 0.3, E s = 1 × 10−6 J, No. of Primary User = 10, no. of secondary user = 90.

6.1 Simulation Results of the Optimization Algorithms 6.1.1

Transmission Power vs. Energy Efficiency

The Energy Efficiency curve for differing transmission power (Qt ) for various Signals to Noise Ratio values is seen in Figs. 2 and 3. It is clear from the Energy Efficiency versus transmission power that if Qt ’s value increases, the EE increases, while if the transmission power increases after reaching a peak value, the EE gradually decreases. Since opportunistic throughput increases as transmission power increases and Energy Efficiency is inversely proportional to power and directly proportional to throughput. Energy efficiency function increases until the improvement in throughput exceeds the rise in power level, at which point the Energy Efficiency function peaks and the increase in power level predominate the throughput, resulting in a steady decline in the EE function. Since the probability of false alarm reaches minimum level at high SNR levels due to EE function peak for various situations is higher, giving the EE

Fig. 2 Analysis with respect to transmission power at signal to noise ratio = −6 dB

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V. Srivastava and I. Bala

Fig. 3 Analysis with respect to transmission power at signal to noise ratio = 8 dB

function a greater chance of finding spectrum gaps. This leads to higher opportunistic throughput and, as a result, a higher EE function.

6.1.2

Sensing Bandwidth Versus Energy Efficiency

EE reduces as sensing bandwidth grows because a higher sensing bandwidth Bs results in a lower transmitting bandwidth, resulting in a lower opportunistic throughput value, leading to a decreased EE value (Figs. 4 and 5).

6.1.3

Probability of Detection Versus Energy Efficiency

In above graph probability of detection is 0.9. Energy Efficiency increase with an increase in detection probability, as seen in Figs. 6 and 7. Higher opportunistic throughput is ensured by a higher detection probability, indicating an improved EE function value. With a high probability of detection, an opportunistic spectrum access scheme may operate with less interference. Because of its better exploitation potential, the SVM-RDA method is good in achieving a better peak for Energy efficiency than ABC, PSO, and PSO-GSA for differing detection probability.

A Novel Support Vector Machine-Red Deer Optimization Algorithm …

Fig. 4 Analysis with respect to sensing bandwidth at signal to noise ratio = −6 dB

Fig. 5 Analysis with respect to sensing bandwidth at signal to noise ratio = 8 dB

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Fig. 6 Analysis with respect to probability of detection at signal to noise ratio = −6 dB

Fig. 7 Analysis with respect to probability of detection at signal to noise ratio = 8 dB

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53

Table 2 Optimum energy efficiency values using Support Vector Machine-Red Deer Algorithm, Particle Swarm Optimization-Gravitational Search Algorithm, Particle Swarm Optimization, and Ant Bee Colony for different Signal to noise ratio values Energy efficiency function

Optimization methodology

Peak values Energy efficiency SNR = −6 dB

SNR = 8 dB

Varying in terms of transmission power (mW)

SVM-RDA

7

15

PSO-GCA

6

14.3

ABC

5.5

9.7

Varying in terms of sensing bandwidth (Hz)

Varying in terms of detection probability

6.1.4

PSO

4.8

8

SVM-RDA

5.2

12

PSO-GCA

4.9

11.2

ABC

4.6

8

PSO

4.4

7.2

SVM-RDA

2.6

6.5

PSO-GCA

2.5

6.1

ABC

2.3

3.9

PSO

2

2.8

Graphical and Tabular-Based Comparative Analysis

Table 2 demonstrates each optimization algorithm’s effectiveness (SVM-RDA, PSO, PSO-GSA, and ABC) with respect to peak energy efficiency and parameter values.

7 Conclusion For a Cognitive Radio Network, the authors propose an SVM-RDA-based optimization algorithm for enhancing energy efficiency in spectrum sensing. Energy efficiency function is calculated with respect to transmission power, sensing bandwidth, and probability of detection. RDA’s performance has been significantly improved as a result of the hybridization of SVM and RDA. In comparison to current PSO-GCA algorithms, the proposed SVM-RDA system’s simulation results show its efficacy in terms of EE for sensing of spectrum. With an SNR of 6 dB and population size of 20, the proposed SVM-RDA is 15%, 30%, and 22% high energy efficient than PSO-GSA, PSO, and ABC, respectively, for varying transmission power. For the varying probability of detection, the proposed SVM-RDA outperformed PSO-GSA, PSO, and ABC by 4%, 24%, and 16%, respectively. In contrast, for varying sensing bandwidth, the proposed algorithm outperforms PSO-GSA, PSO, and ABC by 7%, 19%, and 16%, respectively. Compared to the current PSO-GSA, PSO, and ABC algorithms, the proposed algorithm is effective in spectrum sensing and has obtained optimum energy consumption for sensing of spectrum in Cognitive Radio Network.

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Deceptive Product Review Identification Framework Using Opinion Mining and Machine Learning Minu Susan Jacob and P. Selvi Rajendran

Abstract Reviews of products are an integral part of the marketing and branding of online retailers. They help build trust and loyalty and generally define what separates the goods from others. The competition is so high that at times the company is forced to rely on the third party in producing deceptive reviews to influence readers’ opinions and hence to enhance the sales. This misleads the ethics and purpose of online shopping. This paper proposes a model to identify fake product reviews. The model uses Naive Bayes classifier and Support Vector Machine to classify the fake and genuine reviews. The model uses a set of parameters such as length of the review, usage of personal pronouns, nature of the review, verified purchase status, rating of the review and the type of the product to extract the features for the classification. The experimental results show that the model is working efficiently with a high classification accuracy rate. Keywords Opinion mining · Fake product review · Naive Bayes classifier · Support vector machine · Classification

1 Introduction There are a variety of criteria that help to determine an e-commerce store’s performance and reputation. Product reviews are however a very important factor in raising the credibility, standard and evaluation of an e-commerce shop. Often after a transaction has been made, the organization may provide a URL for printed literature or e-mail marketing to encourage clients to check their service. Consumer reviews do not take anything into account but provide an e-commerce store with one of the important factor-customer reviews. M. S. Jacob (B) · P. Selvi Rajendran Department of Computer Science and Engineering, KCG College of Technology, Chennai, India e-mail: [email protected] P. Selvi Rajendran e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_4

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Merchants very often underestimate the value of product feedback for an ecommerce store, paying more attention and thinking about too many things to handle, such as improving web layout, removing consumer doubts, helping timely customers determine which item to purchase [1], customer care and administrative tasks. A customer knows exactly what he is purchasing, what he is being given and what items he can expect from the product with the aid of product reviews. A customer knows exactly what he is purchasing, what he is being given and what items he can expect from the product with the aid of product reviews. Consumer reviews have become more critical because of the absence of the right to ‘Test’ the item prior to its online purchase. Some good reviews are applied to some of the review websites by the individuals of the product company themselves to generate false-positive product reviews [2] and negative fake feedback [3] for their competitors. For several different goods produced by their own company, they offer good feedback. It would not be possible for users to find out if the review is true or false.

2 Related Work Fake news identification is the need of the hour. The study of literature requires content-based strategies and approaches based on trends. Content-based approaches require two steps: pre-processing content and supervised testing of the learning model on pre-processed content. Generally, the first step includes word tokenization, derivation and/or weighting. [4] and [5]. Term Frequency-Inverse Document Frequency (TF-IDF) [6, 7] can be used in the second phase to construct samples for supervised learning training. TF-IDF will, however, generate very few samples, especially for data on social media. Word embedding methods (like word2vec [8] and GloVe [9]) are used to transform words into vectors to overcome this obstacle. Pennebaker [10], using Language Interrogation and Word Counting (LIWC) [11] also addresses the variations between deceptive and non-deceptive languages in word use. Specifically, more models focused on deep learning have been explored in comparison with other supervised learning models [12, 13]. For example, Mikolov [14], to describe the detection model, two LSTM RNN models were used: one to learn simple word embedding and the other to improve performance by connecting the output of long-term short-term memory (LSTM) with the feature vector of LIWC. In addition, Doc2vec [13] is used to describe the content of each social interaction to achieve better efficiency, attention-based RNN is often used. Lazer [15], in the feedback of the attention-based RNN, add the name of the speaker and the topic of the argument in addition because convolutional neural networks have been successful in many text classification tasks, they are also widely used. A multi-source and multi-class fake news detection system (MMFD) was proposed by Gao [16], in which CNN analyzes the local pattern of each text in the argument and LSTM analyzes the time dependence of the whole text. In the system based on the propagation pattern, the patterns of propagation are derived from news knowledge in the time series. Lan [17], for example, suggested a dynamic sequence time structure (DSTS) to capture

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shifts in social background characteristics (such as Weibo content and users) over time in order to discover rumours as early as possible in order to recognize false news from Weibo. A layered attention RNN (HARNN) was suggested by Hashimoto [18], which uses a Bi-GRU layer with an attention mechanism to capture high-level gossip material representations and uses a Gated Recurrent Unit (GRU). Chapello [19], in time series modifications, imagine the subject structure and seek support from external credible sources to assess the validity of the topic. Most current techniques, in short, are based on supervised learning. In order to understand the detection process, it needs a large amount of labelled data, particularly for deep learning-based methods. However, annotating news on social media is too costly and requires a lot of manpower due to the enormous size of social media info. To enhance the efficiency of identification, it is therefore imperative to combine unmarked data with marked data in the detection of fake news. Semi-supervised learning [20] is a system that can use knowledge that is both labelled and unlabelled. Therefore, for the timely identification of false news, we suggest a new paradigm for supervised learning by Naïve Bayes and SVM classifiers. We will present the proposed structure in the next section and describe in depth how to incorporate a deep, semi-supervised learning model. In an ongoing report, a strategy was proposed by Elmurngi and Gherbi [21] utilize an open-source programming device called ‘Weka apparatus’ to execute AI calculations utilizing feeling examination [22] to group reasonable and out of line audits from Amazon audits dependent on three unique classes positive, negative and impartial words. In this research work, the spam audits are recognized by just including the supportiveness votes cast a ballot by the clients alongside the rating deviation are viewed as which restrains the, generally speaking, execution of the framework. Likewise, according to the analyst’s perceptions and exploratory outcomes, the current framework utilizes Naive Bayes classifier for spam and nonspam arrangement where the exactness is very low which may not give precise results for the client. At first O’Brien [3] and Reis et al. [23] have proposed arrangements that rely just upon the highlights utilized in the information set with the utilization of various AI calculations in identifying counterfeit news on social media. Despite the fact that distinctive AI calculations the methodology needs to demonstrate how precise the outcomes are. Wagh et al. [24] took a shot at Twitter to dissect the tweets posted by clients utilizing notion investigation to characterize Twitter tweets into positive and negative. They utilized K-Nearest Neighbor as a technique to designate them assumption marks by seven preparings and testing the set utilizing highlight vectors. However, the relevance of their methodology to other sorts of information has not been approved.

3 Methodology To overcome the great problem of online websites due to spamming of views, this work proposes the detection of any such spamming fake reviews by grouping them

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into fake in the first layer of the model. The system tries to classify the publicly available reviews datasets available from a variety of sources and categories including service-based, product-based customer feedback, experience and a crawled Amazon dataset. More precision is achieved with Naïve Bayes [25], SVM classifiers [26]. Additional features such as contrast, to improve the accuracy Apart from the review info, the feeling of the review, checked sales, ratings, emoji counts [27, 28], product categories with an overall score are used. A classifier is constructed based on the features identified. And those traits are assigned a likelihood factor or weight, depending on the training sets listed. This involves the usage of different supervised algorithms to find real and fake reviews. The Amazon dataset collected from the Amazon analysis data location is the data set available in this work. For assessment, approximately 10,000 reviews are taken. As it is a broad data collection, 30% of the information is used for training and 70% is used for research. Collected reviews are pre-processed and the data processed is stored in a registry. Based on certain parameters, features are extracted from the pre-processed data. The features extracted are length of the review of the reviewer, the nature of rating, verified purchase. The fake reviews tend to be of smaller length. Review from the same ID multiple times tends to be the sign of a fake review. Reviews collected have been classified using two machine learning algorithms Support Vector Machine and Naive Bayes classification algorithm. Performance both the algorithms are compared in terms of accuracy, precision and recall. The pre-processed data was transformed by applying those parameters into a set of features. Extracted were the following features: • Normalized length of the review-fake reviews tends to be of smaller length. • Reviewer ID—A reviewer posting multiple reviews with the same Reviewer ID. • Rating-fake reviews in most scenarios have 5 out of 5 stars to entice the customer or have the lowest rating for the competitive products thus it plays an important role in fake detection. • Verified purchase—Purchase reviews that are fake have lesser chance of it being verified purchase than genuine reviews. • Nature of the review—The common problem/highlight of a product is discussed or not. Fake reviews often have stronger positive or negative emotions than other reviews, because they are used to affect people’s opinions. Advertisers post fake reviews with more objective information, giving more emotions such as how happy it made them than conveying how the product is or what it does. This is how we used sentiment analysis to classify various reviews as fake and genuine. Classifying the reviews concurring to their feeling figure or opinions being positive, negative or impartial or neutral. It incorporates anticipating the reviews being positive or negative according to the words utilized within the content, emojis utilized, appraisals given to the audit and so on. Related research [29] says that fake surveys have more grounded positive or negative emotions than genuine reviews. The reasons are that fake reviews are utilized to influence people opinions and it is more noteworthy to communicate suppositions

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than to doubtlessly portray the facts. The Subjective versus Objective proportion things: Promoters post fake surveys with more feeling given words or objective data, giving more feelings such as how cheerful it made them than conveying how the item is or what it does. Positive assumption versus negative sentiment: The opinion of the survey is analyzed which in turn offer assistance in making the choice of it being fake or honest to goodness survey. The method used to classify the feedback obtained from publicly available datasets from various sources and categories based on profit, product-based, customer criticism, participation-based and the crept Amazon dataset using Naïve Bayes and SVM with greater accuracy. To move forward the exactness, the extra parameters or features like length of the parameter, rating of the review, nature of the review and verified purchase are used and the scores obtained are utilized in expansion to the survey details. Based on the known highlights, a classifier is constructed. And, depending on the classified planning sets, those highlights are given a probability estimate or a weight. This is a directed learning technique that uses various calculations of machine learning to detect a false or honest analysis. At this point the raw information is pre-processed by applying tokenization, cleaning of words and lemmatization. Feature Extraction: The processed data have distinctive highlights or quirks that can help to resolve the classification issue. For, e.g. Length of surveys (fake audits tend to be littler in length with less realities uncovered almost the item) and fancy words (fake reviews have fancy words). Separated from the fair review text there are other highlights that can contribute towards the classification of surveys as fake. A few of the critical ones that were utilized as extra highlights consideration are ratings, verified purchase, nature of the review and length of the review. The diagram is shown below (Fig. 1) explains the entire flow of the paper. • • • • •

Customer Review Collection Data Pre-processing Sentimental Analysis/Opinion Mining Fake Product Review Detection Evaluation of Results and Result Analysis.

4 Experimental Results We need a method to find the unusual pattern of text, style of writing and pattern [30]. The system should have an internal facility to score the review and any suspicious similarities to be flagged to the authority. The fake surveys calculation requires time and training to begin working faultlessly. The implementation of this work used supervised learning techniques on the datasets and the fake and genuine labels helped to cross validate the classification results of the data. Datasets for such reviews with labels were found from different sources and combined into customerreviews.csv file. Then to make it readable, the labels in the dataset were clearly labelled as fake or genuine. Since the dataset created from multiple sources of information had many

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Customer Review Collection

Data Pre-processing Prepare Train and Test Data Set Opinion Mining

Apply the parameters for classification

Blank Space Removal/Cleaning

Conversion of text to lowercase

Stemming and Lemmatization

Naive Bayes Classifier Fake Product Review

Support Vector Machine Evaluation of Results and Result Analysis

Fig. 1 The implementation architecture

forms of redundant and unclean values, it needed to be pre-processed. Data had been cleaned by removing all the null values, white spaces and punctuations. This raw dataset was loaded in the form of tuple allowing to only focus on the textual review content. Then the raw data was pre-processed by applying tokenization, removal of stop words and lemmatization. This processed data was then analyzed for emotions or sentiment if the review was positive or negative. The significant factors for doing the sentiment analysis of the reviews were use of the parameter’s length of the review, rating of the review and the nature of the review. While pre-processing the input the data was parsed for the personal pronouns to be an exception, so they didn’t remove or discard them by accident while cleaning the dataset. The pre-processed dataset was thus classified using different classification algorithms to analyze a variety of data to classify it. The two classifiers used in this configuration are: • Naïve Bayes classifier • Support Vector Machine Naïve Bayes [25] and Support Vector Machine were used for detecting the genuine (T) and fake (F) reviews across a wide range of data set. The probability for each word calculated was given by the ratio of (sum of frequency of each word of a class to the total words for that class). The dataset was split into 70% training, 30% testing, 7000 for training and 3000 for testing. Finally, the likelihood of each review was determined for each class for the testing of the data using a test collection. The class

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with the highest probability value for which the mark is applied to the analysis, i.e. true/genuine (T) or false (F) review. F-train.txt and T-train.txt were the datasets used for preparation. They included both the Review ID (e.g. ID-1100) and the Review text ID (Great product). The dataset consisting of 10,000 product reviews are read and it is given for preprocessing. The data requires pre-processing. The raw data is transformed into an understandable format for the NLP models. This will help in getting better results while applying the classification algorithms. Besides the other easy to understand steps the following two steps are also performed. The input data includes an identification number for each raw along with the review text and the label for it. We are considering two labels Label 1 and Label 2 in order to indicate fake and genuine reviews. Later the labels are changed to fake and genuine for a better understanding. This helps in analyzing the parameters for feature extraction better. The raw data is transformed into an understandable format for the NLP models. This will help in getting better results while applying the classification algorithms. Besides the other easy to understand steps the following two steps are also performed: • Tokenization • Word Stemming/Lemmatization Tokenization: This is a mechanism by which a text group is broken into words, phrases, images, or other essential components called tokens. For additional planning, the rundown of tokens is supported. NLTK Library has word tokenize and tokenize sent to separately split a flood of text into a rundown of terms or phrases easily. NLP is a branch of artificial intelligence, short for natural language processing, which focuses on allowing computers to understand and interpret human language. The issue with human language comprehension is that it is not a collection of rules or linear data that can be fed into the machine (Figs. 2 and 3). Word Stemming/Lemmatization: Stemming and lemmatization are related. The distinction is that a stemmer works on words without taking into account the meaning, whereas lemmatization is context-based. Consider the following table for better understanding. Pre-processing of the data input helps in cleaning the data and the final result of this will be a set of words in a list that is ready for analysis (Table 1).

5 Feature Extraction The pre-processed reviews are further analyzed with various parameters for the feature extraction. These parameters help in improving the accuracy of the training system and hence the prediction. The following parameters are analyzed. Normalized Length of the Review: Fake reviews appears to be smaller in length (Figs. 4 and 5).

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Fig. 2 The extraction of data

Fig. 3 Analysis of corpus text

Table 1 Stemming versus lemmatization

Word

Stem

Lemma

Studies

Studi

Study

Studying

Study

Study

Policies

Polici

Policy

Nature of the Review: fake negative review will not discuss the common problem of the product. Similarly, the common highlight of the product will not be discussed in the fake positive review.

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Fig. 4 The steps in pre-processing

Fig. 5 The analysis on length of the review

Reviewer ID: A reviewer posting a couple of opinions with the same Reviewer ID is likely to be fake. Rating: Fake reviews in most situations have 5 out of 5 stars to attract the client or have the lowest rating for a negative fake review. Verified Purchase: The review that is of a verified purchase is having less chance of becoming fake. Combination of all these features is selected to enhance the accuracy of the model (Table 2).

66 Table 2 Impact of nature of review

Table 3 Impact of length of the review

M. S. Jacob and P. Selvi Rajendran NOR

Label

Count

Common problem discussed

Fake Genuine

2100 7412

Common problem not discussed

Fake Genuine

8100 1428

Review ID

No. of words

Fake/Genuine

2

8

Genuine

4

15

Fake

9996

10

Genuine

9998

15

Fake

It is analyzed that the length of the review is closely related to the genuinity of the review. If the length of the review is above the threshold (number of words is more than 10), then the chance that the review is fake is high (Table 3). Most of the time the review source will be the same. This can be identified by the Reviewer ID parameter. It is also analyzed that the review for a product posted from the same user multiple times, then those reviews are likely to be fake. This is identified with the Reviewer ID parameter. The rating of the review is considered as another important parameter in finding out the genuinity of the review. Fake reviews in most situations have maximum number of stars to attract the client or have the lowest rating for a negative fake review. A review rating in the midrange is identified as genuine (3 or 4). The review that is of a verified purchase is having less chance of becoming fake. Table 4 indicates the impact of verified purchases on the detection of fake reviews. If the review is from a person who has actually bought the product, the chances of being genuine are more, On the other hand, if the review is from a person whose purchase is not verified then the chances of genuinity are very less (Fig. 6). The following graph (Fig. 7) indicates the nature of the review. The nature of the review is considered an important parameter in finding out whether the review is genuine or fake. It is analyzed that the review which does not address the common problem of a product discussed in majority of the reviews is considered as a fake negative review. Similarly, a review which is not addressing the common. Highlight Table 4 Impact of verified purchase Verified purchase

Label

Count

Yes

Fake Genuine

2577 7234

No

Fake Genuine

7623 1679

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Fig. 6 The analysis on verified Purchase

Fig. 7 The analysis on nature of review

of a product that is discussed in majority of the reviews is considered as a fake positive review (Fig. 8). The corpus will be split into two data sets, Training and Assessment. The training data set will be used to match the model and the predictions will be performed on the test data set. This can be done via the train test split of the Sklearn library. As we have set the test size = 0.3 parameter, the training data will have 70% of the corpus and the test data will have the remaining 30%.

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Fig. 8 Feature extraction

The target variable encoding is performed to transform string type categorical data into numerical values in the data set that can be interpreted by the model. Then it’s done with the word vectorization process. The transformation of a collection of text documents into vectors with numerical features is a general operation. The model can understand many methods for translating text data to vectors, but TF-IDF is by far the most common method. This is an acronym for “Term Frequency-Inverse Document” Frequency, which is the portion of the resulting scores assigned to each expression. Term Frequency: This summarizes how frequently within a text a given word appears. Frequency of the Inverse Document: This downscales terms that appear a ton in documents. TF-IDF are ratings of word frequency that tend to explain words that are more interesting, e.g. daily in a text, without going into the math, but not by documents. TFIDF are ratings of word frequency that tend to explain words that are more interesting, e.g. daily in a text, without going into the math, but not by documents. The following syntax may be used to first fit the TG-IDF model over the entire corpus. This will help TF-IDF build a vocabulary of words it has studied. We have got a vocabulary as shown below from the corpus. This is going to give a performance as (Figs. 9, 10 and 11) {‘even’: 1459, ‘sound’: 4067, ‘track’: 4494, ‘lovely’: 346, ‘paint’: 3045, ‘mind’: 2740, ‘well’: 4864, ‘would’: 4952, ‘recommend’: 3493, ‘people’: 3115,’ hate ‘: 1961,’ video ‘: 4761…} The vectorized data is available in the variable Train_X_Tfidf. Each column in the database is represented with the help of a vector for further analysis. The Naïve Bayes classifier gives an accuracy score of 83.1%. The SVM classifier gives an accuracy score of 84.6%. A comparative analysis is shown in the following graph (Fig. 12). It

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Fig. 9 The label genuine/fake

Fig. 10 Analysis of parameters for feature extraction

is identified that SVM classifier gives a better result as applied in the given dataset.

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Fig. 11 The accuracy score calculation

Fig. 12 The comparison of accuracy in percentage

6 Conclusion The identification of deceptive reviews is intended to filter false reviews. In this approach, the SVM classification of the research work provided an enhanced classification accuracy on the given dataset. Compared to Naïve Bayes classifier. Precisely the model can classify the fake feedback further and forecast them effectively. It is possible to apply this technique to other sampled dataset examples. The visualization of data helped explore the accuracy of the classification contributed to the dataset and also the characteristics found. The different algorithms used and their

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accuracy indicates how each of them worked on the basis of their accuracy factors. The method also provides the consumer with a feature to suggest the most important truthful feedback to make choices about the merchandise possible for the customer. Various variables such as the inclusion of new vectors such as review length, scores, nature of the review and verified purchase have helped to infer and thereby achieve the precision of the data classification.

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M. S. Jacob and P. Selvi Rajendran computing and communications; IEEE 16th International conference on smart city; IEEE 4th International conference on data science and systems (HPCC/SmartCity/DSS). IEEE, pp 282– 289 Hashimoto T, Kuboyama T, Shirota Y (2011) Rumor analysis framework in social media. In: TENCON 2011–2011 IEEE Region 10 conference. IEEE, pp 133–137 Chapelle O, Scholkopf B, Zien A (2009) Semi-supervised learning (Chapelle O et al (eds), 2006) [book reviews]. IEEE Trans Neural Networks 20(3):542–542 Zhu XJ (2005) Semi-supervised learning literature survey. University of Wisconsin-Madison Department of Computer Sciences, Technical report Elmurngi EI, Gherbi A (2018) Unfair reviews detection on Amazon reviews using sentiment analysis with supervised learning techniques. J Comput Sci 14(5):714–726 Conroy NJ, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. In: Proceedings of the annual meeting. Association for Information Science and Technology Reis JCS, Correia A, Murai F, Veloso A, Benevenuto F (2019) Supervised learning for fake news detection. IEEE Intell Syst 34(2):76–81 Wagh B, Shinde JV, Kale PA (2017) A Twitter sentiment analysis using NLTK and machine learning techniques. Int J Emerg Res Manag Technol 6(12):37–44 McCallum A, Nigam K (1998) A comparison of event models for Naive Bayes text classification. In: Proceedings of AAAI-98 workshop on learning for text categorization, Pittsburgh, PA, USA, vol 752, no 1, pp 41–48, July 1998 Wang WY (1999) Liar, liar pants on fire: a new benchmark dataset for fake news detection. In: Proceedings of the annual meeting. Association for Computational Linguistics, pp 422–426 Novak J (2019) List archive Emojis [Online]. Available: https://li.st/jesseno/positivenegativeand-neutral-emojis-6EGfnd2QhBsa3t6Gp0FRP9. Accessed: June 2019 Novak PK, Smailovi´c J, Sluban B, Mozeti I (2015) Sentiment of Emojis. J Comput Lang 10(12):1–4 Liu N, Hu M (2019) Opinion mining, sentiment analysis and opinion spam detection [Online]. Available: https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon. Accessed: Jan 2019 Long Y, Lu Q, Xiang R, Li M, Huang C-R (2017) Fake news detection through multi-perspective speaker profiles. In: Proceedings of the eighth international joint conference on natural language processing, vol 2: Short papers, pp 252–256

Detecting Six Different Types of Thyroid Diseases Using Deep Learning R. Mageshwari, G. Sri Kiruthika, T. Suwathi, and Tina Susan Thomas

Abstract Thyroid is the most common disease worldwide. More than 42 million people are affected by thyroid in India. Thyroid is a small gland in the neck region that secretes thyroid hormone which is responsible for directing all our metabolic functions. When the thyroid malfunctions, it can affect every facet of our health. Thyroid disease has become more challenging for both medical scientists and doctors. Detecting thyroid diseases in early stage is the first priority to save many people’s life. Typically, visual examination and manual techniques are used for these types of thyroid disease diagnoses. Thus, in this project, we apply deep learning algorithms to detect six different types of thyroid diseases and its presence without the need for several consultations from different doctors. This leads to earlier prediction of the presence of the disease and allows us to take prior actions immediately to avoid further consequences in an effective and cheap manner avoiding human error rate. Keywords Deep learning · Prediction · Thyroid

1 Introduction The endocrine system is a part that secretes thyroid hormone in our neck, which is responsible for controlling heart and brain development, muscle, and digestive function [1]. The Thyroid Disease Detection project is focusing on detecting six types of thyroid diseases which are Thyroid cancer, Thyroid nodules, Hyperthyroidism, Goiter, Thyroiditis, and Thyroidism [2]. Hypothyroidism doesn’t have enough thyroid hormones. So, it leads to weight gain, dry skin, and heavy periods [3]. Thyroiditis which produces too much thyroid hormone causes huge pain. Hyperthyroidism is caused when thyroid is overactive and releases much more hormones which lead to appetite, diarrhea, and rapid heartbeat [4]. The thyroid nodules are just an unusual growth in which the cells might be either fluid-filled or solid lump. Thyroid cancer causes malignant cells from the thyroid gland. Prediction of thyroid R. Mageshwari · G. Sri Kiruthika · T. Suwathi · T. S. Thomas (B) Department of Information Technology, KCG College of Technology, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_5

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diseases presence by manually checking the reports by a number of doctors leads to mistakes causing life losing disadvantages. In the existing system, the benign and malignant thyroid is detected using OTL by Ultrasound images [5]. The accuracy of prediction was also compared to VGG-16 based on different input images and the transfer learning model. The purpose of thyroid disease detection is to detect the problems related to thyroid gland without multiple consultations from different doctors. As it is an automated process, people can save time and money. To detect the disease, we use VGG-16 algorithm in Deep Learning.

2 Related Work This literature survey provides detailed documentation about the current state of deep learning technology in information and research systems [6]. It outlines, in general, the functioning and the factors required for the success of deep learning [7]. Here the platform where this technology is used is mentioned and also its uses are described. This technology enables safe and secure interaction between applications and people without the help of doctors [8]. Conclusively, it is noted that this technology in its current state has a long way to go before it can be adopted as a mainstream technology.

3 Objective The main purpose of this project is to determine the presence of thyroid disease at an early age. Predicting the presence of thyroid disease by hand examining the results of reports from various doctors will be time consuming and may sometimes leads to errors that cause even human loss. The purpose of thyroid diagnosis is to diagnose thyroid-related problems without consulting a variety of doctors. An in-depth study has so far been very helpful in health care systems by understanding the source and journey of the product. As is the custom, people can save time and money.

4 Motivation When it comes to health care the thyroid disease can affect anyone—men, women, infants, teenagers and the elderly. It can be present at birth (typically hypothyroidism) and it can develop as you age (often after menopause in women). More than 42 million peoples are affected by thyroid in India. Thyroid leads to loss of life and increased risk. Manual process is time consuming. Thyroid disease is sometimes hard to examine as the symptoms were certainly puzzled with those of other circumstances. Inputs are given as scanned image from the local storage and will get the output as name of disease present with the patient’s details.

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5 Existing System It aimed to put forward a highly mutualized model, a new framework of Online Transfer Learning (OTL) to find the difference between benign and malignant thyroid nodules using ultrasound (US) images [9]. Online Transfer Learning method is the combination of both the transfer learning and the online learning. The datasets of two thyroid nodules have 1750 images which contains 1078 of benign and 672 of malignant nodules, and 3852 of thyroid nodules which contains 3213 of benign and 639 of malignant nodules were collected to develop the model [10]. OTL method was compared with VGG-16 also. One of the most disadvantages is that the transfer learning only works if the initial and target problems of both models are similar enough. Only benign and malignant kind of thyroid presence is detected within the existing system. Accuracy decreases with decrease within the amount of data.

6 Proposed System An intelligent six different thyroid diseases recognition systems can predict the presence of different types of thyroid by integrating the image processing model and deep learning technique. The proposed system discusses the detection of thyroid information to increase accuracy. Both researchers and doctors face the challenges of fighting with thyroid diseases. In this thyroid disease could be a major explanation for formation in diagnosing and within the prediction, onset to which it’s a difficult axiom within the medical research. Thus, in this project we develop and apply a novel deep learning architecture to effectively detect and identify the presence of five different thyroid diseases such as Hyperthyroidism, Hypothyroidism, Thyroid cancer, Thyroid nodules, Thyroiditis and to check for normal thyroid without the need of several consultations from different doctors. Uses VGG16 algorithm to achieve the purpose. Optimizing technique like SGD—stochastic gradient descent and regularization methods like ReLU and ELU to increase the accuracy. Eliminates human error rate. Web application for hospitals will be developed which requires a scanned input image to predict the type of disease.

7 Architecture Diagram In this project given in Fig. 1, the presence of thyroid diseases are highlighted and it will be easy to verify the presence of thyroid diseases within the CT pictures or Xrays. So, initially, the primary steps are dataset assortment where we’ll be aggregating the dataset like CT images or X-rays utilized by the laboratories to analyze the presence of thyroid diseases from numerous resources through internet. After that, we’ll be rending those datasets into totally different classes. That is, the dataset will be

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Fig. 1 Architecture for detection of thyroid

rendered into coaching and testing dataset. In coaching datasets, we’ll be mistreating the dataset for coaching the module whereas a testing dataset is employed to gauge the model when it’s been utterly prepared. Thus, coaching dataset initially undergoes the method referred to as dataset augmentation; wherever the dataset is multiplied into several datasets then it’ll undergo the method referred to as pre-processing which is to form all sizes into single size. We train those datasets by extracting the options using a novel deep learning design. It undergoes a method referred to as improvement that will optimize the model and decreases the loss which will cut back the noises generated throughout training. Then it’ll be undergoing a process termed model seriation which will be able to evaluate the generated mistreatment model. Thereby the presence of thyroid diseases will be predicted from the testing dataset. A JavaScript framework react.js will be developed during scanning the Associate in Nursing input images. This will provide the output of the kind of thyroid disease saving plenty of our time and cash invested by the patients. Thus, this methodology provides Associate in Nursing effective and low-cost methodology to work out the presence of thyroid diseases than the methodologies used these days.

8 Modules Dataset Collection—Ultrasound images are collected for various types of thyroid diseases in various hospitals. There are three steps to collecting data, manually detecting, and downloading X-ray or CT scans that can take a long time due to the amount of work involved. As data has become an important asset in deep learning, more information can be obtained from third-party resources. Start with a pre-trained network on a large database, then connect it to yourself.

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Fig. 2 Dataset collection

Data Addition—Using this method the amount of data collected increases. Data Processing—Data sets collected from various hospitals are in raw format which is not usable for analysis, so we used the data processing process. Algorithm Training—Datasets are trained using an in-depth learning algorithm. Data Uses—To increase efficiency and reliability, the optimization process is applied (Figs. 2, 3 and 4).

9 Algorithm Novel Architecture: In this project given in the diagram Fig. 5 a novel construction is underway to identify five different types of thyroid diseases such as Hyperthyroidism, Hypothyroidism, Prostate cancer, Thyroid nodules, Thyroiditis and to check whether the thyroid gland is normal.. The network can take an input image with height, width as duplicate 32 and 3 as channel width. It will be the size of the input as 224 × 224 × 3. The design of the novel makes the first convolution and max-pooling using the dimensions of 7 × 7 and 3 × 3 respectively. After that, Phase 1 of the network starts and consists of three remaining blocks containing three layers each. The maximum number of characters used to perform the convolution function on all three layers of block 1 is 64, 64, and 128 respectively. Curved arrows point to the connection of the identity. The combined arrow represents that the operation of the convolution in Residual Block is done in stride 2, hence, the input size will be reduced by half in length and width but the channel width will be doubled. Continuing from one stage

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Fig. 3 Efficiency of model obtained

Fig. 4 Graph plot after the training process

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Fig. 5 Steps for processing the thyroid Image

to another, the channel width is doubled and the input size is reduced by half. In deep networks bottleneck design is used. For each remaining F function, three layers are arranged in sequence. The three layers are 1 × 1, 3 × 3, 1 × 1 convolutions. Layers of 1 × 1 convolution are responsible for reducing and restoring size. The 3 × 3 layers are left as a bottle with a small size of input/output. Finally, the network has a Pooling layer followed by a fully connected layer of 1000 neurons (output of the ImageNet class).

10 Flow Chart Flow Chart is given below in Fig. 6.

11 Workflow Step 1: Start.

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Fig. 6 Flow chart

Step 2: Get the scanned image. Step 3: Upload the scanned image from the local storage. Step 4: Get the digital output. Step 5: Stop.

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12 Conclusion Thyroid disease detection model leads to an earlier prediction of the presence of the disease and allows us to take prior actions immediately to avoid further consequences in an effective and cheap manner avoiding human error rate. We use PyCharm and Visual Studio Code as Integrated Development Environment and ReactJS is used as a front-end tool and Node.js as backend tool. We have concluded that by giving the ultrasound images as input we can get the disease name with patient details as output. Therefore, instead of checking the report manually, we can implement the same in digital method.

References 1. Zhou H, Wang K, Tian J (2020) Online transfer learning for differential diagnosis of benign and malignant thyroid nodules with ultrasound images. IEEE Trans Biomed Eng 67(10) 2. Kumar V, Webb J, Gregory A, Meixner DD, Knudsen JM, Callstrom M, Fatemi M, Alizad A (2020) Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE 3. Song W, Li S, Liu J, Qin H, Zhang B, Zhang S, Hao A (2018) Multi-task cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE J Biomed Health Inf 4. Rosen JE∗, Suh H, Giordano NJ, A’amar OM, Rodriguez-Diaz E, Bigio II, Lee SL (2016) Preoperative discrimination of benign from malignant disease in thyroid nodules with indeterminate cytology using elastic light-scattering spectroscopy. IEEE Trans Biomed Eng 61(8) 5. Anton-Rodriguez JM, Julyan P, Djoukhadar I, Russell D, Gareth Evans D, Jackson A, Matthews JC (2019) Comparison of a standard resolution PET-CT scanner with an HRRT brain scanner for imaging small tumors within the head. IEEE Trans Radiat Plasma Med Sci 3(4) 6. Anand R et al (2016) Blockchain-based agriculture assistance. Lecture notes in electrical engineering. Springer, Berlin, pp 477–483 7. Feigin M, Freedman D, Anthony BW (2016) A deep learning framework for single-sided sound speed inversion in medical ultrasound 0018-9294(c) 8. Narayan NS, Marziliano P, Kanagalingam J, Hobbs CGL (2016) Speckle patch similarity for echogenicity based multi-organ segmentation in ultrasound images of the thyroid gland. IEEE J Biomed Health Inf 2168-2194 9. Azizi S, Bayat S, Yan P, Tahmasebi A, Kwak JT, Xu S, Turkbey B, Choyke P, Pinto P, Wood B, Mousavi P*, Abolmaesumi P (2018) Deep recurrent neural networks for prostate cancer detection: analysis of temporal enhanced ultrasound. IEEE Trans Med Imaging 0278-0062 10. Peter TJ, Somasundaram K (2012) An empirical study on prediction of heart disease using classification data mining techniques. In: IEEE-International conference on advances in engineering, science and management, ICAESM-2012, vol 14, no 8, Aug 2012

Random Forest Classifier Used for Modelling and Classification of Herbal Plants Considering Different Features Using Machine Learning Priya Pinder Kaur and Sukhdev Singh

Abstract The advancement of technology in all the fields not limited to one area but today demand in the field of herbal plants has been also increased. As the change in lifestyle of human being leads to various difficulties for their living and healthrelated issues, there is a need for the system that will help for the recognition and identification of herbal plants that are not known to everyone and carries useful properties in life of humankind. The researcher takes advantage to carry out their research in the field of computer science using image recognition, machine learning and extends further into deep learning. In this paper images of herbal plants are taken by mobile camera and then resized to 20% from the original size for easy and fast calculation of features. Then those RGB images are converted into grayscale then to binary for reading features and calculation purposes. The random forest classifier is used to classify different herbal plant species considering shape as the main feature, based on various features such as length, width, area of the leaf, perimeter of leaf, leaf area enclosed in a rectangle, leaf percentage in the rectangle, leaf pixels in four different quadrants and rectangle pixels in four different quadrants are extracted in feature extraction. Training data consists of 85% and for testing 15% data has been used and classification results are discussed. Keywords Random forest · Feature extraction · Classification · Plant recognition

1 Introduction The technological world of today necessitates that technology be used to find solutions to complex problems. Herbal plants can be found abundantly across the globe and in all regions of any country. These herbal plants have been used since millenniums to cure various diseases which have affected humankind. These plants are P. P. Kaur (B) Punjabi University, Patiala, Punjab, India S. Singh M.M. Modi College, Patiala, Punjab, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_6

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most effective for medicinal purposes and also help in keeping the air clean, disinfect germs and dangerous micro air pollutants. Besides providing for food these plants have great use in medicine. Physical feature as shape helps in identification as well as classification of the plant species and hence it is an important feature. The shape is also used by researchers as its availability is without question. In this paper, features extraction is done from the herbal leaves and then machine learning algorithms are employed for training purposes. Afterward, the dataset created is tested and classification is done of the plant leaves to obtain the optimum results using a random forest classifier.

2 Literature Review Today researchers carried out research by using different techniques of machine learning and deep learning. Some of the methods, techniques, and algorithms are already studied and discussed below related to machine learning to classify and recognize herbal plants through various features of plant leaves. Kaur et al. [1] purposed that different machine learning techniques used for the recognition of herbal plants and various methods were discussed that are suitable identification of herbal plant species and which physical features are considered as the most important for the plant to be recognized in image processing, machine learning and in deep learning. Pankaja and Suma [2] introduced two methods WOA (Whale optimization algorithm) and random forest for the classification and recognition of plant leaf. Images of plant leaves taken from Swedish and Flavia datasets. WOA method overcomes the dimensionality problem and random forest classify the leaf of plant and gives 97.58% accuracy using shape, texture, and color features. Singh and Kaur [3] the paper gives a comparative study of features that were discussed, which gives better results and is used most widely in the field of computer vision and image processing for recognition and identification of herbal plants. Shape, color, texture, and vein features were discussed in detail. Singh and Kaur [4] in this review on plant recognition system on the basis of leaf done in which different methods for classification of leaf on the basis of their shape, color, texture, and vein were discussed with their problem addressed and how they solve using best method/technique for recognition, classification, and identification of plant using machine learning. Ramesh et al. [5] proposed a system for identifying healthy and diseased leaves through a random forest algorithm. 160 leaves of papaya are contained in the dataset and texture, color, and shape features were extracted. For object detection the histogram of an oriented gradient method. Accuracy attained through the random forest was 70%, it improved by combining local features with global features such as SURF (speed up robust features) and SIFT (scale invariant feature transform), DENSE along with BOVW (Bag Of Visual Word).

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Vijayshree and Gopal [6] purposed an automatic system that identifies and classifies herbal medicinal plants. 500 leaves along with 21 features containing 50 different species were extracted using color, texture, and shape. The experimental output shows that using a neural network for identification and classification of texture feature resulted in 93.3% accuracy while using all features gives 99.2% accuracy. Dahigaonkar and Kalyana [7] identified ayurvedic medicinal plants that used leaves on the basis of their shape, color, and texture feature. A total of 32 different plants were classified using features such as solidity, eccentricity, entropy, extent, standard deviation, contrast, equivalent diameter, mean, and class. They attained through support vector machine classifier 96.66% of accuracy. Kan et al. [8] purposed classification of herbal plants using an automated system that used leaves. Shape-5 and texture-10 features were extracted and implemented using SVM classifier for classification of the plant. The medicinal plant specimen library of Anhui University of Traditional Chinese Medicine was taken and its dataset contained 240 leaves. The experiment resulted in 93.3% recognition rate. De Luna et al. [9] developed the system for the identification of Philippine herbal medicine plant through leaf features by ANN. Dataset contains 600 images from 12 different herbal plants having 50 from each. Basic features length, width, circularity, convexity, aspect ratio, area, perimeter of convex hull, solidity, vein density were used that defined the venation, shape, and structure of leaf. The classification and analysis of data are done through Python and Matlab. The experiments were conducted for identification of herbal plants through various algorithms mentioned as—Naïve Bayes, Logistic Regression, KNN, classification and regression trees, linear discriminant analysis, SVM, Neural network and resulted in 98.6% recognition. Isnanto et al. [10] proposed a system for recognition of herbal plants using their leaf patterns. Extracted features of the image used Hu’s seven moments invariant, image segmentation-Otsu method and for recognition—Euclidean and Canberra Distance were implemented. The experimental results gave 86.67% accuracy in the identification system through Euclidean distance while using Canberra distance 72% accuracy was achieved. 100% accuracy was attained for nine types of leaves using Euclidean distance and five types of leaves using Canberra distance.

3 Proposed Methodology The images of herbal plant species taken by mobile camera in the daylight and after resized into 20%, preprocessing is done then features of the leaf are extracted and then training and testing are conducted to obtain maximum accuracy by classifying different plants on the basis of their shape features (Fig. 1).

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Fig. 1 Proposed methodology follows

3.1 Image Acquisition The sample of leaves contained in the dataset obtained from herbal plants collected from different herbal and botanical gardens of Punjab. The herbal plant leaf images were taken using mobile camera in the daylight and used for the experimental purpose of training and testing to classify plants through their leaves. Table 1 represented a sample of leaves from plants namely (1) Haar Shinghar, (2) Periwinkle, (3) Currey Patta, (4) Tulsi.

3.2 Preprocessing The RGB image of herbal plant leaf has taken on plain white paper background, and resized the image for fast execution, take low memory space, and for easy evaluation. The RGB image is converted into grayscale and then binary for extracting their features (Table 2).

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Table 1 Sample of leaves from four different plants

Table 2 RGB image, grayscale image, and binary image of different leaves of herbal plants

Haar shinghar

Periwinkle

Currey patta

Tulsi

3.3 Feature Extraction Feature extraction is the part that is used to recognize plants by extracting different features from their leaf. Various morphological features are calculated such as leaf height and width, total area of rectangle enclosing leaf, total leaf area, perimeter of

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Length Width

Leaf pixels of quadrant 1, 2, 3, 4

Rectangle pixels of quadrant 1,2,3,4

Fig. 2 Represents feature extraction of herbal leaf

Fig. 3 Sample output of leaf feature extraction

leaf in pixels, four quadrants of rectangle in pixels, four quadrants of leaf in pixels, and leaf percentage in a rectangle. Figure 2 shows leaf samples with their feature description (Fig. 3).

3.4 Classification For classification, random forest classifier is used where features are extracted from leaves are stored in the dataset and used for training and testing purposes. Training contains 85% and for testing, 15% of data has been used, random forest classifier model has been implemented (taking n_estimator = 10, max_features = auto) for

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classification of herbal plant. The machine has trained to obtain more accuracy using different features.

3.5 Performance Evaluation After classification of herbal plant species then performance has been evaluated on the basis of results given by random forest classifier. It represents how many plants belong to a particular category and also generated a confusion matrix, accuracy score, and classification report that consists of precision, recall, F 1 -score, and support values.

4 Result and Discussion 4.1 Result while using random forest model on 50 leaves and 75 leaves of plants 0: Currey patta 1: Haar shinghar 2: Periwinkle 3: Tulsi has been analyzed using various features. Case I: Using 4 features stated below (Fig. 4): Case II: Using 5 features stated below (Fig. 5): Case III: Using 6 features stated below (Fig. 6): Case IV: Using 8 features stated below (Fig. 7): Case V: Using 10 features stated below (Fig. 8): Case VI: Using 14 features stated below (Fig. 9 and Table 3): (a)

(b)

Fig. 4 a Results of all four plants using random forest model for 50 leaves. b Results of all four plants using random forest model for 75 leaves

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(a)

(b)

Fig. 5 a Results of all four plants using random forest model for 50 leaves. b Results of all four plants using random forest model for 75 leaves

(a)

(b)

Fig. 6 a Results of all four plants using random forest model for 50 leaves. b Results of all four plants using random forest model for 75 leaves

(a)

(b)

Fig. 7 a Results of all four plants using random forest model for 50 leaves. b Results of all four plants using random forest model for 75 leaves

4.1 Discussion (Using Random Forest Model) Plants leaves of Currey Patta, Haar Shinghar, Periwinkle, and Tulsi are taken into consideration.

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Fig. 8 a Results of all four plants using random forest model for 50 leaves. b Results of all four plants using random forest model for 75 leaves

(a)

(b)

Fig. 9 a Results of all four plants using random forest model for 50 leaves. b Results of all four plants using random forest model for 75 leaves

Table 3 Random forest model accuracy result of all four plants (Currey Patta, Haar Shinghar, Periwinkle, and Tulsi) with variable features considering 50 and 75 leaves of each plant Cases No. Number of features 50-Leaves accuracy 75-Leaves accuracy Result output for (%) (%) references I

4

86.2

84.4

Figure 4a, b

II

5

86.6

88.8

Figure 5a, b

III

6

86.2

88.3

Figure 6a, b

IV

8

93.1

86.67

Figure 7a, b

V

10

89.6

86.67

Figure 8a, b

VI

14

90.0

91.1

Figure 9a, b

Case I: For four features [width, height, area of rectangle (in pixels), and area of leaf (in pixels)]. Initially, 50 leaves resulted in accuracy of 86.2%, and afterward, 75 leaves resulted in accuracy of 84.2%. With increase in the number of leaves at same feature level, accuracy decreases, since computational model needs to train and test more datasets.

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Case II: For five features [width, height, perimeter, area of leaf (in pixels), and area of rectangle enclosing leaf]. Initially, 50 leaves resulted in accuracy of 86.6%, and afterward, 75 leaves resulted in accuracy of 88.8%. Even with increase in the number of leaves, accuracy increases, since computational model starts getting familiar with previous derived features of the dataset. Case III: For six features [width, height, perimeter of leaf (in pixel), area of leaf (in pixels), area of rectangle enclosing leaf, and the percentage of leaf in the rectangle]. Initially, 50 leaves resulted in accuracy of 86.2%, and afterward, 75 leaves resulted in accuracy of 88.3%. Even with the increase in number of leaves, accuracy increases with comparison to 50 leaves at six feature levels. Case IV: For eight features [width, height, area of leaf (in pixels), percentage of leaf in the rectangle, leaf pixels in Quadrant (I, II, III, IV)]. Initially, 50 leaves resulted in accuracy of 93.1%, and afterward, 75 leaves resulted in accuracy of 86.67%. With the increase in number of leaves, accuracy decreases, since computational model needs to train and test a large number of datasets. Secondly, selection of features becomes important here. As we can see only four features are common in 6 and 8 features, hence variability of these four new features gave the idea to explore more dimensions to train and test our dataset. Case V: For 10 features [width, height, perimeter of leaf (in pixel), area of leaf (in pixels), area of rectangle enclosing leaf, percentage of leaf in the rectangle, and leaf pixels in Quadrant (I, II, III, IV)]. Initially, 50 leaves resulted in accuracy of 89.6%, and afterward, 75 leaves resulted in accuracy of 86.67%. With the increase in number of leaves, accuracy decreases, since computational model needs to train and test a large number of datasets. Secondly, additional four features are added into six feature datasets, making model more complex. Case VI: For 14 features [width, height, perimeter of leaf (in pixel), area of leaf (in pixels), area of rectangle enclosing leaf, percentage of leaf in the rectangle, and leaf pixels in Quadrant (I, II, III, IV)]. Initially, 50 leaves resulted in accuracy of 90%, and afterward, 75 leaves resulted in accuracy of 91.1%. Finally, even with increase in the number of leaves, accuracy increases, since computational model starts getting familiar with previous derived features of the dataset. Here, all previous combinations of features are introduced making it complex model, but model has been trained enough to provide better desired results (Fig. 10).

5 Conclusion 5.1 Considering all four plant’s data with each 50 leaves parameter, and increasing the number of features from 4 to 14. The model shows better accuracy as the number of features increases. The best accuracy of the model is obtained at the 8th feature

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RFM anaylsis of 50 and 75 leaves Accuracy of model (in %)

94 92 90 88 86 84 82 80

Features 4

5

6

8

10

14

Accuracy of 50 Leaves in %

86.2

86.6

86.2

93.1

89.6

90

Accuracy of 75 Leaves in %

84.4

88.8

88.3

86.67

86.67

91.1

Fig. 10 Analysis of accuracy (in %) of 50 and 75 leaves using random forest model on increasing number of features

Accuracy (%) for 50 leaves 95 90 85 80

Accuracy (%) for 50 leaves

Accuracy (%) for 75 leaves. 95 90 85 80

Accuracy (%) for 75 leaves.

Fig. 11 Accuracy in percentage using RF model for variable features a 50 leaves b 75 leaves

parameter, i.e., 93.1%. While, as we increase more features decrement and again increment in accuracy is obtained due to more complexity of the model (Fig. 11). 5.2 Considering all four plant’s data with each 75 leaves parameter, and increasing the number of features from 4 to 14. The accuracy of the model starts increasing with an increase in the number of features. But after a certain increase, decline in performance of the model is observed with an increase in complexity to detect features. But after sufficient training of the model, best accuracy for 75 leaves is obtained at a maximum number of feature 14 of 91%.

References 1. Kaur PP, Singh S, Pathak M (2020) Review of machine learning herbal plant recognition system. In: 2nd Proceedings of international conference on intelligent communication and computational research ICICCR, India 2. Pankaja K, Suma V (2020) Plant leaf recognition and classification based on the whale optimization algorithm (WOA) and random forest (RF). J Instit Eng (India) Ser B. https://doi.org/ 10.1007/s40031-020-00470-9

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EEG Based Emotion Classification Using Xception Architecture Arpan Phukan and Deepak Gupta

Abstract Electroencephalogram (EEG) is widely used in emotion recognition which is achieved by recording the electrical activity of the brain. It is a complex signal because of its high temporal resolution and thus requires sophisticated methods and expertise to be interpreted with a reasonable degree of accuracy. Over the years, significant strides have been made in the field of supervised and unsupervised feature learning from data, using deep architectures. The purpose of this study is to build on some of these improvements for better classification of emotions from EEG signals. This is a rather challenging task, and more so if the data we are reliant on is noted for being unsteady, as it changes from person to person. There is a need for an intricate deep learning algorithm that can achieve high levels of abstraction and can still dish out robust/accurate results. In this paper, we have used the Xception (Chollet in 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1800– 1807, 2017 [1]) model from Keras API, further reinforced by fine tuning, to classify emotions into three categories namely NEGATIVE, POSITIVE and NEUTRAL. An open-source EEG dataset from Kaggle (Bird et al. in The international conference on digital image and signal processing (DISP’19). Springer, Berlin, 2019 [2]) was used in this study for the purpose of classification. Our experimental results achieved a precision score of 98.34%, a recall value of 98.33%, and an F 1 -score of 98.336%. This result outperforms many other popular models based upon support vector machine, k-nearest neighbor, self-organizing maps, etc., whose accuracy usually ranges from anywhere between 53 and 92%. Keywords EEG · Transfer learning · Emotion classification · Xception

A. Phukan · D. Gupta (B) Department of Computer Science and Engineering, National Institute of Technology, Jote, Arunachal Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_7

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1 Introduction Brain Computer Interface (BCI) bridges the gap between the computer device and human. It differs from neuromodulation as it facilitates bidirectional data flow. The primary objective of the Brain Computer Interface (BCI) is to translate our intentions into workable instructions for robots and other devices. Furthermore, it can also translate our emotions into a machine-friendly language. Emotion is an important aspect of human-to-human interaction and to imitate this relationship in Human-to-Machine interactions has been the focus of research for decades. Emotion identification has been garnering attention in the field of technology as developing empathic robots, consumer analysis, safe driving [3], and numerous other concepts are being actively sought after. Generally, emotions are categorized based on valence and arousal as shown in Fig. 1. The dataset was labeled according to Negative, Neutral, and Positive groups. Using transfer learning, we chose to classify the data into those three classes (Table 1). Researchers have implemented many other practices to classify emotion based on EEG data as well as facial expressions and there are quite a few success stories as well. Bharat and Gupta [6] achieved a staggering 91.42% accuracy for facial expression recognition with their proposed iterative Universum twin support vector machine. Kwon et al. [7] have devised a 2D CNN model which takes EEG spectrogram and GSR features, pre-processed using Short Time Zero Crossing Rate, to classify emotion into four classes, and achieved an arousal accuracy of 76.56% and Valence accuracy of 80.46% with an overall accuracy of 73.43%. Li et al. [8] used multisource transfer learning to facilitate cross-subject emotion recognition based on EEG data and achieved the highest accuracy of 91.31%. They achieved this by first selecting appropriate sources before the transfer learning, calibrating a style transfer mapping, calculating the nearest prototype as well as the Gaussian model before finally executing the transfer learning with the multisource style transfer mapping. A deep belief network was proposed by Zheng et al. [9] way back in 2014 to classify emotions and they managed to achieve a staggering accuracy of 87.6%. Features were extracted from the EEG data by employing the differential entropy feature extraction Fig. 1 Negative, positive, and neutral classes in an arousal and valence diagram [4]

EEG Based Emotion Classification Using Xception Architecture Table 1 Emotions with their valence label [5]

Sl. No.

Emotion

1

Shame

Negative

2

Humiliation

Negative

3

Contempt

Negative

4

Disgust

Negative

5

Fear

Negative

6

Terror

Negative

7

Enjoyment

Positive

8

Joy

Positive

9

Distress

Negative

10

Anguish

Negative

11

Surprise

Negative (lack of dopamine)

12

Anger

Negative

13

Rage

Negative

14

Interest

Positive

15

Excitement

Positive

97

Valence

method or more specifically a Short Time Fourier Transform which had 512 points and a nonoverlapping Hanning window of 1 s. They reasoned that since their data has higher low-frequency values over high-frequency values, differential entropy feature extraction should be able to accurately identify EEG patterns between low well as high-frequency energy. The extracted features were then fed to their deep belief network comprising of two hidden layers. Their proposed architecture for the unsupervised learning stage was 310-100-30 while the supervised learning stage had a structure of 310-100-30-2. Table 2 shows a list of existing models, the features they used, and the accuracy they achieved.

2 Related Work 2.1 Xception Model The very first convolutional neural network design was basically a pile of convolutions meant for feature extraction from the input images and some max pooling operations for subsampling. This design structure was the LeNet style model [20] which was revamped into the AlexNet architecture [21] in 2012. The AlexNet architecture sported multiple convolution operations in between the max pooling operations which facilitated the architecture to learn a diverse and abstract set of features at every spatial scale. The model proposed by Zeiler and Fergus in 2013 [22] and the VGG architecture presented by Simonyan and Zisserman [23] in 2014 were improvements

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Table 2 List of existing models along with their accuracy Sl. No.

Author

Features

Model

Accuracy (%)

1

Kwon et al. [7]

CNN and GSR features

2D-convolutional neural network

73.43

2

Li et al. [8]

Style transfer mapping Style transfer mapping

91.31

3

Zheng et al. [9]

Short time Fourier transform

DBN-HMM

87.6

4

Murugappan et al. [10]

Discrete wavelet transform

Linear discriminant analysis and K-nearest neighbor

83.26

5

Jirayucharoensak et al. [4]

Fast Fourier transform Deep learning network with principal component based covariate shift adaptation

53.42

6

Hosseinifard et al. [11]

EEG band power, detrended fluctuation analysis, Higuchi, correlation dimension, Lyapunov exponent

Linear discriminate analysis, logistic regression, K-nearest neighbor

90

7

Hatamikia and Nasrabadi [12]

Approximate entropy, spectral entropy, Katz’s fractal dimension and Petrosian’s fractal dimension

Self-organization map

55.15

8

Zubair and Yoon [13]

Statistical-based Support vector machine 49.7 features, [14, 15] wavelet-based features

9

Jadhav et al. [16]

Gray-level co-occurrence matrix

K-nearest neighbor

10

Martínez-Rodrigo et al. [17]

Quadratic sample entropy

Support vector machine 72.50

11

Lee et al. [18]

Statistical photoplethysmogram features

Convolutional neural network

82.1

12

Bird et al. [19]

Log-covariance features, Shannon entropy, and log-energy entropy, frequency domain, accumulative features as energy model, statistical features, max, min, and derivatives

Random forest

87.16

79.58

(continued)

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Table 2 (continued) Sl. No.

Author

Features

Model

Accuracy (%)

13

Bharat and Gupta [6]

Principal Component Analysis, Linear discriminant analysis, independent component analysis, local binary pattern, wavelet transform, phase congruency

Iterative universum twin support vector machine

91.4

Fig. 2 Transfer learning model summary

to this design, achieved by increasing the depth or layers of the network. In the same year, Szegedy et al. [24] proposed a novel network dubbed as GoogLeNet (Inception V1). It was based on the Inception architecture and was later reined to Inception V2 [25], followed by Inception V3 [26], the Inception-ResNet [27]. The objective of the Inception module is to make the process of convolution, i.e., the process where a single convolution kernel maps cross channel as well as spatial correlations simultaneously, easier and more efficient. This was achieved by explicitly disseminating the process of mapping correlations to a series of operations that would map the spatial and cross channel correlations independently. In a paper by Chollet [1] he defined Xception as “Extreme Inception”. It is a convolutional neural network designed on depth wise separable convolutional layers. It completely decoupled the mapping of spatial correlations and cross channels in the feature maps. This structure has 36 convolutional layers which extract the features of the input image for the network. These layers are segregated into 14 modules where every node other than the first and last modules, is surrounded by linear residual connections. In this paper, we have made use of the feature extraction base of Xception and defined our output layer with the three output classes, namely POSITIVE, NEGATIVE and NEUTRAL. The activation function of choice was SoftMax. The total parameter, trainable parameter, and non-trainable parameter of the transfer learning model were 20,867,627, 20,813,099, and 54,528 respectively. Refer to Fig. 2.

3 Materials and Methods The subsequent sections explain the implementation or methodology and the dataset used.

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3.1 Proposed Methodology Figure 3 shows the flow chart of this study’s proposed methodology. The first step that had to be undertaken for this study was to download an EEG dataset. Our requirements for one were that it should be easily accessible, free for all, and without any additional terms be it legal or otherwise. We came across many free datasets like DEAP [28], SEED [29, 30], Enterface’06 [31], BCI-NER Challenge [32], cVEP BCI [33], SSVEP [34, 35], SPIS Resting State Dataset [36], EEG-IO [37] and TUH EEG Resources [38] among many others. However, even if they were free, they weren’t exactly available to all. They needed signed EULA among many other preconditions that needed to be satisfied before one can download the dataset. Thus, our final candidate for the dataset was the open-source dataset [2] by Bird. This is a multi-class dataset that had discrete values for the readings. We then used it to generate 2132 python specgram images. A sample of these images can be found in Fig. 5. These generated specgram images are what we used for classification purposes by the Xception model. The Xception model, however, only accepted images that met certain conditions. We proceeded with the necessary prepossessing of the specgram images, i.e., scaling and resizing to bring them within the bounds of the accepted format. We then input these pre-processed images into the Xception model. At this stage, trying to classify 2132 images on a mere 12 GB of RAM was guaranteed to trigger an out-of-memory error on a free Google colab session. Thus, we had to reduce the number of inputs, i.e., the number of images to be used for classification down to 1500. The next step involved putting the Xception model to work to generate our classifier. Since our objective was to capitalize on an already optimized and robust neural network, we decided to freeze all the weights of our Xception model and train it over our trimmed dataset of 1500 specgram images. Although this was not an ideal solution, this allowed us to circumvent the insufficient memory error of the Google colab session. After successfully training our model, we fine-tuned it further by retraining the 120th and successive layers. This implies leaving the weights of the layers between 1 and 119 untouched while the weights of all the layers after the 119th layer were updated. Finally, we plotted our graphs and the confusion matrix of our observed result as shown in Figs. 6, 7, and 8 respectively.

3.2 Dataset There are two methods to acquire EEG data from a person. The first is the subdural method where the electrodes are placed under the skull, on or within the brain itself. On the other hand, forgoing this invasive method, there is also the option of a noninvasive technique where electrodes (either wet or dry) are placed around the cranium and raw EEG data, which is measured in microvolts (µV) are recorded from time t to t + n.

EEG Based Emotion Classification Using Xception Architecture Fig. 3 Flow chart of the proposed method

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Fig. 4 The sensors AF7, AF8, TP10, and TP9 of the Muse headband. The NZ placement (green) was used to calibrate the sensors as a reference point [19]

The dataset used in this study was recorded by four dry extracranial electrodes via a MUSE EEG headband which employs a non-invasive technique for acquiring data. Micro voltage recordings were taken from the AF7, AF8, TP10, and TP9 electrodes as illustrated in Fig. 4. Six film clips were shown to a male and a female subject and the data was recorded for 60 seconds generating 12 min of EEG data. Which translates to 6 minutes of data for each emotional disposition. Neutral EEG brainwave data were also recorded for 6 minutes to add valence to the data. This practice of using the neutral class for classification was found to have significantly reinforced the learning rate for a chatbot [5]. The 36 min of brainwave data thus recorded had its variable frequency resampled to 150 Hz which generated a dataset comprising 324,000 data points. Emotions were evoked from the list shown in Table 1 by activities that were strictly stimuli. To avoid modification of neutral data by emotions the participants might have felt a priori, it was collected first and without any stimuli. This ensured that the resting emotional state data of the subjects were unaltered. To curtail the interference of the resting state, only 3 minutes of data was collected every day (Table 3). In order to keep the Electromyographic (EMG) waves, which are more prominent over brain waves, from modifying the EEG data, the participants were forbidden from making any conscious movements like drinking or eating while watching the video clips. Ding and Marchionini [39] proposed that blinking patterns can prove to be constructive for classifying mental dispositions, which is why there were no restrictions placed on the subjects over their unconscious movements. Table 3 Details of the video clips used as stimuli for the EEG data recording [5] Stimulus

Details

Studio

Year

Valence

Marley and Me

Death scene

Twentieth Century Fox, etc.

2008

Negative

Up

Opening death scene

Walt Disney Pictures, etc.

2009

Negative

My Girl

Funeral scene

Imagine Entertainment, etc.

1991

Negative

La La Land

Opening musical number

Summit Entertainment, etc.

2016

Positive

Slow Life

Nature time lapse

BioQuest Studios

2014

Positive

Funny Dogs

Funny dog clips

Mashup Zone

2015

Positive

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Fig. 5 Four different samples of the spectrograms generated

A combination of Log-covariance features, Shannon entropy and log-energy entropy, Frequency domain, Accumulative features as energy model, Statistical Features, Max, Min, and Derivatives were taken into account to generate a dataset of 2548 source attributes [2, 19].

3.3 Implementation Using the specgram function from the matplotlib.pyplot library in python, we extracted the spectrograms of the 2132 instances of the dataset. Figure 5 shows a sample of the spectrograms which were generated and then fed to the Xception architecture for feature extraction and classification. Those images were then resized to 299 × 299 which is the recommended size for inputs in the Xception model. They were then scaled to the range of 0.0–1.0. However, due to hardware constraints, we could not use the whole set of images of 2132 spectrograms. Instead, we chose to make the first 1500 spectrograms as our image dataset for this study. We compiled our Xception model with the Sparse Categorical Cross entropy being the loss function of choice, Adam optimizer, and the accuracy metrics. It was then executed for 100 epochs with the batch size set to 16 because any batch size greater than 16 crashed our Google colab session by using up all of the available RAM. The validation split which was set to 0.1, was scrutinized for judging the accuracy of the model in this study.

4 Results The model, without any further fine tuning, gave a test accuracy of 97.66%, refer to Fig. 6 for the accuracy versus epochs graph. To fine-tune this model, we trained the weights of the Xception model layers from index 120 onwards, all the while keeping the weights of the preceding layers fixed. We compiled this model without changing any of the other parameters, i.e., the loss function, optimizer, and metrics were still the Sparse Categorical Cross entropy, Adam, and accuracy respectively. Furthermore, the epochs for the purpose of fine

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Fig. 6 Accuracy versus epochs (before fine tuning)

tuning were set to 100, the validation split was set to 0.1 and the batch size was kept at 16 due to hardware constraints. This fine-tuned model gave a weighted average precision score of 98.34%, a weighted average recall value of 98.33%, and a weighted average F 1 score of 98.336%. Refer to Fig. 7 for the accuracy versus epochs graph after fine tuning the Xception model while Fig. 8 visualizes the confusion matrix. One can easily verify from Table 2 that our model outperforms all the models mentioned in it. However, it still leaves much to be desired. A 98% accuracy, while looks good on paper, is not an accurate enough model for the real world. A 2% inaccuracy in the real world where there might be thousands of samples to classify, some of which might even be for medical purposes, is a glaring problem. It is serious enough that most might not consider it trustworthy enough to completely switch over to machine learning alternatives from the human counterparts. Thus, further research and optimizations are in order to successfully bridge this gap. Fig. 7 Accuracy versus epochs (after fine tuning)

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Fig. 8 Confusion matrix

5 Conclusion This study highlighted the application of transfer learning of robust models on EEG data. These architectures were trained on the ImageNet dataset or other significantly larger image datasets, one of which is home to 350 million images and 17,000 class labels thereby guaranteeing higher abstraction and increased accuracy. The Xception model considered for this study gave impressive results with minimal effort. The caveat here is that the windowed EEG data were collected from four points on the scalp, namely the AF7, AF8, TP9, and TP10 by a low resolution commercially available EEG headband. The high precision results prove that architectures like Xception, VGG, ResNet, DenseNet, etc. have astounding potential in the field of BCI applications. Further studies may include an ensemble of transfer learning as well as other classification algorithms like Support Vector Machine, Random Forrest, Deep Belief Network, K-Nearest Neighbor, etc. over more sophisticated datasets like DEAP, SEED, Enterface’06, etc. to gauge performance improvements with respect to existing models.

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Automated Discovery and Patient Monitoring of nCOVID-19: A Multicentric In Silico Rapid Prototyping Approach Sharduli, Amit Batra, and Kulvinder Singh

Abstract Deep learning (DL) has played a vital role in the analysis of several viruses in the area of medical imaging in recent years. The Coronavirus disease stunned the whole world with its quick growth and has left a strong influence on the lives of myriad across the globe. The novel Coronavirus disease (nCOVID-19) is one of the ruinous viruses as conveyed by the World Health Organization. Moreover, the virus has affected the lives of millions of people and resulted in casualties of more than 1,224,563 people as of November 04, 2020. The objective of this manuscript is to instinctively discover nCOVID-19 pneumonia sufferers by employing X-ray pictures along with maximization of accuracy in discovery utilizing deep learning methods and image pre-processing. Deep learning algorithms have been written to examine the X-rays of individuals to fight with this disease. This would be highly beneficial in this pandemic as the manual testing takes a huge amount of time, heeding that the count of those infected from this disease is just augmenting. Several metrics in this paper have been used to predict the performance, i.e., accuracy, sensitivity, specificity and precision. The great accuracy of this computer-assisted analysis mechanism can remarkably enhance the accuracy and speed of determining the nCOVID-19 patients in initial stages. This research for the automated discovery of the nCOVID-19 virus has employed the datasets from some of the widely used public repositories like UCI, Kaggle and GitHub. Likewise, successful efforts are made for the automated discovery and monitoring of nCOVID-19 by using a rapid prototyping approach for timely and authentic decision by implementing machine learning.

Sharduli Department of Biotechnology, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India A. Batra (B) Department of CSE, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India K. Singh Faculty of CSE, Department of CSE, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_8

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Keywords Artificial Intelligence · Convolutional Neural Networks · Coronavirus · Deep Learning · Dropout · Machine Learning · nCOVID-19 · Pandemic

1 Introduction The novel Coronavirus has resulted in pneumonia, which is a contaminated disease which has an intensified effect on the lungs of a human. The thoracic CT and Xray images have been found as an effective tool in the discovery of nCOVID-19. Although several clinical courses of action and treatments exist already, since Artificial Intelligence (AI) offers an innovative model for healthcare, various diverse AI techniques that are based on machine learning (ML) algorithms are utilized for examining dataset and help in making decisions quickly and rapidly. Deep learning and machine learning approaches have become accepted exercises in the application of AI to extract, examine, study and identify graphical forms from images datasets. Additionally, deep learning is a technique in which convolutional neural networks are employed for automated withdrawal of graphical features, which is obtained by the task termed as convolution. Different layers do the processing of data which is nonlinear [1]. The advancements in deep learning algorithms in the recent few years have surged. Deep learning comprises machine learning techniques majorly centered on automated extraction of graphical features and categorization of medical images. It has been seen in March 2020 that there has been a rise in accessible X-rays from fit and well people, along-with from sufferers who are having nCOVID-19. A dataset consisting of 180 nCOVID-19, 1400 pneumonia and 1398 normal thoracic images has been used for in silico investigations. Another image dataset containing 2500 images has been used as a training set of each class for validating and training using deep learning and convolutional neural networks (CNNs). Every layer comprises information for transformation into a more conceptual level. As we go deeper and deeper into the network, the more intricate features are obtained. Because of the small size of the samples pertaining to nCOVID-19 (224 images), transfer learning technique can be employed to train the deep CNNs. Currently, real-time reverse transcriptionpolymerase chain reaction (RT PCR) is the testing that is being employed by the medical professionals to detect nCOVID-19. This method of testing has a comparatively less positive rate in the early phase of the disease. As a matter of fact, the medical clinicians felt the necessity of an alternate technique for the diagnosis of nCOVID-19. The look and aspect of X-ray chest images in nCOVID-19 differs from any other class of pneumonic virus. As a result, the objective of this paper is put forward to discover nCOVID-19 employing chest X-ray images. The automated and early diagnosis of nCOVID-19 may be very advantageous and helpful for the world to prompt recommendation of the sufferer to isolate, frequent intubation of severe instances in special hospitals, and keeping an eye on outspread of nCOVID-19. By utilizing the concept of transfer learning, holding of the information or features extracted is the clue to carry out other jobs.

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2 Related Works The doctors and radiologists are able to discover nCOVID-19 patients centered on chest X-ray inspection, their jobs are manual and take a long amount of time, particularly, when the number of cases becomes very large. Choi et al. [2] collected the information from four United States and three Chinese radiologists to recognize and ascertain nCOVID-19 in contrast to any pneumonia centered on chest CT pictures collected from a group of 425 instances, where 219 instances were in China positive with nCOVID-19 and in contrast 206 instances were in the US with non-nCOVID-19 virus. A novel deep learning technique, termed as Coronavirus discovery network (COVNet), is recently incorporated by Li et al. [3] to discover nCOVID-19 centered on thoracic CT pictures. Three types of CT pictures, comprising of nCOVID-19, community attained pneumonia (CAP), and non-pneumonia images, are employed to determine the effectiveness of the suggested technique, which is depicted in Fig. 1. COVNet is described as a convolutional ResNet-50 [4] technique to which a progression of CT images is fed as input and in turn, estimates the category labels of the CT images as the output. One more deep learning technique centered on the concatenation of a 3-dimensional CNN ResNet-18 network and location attention approach is suggested by Xiaowei et al. [5] to discover nCOVID-19 cases employing pulmonary CT images. In addition to the aforementioned research, it has been observed in plenty of manuscripts about utilizing DL algorithms for nCOVID-19 examination employing medical pictures. Some of the important work related to rapid detection of nCOVID-19 is depicted in Table 1 for contrast and differentiation.

Fig. 1 Architecture of COVID-19 detection neural network model (COVNet)

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Table 1 Outline and review of deep learning techniques for nCOVID-19 identification employing medical images Dataset

AI techniques

Outcomes

CT medical images of 1136 Amalgamation of Specificity value of 0.922 and training cases with 723 cases three-dimensional UNet++ [7] sensitivity value of 0.974 found as positive with and ResNet-50 [4] COVID-19 from five hospitals [6] 1, 065 CT images comprising of 325 Coronavirus and 740 viral pneumonia [8]

Change initiated transfer learning approach

Specificity value of 0.83 with accuracy 79.3% and sensitivity of 0.67

CT images acquired from 157 ResNet-50 cases (United States and China) [9]

0.996 AUC value

618 CT images: 219 from 110 Location attention and COVID-19 people, 224 CT ResNet-18 [4] images from 224 people having influenza-A viral, pneumonia, and 175 CT images from well persons [5]

86.7% accuracy value

On the other side, a stereotype of an Artificial Intelligence technique called as α-Satellite, is suggested by Ye et al. [10] to estimate the contagious chance of a particular topographical region for risk assessment to help combat nCOVID-19 at cluster levels. The data obtained from the social networks for a given region may be finite and restricted so that they are enhanced and improved by the conditional reproductive networks [11] to acquire knowledge about the public realization of nCOVID-19 [12] changes a technique based on discrete-time, termed as ACEMod, earlier employed for influenza pandemic simulation [13] for identifying the and recognizing the patterns of nCOVID-19 pandemic across Australia over a period of time. In other research, Allam and Jones [14] proposed the utilization of AI and information spreading for good comprehension and maintaining the health of the people throughout the period of nCOVID-19 pandemic. A composite AI technique for the early prediction of speed of spread of nCOVID-19 is suggested by Du et al. [15] which integrates the pandemic susceptible infection (SI) technique, natural language processing (NLP), and deep learning methods. In another research, Lopez et al. [16] suggested utilizing the network analysis methods along with NLP and mining of the text to examine multilingual Twitter data to comprehend and interpret guidelines and usual feedback to nCOVID-19 disease through a period of time. After going through some of the important work for the early detection of nCOVID-19, proposed algorithm is induced for the rapid discovery and checking of nCOVID-19.

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3 Proposed Algorithm and Methodology The succeeding terms are trustworthy used so as to become acquainted with the terminology. The term “parameter” is used for a changeable value that is instinctively studied and understood throughout the training procedure. The term “weight” is usually utilized equivalent and interchangeably in place of “parameter”; but an attempt has been made to use this term whenever making mention of parameter external to the layers of convolution, i.e., a kernel, as an instance in entirely connected and associated layers. The term “kernel” is used for sets of learnable parameters applicable in the convolution process. The term “hyperparameter” is used for a variable that is required to be set well before the training begins. CNN is a kind of deep learning technique where the dataset is processed and is having a grid structure, as instance pictures, that is motivated by the structure of organism visible skin and developed to instinctively study three-dimensional characteristics. Pertaining to the categorization process of the CNNs, the following are some of the metrics: (a) correctly recognized diseased cases, termed as (True Positives, abbreviated as TP), (b) incorrectly categorized viral cases (False Negatives, abbreviated as FN), (c) correctly recognized healthy instances (True Negatives, TN), and (d) incorrectly categorized healthy instances (False Positives, FP). It can be observed that TP pertains to the correctly estimated nCOVID-19 instances, FP pertains to particular or pneumonia instances that are categorized as nCOVID-19 by the CNNs. TN pertains to usual or pneumonia instances that are categorized as non-nCOVID-19 instances, whereas FN pertains to Coronavirus instances categorized as usual pneumonia instances. Centered on these metrics, an attempt is made to determine the sensitivity, specificity, and accuracy of the technique employed. Sensitivity = TP/(TP + FN)

(1)

Specificity = TN/(TN + FP)

(2)

Accuracy = (TP + TN)/(TP + TN + FP + FN)

(3)

Convolution is a particular category of linear function employed for characteristics withdrawal, in which a tiny matrix of numbers, termed as kernel, is forced to apply through the input, which is a matrix cum array of numbers, termed as a tensor. Two major hyperparameters that characterize the convolution function are the count and size of kernels. Recent CNNs commonly utilize no padding to keep in-plane measurements so as to put in additional layers. If there is no padding, every consecutive characteristic map would become compact and tinier afterward the convolution function. The span among two consecutive kernel locations is termed as a stride, which as well explains and interprets the convolution function. A usual option of a stride is 1; nevertheless, a stride greater than 1 is occasionally employed so as to acquire downsampling of the characteristic maps. The parameters like kernels are

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Table 2 Convolutional neural network comprising of parameters and hyperparameters Layer type

Hyperparameters

Parameters

Convolution layer

No. of kernels, padding, stride, Kernel size, Activation Function

Kernels

Pooling layer

Filter size, stride, pooling technique, padding

None

Entirely connected layer

Activation function, no. of weights

Weights

instinctively studied and examined throughout the task of training in the layer of convolution; the count of kernels, size of the kernels, padding, and stride are considered as hyperparameters that require to be fixed prior to the beginning of the training task as mentioned in Table 2.

3.1 Training of Network Training a system is a task of locating kernels in convolution layers and weights in entirely associated layers which reduces the dissimilarity among output estimations and provided ground truth tags on training information. Backpropagation technique is the algorithm usually employed for training and guiding neural networks where loss operation and optimization of gradient descent technique has a significant part to play. A loss operation also termed as a cost operation, computes the similarity among output estimations of the network via forward propagation and provided ground truth tags. Frequently employed loss operation for multicategory categorization is cross entropy, while averaged squared error is usually forced to apply to regression to continual values. A category of loss operation is amongst the hyperparameters and requires to be obtained as per the provided jobs. Gradient descent is usually employed as an optimization technique that repeatedly modifies the understandable limits, i.e., weights and kernels, of the system in order to mitigate the loss. Mathematically, the gradient may be defined as a partial derivative of the loss w.r.t. every understandable parameter, and a one modification of a parameter can be written down as follows: w := w − α ∗ ∂ L/∂w

(4)

In the above equation, w is nothing but every understandable parameter, α is speed of learning and L is a loss operation. The gradient of the loss operation gives us the guideline with which the operation has the abrupt speed of growth, and every understandable parameter is modified in the opposite direction of the gradient with a random step size obtained centered on a hyperparameter termed as rate of learning. An activation function that is forced to apply to the multicategory job is a softmax operation that is used to renormalize resultant actual values from the rearmost entirely associated layer to objective category likelihoods, in which every value varies between 0 and 1 and all values total to 1. The following algorithms

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are successfully employed using Python software (Anaconda platform) installed on Windows 10 operating system with 12 GB of RAM and Intel Core i7 (4.80 GHz) processor.

3.2 Algorithms Used

Training Algorithm: 1 # VGG16 network requires to be loaded Model = VGG(w=”imagenet”, inc_top=False, inp_tensor=Inp(shape=(224, 224, 3))) # develop the head of the prototype which may be positioned on topmost of the # the baseline prototype hModel = Model.out hModel = AvgPooling2D_operation(pool_size=(4, 4))(hModel) hModel = Flatten_operation(name="flatten")(hModel) hModel = Dense_operation(64, activation="relu")(hModel) hModel = Dropout_operation(0.5)(hModel) hModel = Dense_operation(2, activation_function=”softmax”)(hModel) # position the head FC prototype on topmost of the baseline prototype (which may constitute # the real prototype which may be trained) model = Model(inp=Model.input, out=hModel) # for loop is employed through all layers in the baseline prototype and fix these, therefore, # may not be modified for the duration of first training task for l in baselineModel.layers: l.train = False

In the aforesaid algorithm, VGG16 network is instantiated with weights pretrained on ImageNet. Training Algorithm: 2 # The model is compiled as underneath: print(“The model is being compiled...”) optimizer = Adam_opt(lr=INIT_LR, decay=INIT_LR/no_of_epochs) model.compilation(loss="binary_cross_entropy", opt=optimizer, measures=[“accuracy”]) # The head of the network is trained print(“The head is being trained...”) Head = model.fitting_generation( train.flow(trainingX, trainingY, size_of_batch=S), steps=length(trainingX) // S, valid_data=(testingX, testingY), valid_steps=length(testingX) // S, epochs=EPOCHS)

In Algorithm 2, the network is compiled with decay in learning rate and the optimizer employed is Adam optimizer. The neural network is trained by calling

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Fig. 2 Training and Validation technique utilized in the fivefold cross-validation step

fit_generator function and chest X-ray information is passed through the data augmentation object.

4 Empirical Results We carried out trials to distinguish and order nCOVID-19 X-ray pictures in distinct situations. In the first place, a DarkCovidNet deep understandable technique has been suggested to characterize X-ray images into threefold classifications: COVID-1, NoFindings, and Pneumonia. Moreover, the DarkCovidNet prototype is constituted to recognize two categories: Coronavirus and No-Findings categorizations. The exhibition of the suggested technique is evaluated employing the 5-overlap cross-approval methodology for both the paired and triple characterization issues. 80% of X-ray images are employed for preparation and 20% for approval. The tests are reused several time periods as depicted in Fig. 2. The entireness of the split k components is enveloped by folds to employ in the approval phase. In Fig. 3, the curves for training loss and accuracy have been depicted. The accuracy of testing, computational complexity, i.e., count of floating-point operations and architectural complexity, i.e., count of parameters of the proposed approach has been shown in Table 3.

5 Conclusion and Future Research Directions In this research paper, a more refined technique is presented to describe different deep learning strategies to detect novel nCOVID-19 at a rapid pace as compared to

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Fig. 3 Accuracy and training loss

Table 3 Contrasting accuracy, parameters, and floating-point operations

Model

Accuracy (%)

Parameters (M)

FLOPs (G)

ResNet-50

98.6

23.54

42.70

Proposed approach

98.7

1.60

4.98

manually employed RT-PCR technique utilizing X-rays of lungs of infected people. The model performed on the approval set truly with just two cases each for False Positives and False Negatives. Proceeding onward to the test information, out of 74 pictures, 70 are accurately anticipated, which is an outer approval exactness of 94.6%. However, the most astonishing thing came when it anticipated the right positive cases. The model never prepared on these images, yet still figured out how to foresee with 99% conviction that the lungs in the pictures are certain for nCOVID-19. This is an extraordinary precision for a profound learning model in characterizing nCOVID19, however, this is just a new beginning to explore different avenues regarding a conspicuous absence of X-ray information, and it has not been approved by outside wellbeing associations or experts. Conflict of Interest All authors confirm no conflict of interest or funding for this study. Transparency Declaration The authors certify that the manuscript is reported clearly, truthfully, and precisely with no omission of any important aspect. Authors’ Contributions Statement Sharduli: Idea formulation, Conceptualization, Effective literature review, Methodology, Formal analysis, Software testing, Writing- review & final editing. Amit Batra: Conceptualization, Software, Methodology, Writing- review & final editing. Kulvinder Singh: Experiments and Simulation, Supervision, Validation, Writing-original draft.

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References 1. Deng L, Yu D (2013) Deep learning: methods and applications. Found Trends Signal Proces 7(3–4):197–387 2. Choi W, My T, Tran L, Pan I, Shi L-B, Hu P-F, Li S (2020) Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology 1:1–13 3. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Xia J (2020) Artificial ıntelligence distinguishes covıd-19 from community acquired pneumonia on chest CT. Radiology 284(2):200905 4. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, 2016-Decem, pp 770–778 5. Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Chen Y, Su J, Lang G, Li Y, Zhao H, Xu K, Ruan L, Wu W (2020) Deep learning system to screen coronavirus disease 2019 pneumonia. In: Applied Intelligence 6. Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, Xu W (2020) AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks. MedRxiv, 2020.03.19.20039354 7. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++ a nested u-net architecture for medical image segmentation. In: 4th International workshop on deep learning in medical ımage analysis, lecture notes in computer science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 3–11 8. Shuai W, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B (2020) A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). MedRxiv 9. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Bernheim A, Siegel E (2020) Rapid AI development cycle for the coronavirus (COVID-19) pandemic: ınitial results for automated detection & patient monitoring using deep learning CT ımage analysis. Retrieved from http:// arxiv.org/abs/2003.05037 10. Ye Y, Hou S, Fan Y, Qian Y, Zhang Y, Sun S, Laparo K (2020) Alpha-satellite: an aı-driven system and benchmark datasets for hierarchical community-level risk assessment to help combat COVID-19. Retrieved from http://arxiv.org/abs/2003.12232 11. Mirza M, Osindero S (2014) Conditional generative adversarial nets, pp 1–7. Retrieved from http://arxiv.org/abs/1411.1784 12. Chang SL, Harding N, Zachreson C, Cliff OM, Prokopenko M (2020) Modelling transmission and control of the COVID-19 pandemic in Australia, pp 1–31. Retrieved from http://arxiv.org/ abs/2003.10218 13. Zachreson C, Fair KM, Cliff OM, Harding N, Piraveenan M, Prokopenko M (2018) Urbanization affects peak timing, prevalence, and bimodality of influenza pandemics in Australia: results of a census-calibrated model. Sci Adv 4(12):1–9 14. Allam Z, Jones DS (2020) On the coronavirus (COVID-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Healthcare 8(1):46 15. Du S, Wang J, Zhang H, Cui W, Kang Z, Yang T, Zheng N (2020) Predicting COVID-19 using hybrid AI Model. SSRN Electron J 1–14 16. Lopez CE, Vasu M, Gallemore C (2020) Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset, pp 1–4. Retrieved from http://arxiv.org/abs/2003. 10359

Estimation of Location and Fault Types Detection Using Sequence Current Components in Distribution Cable System Using ANN Garima Tiwari and Sanju Saini

Abstract This paper introduces a technique to estimate the location and detect types of fault types in distribution underground cable systems. This paper is following by mainly two steps, in which first step is modeling the 20 km, 11 kV underground cable for distribution power system, and second step is collecting the data from it for training the artificial neural network. Sequence current components (positive, negative, and zero-sequence values of current) are used as input data and fault types, and their locations are used as output data. Keywords Underground cable · Artificial neural network · Fault detection · Distribution system

1 Introduction The main problem in power systems is difficulty in the acquisition of data. To retain information at multiple modes in power systems used, different types of traditional measuring instruments, which are current transformer (CT), potential transformer (PT), and remote terminal units (RTU), as well as intelligent electronic devices (IEDs), are being used [1]. For smart online inspections in power systems developed self-power non-intrusive sensors, which formed a smart network [2, 3]. In [4] a brief introduction of the application of phasor measurement unit (PMU) has to attain more impact. The levels of frequency of current signal and voltage signals revolutionize abruptly [5] after occurrence of fault, i.e., 1-line to ground fault and three-phase fault, etc. If these effects are analyzed, it may help the power plant in a great manner. Several methods are produced for frequency characteristics analysis of TD (TimeDomain) signals. In which three techniques are mainly used to identify fault type in power systems are described as follows based on scalable and moving localizing Gaussian window, ST gives frequency-dependent resolution with time–frequency representation, as given by Stockwell et al. in [6]. G. Tiwari · S. Saini (B) Department of Electrical Engineering, DCRUST Murthal, Sonipat, Haryana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_9

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In detection and interruption of a transient event, used an ST representation, i.e., two-dimensional TF (time–frequency) representation, which can efficiently provide local spectral characteristics [7]. S-Matrix can store the calculation of ST, twodimensional visualization plotted with the help of ST contour. ST is better than DWT according to some researchers because it avoids some drawbacks of DWT, i.e., noise sensitivity and particular harmonics reflection characteristics [8, 9]. In [10], problem of faulty section and phase is solved by using standard deviation and signal energy of ST contour. Calculation of autocorrelation and variance of S-matrix is explained in [11]. The introduction of ST based on fast discrete is explained in [12]. Fault point location phase angle, amplitude, and impedance of fault point, location of fault introduced in [13]. Analysis of signals, the frequency domain is needed a mathematical tool and Fourier transform is mainly used for this purpose. Discrete Fourier transform (DFT) is proposed for discrete frequency domain and TD coefficients. In paper [14] authors proposed to calculate the phasor elements of the faulty current signal by using discrete Fourier transform (DFT), full-cycle harmonic component, and DFT half-cycle to eliminate direct current elements. For classification of faults type researcher used half-cycle DFT to find harmonic and fundamental Phasor [15– 17]. Fundamental values of voltage and currents are finding by full-cycle FFT [18]. There are mainly two types of faults, one is symmetrical, and another is unsymmetrical faults. Three phases to ground faults are considered symmetrical faults. Other types of unsymmetrical faults describe below. • • • •

Single-phase to ground fault (L-G fault) Phase-to-phase fault (L-L fault) Double phase to ground fault (L-L-G fault) Three-phase fault (L-L-L fault)

Benefits to detects the type of faults are to enhance reliability, increase the life of cables, protect the overall system from damage, to a continuous flow of energy, because the three-phase fault is more severe compare to single-phase fault, but occurrence of single-phase fault is frequently [1]. This work is using an artificial neural network (ANN) to estimate location and fault type detection in a distribution cable system. A simulated model of distribution underground cable system is given in Sect. 2. An introduction to an artificial neural network-based fault detection scheme is given in Sect. 3 followed by simulation results and conclusion.

2 MATLAB Simulink Model of Distribution Underground Cable A model of distribution underground cable system is simulated as a PI section. MATLAB Simulink model of a distributed underground cable system is shown in Fig. 1. Simulink model of underground cable can generate 11 categories of faults,

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Fig. 1 Simulink model of an underground cable

Fig. 2 Internal structure of the cable

i.e., single-phase to a ground fault (AtoG, CtoG), phase-to-phase fault (AtoB, BtoC, CtoA), double-phase to ground fault (AtoBtoG, BtoCtoG, CtoAtoG), three-phase fault (AtoBtoC), three-phase to ground fault (AtoBtoCtoG), etc. The overall structure of cable in PI section is shown in Fig. 2. Here, mutual inductance block is used for representation of two- or three-windings inductances utilizing identical mutual coupling. Capacitance, ‘C’ represents branch capacitance, in farads (F). By using this model, collection of data has been done in this work to train an artificial neural network. The used qualities of cable parameters are given in Table 1.

3 Collection of DATA Data used for a training set of ANN is the values of positive, negative, and zero sequences (I 1 , I 2, and I 0 , respectively) components of fault current. To get this data, firstly phase A to ground fault (AtoG fault) is applied in the toolbox for 0.2–0.3 s (at a distance of 4 km). This gives the values of zero sequence, positive sequence, and

122 Table 1 Configuration parameters of cable

G. Tiwari and S. Saini Quality of cable parameters

Values

Number of cables

3

Frequency

50 Hz

Ground resistivity

100  m

The geometric mean distance between cables

80.09 cm

Phase conductor Number of strands

58

Strand diameter of cable

2.7 mm

Resistivity of material

1.78e−8  m

Relative permeability of material

1

External diameter of phase conductor

20.9

Phase-screen insulator Relative permeability of material

2.3

Diameter of internal cable

23.3 mm

Diameter of external cable

66.6 mm

Screen conductor The resistivity of screen conductor

1.78e−8  m

Total section of screen conductor

0.000169 m2

The internal diameter of screen conductor

65.8 mm

External diameter of screen conductor

69.8 mm

Outer screen insulator Relative permittivity of outer screen insulator

3.25

The internal diameter of outer screen insulator

69.8 mm

External diameter of outer screen insulator

77.8 mm

negative sequence component of fault current by using sequence analyzer block box of MATLAB. Same procedure is repeated for other types of fault like BtoG (phase B to ground), CtoG (phase C to ground), ABG (lines AB to ground), BCG (lines BC to ground), CAG (lines CA to ground), AB (line A to line B), BC (line B to line C), CA (line C to line A), ABC (three-phase fault), and ABCG (three-phase to a ground fault) in addition to a case of NO FAULT. There are seven different locations of fault like at source end, at a mid-point at load end, at 4, 8, 12, and 16 km. So there are a total of nineteen cases, for which, the training data is collected. Source-end and load-end voltages and currents t for 1-line to ground fault given in Table 2. Collection of training data for artificial neural network which are values of sequence components of fault current which have been noted for different types of faults at 4 km length of cable from source end or final training data for ANN is given in Table 3.

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Table 2 Source-end and load-end voltages and currents t for 1-line to ground fault Quantities

Single-phase to ground fault at phase A

Single-phase to ground fault at phase B

VSA

0.0025 − 0.0003 J

0.0247 − 0.00191 J

0.0210 − 0.0082 J

VSB

−0.0176 − 0.0141 J

−0.00159 − 0.00209 J

−0.014 − 0.020 J

VSC

−0.0107 + 0.0223 J

−0.0034 + 0.0223 J

−0.0010 + 0.0023 J

ISA

0.4526 − 1.928 J

0.6594 − 0.00164 J

0.5280 − 0.204 J

ISB

−0.4407 − 0.355 J

−1.964 + 0.528 J

−0.3310 − 0.5701 J

ISC

−0.327 + 0.5721 J

−0.0875 + 0.559 J

1.556 + 1.3 J

VRA

0.00035 + 0.0005 J

0.026 − 0.0026 J

0.0201 − 0.0102 J

VRB

−0.018 − 0.123 J

0.000253 − 0.00056 J

−0.01529 − 0.0212 J

VRC

−0.0107 + 0.0238 J

−0.00124 + 0.0226 J

−0.00061 + 0.00006 J

IRA

0.0089 + 0.0125 J

0.6529 − 0.065 J

0.5051 − 0.2556 J

IRB

−0.4739 − 0.309 J

0.00629 − 0.0140 J

−0.382–0.53 J

IRC

−0.2096 + 0.598 J

−0.03104 + 0.5654 J

−0.0153 + 0.00148 J

Table 3 Zero sequences, negative sequence, and positive sequence current components

Single-phase to ground fault at phase C

Positive sequence I1

Negative sequence I2

Zero sequence I0

Type of fault

1.0340

0.7889

0.7227

AG

1

0.7569

0.6912

BG

1

0.7353

0.6700

CG

1.9364

1.261

0.5440

ABG

1.8085

1.1140

0.5865

BCG

1.9092

1.2127

0.5610

CAG

1.6588

1.5251

0

AB

1.5075

1.3773

0

BC

1.6275

1.4949

0

CA

2.9790

0.7521

0.7521

ABCG

2.9790

0.7521

0.7521

ABC

1

0

0

No fault

4 Simulation Results In this paper, fault types and their location in an underground cable have been found, by using an artificial neural network. The cable model is simulated for 1 s. Source end and load end currents and voltages are given in Table 2. Fault voltages and currents have been observed for different types of faults. In Figs. 3, 4, 5 and 6, wave shapes for the same are shown for two different types of faults.

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Fig. 3 Waveform of voltage for 1-line to ground fault

Fig. 4 Waveform of current for 1-line to ground fault

Fig. 5 Waveform of voltage for phase to phase to ground fault

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Fig. 6 Waveforms of current for phase to phase to ground fault

Artificial neural network is used to detect types of fault and its location combination of six layers; in which one input layer, four hidden layers (having 4, 2, 5, and 6 neurons, respectively), and one output layer. Other performance parameters of ANN are shown in Fig. 7 The neural network after training has been observed to classify each type of fault at different locations correctly with different accuracy for a different type of fault approx. 99%, which is given in Table 4. Performance chart of ANN is shown in Fig. 8.

5 Conclusion This work concludes that the computational technologies such as artificial neural networks can be effectively utilized for detection of fault types and locations in distribution underground cable systems.

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Fig. 7 Parameters of ANN

Table 4 Accuracy in a type of faults at 4 km

S. No.

Type of faults

Accuracy in %

1

AG

98.2

2

BG

97.3

3

CG

99

4

ABG

99.9

5

BCG

96.3

6

CAG

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References 1. Kezunovic M (2011) Smart fault location for smart grids. IEEE Trans Smart Grid 11–22 2. Ouyang Y, He JL, Hu J et al (2012) A current sensor based on the giant magnetoresistance effect: design and potential smart grid applications. Sensors, 15520–15541 3. Han JC, Hu J, Yang Y et al (2015) A nonintrusive power supply design for self-powered sensor networks in the smart grid by scavenging energy from AC power line. IEEE Trans Ind Electron 4398–4407 4. De La Ree J, Centeno V, Thorp JS et al (2010) Synchronized phasor measurement applications in power systems. IEEE Trans Smart Grid 20–27 5. Bo Q, Jiang F, Chen Z et al (2000) Transient based protection for power transmission systems. IEEE Power Engineering Society Winter Meeting, pp 1832–1837 6. Stockwell RG, Mansinha L, Lowe R (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 998–1001 7. Dash P, Panigrahi B, Panda G (2003) Power quality analysis using S-transform. IEEE Trans Power Deliv 406–411 8. Dash PK, Das S, Moirangthem J (2015) Distance protection of shunt compensated transmission line using a sparse S-transform. IET Gener Transm Distrib 1264–1274 9. Moravej Z, Pazoki M, Khederzadeh M (2015) New pattern-recognition method for fault analysis in a transmission line with UPFC. IEEE Trans Power Deliv 1231–1242 10. Samantaray SR, Dash PK (20085) Pattern recognition based digital relaying for advanced series compensated line. Int J Electr Power Energy Syst 102–112 11. Samantaray SR (2013) A systematic fuzzy rule-based approach for fault classification in transmission lines. Appl Soft Comput 928–938 12. Dash P (2013) A new real-time fast discrete S-transform for cross-differential protection of shunt compensated power systems. IEEE Trans Power Deliv 402–410 13. Samantaray S, Dash P (2008) Transmission line distance relaying using a variable window short-time Fourier transform. Electr Power Syst Res pp 595–604 14. Yu S-L, Gu J-C (2001) Removal of decaying DC in current and voltage signals using a modified Fourier filter algorithm. IEEE Trans Power Deliv 372–379 15. Das B, Reddy JV (2005) Fuzzy-logic-based fault classification scheme for digital distance protection. IEEE Trans Power Deliv 609–616 16. Jamehbozorg A, Shahrtash SM (2010) A decision-tree-based method for fault classification in single-circuit transmission lines. IEEE Trans Power Deliv 2190–2196

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17. Jamehbozorg A, Shahrtash SM (2010) A decision tree-based method for fault classification in double-circuit transmission lines. IEEE Trans Power Deliv 2184–2189 18. Hagh MT, Razi K, Taghizadeh H (2007) Fault classification and location of power transmission lines using artificial neural network. In: 2007 Conference Proceedings of IPEC, vols 1–3, pp 1109–1114

LabVIEW Implemented Smart Security System Using National Instruments myRIO S. Krishnaveni, M. Harsha Priya, and P. A. Harsha Vardhini

Abstract In this paper, a smart security system is proposed based on LabVIEW. Here, abnormal movements are detected from IR sensor and camera record the surroundings and sends information to user through email. This system monitors physical movements are monitored using an efficient image analysis system and communicated using wireless network data managing system. Smart security system design consists of components such as sensors, alarms, cameras, and an SMTP protocol. Continuous monitoring and movements are captured in camera further the frames are analyzed in LabVIEW software. myRIO notifies the user if any intrusions are observed, while notifying user small video of intruder is also sent to registered email. Keywords IR sensor · myRIO · USB 3.0 · Security · Smart system

1 Introduction A smart home security system is significant in residential community. Security and safety are the key purpose of designing smart home security system. Advanced security features are possible through essential wireless communication network protocols. Physical movements are recognized and recorded in camera; further, information is communicated to user [1–7]. This system monitors physical movements are monitored using an efficient image analysis system and communicated using wireless network data managing system. When sensor identifies any movements in premises, then processor triggers the camera to capture picture on real time, and video is streamed and recorded. The code for this application is done by using

S. Krishnaveni (B) · M. Harsha Priya Department of ECE, CMR College of Engineering and Technology, Hyderabad, Telangana, India e-mail: [email protected] P. A. Harsha Vardhini Department of ECE, Vignan Institute of Technology and Science, Deshmukhi, Telangana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_10

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LabVIEW software by taking a flat sequence. This flat sequence consists of individual codes, combined together as a final result. National instrument’s myRIO is an efficient embedded board used for real-time evaluation to enhance the application efficiency. National instruments LabVIEW is a system design development environment provides graphical approach. Due to visualization in LabVIEW, it becomes easier to integrate complex hardware as diagram from any vendor. myRIO utilizes on board FPGA and microprocessors, it served as embedded evaluation board works on real time. Imaging camera captures imaging data is communicated using USB 2.0, 3.0 technologies. Characteristics of surroundings are sensed by electronic instruments called infrared sensor [8–15]. There are two techniques involved in detection one is infrared sensor detects either presence of object which emits infrared radiation; second is it detects if any intrusion occurred in infrared radiation being emitted by the sensor. By combining all the hardware and software components, the streaming video is sent to the specified person, using the SMTP protocol.

2 Technical Approach Smart security system design consists of components such as sensors, alarms, cameras, and an SMTP protocol. Continuous monitoring and movements are captured in camera; further, the frames are analyzed in LabVIEW software. myRIO notifies the user if any intrusions are observed, while notifying user small video of intruder is also sent to registered email. Communication protocol SMTP port 25 is used for communicating from server to client. An electronic sensors here used in infrared sensors identifies the movements in the sensor covered region. Heat emitted from living beings can be measured by IR sensor. This plays vital role in detection of intruders. Immediately, when intruder is observed, a video is recorded and communicates to user on real time using SMTP protocol. USB cameras are picture capturing cameras; here, technology used is USB 3.0 which transfers the captured data. The structure of components that are integrated together to develop an application; components used here are IR sensor, Buzzer, myRIO, camera USB3.0. If any intrusion is found in path covered by IP sensor it notifies the user with a recorded video using USB camera. For authorized user, notification is sent using SMTP protocol.

3 Methodology Smart security system design consists of components such as sensors, alarms, cameras, and an SMTP protocol. As mentioned in Fig. 1, block diagram describes the implementation of project and interfacing of components. Power supply is given to myRIO, and the system. IR sensor and Buzzer are interconnected to myRIO. An USB Camera is connected to system. Whenever IR sensor detects any object, Buzzer

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Fig. 1 Taxonomy of processing

starts alerting. Then, camera starts capturing the footage, records it and sends an email notification to the authorized registered email account.

3.1 Module 1: Buzzer Code First stage of design, Fig. 2 is to detect if any obstacles in program, and two cases are

Fig. 2 Buzzer code

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Fig. 3 Camera code

considered if any intrusion occurs, buzzer alerts the surroundings. Case 2 is when there is no intrusion code returns a string indicating normality. In buzzer mode, the output is considered as global variables so that it can be used as input for next modules.

3.2 Module 2: Camera Code If the sensors detects any abnormal situations in the predefined surroundings, immediately, camera starts capturing the premises. The captured video is stored in extension of ‘.avi’. Further, this is communicated to user. If there is no abnormal situation, then module 2 returns a false to next module 3, i.e., email mode as shown in Fig. 3.

3.3 Module 3: Email Code Email mode is the third mode of the design. After intrusion is observed, camera captures a video of the premises and saves the file. Email code returns ‘True’ and send the email to authorized user through the registered email id. If no video is captured, email code returns zero or ‘False’. Code is as shown in Fig. 4.

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Fig. 4 Email code

4 Experimental Results 4.1 Detection of the Movement When IR sensor detects any object, the USB camera starts to capture the 30 s video by executing true case of the code and sends the email to the registered mail-id. Figure 5 illustrates the results of the work proposed. Fig. 5 Hardware setup while detection

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4.2 Streaming Video Being Recorded When IR sensor detect intrusions, video is recorded and saved in the given file path. The recorded file has been sent as an alert message to the authorized person registered email account. Figure 6 illustrates source code front panel, and Fig. 7 depicts the code results in recording saved in file path mentioned in the code. Figure 7 represent the recording of captured premises.

Fig. 6 Source code–front panel

Fig. 7 Code results in recording saved in file path mentioned in the code

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Fig. 8 Alert message to all

Fig. 9 Source code-block diagram

4.3 Mail Attachment to the Registered Account Figure 8 depicts the email which is sent to the user, along with a streaming video clip attached to the mail. This mail is sent when the sensor detects some intrusion/movement. Final source code is shown in Fig. 9.

5 Conclusion With LabVIEW, abnormal movements are detected from IR sensor and camera as it records the surroundings and sends information to user through email. This system monitors physical movements and are monitored using an efficient image analysis system and communicated using wireless network data managing system. Smart security system design consists of components such as sensors, alarms, cameras, and an SMTP protocol. Continuous monitoring and movements are captured in camera; further, the frames are analyzed with LabVIEW. myRIO notifies the user if any intrusions are observed, while notifying user small video of intruder is also sent to registered email.

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References 1. Juhana T, Angraini VG (2016) Design and implementation of smart home surveillance system. In: 2016 10th International conference on telecommunication systems services and applications (TSSA) 2. Harsha Vardhini PA, Harsha MS, Sai PN, Srikanth P (2020) IoT based smart medicine assistive system for memory impairment patient. In: 2020 12th international conference on computational intelligence and communication networks (CICN), Bhimtal, India, pp 182–186 3. Harsha Vardhini PA, Ravinder M, Srikanth P, Supraja M (2019) IoT based wireless data printing using Raspberry Pi. J Adv Res Dyn Control Syst 11(4):2141–2145 4. Kumar S, Swetha S, Kiran T, Johri P (2018) IoT based smart home surveillance and automation. In: 2018 International conference on computing, power and communication technologies (GUCON), Greater Noida, Uttar Pradesh, India 5. Vardhini PAH (2016) Analysis of integrator for continuous time digital sigma delta ADC on Xilinx FPGA. In: International conference on electrical, electronics, and optimization techniques, ICEEOT 2016, pp 2689–2693 6. Harsha Vardhini PA, Madhavi Latha M (2015) Power analysis of high performance FPGA low voltage differential I/Os for SD ADC architecture. Int J Appl Eng Res 10(55):3287–3292 7. Kumar P (2017) Design and implementation of Smart Home control using LabVIEW. In: Third international conference on advances in electrical, electronics, information, communication and bio-informatics (AEEICB), Chennai, pp 10–12. https://doi.org/10.1109/AEEICB.2017. 7972317 8. Vardhini PAH, Koteswaramma N, Babu KMC (2019) IoT based raspberry pi crop vandalism prevention system. Int J Innov Technol Explor Eng 9(1):3188–3192 9. Darrell T, Demirdjian D, Checka N, Felzenszwalb P (2001) Plan-view trajectory estimation with dense stereo background models. Trevor Darrell, David Demirdjian, Neal Checka, Pedro Felzenszwalb ICCV 2001, pp 628–635 10. Harsha Vardhini PA, Ravinder M, Srikanth Reddy P, Supraja M (2019) Power optimized Arduino baggage tracking system with finger print authentication. J Appl Sci Comput J-ASC 6(4):3655–3660 11. Vardhini PAH, Makkena ML (2021) Design and comparative analysis of on-chip sigma delta ADC for signal processing applications. Int J Speech Technol 24:401–407 12. Tushara DB, Vardhini PA (2016) Effective implementation of edge detection algorithm on FPGA and beagle board. In: 2016 International conference on electrical electronics and optimization techniques (ICEEOT), pp 2807–2811 13. A wide area tracking system for vision sensor networks. In: Kogut G, Trivedi M (2002) 9th World Congress on intelligent transport systems. Chicalgo, Illinois, Oct 2002 14. Bindu Tushara D, Harsha Vardhini PA (2015) FPGA implementation of image transformation techniques with Zigbee transmission to PC. Int J Appl Eng Res 10(55):420–425 15. Bindu Tushara D, Harsha Vardhini PA (2017) Performance of efficient image transmission using Zigbee/I2C/Beagle board through FPGA. In: Saini H, Sayal R, Rawat S (eds) Innovations in computer science and engineering. Lecture notes in networks and systems, vol 8. Springer, Singapore. https://doi.org/10.1007/978-981-10-3818-1_27

Design and Analysis of Modified Sense Amplifier-Based 6/3T SRAM Using CMOS 45 nm Technology Chilumula Manisha and Velguri Suresh Kumar

Abstract SRAM and sense amplifiers are important components in memory design. The choice and design of a sense amplifier define the robustness of bit line sensing, impacting the read speed and power. The primary function of a sense amplifier in SRAMs 6T is to amplify a small analog differential voltage developed on the bit lines by a read-accessed cell to the full swing digital output signal thus greatly reducing the time required for a read operation. Since SRAMs 6T do not feature data refresh after sensing, the sensing operation must be non-destructive, as opposed to the destructive sensing of a DRAM cell. A sense amplifier allows the storage cells to be small, since each individual cell need not fully discharge the bit line. A novel high-speed and highly reliable sense amplifier primarily based 3 T SRAM is proposed for low supply voltage (VDD) operation. By the proposed method, area of the SRAM will reduced by 50% and speed increase up to 40% and reduced power dissipation. The above proposed method will designed by CMOS tanner 45 nm technology. Keywords Sense amplifier · Conventional SRAM · Power dissipation

1 Literature Survey The sense amplifier-based master stage reduces the loading to the clock network. The proposed improvement on the slave stage shortens the data to Q delay. The simulation results indicate that the proposed design has a shorter data to Q delay than the flip-flop design used in Alpha 21,264 microprocessors. The proposed design [1] can be fabricated in the mainstream commercial digital complementary metal oxide semiconductor process. The self-lock problem in the standard sense amplifier-based master stage is avoided by additional discharging paths controlled by the redundant nodes. The improved SR latch design reduces the data to Q delay. A NOR-gate bank is used to prevent a master stage single event transient propagating to two branches C. Manisha · V. Suresh Kumar (B) Department of ECE, Maturi Venkata Subba Rao Engineering College, Telangana State, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_11

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in the slave stage. A novel soft error tolerant latch and a novel soft error-tolerant flipflop are presented for multiple VDD circuit design [2]. By utilizing local redundancy, the latch and the flip-flop can recover from soft errors caused by cosmic rays and particle strikes. By using output feedback, implicit pulsed clock, and conditional discharged techniques, the proposed flip-flop can behave as a level converter and without the problems of static leakage and redundant switching activity. Since the setup time of the new flip-flop is negative, it can further mitigate the impact of single event transient (SET) at the D input of the flip-flop.

2 Introduction Today’s world is craving for an ultra-low-power system on chip (SOC) which is continuously increasing as the interest in high voluminous—constrained mobile SOC application is growing. In particular, for applications where performance is of secondary importance the one which is the simplest and most efficient methods to improve energy consumption to reduce the voltage supply voltage at the cost of loss of speed. Sense amplifier is like the mitochondria of a memory chip. As the number of cells (SRAM/DRAM) increase [3], it tries to reduce the voltage difference created by them. Sense amplifiers in consortium with memory cells are essential elements in defining the performance and environment tolerance of CMOS memories. Because of their high importance in memory design, sense amplifiers became a very large circuit class. SRAM stands for static random access memory, a nonvolatile memory that can store the information as long as the power is applied. The sense amplifier operates only when data stored is read from memory. Sense amplifier is used to detect the very small difference voltage at bit lines and amplify the signals to its full digital voltage swing before the signals are fully charged or discharged [4]. This situation causes the time taken to read the content of memory to shorten since the circuitry doesn’t require waiting until the signals get charged or discharged to determine either if it is 1 or 0. The small spark on glitch [5, 6] at both bit lines may determine its state, so the memory may take it quickly either as 1 or 0 rather than trying to calculate or wait for its voltage full swing level, thus saves time in read operation into memory. The above paper has arranged as: 1. Introduction, 2. Literature survey, 3. Implementation, 4. Experimental results and comparison, 5. Conclusion.

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3 Implementation 3.1 Conventional Sense Amplifier The designing of conventional SAFF is composed of two stages. One is an SA stage followed by NAND-based latch and sense amplifier stage. In the designing first design the NAND gates. For the designing of sense amplifier stage, take both PMOS M2 & M3 and NMOS M5 & M6. The PMOS M2 and NMOS M6 both gates are short together similarly M2 & M5. The output and input sessions of series PMOS and NMOS are short together. Two PMOS M1 & M4 are taken for clock supply. M1 source is connected to M2 source similarly M4 source is connected to M3 source. All drains of four PMOS are connected to VDC now again take NMOS M7, M8 & M9. All drain of NMOS are shorted. With the support of inverter get the DBAR. Inverter is designed with PMOS M10 and NMOS M11 with supply VDC and ground. If input is D, then we get the DBAR through the inverter. Then connect input D to M7. One more NMOS is clocked to M12 (Fig. 1).

Fig. 1 Conventional sense amplifier-based flip-flop

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Designing of Latch

Now, bring two NAND gates both inputs are upwards, and both the grounds are shorted. Supply V DC is given to both NAND gates. One output is Q for NAND1, and QBAR is output for NAND2. Extract a wire from M2 & M5 connected to NAND1. Extract a wire from M3 & M6 connected to NAND2. Now, both NAND gates are cross coupled. Now, to go, spice sources select VDC and GND. Select VPulse for clock and data D. Select spice commands print voltage at Q and QBAR. For the designing purpose, the required number of PMOS is 5 and NMOS are 7, and NAND gates are 2. For the desired output data, test bench plays a crucial role. In this design, the proper output is obtained for the following specifications. Take the delay is 0n if delay is more the response will be decreased, so delay is very small for any design, fall time is 5n, period 200n and pulse width 95n, rise time 5n which means the time taken to reach 10–68% of the final output.

3.1.2

Simulation Wave Form of Conventional SAFF

The time is taken in X-axis, and voltage is taken in Y-axis. For clock equal to logic 1, the data voltage increases initially then saturated and decreases gradually and then repeated same. When clock is 1 or HIGH, the D value gradually increase up to 10n (Rise time); then, it will constant for 50n then it gradually decreases up to 50 to 55n (fall time) then it will remains at 0 V up to 100n. From the graph, it is clear that QBAR is opposite to Q. Q is logic 0, and QBAR is logic 1 (Fig. 2).

Fig. 2 Simulation results of SAFF

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Fig. 3 Proposed sense amplifier

3.1.3

Proposed Sense Amplifier

The designing of proposed version of the sense amplifier has 7 transistors. It has two inverters with PMOS and NMOS back-to-back connection. The PMOS transistors M1 & M3 and NMOS transistors M2 & M4. To form inverter configuration, M1 & M2 connected in series and both gates are shorted similarly, M3 & M4. The input and output of both inverters are shorted together. Both drain of the inverters are connected to V DD . Another two access transistors are arranged these are PMOS transistors M6 & M7 to connect BIT and BIT BAR. The inputs of access transistors are connected to the inverters. To obtain the BITBAR, another inverter M8 & M9 is used between M6 & M7. Both gates of M6 & M7 are shorted and connect read enable. Another NMOS transistor M5 is connected to the memory part. The source of the M5 connected to ground. The gate is connected to read enable (Fig. 3). Go for spice elements and take two voltage pulses V 1 & V 2 for BIT and read enable. Take DC voltage V 3 for VDD and connected to GND. Then, go for spice commands select print voltage and connect at BIT, read enable, out.

3.1.4

Power Analysis

Proposed sense amplifier has been simulated up to 0 to 5e−07 with VV3 VDC voltage source. The average power consumed around 1.129630e+15 W, the maxpower consumption 1.946189e+15 at time 5e−09 and min-power consumption 7.196223e−10.

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Fig. 4 Simulation results of proposed sense amplifier

3.1.5

Simulation Results of 7Transisitor Sense Amplifier

The output waveforms of the 7T sense amplifier as follows: the read enable is high (5 V); then, the bit line is high (5 V), and the output is also high (5 V). When the read enable is low (0 V), the BIT line is remains in high voltage stage, but the output is low voltage stage (0 V). The read enable voltage is increases gradually; the BIT line is decreases gradually and remains at low voltage, and the output is remains at low voltage state (Fig. 4).

3.1.6

Conventional SRAM Architecture

See Fig. 5.

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Schematic Explanation

Inside the circuit part is called memory part, it has two inverters in back to back connection. The inverter is formed using two PMOS M1 & M2 and two NMOS M3 & M4. The M1 & M4 gates are short together. The input and outputs of inverters are shorted. The drain of two PMOS is connected to VDD, and the source of two NMOS is connected to GND. Another two NMOS transistors (access transistors) M5 & M6 those gates are short together. The output of one M5 is BIT, and the output of M6 is BITBAR. WL is connected to both gates. The operation of the CMOS inverter and NOT gate is same. The memory part has TWO inverters; outputs are taken as Q, and the other one is Q bar. The memory part is connected to the access transistors nothing but line. These lines are called bit and bit bar. The bit bar is get from the inverter connected to the bit. These are used for read or write from the

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Fig. 5 Conventional SRAM schematic

memory part. To access the lines or bit and bit bar access, transistors are used. If word line (WL) = 1, the access transistors are ON. If WL = 0, the access transistors are OFF then. The memory in HOLD state. From the above statements, we can conclude that when WL = 1, then only READ and WRITE operations are possible.

3.1.8

Read Operation

Read from the memory, bit and bit bar are used as output lines. The recharge capacitors are used in READ and WRITE operation. When Q = 1 and Q Bar = 0 WL = 1, then only READ operation is possible to make sure that bit and bit bar are OUTPUT lines. The capacitors are act as PRECHARGE (V DD ) capacitors for READ operation. Bit bar voltage will decrease. So, capacitor will discharge. Bit and bit bar values are send to sense amplifier. The sense amplifier acts as comparator. It will check. Bit bar value decreases then the OUTPUT will be 1. It successfully READ from the memory. When Q = 0 and Q bar = 1, WL = 1 bit and bit bar are acts as OUTPUT. The capacitors are act as PRECHARGE (V DD ) capacitors for READ operation. Bit voltage will decrease. So, capacitor will discharge. So, capacitor will discharge. Bit and bit bar values are send to sense amplifier. The sense amplifier acts as comparator. It will check bit voltage decreases then the OUTPUT = 0.

3.1.9

SRAM Write Operation

In write operation, we have to change Q = 0 to Q = 1, lets save memory value has zero that is Q = 0 and Q bar = 1, word line WL = 1 because we have to access the bit lines bit, and bit bar lines are equal to V pulse as input lines, make bit bar line

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as ground for voltage variations. When voltage is decreases m1 and m2 transistors effect. If applied DC voltage less then threshold of M2, then M2 = OFF M1 = on then it get the value of VDD then Q = 1. Let’s say Q = 0 and Q bar = 1. Due to VDD and Q = 0 the capacitor gets discharged due voltage that increase at both the gates. So that voltage greater then threshold voltage. Then, M4 = on the Q bar = 0 so Q = 0. So, we have to make sure DC voltage less than threshold of M4.

3.1.10

Power Results

The average power consumed around 2.685001e−04 watts, max power 1.227270e−02, and min power consumption is 5.882367e−09.

3.1.11

Output Waveforms

The wave form is plotted by taking time on X-axis and voltage on Y-axis. From the graph is clear that when word line is high, the bit line is low. The Q and Q bar are opposite to each other. For 0 to 47.30 µs, the WL is high (4 V), and the bit line is low (0 V). The Q is high (4 V), and QBAR is low (0 V). After 47.30 µm, the WL decreases gradually and reaches 0 V and remains constant up to 47.40 µm in that period the bit line is initially increases and saturated till 47.35 µm and decreases and remains in low state up to 47.40 µm. The Q and QBAR are constant. It is repeated periodically (Fig. 6).

Fig. 6 Simulation results of conventional SRAM

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Fig. 7 Proposed 3T SRAM cell architecture

3.1.12

Proposed 3T SRAM Architecture

A standard 3T SRAM cell has two bit lines for read and writes operation thus it consumes more power. When we change write line BL also changes and changes occur in output 0 or 1. Here, 3 transistors are used to store bits of data. M1 and M2&M3 three transistors. M1, M3 are NMOS, and M2 is PMOS. Each transistor consists of one bit line and one write line. And it always indicates in same direction. Here, we used 3 spice sources they are V 1, V 2, V 3; all three voltages are grounded. V 1 = 1.8 V and V 2 consists of BIT line, and V 3 consists of write line. Transistors length (L) is 45n and width (W ) is 90 nm for NMOS, and for PMOS length is 45 nm, and width is 180 nm (Fig. 7).

3.1.13

Test Bench of 3T SRAM

To simulate or work, the 3T SRAM values are given as, DC voltage of 3T SRAM cell is 1.8 V, the inputs of voltage 1 and voltage 2 are V 1 = 5 V and V 2 = 0 V. The period of 3T SRAM cell is 100 n/s. Delay time of 3T SRAM is 0 n/s. Rise time of 3T SRAM is 5n. Fall time of 3T SRAM is 5n. And the pulse width is 45n.

3.1.14

Power Analysis

To simulate or to run the 3T transistors, the time period will be 200 ns, for the 3T transistors the average power consumed is 7.251771e−08 W. And the maximum

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Fig. 8 Simulation results of proposed 3T SRAM cell

Table 1 Experimental results of conventional with proposed sense amplifier-based SRAM cells S. No.

Conventional

Proposed

1

Sense amplifier

10T

7T

2

Power consumption

10 × e14

1.2 × e09

SRAM

Conventional

Proposed

6T

3T

2.6 × e−3

7 × e−8

power consumed is 2.445337e−05 at time 5e−09, and minimum power consumed is 7.496473e−11 at time 1.28418e−07 where Bl:v is bit line, and Wl:v write line and out:v are output (Fig. 8).

4 Experimental Results Comparison See Table 1.

5 Conclusion and Future Scope A novel high-speed and highly reliable sense-amplifier-primarily based 3T SRAM is proposed for low supply voltage (VDD) operation. By the proposed method, area of the SRAM will reduced by 50% and speed increase up to 40%. And reduced power dissipation of 7 × e−8 by this proposed 3trasisitored SRAM will applicable in all analog and digital circuits to load large data processing systems.

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References 1. Kaul H, Anders M, Hsu S, Agarwal A, Krishnamurthy R, Borkar S (2012) Near-threshold voltage (NTV) design—opportunities and challenges. In: Proceedings of 49th ACM/EDAC/IEEE Design Automation Conference (DAC), June 2012, pp 1149–1154 2. Jeon D, Seok M, Chakrabarti C, Blaauw D, Sylvester D (2012) A super-pipelined energy efficient subthreshold 240 MS/s FFT core in 65 nm CMOS. IEEE J Solid-State Circuits 47(1):23–34 3. Suzuki Y, Odagawa K, Abe T (1973) Clocked CMOS calculator circuitry. IEEE J Solid-State Circuits SSC-8(6):462–469 4. Gerosa G et al (1994) A 2.2 W, 80 MHz superscalar RISC microprocessor. IEEE J Solid-State Circuits 29(12):1440–1454 5. Markovic D, Nikolic B, Brodersen RW (2001) Analysis and design of low-energy flip-flops. In: International Symposium on Low Power Electronics and Design, pp 52–55 6. Sandeep P, Harsha Vardhini PA, Prakasam V (2020) SRAM utilization and power consumption analysis for low power applications. In: 2020 International conference on recent trends on electronics, information, communication & technology (RTEICT), Bangalore, India, 2020, pp 227–231. https://doi.org/10.1109/RTEICT49044.2020.9315558

Design and Implementation of Low Power GDI-LFSR at Low-Static Power Consumption Velguri Suresh Kumar and Tadisetty Adithya Venkatesh

Abstract Gate Diffusion Input (GDI) technique plays a vital role in very large scale integration circuits. GDI modeling is a low power technique which is used to design all digital engineering blocks. This technique is used to design universal gates by using only two CMOS Transistors. This technique will improves the silicon area as well as less power consumption and more power delivered to load. And also, proposed with IDDQ and IDDT fault detection testing techniques to detect faults to operate at low-static current consumption in GDI_CMOS_LFSR circuits. However, previous work has been carried out by using 130 nm technology. The proposed LFSR has been design and simulated using Tanner CMOS 45 nm Mentor graphics Technology. In the proposed technique, parameters such as total number of transistors required to design LFSR has reduced, and rise time, fall time, pulse width, noise analysis, are calculated with fault detection . Comparative analysis carried out along with IDDQ current testing methods between conventional LFSR and GDI_LFSR shows 50% and 20% area and power reduction GDI technique respectively. This Paper will report the innovative low power techniques and its impact on future VLSI applications. Keywords Gate diffusion input · Low power · IDDQ · Rise time and fall time · LFSR · Signature analyzer

1 Introduction A low-power digital circuit design technique which is used to reduce area, power dissipation, delays and to increase the speed. By this technique, circuit complexity is also reduced. As shown in Fig. 1, it is the basic GDI cell. The basic GDI cell has three V. S. Kumar · T. A. Venkatesh (B) Department of ECE, Maturi Venkata Subba Rao Engineering College, Hyderabad, Telangana, India V. S. Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_12

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Fig. 1 CMOS GDI D flip flop

inputs. G-common input to both PMOS and NMOS, [1] P&N inputs to source/drain of PMOS/NMOS. The advantage of GDI technique [2] over conventional CMOS technology is that in this technique N, P&G terminals could be given a supply V dd or can be grounded or can be supplied with input signal depending on the circuit to design and hence minimizing the transistor count. Another technique that is used to test semiconductor devices is known as the IDDQ test. The IDDQ test [3–8] is able to find soft errors that do not affect the logical output, such as certain types of coupling and shorts in the system. These errors often waste the power of the system and can lead to more severe issues such as data retention faults. The IDDQ test works by monitoring the Quiescent current of the circuit, which is the current drawn by the system when it is fully powered but not active. This current is usually extremely small. If there is an error, such as a break, in the system, the current will rise by a few orders of magnitudes. By monitoring the current for this change, certain defects that are otherwise undetectable can be found. The IDDQ test is not commonly used in SRAM testing [9–11] for a number of reasons including sizing and scaling. Due to the decreasing transistor size, the leakage current and parameter variation make it very difficult to accurately detect errors. The IDDQ test has only been successfully used for devices that have a channel length of 0.25 m and above, which is much larger than the current technology In addition to this; changes must be made to either the SRAM cell or the row decoders in order to access the whole memory cell which increases the footprint of the chip. Despite this fact, several methods have been proposed for adapting the IDDQ test for current technology, but as sizing continues to decrease, the IDDQ test is becoming less reliable for accurate fault detection although it is not always a useful method

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for determining faults; IDDQ testing is still used to evaluate current flow and power consumption within memory devices.

2 Literature Survey The main challenging areas in VLSI are performance, cost, testing, area, reliability and power. The demand for comparatively lesser price and portable computing devices and communications system are increasing rapidly. These applications require low power dissipation for VLSI circuits [12]. There are main two sources of power dissipation in digital circuits; these are static (mainly due to leakage current and its contribution to total power dissipation is very small) and dynamic (due to switching, i.e., the power consumed due to short circuit current flow and charging of load capacitances) power dissipation. Hence, it is important aspect to optimize power during testing. Power optimization is one of the main challenges. There has been various low power approaches proposed to solve the problem of power dissipation, i.e., to decrease the power supply voltage, switching frequency and capacitance of transistor during the testing. Here, this paper presents one such approach and that is LFSR counter [13–16] which has low power architecture. The LFSR is used in variety of applications such as built-in-self test (BIST), cryptography, error correction code and in field of communication for generating pseudo-noise sequences. Nowadays, LFSR’s are present in nearly every coding scheme as they produce sequences with good statistical properties, and they can be easily analyzed. Moreover they have a low-cost realization in hardware. Counters like binary, gray suffer problem of power consumption, glitches, speed, and delay because they are implemented with techniques which have above drawbacks. They produce not only glitches, which increase power consumption but also complexity of design. The propagation delay of results of existing techniques is more which reduces speed and performance of system. LFSR counters overcome [17–22] these problems which are implemented using different technologies of CMOS.

3 Implementation 3.1 CMOS D Flip Flop The effect is that D input condition is only copied to the output Q when the clock input is active. This then forms the basis of another sequential device called a D flip flop. The “D flip flop” will store and output whatever logic level is applied to its data terminal so long as the clock input is HIGH.

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Fig. 2 CMOS GDI D flip flop power analysis

Fig. 3 CMOS D flip flop simulation results

Figure 1 shows the CMOS GDI D flip flop. In pull up region deigned with PMOS and in pull down region constructed with NMOS logic. It is designed with Tanner SEDIT Tool. Figure 2 shows the power analysis of CMOS D flip flop. It analyzed by using tanner tool. The average power consumed around 3.80e − 004 W. Max power consumed is around 2.26e−002 and minimum power consumed around 6.12e−009 W. The above simulation is done with 1.8 VDD (Fig. 3). Figure 2 shows the simulation results of CMOS D flip flop. It is simulated using CMOS Tanner-SPICE simulation program with integrated circuit emphasis tool. Wave forms are analyzed using W-EDIT (wave form editor).

3.2 CMOS GDI Linear Feedback Shift Register (LFSR) We have designed three-bit LFSR (linear feedback shift register). The output of NAND gate is given as input to first D flip flop and the output of first D flip flop given to input of second D flip flop and the input to the NAND gate. The output of the second D flip flop is given as the input to the third D flip flop and as the input to the NAND gate (Fig. 4). Figure 5 shows the power analysis of LFSR Using GDI CMOS D flip flop. It

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Fig. 4 CMOS GDI LFSR

Fig. 5 CMOS LFSR power analysis

is analyzed by using tanner tool. The average power consumed is around 4.00e − 004 W. Max power consumed is 2.79e−002. Minimum power consumed around 3.85e − 008 W. For above simulation have done with 1.8 VDD. When clock pulse is two the output Q0 will be zero, Q1 will be one and Q2 will be one. When clock pulse is three the output Q0 will be one, Q1 will be zero and Q2 will be one. When clock pulse is four outputs Q0 will be zero, Q1 will be one and Q2 will be zero. Figure 6 shows the simulation results of CMOS LINEAR FEEDBACK SHIFT REGISTER (LFSR). It is simulated using CMOS Tanner-SPICE simulation program with integrated circuit emphasis tool. Wave forms are analyzed using W-EDIT (wave form editor).

3.3 IDDQ Test IDDQ Test schematic The above schematic represents about the IDDQ test. IDDQ testing is a method for testing CMOS integrated circuits for the presence of representing faults. It depends on measuring the supply current (I d ) in the quiescent state.

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Fig. 6 CMOS LFSR simulation result

IDDQ testing uses the principle that in a quiescent CMOS digital circuit, and there is no current path between power supply and ground. The most common semiconductor is silicon. This semiconductor manufacturing faults will cause the current to increase the order of magnitude, which can be easily detected. IDDQ testing is somewhat complex than just measuring the supply current. The reasons to get the IDDQ are test generation is fast, test application time is fast, area and design time overhead is very low. It catches some defects that other tests, particularly stuck-at logic tests, do not (Fig. 7). The above schematic contains two inverters which has two PMOS and NMOS transistors. In the above schematic, PMOS are represented as M1, M3, and NMOS is represented as M2 and M4. The transistors M1 and M2 are connected to input, and the output of the first inverter is connected to input of the second inverter. The

Fig. 7 Schematic of IDDQ test

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output is taken from the second inverter. There are two pulse voltage switch are taken from the Vpulse in which one is connected at the input, and other is connected at the output. VDD and VSS are taken from the miscellaneous that are connected to the circuit (i.e., VSS is connected at PMOS and ground is connected to NMOS). The above schematic also contains the dc supply and pulse waveform. The function of IDDQ is same as the inverter. When the input = 1, the output of the inverter 1 is 0. When the input is 1 then PMOS is off and NMOS is on and the output will be equal to 0. As the output of inverter 1 is connected to input of inverter 2 so that the input of the inverter 2 is 0. As the input of the inverter 2 is 0 then PMOS is on, and NMOS is off then output is 1. Due to some faults like open faults (struck at 0), closed faults (struck at 1), transistor gate-oxide short circuits and unpowered interconnection opens current will flow from inverter 2 PMOS to inverter 1 NMOS. Here, measurable amount of current is flow through the transistor. When the input = 1 the output should be zero then the drain current (I d ) should be zero. There is no voltage drop across the circuit then the output is 5 V. But in IDSS some current is flows from drain to drain that current is called as quiescent current. The advantages in IDDQ are quality of an IC, identification of bridge faults; it is also used for failure test analysis. The disadvantages of IDDQ are time consuming and more expensive. Test bench of IDDQ The test bench of IDDQ represents about the transient analysis and the properties. It has the fall time about 5 ns, delay is 0, frequency is 5 M, time period is about 200 ns, pulse width is 95 ns, rise time is 5 ns, V High and V Low is 5 V and 0 V, respectively, which is taken from spice. The system parameters of IDDQ are angle is about 0 degrees, MasterCell is Vpulse, and Master Library is SPICE_Sources (Fig. 8). IDDQ test The above simulation represents the IDDQ test. In the above simulation, input is In and output is Out which is represented in terms of volts. In the above simulation, In is 1 from 0 to 105 ns, changed to In = 0 from 105 to 200 ns. Later,

Fig. 8 Simulation of IDDQ test

156 Table 1 Comparison table of conventional, GDI_LFSR module

V. S. Kumar and T. A. Venkatesh GDI-LFSR

Conventional LFSR

Module

Power consumed

Module

Power consumed

D_FF

2.4 * 10−4 W

D_FF

3.24 * 10−12 W

XOR GATE 1.4 * LFSR

10−4

W

XOR GATE 9.7 * 10−4 W

1.12 * 10−15 W LFSR

73.3 * 10−3 W

In is changed to 1 from 200 to 301 ns, In = 0 from 301 to 400 ns and In = 1 from 400 to 500 ns. Out = 1 from 0 to 105 ns, then Out = 0 from 105 to 200 ns, Out = 1 from 200 to 301 ns, Out = 0 from 301 to 400 ns and Out = 1 from 400 to 500 ns. The simulation of IDDQ test is same as functionality of inverter (Table 1).

4 Conclusion In this paper, we have designed and analyzed the GDI LFSR using D flip-flop. Also, we have analyzed the power consumed by the LFSR and its different blocks with IDDQ analyzed. The average power consumed is around 1.12 * 10−15 W. Max power consumed is around 2.79e − 002, and the minimum power consumed is around 3.85e−008 watts. Thus, the above simulation is done with 1.8 VDD. An extension of this work would be to optimize the final block LFSR to reduce the power consumption and the area by reducing transistor count at transistor level. This optimized LFSR can be used in different applications as well as for test pattern generations by reducing the faults.

References 1. Bronzi D, Zou Y, Villa F, Tisa S, Tosi A, Zappa F (2016) Automotive three-dimensional vision through a single-photon counting SPAD camera. IEEE Trans Intell Transp Syst 17(3):782–795 2. Vornicu I, Carmona-Galán R, Rodríguez-Vázquez A (2014) A CMOS 0.18 µm 64 × 64 single photon image sensor with in-pixel 11b time to- digital converter. In: Proceedings of International Semiconductor Conference (CAS), pp 131–134 3. Palagiri, H., Makkena, M., & Chantigari, K. R. (2013). Design development and performance analysis of high speed comparator for reconfigurable  ADC with 180 nm TSMC technology. In: 15th International conference on advanced computing technologies (ICACT 2013) 4. Perenzoni M, Perenzoni D, Stoppa D (2017) A 64 × 64-pixels digital silicon photomultiplier direct TOF sensor with 100-MPhotons/s/pixel background rejection and imaging/altimeter mode with 0.14% precision up to 6 km for spacecraft navigation and landing. IEEE J Solid-State Circuits 52(1):151–160 5. Pavia JM, Scandini M, Lindner S, Wolf M, Charbon E (2015) A 1 × 400 backside-illuminated SPAD sensor with 49.7 ps resolution, 30 pJ/sample TDCs fabricated in 3D CMOS technology for near-infrared optical tomography. IEEE J Solid-State Circuits 50(10):2406–2418

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6. Niclass C, Soga M, Matsubara H, Ogawa M, Kagami M (2014) A 0.18-µm CMOS SoC for a 100-m-range 10-frame/s 200 × 96-pixel time-of-flight depth sensor. IEEE J Solid-State Circuits 49(1):315–330 7. Palagiri AHV, Makkena ML, Chantigari KR (2016) An efficient on-chip implementation of reconfigurable continuous time sigma delta ADC for digital beamforming applications. In: Advances in intelligent systems and computing, vol 381, pp 291–299 8. Kim J, Park S, Hegazy M, Lee S (2013) Comparison of a photon counting-detector and a CMOS flat-panel-detector for a micro-CT. In: IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), pp 1–4 9. Palagiri HV, Makkena ML, Chantigari KR (2012) Performance analysis of first order digital sigma delta ADC. In: 2012 Fourth international conference on computational intelligence, communication systems and networks, Phuket, pp 435–440 10. Mo H, Kennedy MP (2017) Masked dithering of MASH digital delta sigma modulators with constant inputs using multiple linear feedback shift registers. IEEE Trans Circuits Syst I Reg Pap 64(6):1390–1399 11. HarshaVardhini PA, Madhavi Latha M, Krishna Reddy CV (2013) Analysis on digital implementation of Sigma-Delta ADC with passive analog components. Int J Comput Dig Syst (IJCDS) 2(2):71–77 12. Sham KJ, Bommalingaiahnapallya S, Ahmadi MR, Harjani R (2008) A 3 × 5-Gb/s multilane low-power 0.18-µm CMOS pseudorandom bit sequence generator. IEEE Trans Circuits Syst II Exp Briefs 55(5):432–436 13. Ajane A, Furth PM, Johnson EE, Subramanyam RL (2011) Comparison of binary and LFSR counters and efficient LFSR decoding algorithm. In: Proceedings of IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), Aug 2011, pp 1–4 14. Vardhini PAH (2016) Analysis of integrator for continuous time digital sigma delta ADC on Xilinx FPGA. In: International conference on electrical, electronics, and optimization techniques, ICEEOT 2016, pp 2689–2693 15. Kłosowski M, Jendernalik W, Jakusz J, Blakiewicz G, Szczepa´nski S (2017) A CMOS pixel with embedded ADC, digital CDS and gain correction capability for massively parallel imaging array. IEEE Trans Circuits Syst I Reg Pap 64(1):38–49 16. Palagiri HV, Makkena ML, Chantigari KR (2013) Optimum decimation and filtering for reconfigurable sigma delta adc. Far East J Electron Commun 11(2):101–111 17. Clark DW, Weng L-J (1994) Maximal and near-maximal shift register sequences: efficient event counters and easy discrete logarithms. IEEE Trans Comput 43(5):560–568 18. Harsha Vardhini PA, Madhavi Latha M (2015) Power analysis of high performance FPGA low voltage differential I/Os for SD ADC architecture. Int J Appl Eng Res 10(55):3287–3292 19. Abbas TA, Dutton NAW, Almer O, Finlayson N, Rocca FMD, Henderson R (2018) A CMOS SPAD sensor with a multi event folded flash time-to-digital converter for ultra-fast optical transient capture. IEEE Sens J 18(8):3163–3173 20. Tetrault M-A, Lamy ED, Boisvert A, Fontaine R, Pratte J-F (2013) Low dead time digital SPAD readout architecture for realtime small animal PET. In: IEEE nuclear science symposium and medical imaging conference (NSS/MIC), Oct 2013, pp 1–6 21. Vardhini PAH, Makkena ML (2021) Design and comparative analysis of on-chip sigma delta ADC for signal processing applications. Int J Speech Technol 24:401–407 22. Krstaji´c N et al (2014) A 256 × 8 SPAD line sensor for time resolved fluorescence and Raman sensing. In: Proceedings of 40th European Solid State Circuits Conference (ESSCIRC), pp 143–146

Lion Optimization Algorithm for Antenna Selection in Cognitive Radio Networks Mehak Saini and Surender K. Grewal

Abstract Overlay cognitive radio networks have proven to be efficient in 5G wireless communication systems owing to their capability of intelligent utilization of the precious electromagnetic spectrum. In this research paper, lion optimization algorithm is used to optimize the transmit antenna selection technique used by the cognitive radio to send information to its user as well as the main channel’s user at the same time. Results in the form of bit error rate versus signal to noise ratio graph are also depicted. Keywords AS · CR · LOA

1 Introduction As stated by the Federal Communications Commission (FCC), on an average about 15–85% of the spectrum band that is licensed is utilized [1]. To overcome the limitations of the already existing wireless networks and meet the needs of the ever-growing number of mobile devices, newer technologies have to be incorporated. Though cognitive radio networks have been in usage since a long time, when combined with various multiple input multiple output or MIMO techniques and newer modulation schemes such as non-orthogonal multiple access (NOMA) etc., these can prove to be one of the key technologies for future generation wireless networks (Fig. 1). As given in Fig. 2a, cognitive radio systems can be classified broadly in three main categories: Interweave cognitive radio networks: These devices do not cause any interference with the primary users as they utilize the primary user’s spectrum only for the time duration in which the primary users are not using it. These unused bands of spectrums of the primary users are given the name of spectrum holes. M. Saini (B) · S. K. Grewal D. C. R. University of Science and Technology, Murthal, India S. K. Grewal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_13

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Fig. 1 Cognitive radio networks existing simultaneously with the main network [2]

CogniƟve Radio Networks

Overlay CogniƟve radio

Underlay CogniƟve Radio

(a)

Interweave CogniƟve Radio

(b)

Fig. 2 a Classification of cognitive radio networks, b various types of cognitive radio networks, i.e., interweave, underlay and overlay [3]

Underlay cognitive radio networks: These networks involve operation of both primary and secondary users simultaneously over the same spectrum. To avoid interference between the two different types of users, i.e., the main channel’s users and the cognitive radio users, a certain threshold has to be maintained. Overlay cognitive radio networks These networks also require both the primary and secondary users to operate simultaneously. However, the difference from underlay

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method is that here secondary user needs to have information regarding primary user’s data in order to cancel it and decodes its own data [3]. Non-orthogonal multiple access technique (NOMA) is a multiple access technique which permits multiple users in the same time–frequency resource block to coexist owing to communication utilizing the power domain. User with best channel condition (having most SNR) is allocated lowest power as it will be able to decode the original signal correctly even if it is allocated least power by performing successive interference cancelation (SIC). The user with worst channel allocation is allocated maximum power so that it can directly decode its signal by considering the rest as noise. So, each user is given the same signal which is a superposition of all the signals required for all the users, weighted according to the amount of power allocated to each signal [4]. Spatial modulation [5] and transmit antenna selection [6] are closed-loop MIMO techniques. In MIMO wireless communication systems, spatial modulation is the method of transmitting information not only from the antenna but via antenna index. This increases the efficiency of the overall system. Transmit antenna selection methods aim at finding the best antenna/antennas out of a set of multiple antennas at the transmitting side of the communication system.

2 Lion Optimization Algorithm Lion optimization algorithm, abbreviated as LOA, constitutes an optimization method, and it is based on behavior of lions and their social organization. This algorithm (LOA) starts by defining a smaller set that is generated randomly which constitutes the solutions that form the (given by the term ‘lions’). Some lions of initial population act to be nomadic lions and the remaining form the resident population of lions and the latter is partitioned in a random fashion into a number of different sub-sets, termed as prides. Certain percent (denoted by S) of part of prides’ population is taken as female and remaining are in male category. However, in nomads, the percentage of females is (1 − S)%. Best (obtained) solution for every lion in passed iterations will be called as best visited one, and for the entire process of optimization, it is progressively updated. The territory of the pride consists of the best visited position of every member. In every pride, some females (selected randomly) are given the task of performing hunting of prey in a group in order to gather food (for their own pride). Hunters make a movement toward prey for encircling and catching it. Remaining females make a move in different positions or parts of the territory. Male lions that form part of the pride roam in the territory. The females of the prides undergo mating process with either one or some of the resident males. Each and every pride excludes the young males to form part of their maternal side’s pride and hence turns nomadic on reaching maturity. The power of these male lions is considered to be lesser in value than that of pride’s residents (males).

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Generate initial population of lions (Npop) randomly.

2.

Initiate the prides and randomly choose a certain percentage of (initial) population as Nomads & randomly partition the remaining lions in a fixed number of prides, thus forming the territory of each pride. In every pride, take some percentage (S) of members of prides as female & the remaining as males. In nomads, take the percentage of females is (1-S)% & the remaining as males.

3.

For every pride, perform the following steps: a. b. c.

Make some female lions (selected randomly) to go for hunting & the remaining female lions of pride are made to move towards best selected position of territory. In prides, mate a fixed percentage (depending on mating probability) of females with resident males (new cubs becoming mature). Drive the weakest male lion out (from the pride) so that it becomes nomad.

4. a. b. 5.

Make male as well as female Nomad lions to move in the search space randomly & mate a fixed percentage (depending on the probability of mating) of the nomadic females to the best nomadic male (new cubs becoming mature). Make male nomad to randomly attack the pride.

Convert some (depending upon immigrate rate) female lions from each pride into nomad.

6. a. b. 7.

Sort all nomad lions (males & females) on the basis of fitness value, select best females out of them and distribute them to prides to fill empty spaces created by migrated females. Remove nomad lions having minimum fitness value keeping in view the maximum number of permitted lions of each gender,

Go to third step if desired results are yet to be obtained.

Fig. 3 Pseudocode of LOA [7]

The nomadic lion (either male or female) has to move in the search space randomly in order to find a place (or a solution) that is better. There is a possibility that a stronger nomadic (male lion) can invade the resident (male lion), causing the latter to be evicted out of the pride who will now become the resident (male lion). For evolving, few of the females (resident ones) keep on immigrating to a new pride after some time or they might turn nomadic (switching their lifestyles), and there is possibility that a few nomad lions (female) join prides. Weakest lion is killed or it may die depending on various factors, like lack of competition and food, etc. This entire process is carried out in a loop until and unless the stopping criteria is met. The overall process of optimization in this algorithm is represented in pseudocode given in Fig. 3. In present paper, this algorithm is utilized to optimize the process of transmit antenna selection.

3 LOA for Transmit Antenna Selection(TAS) in Cognitive Radio Networks The system model of the proposed work is depicted in Fig. 2. PT and PR stand for the primary transmitter and the primary receiver, i.e., these users form part of the main or primary channel for whom the spectrum is allocated, i.e., they constitute the licensed band. PT has multiple transmitting antennas (N p ), and PR has multiple receiving

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Fig. 4 A model for cognitive radio [8]

antennas (N r1 ). Similarly, ST is the secondary transmitter, having Nr receiving end antennas and Nt transmitting end antennas. SR is short for secondary receiver which uses N r2 number of receiving antennas. Together, ST and SR form parts of the cognitive radio network. The dotted lines depicted that there can be multiple secondary receiver, and here, only one is chosen for sending data. The secondary transmitter acts as a relay by first receiving the signal from the primary transmitter and then in the next step sending that signal to the primary receiver and the data intended for the secondary receiver simultaneously by using NOMA and SM. Further, antenna selection is done between the secondary transmitter and the primary receiver link to enhance the performance (Fig. 4).

4 Results Simulation of the cognitive radio model was performed on MATLAB R2018a. Two datasets were chosen for simulation. The first dataset used 40,000 bits of binary data generated randomly and the second dataset used 60,000 bits of binary data. In Fig. 5, four cases are taken in which BER versus SNR graph is drawn by calculating the BER over a range of SNR values, taken for −10 to +10. It is observed that as SNR increases, BER converges to zero. The statistical values calculated for this dataset are given in Table 1. Similarly, for dataset 2, BER versus SNR graph is depicted for four cases, as shown in Fig. 6a–d. Also, statistical results obtained after simulation are given in Table 2. We have achieved the objective of error minimization. Table 3 summarizes the optimization parameters chosen.

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Fig. 5 a–d BER versus SNR graph for first dataset (i.e., for 40,000 bits) for four cases

Table 1 Optimization statistical report

Best Worst Mean

1.4082 224.99 53.624

Median

38.922

STD (standard deviation)

48.868

5 Conclusions and Future Scope This work shows the potential of using lion optimization algorithm for the task of transmit antenna selection in a cognitive radio network. It can be concluded that this technique results in improvements in results as BER converges as SNR is increased with the usage of optimization technique. Further optimization techniques can be applied to compare the results given in this paper. Also, apart from optimizationdriven techniques, data-driven techniques can be applied to further enhance the results.

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Fig. 6 a–d BER versus SNR graph for second dataset (i.e., for 60,000 bits) for four cases Table 2 Optimization statistical report

Best Worst Mean

Table 3 Optimization parameters

2.3605 148.19 39.556

Median

30.506

STD (standard deviation)

34.919

Population size

10

Chromosome length

[24 32 48 64]

Maximum number of iterations

100

Mature age

3

Mutation rate

0.15

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References 1. FCC Spectrum Policy Task Force (2002) Report of the spectrum effiency working group 2. Tellambura C, Kusaladharma S (2017) An overview of cognitive radio networks. Wiley ecyclopaedia of electrical and electronics engineering 3. Akyildiz IF, Lee WY, Vuran MC, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw 50(13):2127–2159 4. Sun Q, Han S, Chin-Lin I, Pan Z (2015) On the ergodic capacity of MIMO NOMA systems. IEEE Wireless Commun Lett 4(4):405–408 5. Abdzadeh-Ziabari H, Champagne B (2020) Signal detection algorithms for single carrier generalized spatial modulation in doubly selective channels. Sig Process 172 6. Kai X, Tao L, Chang-chuan Y, Guang-xin Y (2004) Transmit antenna selection for MIMO systems. In: IEEE 6th Circuits and Systems Symposium on Emerging Technologies: Frontiers of Mobile and Wireless Communication, vol 2, pp 701–704, China 7. Yazdani M, Jolai F (2016) Lion optimization algorithm (LoA): a nature-inspired metaheuristic algorithm. J Comput Design Eng 3(1):24–36 8. Emam S, Celebi ME (2018) Non-orthogonal multiple access protocol for overlay cognitive radio networks using spatial modulation and antenna selection. AEU-Int J Electron Commun 86:171–176

Sub-band Selection-Based Dimensionality Reduction Approach for Remote Sensing Hyperspectral Images S. Manju and K. Helenprabha

Abstract Hyperspectral image classification and segmentation are unique technique to examine diversified land cover images. The main resource controlling productivity for terrestrial ecosystem in land cover classification and the challenges are subjected to curse of dimensionality, which is termed as Hughes phenomenon. Nowadays, it is vital to distinguish how land cover has distorted over time. Over the globe, land area is confined only to 29% of the surface, while the majority share is covered by enormous spread of water. Human has to depend upon less than one-third of the surface, which is around 148,300,000 km2 area, as a habitable part. It is estimated that more than two-third of the land cover area is not suitable for habitation. Approximately, 27% of land area is characterized by cold environment. Dry and extreme weather conditions also account for 21% of the land surface. Nearly, 4% of the total land surface accounts for uneven geographic conditions. Hence, it exemplifies that land is inimitable and ubiquitous resource. This research is focussed on provision for land use vegetation classification using AVIRIS hyperspectral images. The contiguous narrow bandwidth of hyperspectral data facilitates detailed classification of land cover classes. The issues of utilizing hyperspectral data are that they are normally redundant, strongly correlated and subject to Hughes phenomenon. This paper proposes sub-band selection techniques to overcome the Hughes phenomenon in classification. Keywords Hyperspectral image · Hughes phenomenon · Feature extraction · Sub-band selection · GLCM parameters · Linear and nonlinear methods

S. Manju (B) Department of Medical Electronics, Saveetha Engineering College, Thandalam, Chennai 602105, India K. Helenprabha Department of Electronics and Communication Engineering, R.M.D. Engineering College, Kavaraipettai 601206, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_14

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1 Introduction Mapping and recognizable proof of vegetation is a most significant issue for biodiversity management and preservation. The different common marvels like agricultural escalation, urban development, environmental change and scrub advancement are the significant focal point of research, and their spatiotemporal elements are the fundamental concern. In this situation, satellite images develop to be a promising answer for common vegetation mapping. Contrasted to aerial photography, satellite symbolism can give images at certain time slack with more noteworthy area of coverage. A few investigations have demonstrated the capability of very high spatial resolution (VHSR) sensors to map vegetation networks. Most of the VHSR satellite sensors are confined to just four spectral bands (blue, green, red, near infrared) and from there on lacking to segregate a few characteristic vegetation networks. The WorldView-2 satellite has been giving VHSR images in eight spectral bands, beginning from blue to near infrared, together with added substance groups like coastal-blue, yellow and red-edge bands. A few examinations have just demonstrated the capability of WorldView-2 spectral imagery to appraise woodland biomass and basic parameters, and to evaluate fine-scale plant species beta diversity in grassland, Dalmayne et al. (2013) considered vegetation mapping assignments applied to urban tree species. The phrase “hyperspectral” imaging was derived from works in remote sensing first cited by Goetz et al. in (1985) to make a straight recognition of surface materials in the form of images. Hyperspectral images are progressively found to have advantages over the field of agriculture. The primary difficulties of HSI grouping cannot be viably overwhelmed by machine learning techniques (Li et al. 2019) and furthermore present the benefits of deep learning and how to deal with these issues. At that point, they developed a structure which partitions the relating works into spectral feature systems, spatial-features systems and spectral spatial feature systems to systematically review the ongoing accomplishments in deep learning-based HSI classification. Also, considering the way that accessible preparing analysis in the remote detecting field is generally restricted and preparing deep networks requires an enormous number of tests, they include a few techniques to improve characterization execution, which can give a few rules to future examinations on this point. A few representative deep learning-based characterization techniques are directed with respect to genuine HSIs in their model. The purpose of hyperspectral imaging is to acquire the spectrum for each pixel in the image of the scene, with the principle of finding objects, identifying materials or classifying land cover classes. Therefore, the accurate way of classification of land cover classes is essential in remote sensing images. The rest of this proposed research work organized as follows: Sect. 2 explains in details the literature survey accomplished in the areas related to land cover classes in context of preprocessing with its advantages and disadvantages; Sect. 3 describes the high dimensionality reduction technique employed. Sect. 4 describes the results

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part with various aspects. Lastly, Sect. 5 ends the comparison of various sub-band selection with the proposed method.

2 Literature Survey Zhou et al. (2018) have novel strategy for CCPR by coordinating fuzzy support vector machine (SVM) with hybrid kernel function, and genetic technique (GA) is proposed. Initially, two shape highlights and two factual highlights that do not rely upon the appropriation parameters and number of tests are displayed to unequivocally depict the qualities of CCPs. At that point, a novel multiclass strategy dependent on fuzzy support vector machine with a hybrid kernel function is proposed. In this strategy, the impact of anomalies on characterization exactness of SVM-based classifiers is weakened by allocating a level of participation for each preparation test. In the interim, a hybrid kernel function consolidating Gaussian part and polynomial bit is received to additionally improve the speculation capacity of the classifiers. To understand the issue of highlights choice and parameters enhancement, GA is utilized to upgrade the info highlights subsets and parameters of fuzzy SVM-based classifier. At last, a few simulation tests and a genuine model are routed to approve the practicality and viability of the proposed procedure. Borsoi et al. (2019) proposed that hyperspectral multispectral (HS-MS) image combination is right now drawing in remote sensing for remote detecting since it permits the age of high spatial goals HS images and going around the primary confinement of this imaging methodology. Previous HS-MS combination technique, in any case, disregard the otherworldly inconstancy frequently existing between images obtained at various time moments. This time distinction causes varieties in spectral signatures of the fundamental constituent materials because of the diverse securing and occasional conditions. This work presents a novel HS-MS image combination system that consolidates an unmixing-based definition with an unequivocal parametric model for typical spectral variability between the two images. Recreations with manufactured and genuine information show that the proposed methodology prompts a critical exhibition improvement under spectral variability and state-of-the-art execution generally. Echanobe et al. proposed that extreme learning machines (ELM) have been effectively applied for the classification of hyperspectral images (HSIs), regardless of the ill effects of three fundamental disadvantages. These include: (1) inadequate feature extraction (FE) in HSIs because of a single hidden layer neuron organize utilized; (2) ill-posed issues brought about by the arbitrary info loads and biases; and (3) absence of spatial data for HSIs classification. To handle the main issue, they build a multilayer ELM for compelling FE from HSIs [16]. The sparse representation is received with the multilayer ELM to handle the poorly presented issue of ELM, which can be tackled by the elective course strategy for multipliers. This has resulted about in the presented multilayer sparse ELM (MSELM) model. The neighbouring pixels are almost certain from a similar class, a nearby block expansion

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is acquainted for MSELM with extricate the local spatial data, prompting the nearby block MSELM (LBMSELM). The loopy conviction spread is likewise applied to the proposed MSELM and LBMSELM ways to deal with further use the spectral and spatial data for improving the classification. Iturrino et al. (2019) concluded that multispectral imaging devices as of now being utilized in an assortment of uses ordinarily follow opto-mechanical plans. These plans regularly present the weakness of a massive and substantial development, restricting their relevance in extraordinary failure elevation remote detecting applications conveyed on procurement frameworks with compelled weight and measurement prerequisites. This work introduces the plan and development of a straightforward optical framework dependent on a transmission grating and a lightweight business camera for depiction of multispectral image acquisition. The framework can be mounted on a little automaton for low-elevation flying remote detecting and is equipped for isolating the spectral data from the spatial scene and creating multispectral HD images and 4 K recordings. Therefore, they propose a quick technique for recouping multispectral scenes from their individual procured unearthly projections. Trial results show similarity of the compared waveforms, and that the greatest peak of the reproduced wavelength changes up to 13 nm from reference spectroradiometer information.

3 High Dimensional Reduction Technique Hyperspectral bands generally have a lot of redundant information, leading to the Hughes phenomenon and an increase in computing time. As a popular dimensionality reduction technology, band/feature selection is essential for HSI classification. The dimensions of dataset can be reduced by using feature selection and feature extraction. The curse of dimensionality is categorized as shown in Fig. 1.

CURSE OF DIMENSIONALITY

FEATURE SELECTION

FEATURE EXTRACTION

LINEAR METHODS 1.PCA 2.MCIA 3.Joint NMF 4.MOFA

NON LINEAR METHODS 1.Autoencoders 2.Representative learning

FILTER METHOD 1.mRMR 2.Information Gain

Fig. 1 Dimensionality reduction techniques

WRAPPER METHOD 1.RFE SVM

EMBEDDED METHOD 1.Elastic Net 2.Stability Selection

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3.1 Feature Selection This is the process of selecting dimensions of the dataset which contributes more to the machine learning tasks which can be achieved by various techniques like correlation analysis, univariate analysis, etc. In filter method, features are selected on the basis of their scores in various test, and best score is selected for learning rule. Finally, performance measures are calibrated. Figure 2 depicts the process of filter method. Figure 3 shows the wrapper method, where subset of features are used to train the Fig. 2 Filter method

Fig. 3 Wrapper method Input Features

Subset Generation

Learning Algorithm

Performance

172 Fig. 4 Embedded method

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Input Features

Subset Generation

Learning Algorithm

Performance

model. It measures the usefulness of a subset of feature by actually training a model. Hence, the best subset of features is selected. In embedded method, the feature selection is performed during the model training and so much less prone to overfitting. Figure 4 shows the process of embedded method feature selection.

3.2 Feature Extraction This is the process of finding new features by selecting or combining features to create reduced feature space while still accurately and completely describing the dataset without loss of information. Some of the popular dimensionality reduction techniques are PCA, ICA, LDA, kernel PCA, etc. In the proposed sub-band selection method, the subspace decomposition is achieved by calculating the correlation coefficients between adjacent bands. Figure 5 illustrates the flow graph of the proposed method. It takes into account the correlation coefficient of adjacent bands and spectral characteristics simultaneously to determine the reduce the redundant information among the selected bands.

4 Results and Discussion 4.1 Salinas Dataset This scene was gathered by the 224-band AVIRIS sensor over Salinas Valley, California, and is described by high spatial goals (3.7-m pixels). The region secured involves 512 lines by 217 samples. This image was accessible just at-sensor radiance information. It incorporates vegetables, exposed soils and vineyard fields. Salinas ground truth consists of 16 classes. The number of classes, training and testing samples is shown in Table 1.

Sub-Band Selection-Based Dimensionality Reduction … Fig. 5 Proposed method

173

Remote Sensing Image (All N bands)

Pre-processing

CURSE OF DIMENSIONALITY

Existing Method PCA, MCA, Autoencoders

Proposed Method Band selection method

Reduced Band Set of Bands Classification of crops

4.2 Sub-band Selection The GLCM is utilized to register a lot of scalar amounts that describe the various parts of the surface in the image. The GLCM features, for example, inertia, sum entropy, difference entropy, homogeneity, angular second moment (ASM), local homogeneity, inverse difference moment (IDM), contrast, entropy, correlation, sum of squares, variance and sum average can well depict the image surfaces. Grey-level co-occurrence matrix (GLCM) gives conveyance estimations of image grey level as grid. In this presented framework, the GLCM figured for the block encompassing the pixel of concern. In this matrix, a few features like contrast, energy, homogeneity, correlation, etc. can be determined. Twenty-two features are removed from this GLCM. Four out of propositions 22 highlights are chosen utilizing correlation feature selection technique. These are homogeneity, sum variance, sum entropy and information measure of correlation-2 (IMC-2). The features are processed utilizing the equation appeared in Table 2. In this, p(i, j) is the (i, j)th entry in a standardized grey tone spatial dependence grid, px(i) is the ith section in the marginal probability grid got by adding the rows of p(i, j), py(i) is the ith passage in the minor marginal probability grid acquired

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Table 1 Indicate the number of classes and their samples information No

Class

Samples

Training/Test

1

Brocoli_green_weeds_1

2009

150/1570

2

Brocoli_green_weeds_2

3726

200/2400

3

Fallow

1976

175/1500

4

Fallow_rough_plow

1394

200/1100

5

Fallow_smooth

2678

250/1750

6

Stubble

3959

350/2500

7

Celery

3579

275/3000

8

Grapes_untrained

11,271

150/1000

9

Soil_vinyard_develop

6203

400/2000

10

Corn_seneseed_green_weeds

3278

300/2400

11

Lettuce_romaine_4wk

1068

170/850

12

Lettuce_romaine_5wk

1927

180/1200

13

Lettuce_romaine_6wk

916

150/700

14

Lettuce_romaine_7wk

1070

100/750

15

Vinyard_untrained

7268

500/2800

16

Vinyard_vertical trellis

1807

180/1100

Table 2 Formula used to compute GLCM features S. No.

GCLM Features

1

Homogeneity

Formula   F1 = i j F2 =

2

Sum variance =

3

Sum entropy

2Ng i=2 2Ng

F3 = −

i=1 2N g

1 1+(i− j)2

p(i, j)

(i−F3 )2 px+y (i) where px+y (k) 2Ng p(i, j), i + j = k = 2, 3, . . . , N g j=1

  px+y (i) log px+y (i)

i=2

4

Information measure of correlation (IMC-2)

    1/2 F4 = 1− exp −2.0 H X Y 2 − H X Y   Where H X Y = − p(i, j) log( p(i, j)) i j     and H X Y 2 = px (i) p y ( j) log px (i) p y ( j) i

j

by adding the columns of p(i, j), and Ng = Number of particular grey levels in the quantized image. The GLCM features extracted band wise, for Salinas and Indian pine images, are presented in Table 3. Figure 6a shows the input image of Salinas data for band 20. The classified result is shown in Fig. 6b. It is explored that the measure of correlation which measures

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Table 3 GLCM features extracted for Salinas image BAND

Homogeneity

Sum variance

Sum entropy

Information measure of correlation (IMC-2)

1:20

0.0337

0.9159

0.9831

0.4664

21:40

0.0340

0.9305

0.9830

0.5786

41:60

0.0352

0.9184

0.9824

0.5340

61:80

0.0351

0.9258

0.9824

0.5340

81:100

0.0351

0.9258

0.9824

0.4933

101:120

0.0302

0.9150

0.9849

0.6732

121:140

0.0346

0.9276

0.9827

0.4884

141:160

0.0321

0.9127

0.9839

0.6015

161:180

0.0320

0.9133

0.9840

0.6000

181:200

0.0345

0.9235

0.9828

0.4162

201:220

0.0337

0.9159

0.9831

0.4664

Fig. 6 a Salinas input image. b Land covered class. c Sub-band selection values

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the structural similarity between various bands seems to be similar for band 21 to band 40. And graph is depicted in Fig. 6c. The result of the sub-band selected in the nearby neighbourhood of every pixel the mean and standard deviation is determined as from these μi =

σi =

N 1  xi j N j=1

1 N (xi j − μi ) j=1 N

(1)

(2)

where μi and σi are mean and standard deviation of pixels in a specific block, x ij is characterized the pixel esteems in block and N describes to the absolute number of pixels in every local block. In the proposed method, the subspace decomposition is achieved by calculating the correlation coefficients between adjacent bands. The redundant information from the selected bands can be obtained from the spectral characteristics of huge spectral bands. From Fig. 6c, it is observed that bands from 21 to 40 have similar distinguishing capacity for different land covers, and thus, the huge numbers of bands are reduced while preserving the information of the original bands in hyperspectral image processing. After all the 1D, 2D, GLCM features are separated, a standardization procedure is applied. It is utilized in the features to bring them into a comparative scope of qualities, helping the successive classification. From this procedure, they are described to zero mean with a standard deviation of one. These esteems are forwarded as input to IRVM training procedure. Satellite image order can be accepted as a coordinated methodology that includes both image processing and machine learning. GLCM indices were created for the evaluation of heterogeneous surface patterns and roughness appeared in digital imagery. Each record can feature a specific property of surface, for example, smoothness, roughness and abnormality.

5 Conclusion One of the most vital challenges tasks in remote sensing applications is the segmentation and classification of hyperspectral images. In order to segment and classify the regions in hyperspectral image, the clear depiction of the edge information is necessary. In this paper, the high dimensionality reduction technique using correlation analysis of subspace decomposition is achieved by calculating the correlation coefficients between adjacent bands. Thus, the huge number of bands are reduced which is the curse of satellite image dataset, and hence, the original bands information is preserved.

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References 1. Gao Q, Lim S, Jia X (2018) Hyperspectral image classification using convolution neural networks and multiple feature learning. Remote Sens 10:1–18 2. Toksoz MA, Ulusoy I (2016) Hyperspectral image classification via basic threshold classifier. IEEE Trans Geosci Remote Sens 54(7):4039–4051 3. Damodaran BB, Nidamanuri RR, Tarabalka Y (2015) Dynamic ensemble selection approach for hyperspectral image classification with joint spectral and spatial information. IEEE J Sel Top Appl Earth Obser Remote Sens 8(6):2405–2417 4. Manju S, Venkateswaran N (2017) An efficient feature extraction based segmentation and classification of Antarctic Peninsula ICE shelf. Int J Control Theory Appl 10(20):231–241 5. Manju S, Venkateswaran N (2018) An improved relevance vector machine with metaheuristic optimization based vegetation classification using worldview-2 satellite images. TAGA J Graph Technol 14:562–574 6. Huo H, Guo J, Li Z-L (2018) Hyperspectral image classification for land cover based on an improved interval type-II fuzzy C-means approach. Sensors 18(2):1–22 7. Hasane Ahammad SK, Rajesh V, Zia Ur Rahman MD (2019) Fast and accurate feature extraction-based segmentation framework for spinal cord injury severity classification. IEEE Access 7:46092–46103 8. Saravanakumar V, Suma KG, Sakthivel M, Kannan KS, Kavitha M (2018) Segmentation of hyperspectral satellite image based on classical clustering method. Int J Pure Appl Math 118(9):813–820 9. Benediktsson JA, Sveinsson JR (2005) Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans Geosci Remote Sens 43(3):480–491 10. Kang X, Li S, Benediktsson JA (2017) Hyperspectral image classification: a benchmark. In: IEEE International Geoscience and Remote Sensing Syposium, pp 3633–3635 11. Zhang X, Weng P, Feng J, Zhang E, Hou B (2014) Spatial-spectral classification based on group sparse coding for hyperspectral image. In: IEEE International Geoscience and Remote Sensing Symposium, pp 1745–1748 12. Braun AC, Weidner U, Hinz S (2012) Classification in high-dimensional feature spaces— assessment using SVM, IVM, and RVM with focus on simulated EnMAP data. IEEE J Sel Top Appl Earth Obser Remote Sens 5(2):436–443 13. Mianji FA, Zhang Y (2011) Robust hyperspectral classification using relevance vector machine. IEEE Trans Geosci Remote Sens 49(6):2100–2112 14. Demir B, Erturk S (2007) Hyperspectral image classification using relevance vector machines. IEEE Geosci Remote Sens Lett 4(4):586–590 15. Damodaran BB, Nidamanuri RR, Tarabalka Y (2015) Dynamic ensemble selection approach for hyperspectral image classification with joint spectral and spatial information. IEEE J Sel Top Appl Earth Obser Remote Sens Soc 8(6):2405–2417 16. Echanobe J, del Campo I, Martinez V, Basterretxea K (2017) Genetic algorithm based optimization of ELM for on-line hyperspectral image classification. In: IEEE International Joint Conference on Neural Networks, pp 4202–4207 17. Lopez-Fandino J, Quesada-Barriuso P, Heras DB, Arguello F (2015) Efficient ELM-based techniques for the classification of hyperspectral remote sensing images on commodity GPUs. IEEE J Sel Top Appl Earrh Obser Remote Sens Soc 8(6):2884–2893 18. Lv Q, Niu X, Dou Y, Wang Y, Xu J, Zhou J (2016) Hyperspectral image classification via kernal extreme learning machine using local receptive fields. In: IEEE International conference in image processing, pp 256–260 19. Samat A, Du P, Liu S, Li J, Cheng L (2014) Ensemble extreme learning machines for hyperspectral image classification. IEEE J Sel Top Appl Earth Obser Remote Sens 7(4):1060–1069 20. Shen Y, Xu J, Li H, Xiao L (2016) ELM-based spectral–spatial classification of hyperspectral images using bilateral filtering information on spectral band-subsets. In: IEEE International geoscience and remote sensing symposium, pp 497–500, Nov 2016

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An Efficient Network Intrusion Detection System Based on Feature Selection Using Evolutionary Algorithm Over Balanced Dataset Manisha Rani and Gagandeep

Abstract Intrusion detection system is the substantial tool for securing the network traffic from malicious activities. Although it provides protection to real-time networks, however, they suffer from high false alarm rate and low testing accuracy in classification task. Because of large-sized IDS datasets, the high dimensionality and class imbalance problem are the crucial barrier in achieving high performance of IDS model. To reduce the dimensions of dataset and to address class imbalance issue, we have used artificial bee colony algorithm for feature selection and SMOTE data oversampling technique to balance the dataset respectively. At the outset, data is preprocessed using min–max normalization method and data is balanced by oversampling the minority instances in the dataset. After that, features are selected and evaluated using random forest classifier. Further, RF classifier has also been chosen based on empirical analysis. The experimental results are performed over widely used NSL KDD dataset, and the superiority of proposed work is evaluated by comparing it with the existing literature. Keywords Intrusion detection system · Artificial Bee Colony · Class imbalance · SMOTE · NSL KDD · UNSW-NB15

1 Introduction In digital world, security has become the critical issue in modern networks from past two decades. It leads toward rapid increase of security threats too along with the growth of computer networks. Despite strong security system against threats, still there exist numerous intruders over the network that can violate the security policies such as confidentiality, integrity and availability, i.e., (CIA), of the network. An intruder may attempt unauthorized access to information, manipulate the information and make the system unreliable by deploying unnecessary services over the network. Therefore, to secure network from these intruder activities, strong security M. Rani (B) · Gagandeep Department of Computer Science, Punjabi University, Patiala, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_15

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mechanisms need to be designed in order to protect system resources from unauthorized access [1]. There are a number of security mechanisms available such as cryptographic techniques, firewalls, packet filters, intrusion detection system and so on. Subsequently, malicious users use different techniques such as password guessing, unnecessary loading of network with irrelevant data and unloading of network traffic to exploit system vulnerabilities. Therefore, it is very unrealistic to protect the network completely from breaches. But it is possible to detect the intrusions so that the damage can be repaired by taking an appropriate action. Thus, intrusion detection system plays a significant role in network security field by providing a solid line of defense against malicious users. It detects any suspicious activity performed by intruder by monitoring network traffic and issues alerts whenever any abnormal behavior is sensed [2]. It is broadly classified into two types, i.e., NIDS and HIDS. NIDS stands for network-based IDS which analyzes the network traffic by reading individual packets through network layer and transport layer, whereas HIDS stands for host-based IDS which monitors every activity of individual host or device on the network. IDS can detect known attacks through signature-based detection and can detect unknown attacks through anomaly-based detection. Both approaches have their own limitations such that former detection approach is good for finding known attacks only but not for unknown attacks because it can match the patterns with the stored patterns only. The system must update the database with new attack signature whenever novel attack is identified. Whereas anomaly-based detection can detect both known and unknown attacks, but it suffers from high false alarm rate because of its nonlinear nature, high-dimensional features, mixed type of features in datasets. To overcome these challenges, there exist various machine learning algorithms and evolutionary algorithms that are mainly used for classification and feature reduction process, respectively. Recently, various algorithms such as random forest, naïve Bays and k-nearest neighbor are used as IDS classifiers. However, the presence of irrelevant features in the dataset deteriorates the performance of classifier [3]. Thus, to improve the performance of classifier, it is very important to reduce the dimensionality of feature space by identifying and selecting relevant features from original feature set which are needed for classification. But selecting the relevant features from full set is itself a challenging task. Currently, bio-inspired algorithms such as genetic algorithm [4], particle swarm optimization [5] and ant colony optimization [6] are emerging techniques used for feature selection because of their high convergence power and searching behavior. They are inspired from biological nature of animals, insects and birds and works based upon the principle of their intelligent evolutionary behavior. These algorithms help in solving the complex problems with improved accuracy and are very useful in finding an optimal solution to a given problem. Although feature selection approach helps in reducing the false alarm rate of IDS, however, due to imbalanced nature of IDS datasets, it could not improve the performance of classifiers significantly. Generally, IDS datasets contain large number of normal data (i.e., majority class instances) as compared to attack data (i.e., minority class instances), resulting in class imbalance problem. It can be addressed at either data level or classifier level by changing class distribution of training set itself or by altering the training algorithm

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rather than training data, respectively. At data level, there are various under-sampling techniques such as one-sided selection and oversampling techniques like SMOTE, clustering approach, etc., are available to balance the instances by inserting some random instances to minority samples or by removing some instances from majority instances, respectively. At classifier or algorithmic level, either threshold approach can be used by adjusting decision threshold of a classifier or cost-sensitive approach can be used by modifying the learning rate parameter of algorithm. In this paper, initially, data is preprocessed using min–max normalization technique. Then, data is balanced using SMOTE [7]. Then, subset of features is chosen from original set using ABC algorithm. The optimality of feature subset is evaluated using random forest classifier. The performance of IDS is evaluated using NSL KDD dataset. After empirical analysis of classification accuracy of various classifiers, random forest has been chosen as classifier among them. The rest of the paper is organized as follows: Sect. 2 deals with survey related to different feature selection and class imbalance approaches. Section 3 describes the dataset used in the experimental analysis. Section 4 deals with the methodology followed for proposed work. Then, experimental results are reported in Sect. 5 and the performance of [8–10] proposed results is superior than existing work. The story of the paper is concluded in Sect. 6.

2 Related Work Numerous ML-based classifiers and evolutionary algorithms have been proposed for classification and feature selection process for IDS. To identify attack from normal traffic, researchers employ various steps such as data preprocessing, feature selection and reduction techniques and classification step. First of all, existing work related to IDS needs to be reviewed to identify research gaps in the previous work and then some novel work would be proposed for further research. Tavallaee et al. [11] analyzed the publicly available KDDCup’99 dataset in order to solve the inherent problems of the dataset. The major problem occurs due to large number of redundant records, and level of difficulty in both training and testing sets leads ML algorithm to be biased toward majority class instances and large gap between training and testing accuracies. To solve these problems, new version of KDDCup’99, i.e., NSL KDD, is proposed. Although NSL KDD can be used as benchmark dataset, it still suffers from few problems such as data imbalancing. Pervez and Farid [12] proposed SVM-based feature selection algorithm and classifier to select subset of features from original set. It had achieved 82.38% of testing accuracy using 36 features and compared its results with existing work. Ingre and Yadav [13] evaluated the performance of NSL KDD using back propagation algorithm of artificial neural network (ANN) architecture. Before learning, data was preprocessed by converting each feature into numerals and normalizing the data into range [0, 1] using Z-score approach. Then, 41 features of KDDTrain+ and KDDTest+ were reduced to 23 using information gain technique, and then, reduced

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features were learnt through neural network. Aghdam and Kabiri [14] proposed IDS model by selecting new feature subset using ant colony optimization (ACO) approach followed by nearest neighbor classifier to identify attacks from normal traffic. Although it achieved better results than existing work, ACO algorithm suffers from computational memory requirements and low speed due to separate memory required by each ant. Kaur et al. [14] compared the performance of hybridization of K-means with firefly algorithm and bat algorithm with other clustering techniques, and it had shown that the proposed work outperformed other techniques with huge margin. However, data preprocessing involving data normalization might also improve the performance of the proposed work. Hajisalem and Babaie [15] proposed another hybrid model using artificial bee colony (ABC) and artificial fish swarm (AFS) evolutionary algorithms over NSL KDD and UNSW-NB15 datasets. Mazini et al. [15] designed a hybrid model for anomaly-based NIDS using ABC algorithm and AdaBoost algorithm to select features and to classify attack and normal data using NSL KDD and ISCXIDS2012 datasets, respectively. Although evolutionary algorithms outperformed various existing works, it could not address the imbalance problem in research work. Recently, Jiang et al. [16] designed hierarchical model based on deep learning approach by addressing class imbalance problem using feature selection process to classify attack data. It combined the one-sided selection (OSS) technique and SMOTE for handling under-sampling and oversampling, respectively. Then, it used convolutional neural network (CNN) to choose spatial features and used bidirectional long short-term memory (Bi-LSTM) to extract temporal features from NSL KDD and UNSW-NB15 dataset. It identified attack data from normal by achieving 83.58% and 77.16% testing accuracy using NSL KDD and UNSW-NB15 datasets, respectively. Alkafagi and Almuttairi [17] designed proactive model for swarm optimization (PMSO) for selecting individual optimal feature subset using PSO and bat algorithms followed by DT classifier. Both researches were successful in improving the performance of the proposed work as compared to existing work but did not address common issue, i.e., class imbalance problem. Wu et al. [18] achieved classification accuracy up to 78.47% by combining K-means clustering approach with SMOTE algorithm to equalize the minority data instances with majority instances in NSL KDD dataset and then by using enhanced RF algorithm for classification process. Although it addressed the class imbalance, it could not achieve effective results for minority attack data such as R2L and U2R attack types. Liu and Shi [19] designed hybrid model for anomaly-based NIDS using GA algorithm for feature selection and RF for classification. It achieved high classification accuracy rate using NSL KDD and UNSW-NB15 training sets but did not test or validate the data results using testing set of both datasets. Priyadarsini [20] solved data imbalanced problem using borderline SMOTE algorithm through RF technique and then selected feature subset using ABC algorithm. Various classifiers such as SVM, DT and KNN were applied for classification of attack data from normal data using KDDCup’99 dataset only. From existing work, it is found that evolutionary algorithms are being extensively used for feature selection process followed by ML classifiers. It is also predicted

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that lesser research has been done to address class imbalance problem. In this paper, data sampling technique such as SMOTE has been used for balancing the NSL KDD dataset. Then, ABC algorithm has been applied for finding optimal subset of features followed by RF classifier to build an effective model for IDS.

3 Dataset Used To empirically analyze the results of IDS model, widely used dataset, i.e., NSL KDD, has been considered in this paper. The experiments are performed using 100% of dataset. The data is trained over 70% of training set, and remaining 30% of training set is used for validating the results. Then, the results of dataset are tested using testing set.

3.1 NSL KDD Dataset Due to inherent problems in KDDCup’99 dataset, performance of various researchers’ work was affected. To overcome the issues, its improved version was made and renamed as NSL KDD dataset [11]. It is the benchmark dataset used by every researcher to compare the performance of IDS models. It contains total of 42 attributes out of which 41 attributes belong to network flow type whereas last 42nd attribute indicates the label assigned to each attribute. All attributes except last are categorized into four types such as basic, content-related, time-related and host-based contents. On the other hand, last attribute represents class that indicates whether the given instance is normal or attack connection instance. The four attack classes are distributed denial of service (DDoS), probe, user to root (U2R) and Remote2Local (R2L). It is publicly available over the Internet consisting of training set and testing set as KDDTrain+ and KDDTest+, respectively. The number of instances present in each set is described in Table 1: Table 1 Record distribution of NSL KDD dataset

Dataset

Total instances

Record distribution Normal

Attack

KDDTrain+

125,973

67,343

58,630

KDDTest+

22,544

9711

12,833

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4 Intrusion Detection Process Intrusion detection system follows some basic steps to detect intrusions from network traffic data. To monitor network traffic, there must be some genuine data containing information about network packets. The first step involves collection of data from available sources over which entire detection process has to be done. In this paper, a benchmark set, i.e., NSL KDD, has been used for empirical analysis of the data. These datasets contain metadata of network-related data and different attacks that can be generated by the intruders. Second step involves data preprocessing step which consists of two sub-steps, i.e., data conversion and data normalization. In order to reduce the dimensions of data, redundant and noisy data are removed from entire dataset and only relevant features are selected through feature reduction and feature selection process. Then, reduced feature set of data is fed into classifier to discriminate normal and attack data. The block diagram of whole intrusion detection process is shown in Fig. 1.

4.1 Data Preprocessing Because of vast amount of network packets, unequal distribution of data in datasets and instability of data toward changing behavior of network, it is very difficult to classify attack data from normal with high accuracy rate. Therefore, there is a need of some sought of preprocessing to data before putting it over detection model. Before classification, data is preprocessed through data conversion and data normalization.

Fig. 1 Block diagram of IDS model

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Data Conversion: The NSL KDD contains heterogeneous data such as numerical and non-numerical or string type of data. Most of the IDS classifiers accept numerical data type only. So, it is necessary to convert the entire data into homogenous form, i.e., numeric type. First of all, data conversion is involved in preprocessing step which converts all non-numerical features into integers. For instance, protocol feature in NSL KDD contains string type of values such as TCP, ICMP and UDP which are converted into numbers by assigning values 1, 2 and 3 to them, respectively. Data Normalization: After converting all features into integer type, they can be of either discrete or continuous in nature, which are incomparable and may degenerate the performance of the model. To improve the detection performance significantly, normalization technique is applied in order to rescale the dataset values into range of interval [0, 1]. There are various normalization techniques such as Z-score, Pareto scaling, sigmoidal and min–max available. Based on empirical analysis, min–max normalization approach has been used among them in this paper. Every attribute value is rescaled into [0, 1] by transforming maximum value of that attribute into 1 and minimum value of that attribute into 0, while remaining attribute values are transformed into decimal values lying between 0 and 1. For every feature x i , it is calculated mathematically as follows: Xi =

xi − min(xi ) max(xi ) − min(xi )

(1)

where xi denotes ith feature of dataset x, and min(xi ) and max(xi ) denote minimum and maximum values of ith feature of dataset x, respectively. X i represents the corresponding normalized or rescaled value of the ith feature value.

4.2 Data Sampling Generally, large-size datasets suffer from unequal distribution of records known as class imbalance problem. The training set of IDS datasets contains more number of normal data known as majority instances than attack data, i.e., minority class instances. For example, the large difference between normal and attack instances is 8713 in KDDTrain + set of NSL KDD, which makes the results biased towards normal data instances. It is difficult to detect U2R and R2L attacks in NSL KDD because it contains only 52 U2R and 995 R2L instances which are lesser than DoS and probe class instances. Therefore, it fails to detect minority attack types in the presence of vast amount of normal data effectively. In order to give importance to minority class instances, we have used synthetic minority oversampling technique (SMOTE) in which minority instances are oversampled by adding some random instances to it so that it can be equally represented as majority instances in dataset. The pseudoinstances are added by joining it with k-nearest neighbors of each minority sample of feature vector. The number of neighbors used to introduce pseudo-instances in

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minority instances is taken as five in the paper. The pseudo-instance value is decided by taking difference between original feature value and its corresponding nearest neighbor value. To avoid the zero difference, it is multiplied by some random value lying in the interval range (0, 1). It is calculated using Eq. (2) as follows: Newinst = ( f v − n v ) ∗ rand(0, 1)

(2)

where Newinst indicates the pseudo-value to be added to the set, f v denotes the feature value of minority class instance, n v denotes value of nearest neighbor of that minority instance and rand(0, 1) is the random function which generates the random value that lies in between 0 and 1. The sampling ratio is determined by dividing the number of samples available in minority class instances after resampling to the total number of samples present in majority class instances. Mathematically, it is defined using Eq. (3) as follows:  Sr = NMin NMaj

(3)

where Sr denotes sampling ratio, NMin indicates the number of minority class instances after resampling and NMaj indicates the number of majority class instances present in the dataset. In this way, balanced dataset is generated using SMOTE to equally represent the minority and majority class instances.

4.3 Feature Selection Apart from data preprocessing, it is very challenging task to monitor large amount of network traffic having ambiguous and redundant data. It is very crucial to reduce the dimensions of full data in order to secure network from intrusions in real time. The dimensionality reduction of large datasets further improves the performance of IDS classifiers of system. It can be achieved by selecting informative or relevant features while rolling out redundant and irrelevant features from original set through feature selection process. On the basis of evaluation criteria, feature selection is of two kinds, i.e., filter-based and wrapper-based feature selections. Filter-based FS assigns weights to features and filters out the irrelevant features based upon order of weight. Although it saves finding time of method, there is no role of classification algorithm. On the contrary, wrapper-based FS takes into account the effect of classification algorithm in finding feature subset and results in high classification accuracy as compared to filter-based FS [21]. That is why, we have preferred wrapper-based FS approach in this paper. The basic procedure of wrapper-based feature selection involves selecting the subset of features from original set, and then, the generated set is evaluated for its optimality. If generated set is evaluated to be optimal, it is chosen as best subset; otherwise, another subset is evaluated. In recent years, swarm intelligence emerged out as an effective approach for feature selection as

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compared to other approaches. It is inspired from collective behavior of swarms such as honey bees, birds, flocks and insects and interacts with each other through specific behavioral patterns. They are capable of solving nonlinear complex problems within less computational time with low computational complexity [22]. After learning pros and cons of various evolutionary techniques, we have used ABC for feature selection in this paper. ABC has been used to find optimal subset of features from NSL KDD dataset.

4.3.1

Artificial Bee Colony

ABC is meta-heuristic evolutionary algorithm based on swarm intelligence introduced by Karaboga [23]. It is biologically inspired from collective behavior of honeybees. It mainly consists of three kinds of bees such as employed bees, onlooker bees and scout bees that accomplish the task through global convergence. The employed bees search for food source in the hive on the basis of few properties of food like concentration of energy (i.e., nectar amount), closeness to the hive, etc. They share their information about searched food source with the onlooker bees in dancing area of the hive. Then, onlooker bees further search for most profitable food source on the basis of information acquired from employed bees in the hive. The location of most profitable food source is memorized by the bees which start exploiting that location. On the other hand, the employed bees turn into scout bees when the food source is abandoned. Then, scout bees start finding for new food source in a random manner. In this algorithm, the solution is initiated by number of employed bees present in the hive. The solution is represented by number of features contained in the dataset. Initially, the solution in the population is denoted by S which is defined as S i = {1, 2, 3, …, N} where N denotes the number of attributes and S i denotes the ith food source. For NSL KDD, value of N is 41. The values or records contained in the attribute are taken from balanced set of dataset. In this paper, ABC algorithm has been divided into three phases as follows: Initialization Phase: In this phase, the food source S of the population is initialized using Eq. (1):   Si, j = l j + u j − l j ∗ rand(0, 1)

(4)

where l j and u j indicate the lower and upper bound of solution Si, j , respectively, and rand(0, 1) is the random function used to generate the number in the range (0, 1). Employed Phase: After initializing the food source, employed bee starts searching for another food source in the neighborhood of it and updates the position of food source as shown in Eq. (5):   NSi, j = Si, j + ∅i, j ∗ Sk, j − Si, j

(5)

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where NSi, j denotes the possible candidate food source, Sk, j represents new food source searched by employed bees in neighborhood of prior food source Si, j and ∅i, j is the random number that lies in the interval range [−1, +1]. Onlooker Phase: After exploring the neighborhood, employed bees share its information with onlooker bees in the dancing area. Then, onlooker bees exploit that food source based on the probability function using Eq. (6):  Probfunc(i) = fitnessi  fitnessi

(6)

where fitnessi denotes the fitness value of ith food source. This fitness value is evaluated by employed bees as shown in Eq. (7). Then, neighborhood is again explored by employed bees as shown in Eq. (5). If the new food source is better than previous food source, it is replaced by new one; otherwise, it finds for another food source in the hive.  1 , Oi ≥ 0 (7) fitnessi = Oi +1 1 + |Oi |, Oi < 0 where Oi represents the objective function of ith food source. When food source gets abandoned, employed bee becomes scout bee and randomly looks for new food source as shown in Eq. (4). In this way, this algorithm is iterated again and again until each and every feature gets explored and optimal feature subset is generated by ABC algorithm.

4.4 Classification To classify normal data from attack data and to evaluate the chosen optimal feature subset using ABC algorithm in the previous step, random forest classifier has been used based upon empirical analysis of classification accuracy with various classifiers such as K-nearest neighbor (KNN), multilayer perceptron (MLP) and classification and regression tree (CART) in this paper. It mainly solves the problems based upon classification and regression by building multiple decision trees to reduce the noise effect so that more accurate results can be achieved. The multiple decision trees are created randomly using feature subset generated by ABC algorithm in the previous step. Then, it predicts the data as either normal or attack data based upon majority of aggregated votes made from each sub-tree. For binary classification, it usually gives output in 0 and 1 form in such a way that if attack gets detected, it gives value 1; otherwise, it gives output value 0. The proposed flowchart for IDS model is shown in Fig. 2.

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Fig. 2 Flowchart of proposed IDS model

5 Results and Discussion The proposed work is implemented using Python language using TensorFlow and sklearn using Intel® Core™ i5 Processor and storage of 8 GB RAM. For experimental analysis, NSL KDD, a widely used dataset, has been considered in this paper. The whole training sets of both datasets are used to train the data, out of which 70% is used for training the algorithm whereas remaining 30% of set is used for validating the proposed results. The classification is done using tenfold cross-validation approach. The features are selected using ABC algorithm whose initial values of parameters are defined in Table 2. For binary classification, random forest classifier is chosen based on empirical analysis. The classification accuracy of four classifiers is evaluated over NSL KDD dataset. The random forest mainly depends upon the number of decision trees. The accuracy is calculated using 50 decision trees in this paper. Next, KNN classifier is chosen for classification process which works on the principle of close proximity of similar type of data which is calculated using Euclidean distance between different features. The Euclidean distance is represented by k, and value of k is selected as 10 for finding accuracy rate for both datasets. Then, MLP classifier is selected for Table 2 Parameter initialization of ABC algorithm

Parameters

Values

Maximum no. of iterations

100

Population size

10

Population dimension size

N (number of features) For NSL KDD, N = 42

190 Table 3 Classification accuracy of four classifiers

M. Rani and Gagandeep Classifier

NSL KDD

Random forest

75.41

KNN

74.83

MLP

74.22

CART

75.41

comparison which entirely depends upon the number of neurons in the input layer. The number of neurons is set same as number of attributes in the dataset, i.e., 42 neurons in the input layer. The CART classifier gives similar results to random forest classifier. Among four classifiers, random forest gave best accuracy rate as compared to others. That is way, random forest classifier is chosen for this work. The classification accuracy is shown in Table 3.

5.1 Impact of Class Imbalance Because of unequal distribution of class instances in NSL KDD, they suffer from class imbalance problem. This problem has adverse effect on the performance of IDS model. Therefore, it is very important to solve this problem using some suitable approach. It can be solved at either data level or classifier level. In this paper, it is solved at data level using SMOTE. It balances the dataset by oversampling the minority class instances in such a way that both majority and minority class instances are equally represented in the dataset. The normal and attack data are classified using random forest classifier without any feature selection process in this section. The classification accuracy without using any imbalance technique is found to be 76.94, whereas using SMOTE it is evaluated to be 78.87%. From the results, it is evident that the accuracy has been improved by 1.93% for NSL KDD which is of great importance.

5.2 Performance of the Proposed Work In order to find optimal feature subset, we have used ABC algorithm for feature selection because of its ability of exploration and exploitation. Further, to address the data imbalance problem, oversampling is done using SMOTE. The impact of only data imbalance has been discussed in the previous section. The experimental results are drawn after training the data over 70% of training set, and then, it is cross-validated using tenfold of remaining 30% validation set after going through 100 numbers of iterations. The confusion matrices using original set without any modification and after applying SMOTE and ABC feature selection for NSL KDD dataset are given in Table 4.

An Efficient Network Intrusion Detection System … Table 4 Confusion matrix using NSL KDD (a) using original set and (b) using SMOTE and ABC algorithm

191 Normal

Anomaly

Normal

9444

267

Anomaly

4930

7903

Normal

9421

290

Anomaly

3989

8844

(a)

(b)

It has selected 27 attributes out of 42 attributes using NSL KDD dataset which shows the dimensionality reduction of full dataset. With this, it is able to minimize the storage requirements of the program effectively. The list of features selected using ABC algorithm over NSL KDD is shown in Table 5. The proposed work is evaluated in terms of various parameters such as classification accuracy, precision, recall, F-score and ROC curve. The accuracy is tested over KDDTest+ set of NSL KDD, and it is evaluated to be 81.01% which shows large improvement against original accuracy. Similarly, other parameters have shown tremendous results as compared to unbalanced full datasets. Therefore, data imbalance problem plays a crucial role along with feature selection process in classifying normal and attack data in IDS model. Even the ROC curve values are also above 95% over dataset which shows the supremacy of the proposed work. The empirical results for NSL KDD dataset are given in Table 6. The performance of the proposed approach is also compared with the existing literature in Table 7. From the comparison table, it is evident that the proposed work has shown great improvement as compared to the previous work. Table 5 List of selected features using NSL KDD Dataset

Number of features selected

List of selected features

NSL KDD

27

{dur, service, flag, src_bytes, wrong_fragment, urgent, hot, logged_in, num_compromised, root_shell, su_attempted, num_root_access, num_shells, num_outbound_cmds, is_hot_login, is_guest_login, count, srv_count, srv_serror_rate, rerror_rate, same_srv_rate, diff_srv_rate, dst_host_count, dst_host_diff_srv_rate, dst_host_srv_diff_host_rate, dst_host_serror_rate, dst_host_rerror_rate}

192 Table 6 Proposed results for binary classification

Table 7 Comparison of the proposed work with the existing literature

M. Rani and Gagandeep Parameters

NSL KDD Original dataset

SMOTE + ABC

Accuracy

76.94

81.01

Precision

94.73

96.82

Recall

61.58

68.91

F-score

75.25

80.52

ROC

95.20

96.58

References

Classification accuracy (%)

Ibrahim et al. [24]

75.49

Li et al. [8] (using GoogLeNet)

77.04

Li et al. [8] (using ResNet 50)

79.14

Al-Yaseen et al. [9]

78.89

Tao et al. [18]

78.47

Rani and Gagandeep [10]

80.83

Proposed work

81.01

6 Conclusion In this work, an effective NIDS is designed using swarm intelligent-based ABC optimization algorithm followed by random forest classifier for binary classification. Due to large-sized IDS datasets, they usually suffer from class imbalance problem which becomes a crucial barrier to performance of the system. To address this issue, data is balanced using SMOTE in order to give importance to minority instances in the dataset. The impact of class imbalance is effectively evaluated based upon empirical analysis of classification accuracy in this paper. In order to improve the performance of system to a large extent, data is preprocessed using min–max normalization technique followed by data balancing using oversampling approach. Later on, the dimensions of large-size datasets are reduced by extracting relevant feature set using ABC optimization algorithm followed by random forest classifier. The classification accuracy is calculated to be 81.01% through empirical analysis using NSL KDD dataset. The proposed work has successfully outperformed the existing literature also. The future work of this paper is to reduce the computational time of ABC algorithm as it takes long time to train the large datasets which is very time consuming.

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References 1. Madhavi M (2012) An approach for intrusion detection system in cloud computing. Int J Comput Sci Inf Technol 3:5219–5222 2. Rani M, Gagandeep (2019) A review of intrusion detection system in cloud computing. In: Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), pp 770–776 3. Montazeri M, Montazeri M, Naji HR, Faraahi A (2013) A novel memetic feature selection algorithm. In: The 5th Conference on information and knowledge technology, pp 295–300 4. Sampson JR (1976) Adaptation in natural and artificial systems. John H. Holland 5. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1:33–57 6. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1:28–39 7. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority oversampling technique. J Artif Intell Res 16:321–357 8. Li Z, Qin Z, Huang K, Yang X, Ye S (2017) Intrusion detection using convolutional neural networks for representation learning. International conference on neural information processing, pp 858–866 9. Al-Yaseen WL (2019) Improving intrusion detection system by developing feature selection model based on firefly algorithm and support vector machine. IAENG Int J Comput Sci 46 10. Rani M, Gagandeep (2021) Employing Artificial Bee Colony for feature selection in intrusion detection system. In: Proceedings of 8th International conference on computing for sustainable global development, pp 1–5 11. Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) 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 12. Pervez MS, Farid DM (2014) Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs. In: The 8th International conference on software, knowledge, information management and applications (SKIMA 2014), pp 1–6 13. Ingre B, Yadav A (2015) Performance analysis of NSL-KDD dataset using ANN. In: 2015 International conference on signal processing and communication engineering systems, pp 92–96 14. Aghdam MH, Kabiri P et al (2016) Feature selection for intrusion detection system using ant colony optimization. Int J Netw Secur 18, 420–432 15. Kaur A, Pal SK, Singh AP (2018) Hybridization of K-means and firefly algorithm for intrusion detection system. Int J Syst Assur Eng Manag 9:901–910 16. Jiang K, Wang W, Wang A, Wu H (2020) Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE Access 8:32464–32476 17. Alkafagi SS, Almuttairi RM (2021) A proactive model for optimizing swarm search algorithms for intrusion detection system. Int J Phys Conf Ser 1–17 18. Tao W, Honghui F, HongJin Z, CongZhe Y, HongYan Z, XianZhen H (2021) Intrusion detection system combined enhanced random forest with smote algorithm, pp 1–29 19. Liu Z, Shi Y (2022) A hybrid IDS using GA-based feature selection method and random forest. Int J Mach Learn Comput 12(2):43–50 20. Priyadarsini PI (2021) ABC-BSRF: Artificial Bee colony and borderline-SMOTE RF algorithm for intrusion detection system on data imbalanced problem. In: Proceedings of international conference on computational intelligence and data engineering: ICCIDE 2020, pp 15–29 21. Li X, Yi P, Wei W, Jiang Y, Tian L (2021) LNNLS-KH: A feature selection method for network intrusion detection. Secur Commun Netw 1–22 22. Blum C, Li X (2008) Swarm intelligence in optimization. Int J Swarm Intell 43–85 23. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization 24. Ibrahim LM, Basheer DT, Mahmod MS (2013) A comparison study for intrusion database (Kdd99, Nsl-Kdd) based on self organization map (SOM) artificial neural network. J Eng Sci Technol 8:107–119

A Novel Hybrid Imputation Method to Predict Missing Values in Medical Datasets Pooja Rani, Rajneesh Kumar, and Anurag Jain

Abstract The performance of machine learning-based decision support systems for predicting any disease is adversely affected by missing values in the dataset. Prediction of these missing values in an accurate manner is a critical issue. In this paper, a new imputation method called hybrid imputation optimized by classifier (HIOC) is proposed to predict missing values in the dataset. The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations (MICE), K-nearest neighbor (KNN), mean and mode imputation methods in an optimum way. Proposed HIOC has been validated on the datasets of heart disease and breast cancer. Performance of HIOC is compared to MICE, KNN, mean and mode using root-mean-square error (RMSE). The results suggest that HIOC is the most appropriate method for predicting missing data in medical datasets and provides stable performance even when the rate of missing data is increased from 10 to 30%. It provided a reduction in RMSE up to 18.06% in heart disease dataset and 24.62% in breast cancer dataset. Keywords Imputation methods · KNN imputation · Mode imputation · Mean imputation · Hybrid imputation

P. Rani (B) MMICT&BM (A.P.), MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India e-mail: [email protected] R. Kumar Department of Computer Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India e-mail: [email protected] A. Jain School of Computer Sciences, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_16

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1 Introduction The impact of machine learning on the healthcare sector is increasing day by day. Decision support systems developed by training machine learning algorithms on medical datasets have become useful tools in the prediction of diseases. Machine learning algorithms can be used to develop a decision support system to predict any disease using clinical data [1, 2]. Medical datasets used for training these algorithms can include some clinical data whose values are not available. This missing data in the dataset reduces the prediction accuracy of the system. Medical datasets with missing data make data mining very difficult. Before these datasets are mined, these missing values must be effectively handled [3]. An easy way to handle missing values is to use only those records from the dataset that have no missing values. This method is known as complete case analysis. But it can reduce the size of the dataset if there are a large number of records with missing values. In such a situation, imputation methods are the better alternative [3]. Imputation methods predict missing values in the dataset by using characteristics that are hidden in the dataset. Efficiency of imputation methods is reflected by their ability to accurately predict missing values. Several methods of imputation exist to predict missing values. Imputation methods are classified into two categories: single and multiple imputation. In single imputation, each missing value is predicted once and this prediction is considered true. The drawback to this method is that it cannot handle the uncertainty in missing data efficiently. Uncertainty is handled efficiently by multiple imputation because it has multiple cycles of imputation [4]. The major motivation of this research work is to develop a classifier-independent imputation method that can be used efficiently on any dataset. In this paper, a new hybrid imputation optimized by classifier (HIOC) method for predicting missing values is proposed. HIOC is developed by combining MICE, KNN, mean and mode imputation in an optimum manner through a classifier. It is a classifier-independent method that improves the performance of any classifier. Authors have validated the proposed HIOC on two medical datasets: datasets of heart disease and breast cancer. Proposed HIOC has also been compared to already existing imputation methods: MICE, KNN, mean and mode. MICE is a multiple imputation method. KNN, mean and mode are single imputation methods. The major research contributions of the paper are as follows: (1) (2) (3)

A new method for imputation of missing values, called hybrid imputation optimized by classifier (HIOC), is proposed. The proposed HIOC has been validated on two medical datasets, namely Cleveland heart disease dataset and Wisconsin breast cancer dataset. The performance of the proposed HIOC method has been compared to other existing imputation methods.

The remaining paper is structured as follows: Imputation methods applied on different datasets are described in Sect. 2. Description of the proposed method is given in Sect. 3. Results obtained using proposed method is discussed in Sect. 4. Conclusion of the paper and future scope are given in Sect. 5.

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2 Literature Review Sim et al. [5] used seven imputation methods i.e., mean, listwise deletion, hot deck, group mean, predictive mean, k-means clustering and KNN imputation methods on six datasets. Values were randomly removed from the dataset to introduce missing values in the datasets. Missing value imputation methods were applied with 5, 10 and 15% missing values in the datasets. Datasets imputed with values were classified using six classifiers, and the performance of each imputation method was measured. Imputation method which performed best for all the datasets was not found, and in each dataset, different imputation method provided the best results. However, performance of listwise deletion decreased with increasing missing data. Nahato et al. [6] applied imputation methods on hepatitis and cancer datasets. In hepatitis dataset, tuples having more than 24% missing values were removed, and in remaining tuples, mode imputation was used to fill missing values. In the breast cancer dataset, the mode imputation method was applied to all the tuples with missing values. Kumar and Kumar [7] applied four imputation methods KNN, mean imputation, fuzzy KNN and weighted KNN on hepatitis, breast cancer and lung cancer datasets. Fuzzy KNN provided the best performance in imputation. Kuppusamy and Paramasivam [8] introduced missing values randomly in nursery dataset and predicted these missing values with missForest algorithm. Venkatraman et al. [9] performed prediction of cardiovascular disease on DiabHealth dataset. Missing data was filled by using mean method for continuous features and mode method for categorical features. AlMuhaideb and Menai [10] removed records having missing values in their proposed preprocessing procedure for the classification of medical data. Authors validated the proposed preprocessing architecture on 25 medical datasets. Sujatha et al. [11] proposed the imputing missing values classifier (IMVC) method to impute missing values. Imputation was done using three classifiers Naive Bayes, neural network and C4.5. The method was applied on Cleveland heart disease dataset. Abdar et al. [12] used Wisconsin breast cancer dataset (WBCD) for the diagnosis of breast cancer. Authors did not use records of patients having missing medical parameters for training the model. This method of handling missing values is known as complete case analysis. Nikfalazar et al. [13] proposed a missing value imputation method DIFC by combining decision tree and fuzzy clustering. Proposed method was tested on six datasets, adult, housing, pima, autompg, yeast and cmc. Adult and autompg datasets contain missing values, whereas remaining four datasets do not contain any missing values. Records of missing values in adult and autompg datasets were removed. Missing values were introduced artificially in all six datasets to analyze the performance of imputation method. Zhang et al. [14] performed breast cancer prediction from Wisconsin breast cancer dataset. Authors used complete case analysis for handling missing values. Qin et al. [15] performed a diagnosis of chronic kidney disease using kidney disease dataset. Missing values were filled in the dataset using KNN imputation method. Mohan et al. [16] used Cleveland heart disease dataset for developing a system for heart disease prediction. The authors used listwise deletion for handling missing values,

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i.e., removed all the instances having missing values and used remaining records for training and testing the system. Almansour et al. [17] applied different classification algorithms on chronic kidney disease dataset. Authors replaced missing values in each attribute with mean of available values in the corresponding attribute. Supriya and Deepa [18] performed breast cancer prediction on Wisconsin breast cancer dataset. Missing values were filled using mean method of imputation. Different methods of imputation used on various datasets by several researchers are shown in Table 1.

3 Materials and Methods 3.1 Methodology In this paper, a new imputation method hybrid imputation optimized by classifier (HIOC) is proposed. Proposed method has been validated on two medical datasets: Cleveland heart disease dataset and Wisconsin breast cancer dataset [19, 20]. Artificial missing values were introduced in the datasets through random distribution. After introducing missing values, HIOC was applied on the datasets to predict missing values. HIOC combines MICE, KNN, mean and mode algorithms in an optimum manner through a classifier. The algorithm iteratively performs column-wise imputation by selecting the best imputation method for each column. Four temporary datasets are produced by using MICE, KNN, mean and mode imputation methods. After that, algorithm performs n iteration where n is number of columns in dataset. In all iterations, a column is selected from one dataset out of four temporary datasets so that RMSE of the final imputed dataset is reduced to a minimum value. Methodology of proposed method is shown in Fig. 1.

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Table 1 Imputation methods utilized by researchers in various datasets S. No.

Authors

Name of dataset

No. of records

No. of features

Imputation methods

1

Sim et al. [5]

Wine

178

16

Iris

150

7

Liver disorder 345

8

Glass

214

16

Statlog shuttle

57,999

14

Ionosphere

351

36

Listwise deletion, mean, KNN, group mean, k-means clustering, predictive mean and hot deck imputation

Hepatitis

155

20

Breast cancer 699

11

Breast cancer 699

11

Hepatitis

155

20

Lung cancer

32

56

2

Nahato et al. [6]

3

Kumar and Kumar [7]

Mode method Mean, KNN, weighted KNN and fuzzy KNN imputation

4

Kuppusamy, and Paramasivam [8]

Nursery

12,960

8

MissForest algorithm

5

Venkatraman et al. [9]

DiabHealth

2800

180

Mean and mode imputation method

6

AlMuhaideb and Menai [10]

Liver

345

6

Hepatitis

155

20

Listwise deletion

Cleveland heart disease

294

13

Pima

768

9

Hungarian heart disease

303

13

Statlog heart disease

270

13

7

Sujatha et al. [11] Cleveland heart disease

303

13

Imputing missing values classifier (IMVC) method

8

Abdar et al. [12]

Wisconsin breast cancer

569

32

Complete case analysis

9

Nikfalazar et al. [13]

Housing

506

14

Adult

32,561

15

Autompg

398

8

Pima

768

9

CMC

1473

10

Yeast

1484

9

Support vector regression, iterative fuzzy clustering, decision tree-based imputation, DIFC (continued)

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P. Rani et al.

Table 1 (continued) S. No.

Authors

Name of dataset

No. of records

No. of features

Imputation methods

10

Zhang et al. [14]

Wisconsin breast cancer

569

32

Complete case analysis

11

Qin et al. [15]

Chronic kidney disease

400

24

KNN imputation

12

Mohan et al. [16] Cleveland heart disease

303

12

Listwise deletion

13

Almansour et al. [17]

Chronic kidney disease

400

24

Mean imputation

14

Supriya and Deepa [18]

Wisconsin breast cancer

569

32

Mean imputation

Imputed dataset using MICE imputation

Imputed dataset using KNN imputation Dataset with missing values Imputed dataset using Mean imputation

Imputed dataset using Mode imputation

Fig. 1 Methodology of proposed HIOC method

Select each column from one dataset to reduce RMSE

Final imputed dataset

A Novel Hybrid Imputation Method …

201

Algorithm: Hybrid Imputation Optimized by Classifier (HIOC) Algorithm HIOC( ) { • Dataset DS with missing values is the input. • Apply MICE imputation on dataset DS to predict missing values creating dataset DS1. • Apply KNN imputation on dataset DS to predict missing values creating dataset DS2. • Apply MEAN imputation on dataset DS to predict missing values creating dataset DS3. • Apply MODE imputation on dataset DS to predict missing values creating dataset DS4. • Copy dataset DS1 into DSFinal. • N=Number of columns in DS For (i=1 to N) { o Replace column i of DSFinal with column i of DS1 and calculate RMSE using a classifier. o Replace column i of DSFinal with column i of DS2 and calculate RMSE using a classifier. o Replace column i of DSFinal with column i of DS3 and calculate RMSE using a classifier. o Replace column i of DSFinal with column i of DS4 and calculate RMSE using a classifier. o Choose one of the DS1, DS2, DS3 and DS4 datasets with the least RMSE. o Replace the ith column of the dataset DSFinal with the dataset selected in the previous step. } • Dataset DSFinal with imputed missing values is the output. }

3.2 State-of-the-Art Imputation Methods Performance of HIOC has been compared to four existing imputation methods: MICE, KNN, mean and mode. Mean Imputation. In this method, missing values of each column are filled with mean value of available values of the column [6].

202

P. Rani et al.

Algorithm: Mean Imputation Algorithm MEAN(DS) { Input: Dataset DS with missing values. Output: Imputed Dataset DS with zero missing values N=Number of Columns in DS I=1 While ( I =4

95

94

MAXENTROPY 58.33

58.00

58.00 92.88

n> =5

86

96

SVM

59.33

56.00

56.67 89.82

n> =1

100

85

SLDA

62.33

65.33

63.33 91.18

n> =2

100

85

LOGITBOOST

58.33

53.33

54.33 94.51

n> =3

99

86

GLMNET

63.00

57.33

59.33 59.58

n> =4

88

86

MAXENTROPY 56.00

61.00

57.67 94.84

n> =5

79

95

SVM

96.00

80.00

86.33 95.92

n> =1

100

94

SLDA

95.00

69.33

77.00 95.76

n> =2

100

94

LOGITBOOST

94.67

70.67

78.67 96.71

n> =3

100

94

GLMNET

96.33

77.33

84.33 71.01

n> =4

99

94

MAXENTROPY 88.67

81.33

84.33 96.52

n> =5

92

95

SVM

84.67

82.33

83.33 80.73

n> =1

100

85

SLDA

80.67

78.67

79.67 77.19

n> =2

100

85

LOGITBOOST

81.67

77.33

79.00 94.30

n> =3

97

87

GLMNET

87.33

81.33

83.67 43.29

n> =4

88

90

MAXENTROPY 77.67

79.33

78.00 79.48

n> =5

72

96

67.67

74

n> =1

100

95

CM SVM Haryana SLDA

90.67

95.48

88.67

62.67

70

94.73

n> =2

100

95

LOGITBOOST

97.67

67.33

75

95.90

n> =3

100

95

GLMNET

97.33

67.67

75

85.52

n> =4

100

95

MAXENTROPY 66.67

82.33

64.33 74.98

n> =5

82

99

References 1. Berry MW (2004) Automatic discovery of similar words, in survey of text mining clustering, classification, and retrieval. Springer Verlag, New York, LLC, pp 24–43 2. Boyd D, Golder S, Lotan G (2010) Tweet, tweet, retweet: conversational aspects of retweeting on twitter, system science (HICSS) 3. Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews 4. Pang B, Lee L, Vaithyanathan SK (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the conference on empirical methods in natural language processing, vol 10 5. Smeaton AF, Bermingham A (2010) Classifying sentiment in microblogs: is brevity an advantage? In: Proceedings of the 19th ACM international conference on information and knowledge management, pp 1833–1836

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6. Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the seventh conference on international language resources and evaluation, pp 1320–1326 7. Liang P-W, Dai B-R (2013) Opinion mining on social media data. In: IEEE 14th international conference on mobile data management, Milan, Italy, June 3–6, pp 91–96 8. Rochmawati N, Wibawa SC (2013) Opinion analysis on Rohingya using twitter data. IOP Conf Ser: Mater Sci Eng 9. Younis EMG (2015) Sentiment analysis and text mining for social media microblogs using open source tools: an empirical study. Int J Comput Appl (0975–8887) 112(5) 10. Pennebaker JW, Chung CK, Ireland M, Gonzales A, Booth RJ (2007) The development and psychometric properties of liwc 11. Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with Twitter: what 140 characters reveal about political sentiment. ICWSM, pp 179–185 12. Choy M, Cheong LFM, Ma NL, Koo PS (2013) A sentiment analysis of Singapore presidential election 2011 using Twitter data with census correction 13. Wang H, Can D, Kazemzadeh A, Bar F, Narayanan S (2012) A system for real-time twitter sentiment analysis of 2012 US presidential election cycle. In: Proceedings of the ACL 2012 system demonstrations, Association for Computational Linguistics, pp 115–120 14. Mishra P, Rajnish R, Kumar P (2016) Sentiment analysis of twitter data: case study on digital India. In: International conference on information technology, pp 148–153 15. Das A, Bandyopadhyay S (2010) SentiWordNet for Bangla. Knowledge sharing event-4: task, vol 2 16. Joshi A, Balamurali AR, Bhattacharyya P (2010) A fallback strategy for sentiment analysis in Hindi: a case study. In: Proceedings of ICON 2010: 8th international conference on natural language processing 17. Chesley P (2006) Using verbs and adjectives to automatically classify blog sentiment. In: Proceedings of AAAI-CAAW-06, the spring symposia on computational approaches 18. Gune H, Bapat M, Khapra MM, Bhattacharyya P (2010) Verbs are where all the action lies: experiences of shallow parsing of a morphologically rich language. In: Proceedings of the 23rd international conference on computational linguistics, pp 347–355 19. Mittal N, Agarwal B, Chouhan G, Bania N, Pareek P (2013) Sentiment analysis of Hindi review based on negation and discourse relation. In: Proceedings of international joint conference on natural language processing, pp 45–50 20. Godbole N, Srinivasaiah M, Skiena S (2007) Large-scale sentiment analysis for news and blogs. In: Proceedings of the international conference on weblogs and social media (ICWSM)

Efficient Key Management for Secure Communication Within Tree and Mesh-Based Multicast Routing Protocols Bhawna Sharma and Rohit Vaid

Abstract An important component for secure multicast is key management which is used for security. Security is a major challenge in mobile ad-hoc networks, because of its inclined nature towards the MANETs and it cannot be removed. For this reason, a key control in multicast routing protocol on mobile ad-hoc networks (MANETs) is applied for centralized authority, limited bandwidth, dynamic topology and strong connectivity. In other words, Key distribution may be the most important challenge for the type of this dynamic kind of network. In this paper, we use different parameters (average packet delivery ratio, overhead, throughput, end to end delay and packet drop) for measuring the overall performance of tree based and mesh based routing protocols with the use of Traffic Encryption Keys (TEKs), i.e. private key infrastructure and public key infrastructure inside the multicast group. Keywords Multicast · Protection · Symmetric key · Public key cryptosystem · Key management center · Security · TEK

1 Introduction Mobile Ad-Hoc Network (MANET) [1, 2] is a decentralized, self-control system, not rely upon fixed verbal exchange facilities. Ad-Hoc Network can be a collection of nodes in shape which flow everywhere at will, the community nodes distribute dynamically, nodes connect others through wireless network, each connected node has the double features first is terminal and second is routers. Multicast communications are the best way to keep records of the participated nodes as compared to unicast communications. Multicast network is dynamic in nature. Secure multicast is that the internetwork provider that securely grants records from a supply to a couple of selected receivers. The inherent advantages of multicast routing can also provide a few benefits making it prone to attack unless they B. Sharma (B) · R. Vaid Department of Computer Science and Engineering, MMEC, MM (Deemed To Be University), Mullana, Ambala, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_37

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are secured. The aim is to stable those benefits maintain the advantages of multicast providers.

1.1 Multicasting Routing There are three different types of routing in networks: unicast, multicast and broadcast. Unicast is one-to-one communication. Unicast communication is suitable for small networks. Whereas in multicast, it is one to many routing take place. In multicast, data is shipped from one node to other nodes [3]. In broadcast one to all communication done [4]. However, multicast can be an aggregate of unicast and multicast systems. In broadcast, packets have been sent to all nodes inside the network. In this, some nodes will drop the packets if they are no longer multicast networks. Below are a few necessities for multicasting [5]: 1. 2. 3. 4. 5.

6. 7.

Multicast addresses used to be identified in order that in IPv4, class D deal is fixed for this purpose. Each node used to translate the IP multicast deal with inside the multicast organization. A router has to send among an IP multicast deal with and a sub-communication multicast deal with so as to supply a multicast IP datagram at the network [5]. Multicast is dynamic in this any host can be a part of or depart the multicast network. Routers have to alternate statistics. Firstly, routers used to recognize which subnet use the nodes of multicast network and secondly, routers calculate the shortest distance among the different multicast network. A routing algorithm is used to calculate the shortest paths to all or any network. Each router determines the shortest path to the sender node and the destination node.

Multicast routing will be a network-layer carry out that constructs techniques on that understanding packets from a deliver are allotted to achieve several, but now no longer all, destinations throughout a network. Multicast routing sends one reproduction of a statistics packet at an identical time to more than one receiver over a connected hyperlink that is shared via way of means of the techniques to the receivers. The sharing of hyperlinks within the collection of the techniques to receivers implicitly defines a tree wont to distribute multicast packets (Fig. 1).

1.2 Multicast Tree Classification Classification of multicast tree based on the two categories—(i) Source based tree (ii) Group Shared tree.

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Fig. 1 Classification of multicast routing protocols [6]

Source based tree (SBT) The Source Specific Multicast (SSM) protocol. There is minimum delay between End-to-stop communication. It is not suitable for large networks. There are different source based protocols such as PIMDM, DVMRP and MOSPF. Group Shared tree It is Core-based tree in which data is transmitted through the router between nodes inside the network. The delay inside the tree is more than the SBT (Source-primarily based tree). It is scalable (suitable for big networks). CBT and PIM-SM are group based shared protocols.

1.3 Multicast Mesh Classification Mesh primarily based schemes set up a mesh of paths that join the nodes and destinations [7]. This repetition brings approximately high dependability/energy but may also basically construct package deal overhead. For example—On demand multicast routing protocol (ODMRP), Dynamic core-based multicast protocol (DCMP) Middle assisted mesh protocol (CAMP), Neighbor support ad-hoc multicast routing protocol (NSMP), Location-primarily based multicast protocol and Forwarding group multicast protocol (FGMP) [8].

2 Security in Multicasting in MANET The basic security aspects in MANET are as follows: • Authentication offerings offer guarantee of a host’s identity. Authentication mechanisms are regularly implemented to many factors of multicast communications

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completely strong authentication mechanisms are suggested for stable multicast applications. Digital signatures schemes, equal to the Digital Signature Standard (DSS), are valid samples of authentication mechanisms supported public key technology [9]. • Confidentiality ensures that the community data cannot be found out to the embezzled unit. Integrity is essential to attend to transfer the data between the nodes. • Availability—in this service, all data and services that are available in the network are only accessed by each authorized node. Due to dynamic topology and open boundary nature of MANET’s an availability challenge arises. The main security parameter is time which is used to access the network services or data is important. • Nonrepudiation ensures that the message forwarded cannot be refused with the aid of using the message instigator [10].

3 Key Management The Key control is the method of creating, applying and updating the keys for a stable and secure communication network [11]. The Traffic Encryption Keys (TEKs) and Key Encryption Keys (KEKs) are used for encryption and decryption. In a stable multicast communication, every member process a key to encrypt and decrypt the multicast data. The method of updating and implementing the keys to the nodes in the network is rekeying operation [12]. However, at some point of network modulation, key control apply several exchanges according to unit time for forwarding and backward sending [13]. The multicasting is classified into types like centralized and distributed schemes. The Group Controller (GC) plays network key control and the best small operations are implemented at the nodes of the network simply in case of a centralized scheme. For distributed scheme, the important thing is control done by every user to strongly connect to the other user [14, 15].

3.1 Traffic Encryption Keys Numerous correspondence frameworks empower steady interchanges among sending and getting messages via way of means of the usage of symmetric visitors encryption keys (TEKs) containing an encryption key and a deciphering key which are indistinguishable and which are utilized to hold non-public information join among the nodes.

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4 Related Work In general, there are three major methods for key control in WSNs, supported symmetric key cryptography, public key cryptography and hybrid [16]. Particularly throughout a hierarchical architecture, it’ll upload up to apply a hybrid method, throughout which the major process and large operations are performed. During this case, authentication and integrity are regularly received via way of means of digital signatures. However, as there is a large overall performance distinction among symmetric key and public key cryptography, it is still exciting to seem at symmetric key-primarily based on solutions, thanks to the limited energy, verbal exchange bandwidth and process capacities of the tool nodes. Many proposals for key control using a symmetric key-primarily based totally method in WSNs are published. Looking at the assumed topology, this method is split into two categories: a hierarchical and nonhierarchical (or distributed) layout of the nodes [17]. Most methods for symmetric key-primarily based on key control protocols expect a hierarchical network, where the nodes have a predefined role within the network. Mesh certification authority (MeCA), given in [18], addresses the nodes for authentication and key control in wireless mesh networks. It depends on the selfconfiguring and self-organizing methods of WMN to distribute the capabilities of a certification authority over many mesh routers [19]. The basic key functions like data sharing and key distribution are done through it. MeCA helps the multicast tree method given in [20] to reduce the operational overhead.

5 Proposed System In the proposed system, Traffic Encryption Keys (TEKs) and Key Encryption Keys (KEKs) are generated. The source can be tree and mesh based. The key is generated in both phases for encryption and decryption (Fig. 2).

6 Simulation Results (see Table 1)

6.1 Metrics We use the following parameters in evaluating the performance of the tree and mesh multicast routing protocols (Figs. 3, 4, 5, 6 and 7).

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Fig. 2 Proposed architecture [21]

Table 1 Simulation parameters [12]

Number of nodes (receiver)

5, 10, 15, 20, 25

Area size

2000 × 2000

Simulation time

40 s

Rate

250 Kb

Mobility model

Random waypoint

Mac

802.1

Speed

5, 10, 15, 20, 25 and 30

• Average Packet Delivery Ratio = number of packets received/ total number of packets sent. • Overhead = The total number of control packets sent/the number of data packets delivered successfully. • Packet Drop—It is defined as the average number of packets dropped at each receiver end.

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Fig. 3 Comparison of delivery ratio with Traffic Encryption Keys (TEKs) for varying receivers in trees and mesh based multicast routing protocols

Fig. 4 Comparison of overhead with Traffic Encryption Keys (TEKs) for varying receivers in trees and mesh based multicast routing protocols

• End to End Delay: It can be defined as the time taken for a packet to travel from source to destination. • Throughput = Total number of packets received per unit of time.

7 Conclusion Research is given around various multicast routing protocols and their examination. All conventions have their personal advantages and drawbacks. This paper clarified exceptional highlights of multicast routing. The examination consists of

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Fig. 5 Comparison of packet drop with Traffic Encryption Keys (TEKs) for varying receivers in trees and mesh based multicast routing protocols

Fig. 6 Comparison of end to end delay with Traffic Encryption Keys (TEKs) for varying receivers in trees and mesh based multicast routing protocols

Fig. 7 Comparison of throughput with Traffic Encryption Keys (TEKs) for varying receivers in trees and mesh based multicast routing protocols

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the important difference and likenesses of mesh and tree primarily based on multicast routing protocols which could help with selecting which routing is affordable wherein circumstance. Multicast tree primarily based totally directing conventions are powerful and satisfy versatility issue, they have got some downsides in particular appointed far flung corporations due to transportable nature of hubs that partake in the course of multicast meeting. In the mesh, primarily based protocols store the good sized length of manipulating overhead applied in tree support.

References 1. Younis M, Ozer SZ (2006) Wireless Ad hoc networks: technologies and challenges. Wirel Commun Mob Comput 6(7):889–892 2. Guo S, Yang OWW (2007) Energy-aware multicasting in wireless Ad hoc networks: a survey and discussion. Comput Commun 30(9):2129–2148 3. Liebeherr J, Zarki M (2003) Mastering networks: an internet lab manual. Addison Wesley 4. Maharjan N, Moten D Secure IP multicasting with encryption key management 5. Stallings W (2004) Data and computer communications, 7th edn. Pearson Prentice Hall, Upper Saddle River, NJ 6. Jain S, Agrawal K (2014) A comprehensive survey of multicast routing protocols for mobile Ad Hoc networks. Int J Comput Appl (IJCA) 7. Biradar R, Manvi S, Reddy M (2010) Mesh based multicast routing in MANET: stable link based approach. Int J Comput Electr Eng 2(2) 8. Sung-Ju WS, Lee MG (2002) On-demand multicast routing protocol in multihop wireless mobile networks. Mobile Netw Appl 7:441–453 9. Maughan D, Schertler M, Schneider M, Turner J (1997) Internet security association and key management protocol (ISAKMP), Internet-Draft 10. Rajan C, Shanthi NS (2013) Misbehaving attack mitigation technique for multicast security in mobile ad hoc networks (MANET). J Theor Appl Inf Technol 48(3):1349–1357 11. Devi DS, Padmavathi G (2009) A reliable secure multicast key distribution scheme for mobile Ad hoc networks. World Acad Sci Eng Technol 56:321–326 12. Madhusudhanan B, Chitra S, Rajan C (2015) Mobility based key management technique for multicast security in mobile Ad Hoc networks. Sci World J 2015 13. Devaraju S, Padmavathi G (2010) Dynamic clustering for QoS based secure multicast key distribution in mobile Ad hoc networks. Int J Comput Sci Issues 7(5):30–37 14. Lin H-Y, Chiang T-C (2011) Efficient key agreements in dynamic multicast height balanced tree for secure multicast communications in Ad Hoc networks. EURASIP J Wireless Commun Netw 2011. https://doi.org/10.1155/2011/382701 15. Srinivasan R, Vaidehi V, Rajaraman R, Kanagaraj S, Kalimuthu RC, Dharmaraj R (2010) Secure group key management scheme for multicast networks. Int J Netw Secur 10(3):205–209 16. Carlier M, Steenhaut K, Braeken A (2019) Symmetric-key-based security for multicast communication in wireless sensor networks. In: MDPI 17. Bala S, Sharma G, Verma A (2013) Classification of symmetric key management schemes for wireless sensor networks. Int J Secur Appl 7:117–138 18. Kim J, Bahk S (2009) Design of certification authority using secret redistribution and multicast routing in wireless mesh networks. Comput Netw 53(1):98–109 19. Matam R, Tripathy S (2016) Secure multicast routing algorithm for wireless mesh networks. J Comput Netw Commun 2016 20. Ruiz PM, Gomez-Skarmeta AF (2005) Heuristic algorithms for minimum bandwidth consumption multicast routing in wireless mesh networks. In: Syrotiuk VR, Chávez E (eds) Ad-hoc, mobile, and wireless networks, vol 3738. Lecture notes in computer science, pp 258–270

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21. Chaitra KV, Selvin Paul Peter J (2018) A reverse tracing scheme and efficient key management for secured communication in MANET. Int J Eng Res Technol (IJERT) ISSN: 2278–0181 ICESMART-2015

Detection of Signature-Based Attacks in Cloud Infrastructure Using Support Vector Machine B. Radha

and D. Sakthivel

Abstract In modern years, cloud computing has arisen as a widely utilized innovation in IT area. With increase in the use of cloud computing, it has become more prone to intrusions. Indeed, even a little interruption assault can bargain whole framework; subsequently, interruption can be considered as a basic issue for cloud-based platforms. The substantial growth in the number of applications using cloud-based infrastructures calls for the need of security mechanisms for their protection. Intrusion detection systems are one of the most suitable security solutions for protecting cloudbased environments. The signature-based intrusion detection and support vector machine have emerged as a recent interest and research area. With their robust learning models and data-centric approach, SVM-based security solutions for cloud environments have been proven effective. Attack features are extracted from organization’s network and application logs. Attack presence is confirmed by performing support vector machine. Performance measures such as average detection time are used to evaluate the performance of the detection system. Keywords Machine learning · SVM · Cloud

1 Introduction The exponential growth in modern technology has produced a ubiquitous global networking system of services and communications along with its attendant problems. Given this state of affair and the cost-saving paradigm of providing resources locally, companies around the globe are increasingly leaning to providing their services and resources for users through cloud computing networks, which, in turn,

B. Radha Department of Information Technology, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu 641105, India D. Sakthivel (B) Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, Tamil Nadu 642107, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_38

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pushed the security risks to new heights. Eventually, cyber security became a major issue for cloud computing [1]. Firewalls and other rule-based security approaches have been utilized broadly to give assurance against assaults in the server farms and contemporary networks. However, firewalls are not capable of detecting insider attacks or advanced attacks, such as distributed denial of service (DDoS) attacks, since they do not occur where the firewalls are set up or the rules are not enough to detect them. Additionally, large distributed multi-cloud environments would require a significantly large number of complicated rules to be configured, which could be costly, time-consuming, and error-prone. Cloud providers must take the necessary measures to either prevent these attacks before their occurrence or detect them as soon as they occur. Generally, attacks can be identified and forestalled utilizing intrusion detection systems (IDS) and intrusion prevention systems (IPS). Traditionally, IDSs use predefined rules or behavioral analysis over the network to detect attacks [2]. In this paper, we tackle the problem of intrusion detection in cloud platforms by leveraging machine learning (ML) technique, namely support vector machines (SVM). The aim has been to improve signature-based detection accuracy, while reducing the model training time and complexity. ML models used in intrusion detection systems need to have a relatively short training time since they need to be retrained to classify existing types of attacks. To evaluate the proposed model, UNSW-NB15 dataset was used with some of the normalization methods that help to increase the overall accuracy. Our contributions in this work are twofold [2, 3]: (1)

(2)

We implement SVM using the tool provided by Python to achieve high accuracy for signature-based detection in cloud environments. We perform parameter tuning to achieve the best accuracy. We perform feature engineering to find out optimal set of features to achieve maximum accuracy in minimal training time and complexity. We aim to reduce the training time by selecting optimal set of features while accuracy is not compromised.

2 Related Work Sakthivel and Radha, Cloud Computing has remarkable area in conceptual and infrastructural computing. Cloud computing provides user to access and keep their resources in cloud by multitenant architecture. Infrastructure as a service (IaaS) is a form of cloud computing that provides virtualized computing resources over the internet [4–6]. In an IaaS model, a cloud provider hosts the infrastructure components traditionally present in an on-premises data center, including servers, storage, and networking hardware, as well as the virtualization or hypervisor layer. Benefits of IaaS are its full control of the computing resources through administrative access to VMs, flexible and efficient renting of computer hardware and portability, interoperability with legacy applications. IaaS has some common issues such as network

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dependence and browser-based risks. It also has some specific issues such as compatibility with legacy security vulnerabilities, virtual machine sprawl and robustness of VM-level isolation and data erase practices [7]. The recent cloud-based computer networks have created number of security challenges associated with intrusions in network systems. The increase in the huge amount of network traffic data, involvement of humans in such detection systems is time-consuming and a non-trivial problem. The network traffic data is highly dimensional, consisting of numerous features and attributes, making classification challenging and thus susceptible to the dimensionality problem. The threats to the security of the company’s data and information are highly involved and to be addressed. The data mining techniques and machine learning algorithms have evolved for securing cloud databases and monitor the malicious activity and threats inside the networks. Therefore, this paper reviews the various machine learning algorithms for securing cloud-based environment [3]. Deep learning approaches are mainly categorized into supervised learning and unsupervised learning. The difference between these two approaches lies in the use of labeled training data. Specifically, convolution neural networks (CNN) that use labeled data fall under supervised learning, which employs a special architecture suitable for image recognition. Unsupervised learning methods include deep belief network (DBN), recurrent neural network (RNN), auto-encoder (AE), and their variants. Next, we describe recent studies related to our work; these studies are mainly based on KDD Cup 99 or NSL-KDD datasets [1]. Studies on intrusion detection using the KDD Cup 99dataset have been reported. Kim et al. specifically targeted advanced persistent threats and proposed a deep neural network (DNN) [8] using 100 hidden units, combined with the rectified linear unit activation function and the ADAM optimizer. Their approach was implemented on a GPU using TensorFlow [9]. Papamartzivanos et al. proposed a novel method that combines the benefits of a sparse AE and the MAPEK framework to deliver a scalable, self-adaptive, and autonomous misuse IDS. They merged the datasets provided by KDD Cup 99 and NSL-KDD to create a single voluminous dataset [3].

3 Signature-Based Intrusion Detection System The problem of intrusion detection can be modeled as a classification problem. This approach consists of first obtaining labeled traffic data and then training a classifier to discern between the normal traffic and intrusions. Each record belonging to the training set consists of a certain number of traffic features, such as the protocol type, service requested, and size of payload [10]. Each of these records has a label indicating the class of traffic (normal/intrusion) they belong to [11, 12]. UNSW dataset has been released, and it includes ten different types of traffic packets; it is more suitable to be used in the signature base detection models for SVM [8]. It includes normal packets as well as nine types of attacks, which are Analysis,

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Backdoor, DoS, Exploits, Fuzzers, Reconnaissance, Shellcode, and Worms. UNSWNB-15 is composed of two parts: a training set UNSW-NB-15 training-set.csv which has been used for model creation and a testing set, UNSW-NB-15-testing-set.csv used for the testing step and modeling the received real-time packets. The number of records in the training and testing sets is 175,341 and 82,332, respectively.

3.1 Support Vector Machines Support vector machine (SVM) is a classification technique. Based on the concept of optimal margin classifiers, this classification method gives a very high accuracy rate for a large number of problem domains and is highly suited for high-dimensional data. H1, H2, and H3 are three of the infinite possible decision hyperplanes. The hyperplane H3 (green) does not separate the two classes and is not suitable for use in classification. The hyperplane H1 (blue) does separate the two classes but with a small margin and H2 (red) separates the two classes with the maximum margin [13].

3.2 Maximal Margin Hyper Planes For the purpose of illustration, let us consider a dataset that is linearly separable. Given a set of labeled training data, we can find a hyperplane such that it completely separates points belonging to the two classes. This is called the decision boundary [9]. An infinite number of such decision boundaries are possible (Fig. 1). Decision Boundary edge alludes to the briefest distance between the nearest focuses on the either side of the half plane (Fig. 2). It is evident by intuition and has been mathematically proven that the decision hyperplane with the maximal margin provides better generalization error. Support vectors refer to train samples lying on the margins of the decision plane, and the process of training the SVM involves finding these support vectors. Figure 2 shows the maximum margin hyperplane and margins for an SVM trained with samples from two classes. Samples on the margin are called the support vectors.

3.3 Signature-Based Intrusion Detection Using SVM The formulation for a standard SVM for a binary classification problem is given as  1 ∈i min ||w||2 + C 2 l=1 n

Detection of Signature-Based Attacks in Cloud Infrastructure … Fig. 1 Infinite decision hyperplanes for a binary classification problem

Fig. 2 Optimal margin classifier for binary classification problem

under the constraints   yi W T xi + b ≥ 1− ∈i ∈i ≥ 0, i = 1, . . . N ,

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where x i ∈ Rn is the feature vector for the ith training example. yi ∈ −1, 1 is the class label of x i , i = 1, …, N, C > 0 is a regularization constant. The pseudo-code for the self-training wrapper algorithm is given below: Algorithm 1 Self-Training-SVM Input: FI, FT and 0 FI : The set of N1 labeled training examples xi , i = 1, ..., N. Labels of the examples are y0(1), ..., y0(N) FT : The set of N2 training examples for which the labels are unknown. 0: The threshold for convergence Output: A Trained SVM 1: Train a SVM using FI and classify FT using the model obtained 2: k=2 3: while TRUE do 4: FN = FI + FT where labels of FT are the ones predicted using the current model 5: Train a new SVM using FN and again classify FT 6: Evaluate objective function

1 ( (k), ∈ ( )) = || 2

k||2

+

∑ +N2 ∈ i =1

7: if ( (k), ∈ ( )) − ( (k 1), ∈ ( − 1)) < then 8: break 9: end if 10: k=k+1 11: end while

0

3.4 Dataset UNSW dataset has been released, it includes 10 different types of traffic packets, and it is more suitable to bemuse in the contemporary anomaly detection models. It includes normal packets as well as 9 types of attacks, which are Analysis, Backdoor, DoS, Exploits, Fuzzers, Reconnaissance, Shellcode, and Worms. Table 1 shows the notation of these UNSW-NB-15 is composed of two parts: a training set UNSWNB-15 training-set.csv which has been used for model creation and a testing set, UNSW-NB-15-testing-set.csv used for the testing step and modeling the received real-time packets. The number of records in the training and testing sets is 175,341 and 82,332, respectively.

Detection of Signature-Based Attacks in Cloud Infrastructure … Table 1 Classes notation

Number

Class

1

Normal

2

Analysis

3

Backdoor

4

DoS

5

Exploits

6

Fuzzers

7

Generic

8

Reconnaissance

9

Sellcode

10

Worms

517

During this phase, two sets of datasets are extracted from the UNSW-NB-15 training set which consists of over 2 lakh records. The first set FI is a set of labeled records and is used to train the initial SVM. The subsequent set, FT, is the arrangement of unlabeled records and is utilized to retrain the SVM model during the cycles of the calculation [14]. All the 10 different attacks of UNSW-NB-15-features were used in the simulation. For the purpose of this simulation, the size of FI was taken to be much smaller than that of FT so that the efficiency of the proposed scheme in reducing the requirement of labeled data may be properly tested [1].

4 Result The degree of improvement in the detection accuracy with the iterations of the selftraining algorithm depends on the size of the labeled and unlabeled training set. This outcome can be construed from the way that after 6 emphases, the adjustment of the location precision for the re-enactment with 5000 marked records set is practically twofold that of the reproduction with 500 named records set. The results also show that the overall accuracy is most sensitive to the size of the labeled set. In case of the simulation with 500 labeled records, the final detection accuracy was around 75.5%, whereas for the simulation with 5000 labeled records, it was found to be around 86%. Finally, the results validate the hypothesis that self-training can be used for the reduction of the labeled training set size in the domain of intrusion detection as well. A reduction of upto 90% has been achieved in the number of labeled training Fig. 3.

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Fig. 3 Self-training SVM with a labeled training set of size 5000 and unlabeled training set (selftraining set) of size 2 K

5 Conclusion A new method for intrusion detection using SVM algorithm and its effectiveness for the intrusion detection problem domain has been verified by simulation on the standard UNSW-NB-15 dataset. Further, the given algorithm achieves good results in reduction of the requirement of labeled training data. Simulation results showed that this scheme is more efficient for attack detection using SVM can increase the classification accuracy from 66% to up to 90%. Future work will address anomaly detection of attacks in cloud infrastructure and more advanced techniques for profiling normal packets while improving the classification performance using methods such as data mining and data clustering.

References 1. Chkirbene Z, Erbad A, Hamila R (2019) A combined decision for secure cloud computing based on machine learning and past information using. In: IEEE wireless communications and networking 2. Wang W, Du X, Shan D, Qin R, Wang N (2020) Cloud intrusion detection method based on stacked contractive auto-encoder and support vector machine. In: IEEE transactions on cloud computing 3. Sakthivel D, Radha B (2020) Novel study on machine learning algorithms for cloud security. J Crit Rev 7(10) 4. Kumar R, Sharma D (2018) Signature-anomaly based intrusion detection algorithm. In: Proceedings of the 2nd international conference on electronics, communication and aerospace technology

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5. Guo C, Ping Y, Liu N, Luo S-S (2016) A two-level hybrid approach for intrusion detection. Neurocomput 214:391–400, ISSN 0925–2312 6. Sakthivel D, Radha B (2018) SNORT: network and host monitoring intrusion detection system. Int J Res Appl Sci Eng Technol (IJRASET) 6(X) 7. Sakthivel D, Radha B (2018) A study on security issues and challenges in cloud IaaS. Int J Res Appl Sci Eng Technol (IJRASET) 6(III) 8. Chkirbene Z, Erbad A, Hamila R (2019) A combined decision for secure cloud computing based on machine learning and past information. In: IEEE wireless communications and networking conference 9. Bhamare D, Erbad A, Jain R, Zolanvari M, Samaka M (2018) Efficient virtual network function placement strategies for cloud radio access network. Comput Commun 127:50–60 10. Cisco Systems (2016) Fog computing and the internet of things: extend the cloud to where the things are. www.cisco.com 11. Aljamal I (2019) Hybrid intrusion detection system using machine learning techniques in cloud computing environments. In: IEEE SERA 2019 12. Aboueata N, Alrasbi S, Erbad A (2019) Supervised machine learning techniques for efficient network intrusion detection. IEEE 13. Ghanshala KK (2018) BNID: a behavior-based network intrusion detection at network-layer in cloud environment. In: First international conference on secure cyber computing and communication 14. Din MF, Qazi S (2018) A compressed framework for monitoring and anomaly detection in cloud networks. In: International conference on computing, mathematics and engineering technologies (iCoMET), IEEE

A Modified Weighed Histogram Approach for Image Enhancement Using Optimized Alpha Parameter Vishal Gupta, Monish Gupta, and Nikhil Marriwala

Abstract Enhancement of images is a critical part of Computer vision for the processing of images. The objects with a low contrast ratio and low intensities are difficult to interpret by the system models for their further processing. In the present work, we have emphasized our approach in finding the correct methodology for the enhancement of images collected from different data sources. In the proposed approach, we have considered the maritime satellite imagery data and the images captured through camera (especially of ships). We have proposed a novel approach Modified Weighted Histogram Equalization method in which the alpha parameter is adjusted to the best optimized value and the presented work have been compared with other existing approaches such as adaptive gamma correction weighted distribution in terms of PSNR (Peak Signal to Noise Ratio), AMBE (Absolute mean brightness error) and processing time. The work gives the best accuracy results in enhancing the images which further may be used for different application of computer vision such as object detection, vessel classification, etc. Keywords Computer vision · Object detection · PSNR · AMBE · Maritime images · Etc.

1 Introduction The omnipresent and wide applications like scene understanding, video observation, mechanical technology, and self-driving frameworks set off tremendous exploration in the area of PC vision in the latest decade [1]. Being the center of every one of these applications, visual acknowledgment frameworks which incorporate picture arrangement, restriction and recognition have accomplished incredible exploration V. Gupta (B) University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India M. Gupta · N. Marriwala Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_39

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energy. Picture order, being the generally explored territory in the field of PC vision has accomplished momentous outcomes in overall rivalries, for example, ILSVRC, PASCAL VOC, and Microsoft COCO with the assistance of profound learning [2]. Inspired by the consequences of picture characterization, profound learning models have been created for object identification and profound learning-based article recognition has additionally accomplished condition of the results [3]. The authors [4–6] proposed three novel profound learning designs, which can play out a joint location and posture assessment. Pathaka et al. [7] demystified the part of profound learning strategies dependent on convolutional neural organization for object identification. Profound learning structures and administrations accessible for object identification were likewise articulated. Hou et al. [8] tended to the issue to facilitate the profundity all the more adequately with RGB targeting boosting the presentation of RGB-D article identification. With numerous applications in significant level scene understanding, striking article location is a significant target. The objective of notable item location is to recognize the most remarkable areas and afterward portion whole striking articles out. It has as of late pulled in more consideration in PC vision local area as it tends to be utilized as a significant pre-preparing venture before additional handling. Regularly, PC vision models which are intended to recognize striking districts are enlivened by human visual consideration and insight frameworks. Visual saliency identification has exhibited its value in different applications, for example, object discovery and acknowledgment [9–14], picture quality evaluation [15], picture thumbnailing [16], video preparing, and picture information pressure. [17, 18], GPS area assessment and human–robot association. The moving item discovery fills in as a pre-handling step to more elevated level cycles, for example, object characterization or following. Thus, its exhibition can have an enormous impact on the presentation of more elevated level cycles. Foundation deduction is the most widely recognized technique to distinguish forefront objects in the scene. In the foundation sub-foothold strategy, every video outline is contrasted and a foundation model and the pixels whose force esteems digress altogether from the foundation model are considered as frontal area. Exact forefront identification for complex visual scenes is a troublesome errand since genuine video arrangements contain a few basic circumstances [19, 20]. Some key difficulties experienced in foundation deduction strategies are: dynamic foundation, moving shadows of moving articles, abrupt brightening changes, disguise, closer view gap, loud picture, camera jitter, bootstrapping, camera programmed changes, stopped and sluggish items [21]. Zhang et al. [10] proposed a novel diagram-based streamlining outline work for notable item identification. The trial results on four benchmark information bases with correlations with fifteen agent strategies were likewise illustrated. Liang et al. [22] expanded the idea of remarkable article recognition to material level dependent on hyper-ghostly imaging and introduced a material-based notable item identification technique that can successfully recognize objects with comparable saw tone however extraordinary phantom reactions. Naqvia et al. [23] proposed methods that misuse the part of shading spaces for tending to two significant difficulties comparable to notable article identification. This technique self-governing distinguishes the shading space which is locally the most fitting for saliency discovery on a picture by picture premise.

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Shading uniqueness is perhaps the most broadly adjusted sign for remarkable district identification because of the significance of shading in human visual insight [16, 24–28]. Different distinctive shading models (shading spaces) are discovered to be appropriate for different various applications: picture preparing, media, designs, and PC vision. Picking a shading portrayal that safeguards the significant data and gives understanding into the visual cycle is a troublesome cycle according to numerous applications [16, 25–28]. Raj et al. introduced a novel calculation dependent on bandlet change for object recognition in Synthetic Aperture Radar (SAR) pictures. Here, an initial bandlet put together despeckling plan was utilized with respect to the information SAR picture and afterward, a consistent bogus caution rate (CFAR) indicator was utilized for object discovery. Lua et al. read the model for proficient profound organization for vision-based item discovery in mechanical applications [22, 29]. In our previous work [27, 29], we have designed an automated system for the classification and detection of different objects whose results might be improved if the dataset considered over there was first processed through an enhancement technique. In future publications, the work may be combined to give the hybrid approach for the improvement of results. The paper is organized such as Sect. 1 explains the introduction and the previous researchers work, Sect. 2 gives the proposed methodology and Sect. 3 represents the experimental results and analysis.

2 Proposed Approach In our previous published work [27, 29], we have emphasized our study on ships classifications and object detection in coastal areas. As stated, the detection of such objects depends on various factors; one of them might be the dullness of the captured images especially the case of aerial images. The accuracy of the work may be improved if the processed images or datasets are enhanced before being passed through the proposed algorithms. In this work, we have considered different categories of images for their enhancement. In future publications, the work will be expanded for the detection of vessels of the enhanced datasets. In this work, we have first defined the previous approach AGCWD (Adaptive gamma correction weighted distribution) used for the enhancement of aerial images and then the proposed method MWHE (Modified Weighted Histogram Equalization) which gives the better results as compared to the previous approach. In AGCWD method we utilized the adjusted parameter for brightness preservation; this adjusted parameter is also known as alpha parameter. We gave the value of this adjusted parameter manually, so we decided that this adjusted parameter is calculated through optimization. Then we utilized the MWHE method to optimize the adjusted parameter. Through the second approach we received the better results than AGCWD method in which both the parameters (PSNR & AMBE) show the good results according to their requirement. We can see in section experimental results III shows the qualitative results and quantitative

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results between AGCWD method and new proposed method. And through these results we can conclude that proposed method is better than AGCWD method. The mathematical equations for Peak to Signal Noise Ration and Absolute Mean Brightness Error are defined below: PSNR = 10Log10

(L − 1)2 MSE

(1)

MSE is known as Mean Square Error and given by Eq. 2  MSE =

|x(i, j) − y(i, j)|2 N

(2)

where x(i, j) is the input image and y(i, j) is the output image. AMBE (X, Y ) = |X M −Y M |, where X M is the mean of the input image X = X(i, j) and Y M is the mean of the output image Y = Y (i, j). In our work, the main motive is to preserve brightness and contrast enhancement. So AMBE is used to assess the degree of brightness preservation, while PSNR is used to assess the degree of contrast enhancement. In this section, we will discuss the enhancement method with the help of a flow chart. These methods are used for image enhancement. Technique AGCWD (Adaptive Gamma Correction Weighted Distribution) is the previous method in which we optimize the gamma value with the help of Cumulative Distribution Function (CDF). In proposed Modified Weighted Histogram Equalization method, we optimize the alpha parameter that is adjustable. Figure 1 shows the flow chart diagram of the proposed method. Fig. 1 Flow chart of proposed method

Input Image

Binarization of Image

Modified Histogram Equalization

Weighted Correction

Output Image

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Fig. 2 (i) Input image; (ii) AGCWD method; (iii) MWHE method

Flow chart of proposed method as given below:

3 Simulation Results and Discussion We have considered the MARVEL (marine vessels) dataset available at https://github. com/avaapm/marveldataset2016 and www.shipspotting.com. Some of the dataset used in the work is gathered from the Karnal Lake captured in the daytime. The work analysis is carried out considering the qualitative as well as quantitative approaches. The qualitative analysis gives the results in terms of visualization for the observation of humans to get the enhanced images for further analysis and processing. While the quantitative approach gives mathematical acceptance to the model. Here in this paper, we have shown the samples of 10 images of different categories out of complete dataset used. In Figs. 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11, we can see that (i) Input image; (ii) Output of Adaptive Gamma Correction Weighted Distribution method; (iii) Output of Modified Weighted Histogram Equalization method. After the quantitative analysis of the methods below are shown the mathematical results obtained. Table 1 shows the PSNR of the two methods. The proposed approach shows the higher PSNR values which show the enhancement over the existing method. In the same manner, Table 2 shows the AMBE (Absolute mean brightness error) values which must be less to visual the images in a better way.

4 Conclusion A Modified Weighted Histogram Equalization method is proposed and a novel technique for the enhancement of images is given. The present work is implemented to

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Fig. 3 (i) Input image; (ii) AGCWD method; (iii) MWHE method

Fig. 4 (i) Input image; (ii) AGCWD method; (iii) MWHE method

Fig. 5 (i) Input image; (ii) AGCWD method; (iii) MWHE method

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Fig. 6 (i) Input image; (ii) AGCWD method; (iii) MWHE method

Fig. 7 (i) Input image; (ii) AGCWD method; (iii) MWHE method

Fig. 8 (i) Input image; (ii) AGCWD method; (iii) MWHE method

improve the low contrast ratio or dimmed intensity images and to reduce the noise error in captured image so that the visualization of the dataset enhances. The proposed method is compared with the existing algorithm and it is shown that the proposed work gives better results when compared to the existing approach. The results are

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Fig. 9 (i) Input image; (ii) AGCWD method; (iii) MWHE method

Fig. 10 (i) Input image; (ii) AGCWD method; (iii) MWHE method

shown in a qualitative as well as quantitative manner. The algorithm further may be used for many different application of image processing like detection, classification, segmentation of images.

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Fig. 11 (i) Input image; (ii) AGCWD method; (iii) MWHE method Table 1 Comparison between PSNR values

Table 2 Comparison between AMBE values

Images

PSNR (previous approach) (dB)

PSNR (proposed approach) (dB)

Figure 2

+ 17.63

+ 18.53

Figure 3

+ 15.41

+ 15.99

Figure 4

+ 16.55

+ 17.17

Figure 5

+ 13.95

+ 14.89

Figure 6

+ 15.55

+ 16.27

Figure 7

+ 16.57

+ 16.58

Figure 8

+ 16.28

+ 16.56

Figure 9

+ 15.38

+ 18.88

Figure 10

+ 10.14

+ 12.92

Figure 11

+ 17.80

+ 23.39

Images

AMBE (previous approach)

AMBE (proposed approach)

Figure 2

0.1262

0.1119

Figure 3

0.1535

0.1424

Figure 4

0.1459

0.1390

Figure 5

0.1758

0.1745

Figure 6

0.1477

0.1350

Figure 7

0.1499

0.1401

Figure 8

0.1397

0.1365

Figure 9

0.1883

0.1400

Figure 10

0.2831

0.2776

Figure 11

0.1539

0.1132

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References 1. Kyung Hee M (2007) A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(2):593–600 2. Kim Y-T (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8 3. Wan Y, Chen Q, Zhang B (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans Consum Electron 45(1):68–75 4. Gupta V, Gupta M, Singla P (2021) Ship detection from highly cluttered images using convolutional neural network. Wireless Pers Commun 121:287–305. https://doi.org/10.1007/s11277021-08635-5 5. Gupta V, Marriwala N, Gupta M (2021) A GUI based application for Low Intensity Object Classification & Count using SVM Approach. In: 2021 6th International conference on signal processing, computing and control (ISPCC), pp 299–302. https://doi.org/10.1109/ISPCC5 3510.2021.9609470 6. Gupta V, Gupta M, Zhang Y (2021) IoT-based artificial intelligence system in object detection. Taylors and Francis Group, CRC Press, ISBN:9781003140443 7. Pathak AR, Pandey M, Rautaray S (2018) Application of deep learning for object detection. Proc Comput Sci 32:1706–1717 8. Hou S, Wang Z, Wu F (2018) Object detection via deeply exploiting depth information. Neurocomputing 286:58–66 9. Ramli (2009) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319 10. Zhang J, Ehinger KA, Wei H, Zhang K, Yang J (2017) A novel graph-based optimization framework for salient object detection. Pattern Recogn 64:39–50 11. Wang C, Ye Z (2005) Brightness preserving histogram equalization with maximum entropy: a variation perspective. IEEE Trans Consum Electron 51(4):1326–1334 12. Ibrahim (2007) Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(4):1752–1758 13. Lamberti F, Montrucchio B, Sanna A (2006) CMBFHE: a novel contrast enhancement technique based on cascaded multistep binomial filtering histogram equalization. IEEE Trans Consum Electron 52(3):966–974 14. Kim L-S (2001) Partially overlapped sub-block histogram equalization. IEEE Trans Circuits Syst Video Technol 11(4):475–484 15. Celik T, Tjahadi T (2011) Contextual and variation contrast enhancement. IEEE Trans Image Process Appl 20(2):3431–3441 16. Fu K, Gu IY-H, Yang J (2018) Spectral salient object detection. Neurocomputing 275:788–803 17. Rodriguez Sullivan M, Shah M (2008) Visual surveillance in maritime port facilities. In: Proceedings of SPIE, vol 6978, p 29 18. Liu H, Javed O, Taylor G, Cao X, Haering N (2008) Omni-directional surveillance for unmanned water vehicles. In: Proceedings of international workshop on visual surveillance 19. Wei H, Nguyen H, Ramu P, Raju C, Liu X, Yadegar J (2009) Automated intelligent video surveillance system for ships. In: Proceedings of SPIE, vol 7306, p 73061N 20. Fefilatyev S, Goldgof D, Lembke C (2009) Autonomous buoy platform for low-cost visual maritime surveillance: design and initial deployment. In: Proceedings of SPIE, vol 7317, p 73170A 21. Kruger W, Orlov Z (2010) Robust layer-based boat detection and multi-target-tracking in maritime environments. In: Proceedings of international waterside 22. Liang J, Zhou J, Tong L, Bai X, Wang B (2018) Material based salient object detection from hyperspectral images. Pattern Recogn 76:476–490 23. Naqvi SS, Mirza J, Bashir T (2018) A unified framework for exploiting color coefficients for salient object detection. Neurocomputing 312:187–200 24. Fefilatyev S, Shreve M, Lembke C (2012) Detection and tracking of ships in open sea with rapidly moving buoy-mounted camera system. Ocean Eng 54:1–12

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25. Westall P, Ford J, O’Shea P, Hrabar S (2008) Evaluation of machine vision techniques for aerial search of humans in maritime environments. In: Digital image computing: techniques and applications (DICTA) 2008, Canberra, pp 176–183 26. Herselman PL, Baker CJ, Wind HJ (2008) An analysis of X-band calibrated sea clutter and small boat reflectivity at medium-to-low grazing angles. Int J Navig Obs. https://doi.org/10. 1155/2008/347518 27. Gupta V, Gupta M (2020) Ships classification using neural network based on radar scattering. Int J Adv Sci Technol 29:1349–1354 28. Onoro-Rubio D, Lopez-Sastre RJ, Redondo-Cabrera C, Gil-Jiménez P (2018) The challenge of simultaneous object detection and pose estimation: a comparative study. Image Comput 79:109–122 29. Gupta V, Gupta M (2021) Automated object detection system in marine environment. In: Mobile radio communications and 5G networks, Lecture notes in networks and systems, vol 140. https://doi.org/10.1007/978-981-15-7130-5_17

Spectrum Relay Performance of Cognitive Radio between Users with Random Arrivals and Departures V. Sreelatha, E. Mamatha, C. S. Reddy, and P. S. Rajdurai

Abstract Rapid changes and its advancement in wireless communication and palmtop computing devices lead to technology revolution in the present world. The demand for wireless networks with limited spectrum drives to find various paths to enhance communication sector. During study, it is observed that cognitive radio is one of the promising technologies for better communication service. It improves the service by making utilization of the spectrum to the secondary user by allocating spectrum from, licensed users during free time. In this paper, we proposed to develop a mathematical model for the system, and results are presented in figures. Keywords Spectrum · Multiple users · Cognitive radio · Wireless technology

1 Introduction The demand for wireless communication and its rapid growth in communication sector leads to effective resource utilization of the spectrum. It has a limited resource, but according to research studies the licensed spectrum is not efficiently utilized in most of the time [1, 2]. The recent technology proposed the cognitive radio (CR) networks to overcome the shortage and to increase the traffic service. It expands the service from early generations, 1G and 2G of simple voice and short text service to high streamed data applications. In the present days, wireless local area networks (WLANs) are developed based on IEEE 802.11, and wireless personal area networks (WPANs) are developed based on IEEE 802.15 to address the network problems V. Sreelatha · E. Mamatha (B) School of Science, GITAM University, Bangalore, India e-mail: [email protected] C. S. Reddy Department of Mathematics, Cambridge Institute Technology - NC, Bangalore, India P. S. Rajdurai Department of Mathematics, Srinivasa Ramanujan Center, SASTRA University, Kumbakonam, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_40

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with optimal cost and reliably high-speed data service. In most of the needed places, WLAN nodes serve as hotspot connectivity points, especially required in the places such as complex, colleges and universities, railway stations, and residence houses. The future proposed technologies, cognitive radio, and softwaredefined radio (SDR) technologies entrust multi-node, multiband communications with diversified interface connectivity environment. According to research study from Federal Communications Commission, it is observed that in US approximately 70% of the spectrum is not utilized effectively. Networks are primarily divided into two types of wireless users, primary user (PU) and secondary user (SU) [2–4]. To tackle the ideal spectrum utilization, cognitive radio technology evolved for a better service. In the cognitive radio system, secondary user is exploited the usage of spectrum bandwidth during free time, whereas license is primarily provided for primary users [5–7]. To overcome intolerable usage of the secondary users, a nice frequently spectrum monitoring technology is required, so that the service is provided seamlessly to primary users and maximum throughput achieved [8, 9]. Since, maximum utilization of spectrum does not depend on primary users. So, throughput gain of the network depends on the secondary users, allocated by cognitive radio system. Hence, we primarily focus on the throughput of the CR network as the throughput of the secondary users only [10]. In this structure, the spectrum utilization is maximized by providing to secondary user, so that interference level to primary user should be under threshold stage. Under this condition, a good sensible data transmission slots are set to coordinate unit frame such that network collisions should be optimized, and data pocket transmission is maximized. In almost all countries, governmental bodies manage the electromagnetic frequency spectrum and is considered as a natural resource to all the people. Naturally, most of the spectrum is dedicated to the license organizations or service sectors; the remaining left-over spectrum is allotted for future wireless networks. It is monitored that within a stipulated time the requirement of wireless communications swiftly increased, which tends to the shortage of the spectrum. But, many research studies show that a huge volume of dedicated spectrum is not properly utilized in the given stipulated time in any specific locality [8]. That is the allocation of available spectrum to other users for the usage of the spectrum is not bad thought, if it will not affect interference to the licensed network. It gives a better solution to deal with scarcity problem of the wireless spectrum. It can be accomplished by cognitive radio which is a smart technology for wireless communication that can handle sensing, learning, and dynamically allocating physical equipment to frequency radio environment. With these cognitive radios technology by allowing secondary user, the usage of frequency spectrum not only increases but also permits additional users for the service required. In this paper, we primarily focus to develop three distinct performance models for cognitive radio networks to achieve maximum throughput of the spectrum. In the first one, we develop a model to cooperate secondary user in relaying to primary user’s packets requirement. The cooperation should not be affected for both users’ data transmission pockets. In the second stage, cognitive radio networks completely dedicated to all multiple primary users. In this case, the occupancy of the spectrum

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monitored by secondary receiver and transmitter is not indistinguishable, and hence, it infringes the functioning of the secondary user. In the final stage, we consider the networks to allow interference cancelation with multiple antennas so that secondary user can transmit data packets uninterruptedly. This is processed without destructing the interference for the primary user. This method is supposed to give better result for networks sharing between primary users and secondary users by using cognitive radio technology. We forecast the coordination between users will enhance the throughput for the primary user, by optimizing the channel access time. Subsequently, it will allow higher level of accessing the channels for the secondary users. In this paper, authors attempted to verify their hypothesis by modeling as a M/G/1 queuing system for primary and secondary users on defining in a closed form for the delay of data packets to the secondary user.

2 System Model In the present work, we consider the cognitive radio technology having multiple primary users explained in Fig. 1. All these primary users use the same channel for data transmitting at various geographical locations. It is assumed that all secondary users have its own range region with the boundary range of radius Rg . The primary user active in this area Ag must be detected the signal with some probability, where Ag = πRg 2 . Observe that the radius Rg must be chosen, such that it guarantees to a primary user beyond the sensing region endure the secondary user transmission access. Suppose in the transmission region of primary user, if secondary transmitter of another primary is arranged then channel usage may be distinguishable from secondary transmitter and receiver nodes. It leads to the secondary user successful

Fig. 1 Wireless communication network with primary and secondary users

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Fig. 2 Transition state architecture between nodes

probability transmission rate drastically shrivel. In this paper, we proposed to develop secondary user mean packet delay. Let us consider the system presented in the Fig. 2 for two primary users. In the system, first primary user (FPU) and second primary user (SPU) operate the same channel of networks at different geographical locations. Within the of FPU’s and SPU’s transmission range, another secondary transmitter (ST) and receiver (SR) are arranged. This system can be modeled by an M/G/1 queuing network system for FPU and SPU. At the points FPU and SPU data pockets arrive according to Markov Poisson process with mean rates σ1 , σ2 packets per unit time, respectively. There is an influence between primary users; hence, accessibility of the channel at ST is distinct compared to SR. As a result, the chance of utilization of transmission for secondary user occurs only if both FPU, SPU are in idle state at the same time. It is also possible to model and M/G/1 queuing networks system for secondary user. Packets arrive at a rate α packets/s that follows Poisson distribution, whereas service rate follows general distribution. It is assumed that a packet either transmitted successfully or it may be dropped at both primary user and secondary user owing to channel defectives. It follows the Bernoulli process independent from the system. If the packets are missed, then the transmission of packets further repeated between users until it positively reached the destination. Further, it is assumed that the service time is taken as the time difference between initial transmission time of the packet to successful destination transmission time. The system is considered as idle state if both FPU, SPU are instantaneously at rest, otherwise the communication system is considered as a busy state. In the next section, we discuss idle and busy period times for primary user. IKPU (S) =

σk ; s + σk

where k = 1, 2

Next, the Laplace transform of the primary user packet service time M kpu (s) is as follows:

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MKPU (S) =

∞ n=1

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L KPU (ns)Pr(Nk = n)

Pr(Nk = n) = (1 − pKPU )n−1 p K PU, where N k follows geometric distribution. And primary user busy period is obtained as: BKPU (s) = MKPU [s + σk − σk BKPU (s)] The summation of primary user’s idle and busy periods is considered as cyclic period, and is defined as: Q KPU = B KPU + I KPU

3 Probability Distribution of System Idle Period As mentioned earlier, KPU idle periods are follows Poisson distribution with σk . The system idle period is given by the minimum of a complete and a residual idle period of the two primary users. Let I a and I b represent complete and residual periods of two primary users. So, we have Isys = min(Ia , Ib ) Since exponential distribution follows memoryless property, hence for two independent primary users the probability of idle time becomes: Pr(Isys > t) = Pr(Ia > t, Ib > t) = [ 1 − Pr(Ia < t)][1 − Pr(Ib < t) ] = [ 1 − (1 − e−σ1 t )][1 − (1 − e−σ2 t ) ] = e−(σ1 +σ2 )t Therefore, for idle period, the system cumulative distribution function is as follows: Pr(Isys ≤ t) = 1 − e−(σ1 +σ2 )t

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And probability density function is as follows: f Isys (t) = (σ1 + σ2 )e−(σ1 +σ2 )t

4 Results Numerical results are presented in this section in graphical form in Figs. 3, 4, 5, 6, respectively. Mean packet delay of the primary and secondary users with respect to packet traffic load in wireless communication is computed and plotted. To compute the simulation values, mean arrival rate of the primary user 1 is considered as 2 and 3 packets/s per primary user 2. Secondary user packet transmission time is chosen as 0.1 s.

Fig. 3 Shows a graph between secondary users versus traffic load

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Fig. 4 Graph between number of packet and mean packet arrival

5 Conclusion In this paper, authors attempted to address performance of wireless transmission spectrum by adopting cognitive radio technology for multiple primary users. It shows that cognitive radio technology is one of the promising technologies to effectively utilize spectrum. Probabilistic formulae are developed to estimate performance measures among various primary users.

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Fig. 5 Graph between secondary users and traffic load density

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Fig. 6 Plot between number of packet and mean packet arrival size

References 1. Ma X, Ning S, Liu X, Kuang H, Hong Y (2018) Cooperative spectrum sensing using extreme learning machine for cognitive radio networks with multiple primary users. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC), pp 536–540, IEEE 2. Cavalcanti D., Agrawal D, Cordeiro C, Xie B, Kumar A (2005) Issues in integrating cellular networks WLANs, AND MANETs: a futuristic heterogeneous wireless network. IEEE Wireless Commun 12(3):30–41 3. Wei L, Tirkkonen O (2012) Spectrum sensing in the presence of multiple primary users. IEEE Trans Commun 60(5):1268–1277 (2012) 4. Mamatha E, Sasritha S, Reddy CS (2017) Expert system and heuristics algorithm for cloud resource scheduling. Rom Stat Rev 65(1):3–18 5. Pradhan H, Kalamkar SS, Banerjee A (2015) Sensing-throughput tradeoff in cognitive radio with random arrivals and departures of multiple primary users. IEEE Commun Lett 19(3):415– 418 6. Saritha S, Mamatha E, Reddy CS, Anand K (2019) A model for compound poisson process queuing system with batch arrivals and services. Journal Europeen des Systemes Automatises 53(1):81–86 7. Saritha S, Mamatha E, Reddy CS (2019) Performance measures of online warehouse service system with replenishment policy. Journal Europeen Des Systemes Automatises 52(6):631–638 8. Mamatha E, Saritha S, Reddy CS, Rajadurai P (2020) Mathematical modelling and performance analysis of single server queuing system-eigenspectrum. Int J Mathemat Oper Res 16(4):455– 468

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9. Mamatha E, Reddy CS, Krishna A, Saritha S (2021) Multi server queuing system with crashes and alternative repair strategies. Commun Statist Theor Meth. https://doi.org/10.1080/036 10926.2021.1889603 10. Bayat S, Louie RH, Vucetic B, Li Y (2013) Dynamic decentralised algorithms for cognitive radio relay networks with multiple primary and secondary users utilising matching theory. Trans Emerg Telecommun Technol 24(5): 486–502 (2013)

Energy Efficient Void Avoidance Routing for Reduced Latency in Underwater WSNs Swati Gupta and N. P. Singh

Abstract Given the unique characteristics of UWSNs, the provision of mountable and energy effective routing in underwater sensor networks (UWSNs) is very hard to achieve. Because of the fact that the nodes are randomly deployed and energy harvesting cannot be employed, the problem of void hole arises which is regarded as the most challenging problem in design of routing protocols. These are the areas where the packet cannot be further forwarded using greedy mode. The void or energy hole creation leads to performance degradation in these networks in terms of energy consumption, throughput, and network lifetime. The paper works on the concept of detecting the void nodes prior and follow proactive routing in which paths can be updated at regular intervals. Holding times can be reduced by using two-hop information. The paper deals with these aspects of the routing process. In terms of energy economy, packet delivery ratio, and end-to-end delay, simulation results show that the proposed protocol outperforms Depth based Routing and WDFAD-DBR routing. Keywords UWSN · Void hole · Routing · Energy efficiency · Packet delivery ratio · E2E delay

1 Introduction Because of its broad and varied applications, undersea sensor networks have triggered interest in scientific and business communities. Underwater environmental monitoring, mine exploration, disaster warning and prevention, oceanographic data S. Gupta (B) · N. P. Singh Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, India N. P. Singh e-mail: [email protected] S. Gupta Panipat Institute of Engineering and Technology, Panipat, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_41

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collecting, and tactical surveillance applications are only a few of the key uses of UASN [1]. The underwater nodes gather information from the tough maritime environment and transmit it to stations offshore. Sensor nodes deployed underwater have acoustic modems since they work in aquatic environments only but the sink nodes have both acoustic as well as radio modems because they have to receive messages from deployed underwater sensors and had to convey this information through radio links to offshore base stations [2]. UWSN suffers from a lot of drawbacks. The speed of propagation of acoustic signals is 1500 m/s and that is very low as compared to that of the speed of radio signals which is 3 × 108 m/s. Also due to the mobility of nodes, no fixed topology can be considered. Acoustic communication has limited bandwidth, leading to low data rates. Moreover, sensor nodes cannot be recharged and battery replacement is not feasible. Delay and Doppler spread cause intersymbol interference (ISI) in underwater signals [3, 4]. Prominently, two communication architectures have been projected for underwater networks, two dimensional and three dimensional. Static two-dimensional UWSN can be extended to ocean bottom tracking. They comprise sensor nodes that are fixed to the ocean floor and are linked by wireless acoustic links to one or more underwater sinks. The underwater sinks transfer information from inside the sea network to a surface station. Characteristic uses are the ecological control or surveillance of tectonic marine plates. Since only random deployment is possible in underwater sensor networks, so this may lead to high population of nodes in some areas whereas, some regions may be sparsely populated. This may also lead to coverage gaps or void holes in some areas. The void holes may also arise due to sudden death of nodes because of high energy drainage of these nodes or unpredicted movement of nodes because of water currents [5]. In the literature survey many void avoiding algorithms have been discussed some of which work on depth information, others on residual energy, and other forwarding metrics. The proposed protocol works proactively working on two-hop information and bypasses the void nodes in the routing process. Results of the simulation demonstrate how the systems proposed to outperform the selected existing systems in terms of the output measures chosen. The rest of the paperwork is as follows: • • • • •

A summary of current protocols is given in second section, Problem statement is given in third section, System model is presented in the fourth section, Results for simulation and discussion are given in fifth section, Conclusion is provided in sixth section.

2 Related Work Due to water currents, UWSN is dynamic and hence terrestrial protocols are not suited for underwater sensor networks. UWSN routing protocols are classified into two main categories—geographic information based and geographic information free

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routing. Geographic routing uses sensor node location information to route between source and destination [2]. It is further divided as receiver based and sender based and more further as location information based routing or Depth Based Routing. Some of the receiver based protocols are DBR, WDFAD-DBR, VAPR, DSD DBR, H2 DAB, and senders based are VAR, Hydrocast, ARP. DBR adopts routing decisions based on sensor node depth [6]. The packet header contains the sender’s depth information. When opening this packet at a separate node, it compares its own depth to the information about the depth found in that packet. If the receiving node’s depth is smaller, packet will be accepted for further transmission, then if the depth of the receiving node is larger it will be discarded. WDFAD-DBR [7] is likewise a protocol dependent on scope. In this protocol, routing decisions are based on current hop and forwarding nodes from next hop. WDFADforwarding DBR’s region is split into two auxiliary zones and a fixed primary zone that can be increased based on node density. The primary downside is that the fixed region limits routing options, and the received Acknowledgements in response to each packet sent wastes network resources and proposed in VAPR which also calls the depth knowledge for the data packets forwarding. By exchanging beacon messages VAPR [5] predicts all the information about the path to the destination in advance. HydroCast [8, 9] is a routing protocol based on a load. It functions in two parts, a covetous algorithm founded on pressure and a lower-depth-first method of recovery locally. Recovery pathways are established a prior in HydroCast [10], by utilizing the depth attributes of the deployed nodes. But the exploration and management of the recovery route is high overhead, particularly when the path is very long. In the Vector Dependent Forwarding protocol [11], a virtual pipe to be used for data transmission is generated from source to destination. Only the nodes in the pipe are selected for data forwarding. However, repeatedly picking forwarders within the pipe does not balance energy usage. Network lifespan is shortened due to imbalanced energy consumption. In [12], a hop-by-hop vector-based forwarding protocol (HHVBF) is given to address this problem and improve the performance of VBF. In HH-VBF, each node makes the virtual pipe, because of which the direction of the vector changes according to the position of the node. Compared to VBF the energy consumption is adjusted. However, the hop-by-hop computer pipe system generates significant overhead communication. Compared to that of VBF the overhead is higher [13]. Another protocol Adaptive hop-by-hop forwarding dependent vector is given in [14]. It is based on a fixed radius of the virtual routing pipeline. Once a packet is received at a node, it is initially tested to see if it’s under a predefined range for its distance from the forwarder. It uses time-holding to eliminate unwanted broadcasts. The node with more neighbors has a high held time and vice versa [15]. While the packet overlap is minimized, end-to-end delay does not increase. Table 1 offers a comparison of various routing protocols in terms of latency, energy consumption, and bandwidth. The proposed protocol uses adjacent node location information to decide the next hop forwarder neighbors set. To avoid unnecessary transmissions it is forbidden to forward process nodes with higher depths. This increases the network’s residual capacity and increases the packet distribution ratio by eliminating unwanted transfers and collisions [8, 16]. Although we presume that the sinks deployed once are

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stationary, the sensor nodes follow the random-walk mobility model—sensor node selects a path randomly and, unless otherwise specified, moves to the new position with a random velocity between the minimum and maximum velocity, respectively 1 and 5 m/s. A performance analysis will be carried out between the protocol proposed and the DBR and WDFAD-DBR protocol, using a simulation environment, analyzing the metrics: PDR, residual energy, end-to-end delay, and forwarding number.

3 Problem Statement Energy harvesting cannot be employed in underwater sensor nodes so every node has to work with its inbuilt battery and hence have a limited lifespan concluding that energy consumption and lifetime are the main concerns in designing an underwater sensor network. Void recovery in underwater networks consumes a large amount of energy because of the search for alternate paths in case of encountering voids leading both to energy drainage and time delay [11, 17]. So avoidance of void holes is one of the major concerns while developing a routing algorithm in underwater sensor networks. In some routing algorithms such as DBR and WDFAD-DBR. The forwarder selection for the next hop is based on the depth difference between the current forwarder node and the next forwarder node. In DBR the only criteria for selection of the next forward forwarding node are one hope distance and no provision for handling voids has been provided whereas in WDFAD-DBR, the selection criteria include two-hop neighbor information but in these cases, Local optimal solution problem occurs where the current source node is considered the higher depth node as the next forward node, rather than the lower depth node [18, 19]. In this paper, a routing protocol is proposed which removes the problem of local optima and works on the concept of defining the routing paths prior by maintaining a table of two-hop forwarding neighbors. Definition of void node. Taking a set of sensor nodes K = [K n |0 ≤ n ≤ i] and d K n represents the depth of each nodeK n . Further, N K n is the set of neighboring nodes of K n and S = [Sn |0 ≤ n ≤ m] is a set of sink nodes located on the surface. A void node is seen as a node that is not able to find any node with a lower depth than itself as a neighbor node towards the surface. Void node problem is satisfied by the equation [K i ∈ K |d K i < d K v , K i ∈ TK v = ]. In Fig. 1, nodes f and t qualify as void nodes according to the above definition, since they have no node with a lower depth than theirs in their communication range [20, 21]. In greedy depth based protocols, the void nodes may get a higher priority to act as a forwarder node since it is at a lower depth than its neighboring node and hence results in packet drop. Our proposed protocol will bypass these void nodes by using forwarding algorithm based on the local node density by selecting 2-hop forwarding and saving this information in routing table and hence works towards finding a faster and the shortest path from every sensor node towards the sink node.

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Fig. 1 Void hole problem in UWSNs [22]

4 System Model The UWSN architecture proposed is discussed as follows. The proposed architecture is multi sink with the sinks randomly deployed on water body surface and some randomly deployed at the bed of water body and others floating throughout the area of study attached through buoys and can adjust their depths by adjusting the tethers. They are the relay nodes that use acoustic signals to communicate collectively and to the sinks which use radio signals to communicate with the terrestrial base stations. Hence, the sinks use both radio modems as well as acoustic modems. Also, it is assumed that. 1. 2. 3. 4.

Sinks have more energy. The packet once received by any of the sinks is assumed to be received by all others also. To balance the load the sinks are attached to each other. The mobility of the nodes in upright direction is presumed to be insignificant. Mobility of nodes is considered only in the horizontal direction.

The channel model used to calculate signal attenuation in underwater acousticsis detailed here. At a given frequency f , Thorps channel model calculates absorption loss α(f ) [5] as follows: 44 f 2 11 f 2 + + 2.75 ∗ 104 f 2 + 0.003 f ≥ 0.4 (1) 10 log α( f ) =  4 ∗ 100 + f 1+ f2   f + 0.011 f f < 0.4 (2) = 0.002 + 0.11 1+ f Here α(f ) is in dB/km and if in kHz. We determine the value of α by using the value of absorption loss, as follows:

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α=

10α( f ) 10

(3)

likewise, all tunable parameters are given in dBreμPa. The total attenuation A(l, f ) can be computed by combining absorption loss and spreading loss, as follows: 10 log(A(l, f )) = k ∗ 10 log(l) + l ∗ 10 log(α( f ))

(4)

where, first term corresponds to spreading loss and the second term to the absorption loss [11]. The spreading coefficient k defines the geometry of the signal propagation in underwater acoustics (i.e., k = 1 for cylindrical, k= 2 for spherical, and k = 1.5 for practical spreading). The Signal to Noise ratio for an acoustic signal is given as. SNR( f, d) = Tp ( f ) − A(d, f ) − N ( f ) + Di

(5)

where f is the frequency, T p (f) denotes the transmission power, and Di is the directive index, and N(f ) is given as. N ( f ) = Nth ( f ) + Ns ( f )

(6)

i.e., only thermal and shipping noise are taken under consideration.

4.1 Description of the Algorithm The initial simulation parameters as discussed in Table 1 in the next section are initialized for the proposed routing protocol. The sensor nodes to act as source nodes for collecting information from the water body are deployed randomly throughout the region of interest and the sinks are placed on the ocean surface. Every node broadcasts a beacon message at regular intervals whereas the sink nodes broadcast the beacon message only once. The message is being broadcasted to locate the neighboring nodes within its range of transmission. With the help of this message, the nodes are able to locate neighbor nodes and get the hop count numbers. As time lapses the location of the nodes becomes obsolete and hence becomes ineffective so regular updating of the information is necessary which is why beacon messages are being generated periodically. Each node uses a beacon message to locate its neighboring nodes and the hop counts. At this point, each sensor has its neighbor node information, its distance from the nearest sink, and the hop count number from that sink. A beacon message consists of two fields (source ID, NFN ID) where source ID is the ID of the current node and NFN ID is the ID of the next forwarder node. By generating beacons, the neighbor table is updated and maintained. Through this process, the sensor nodes remain synchronized with their routing tables. The routing table format is given in Fig. 2. It consists of the neighboring node’s ID and these neighbors’ next

Energy Efficient Void Avoidance Routing for Reduced Latency … Neighbors’ Node ID

Neighbors in Range

Distance from neighbor

549 No. of hops from the sink

Fig. 2 Routing table format

hop neighbors, the source node’s distance from the next neighbor’s hop, and the number of hops from the sink’s distance. The routing algorithm consists of the updation stage and the routing stage [23]. All the nodes are initially regular nodes and identify themselves as voids through collection of the information from the neighboring nodes. As time passes, this information is updated to keep track of the new voids generated if any, or any void node becoming a regular node because of the nodes being in motion. During the routing, the void nodes exclude themselves from the list of probable forwarding nodes, hence leading to the increase in the probability of the regular nodes participating in the routing and increasing the packet delivery ratio. The size of the forwarding area can also be customized according to the density of the nodes. In the harsh underwater world, the delay in propagation is very high. When a packet is generated/received from its previous node, the node is required to forward it to other nodes in its communication range, which results in the generation of a large number of duplicate packets. High energy depletion results if all nodes are involved in the transmission. And rising the forwarding nodes to an appropriate level is very difficult. Additionally, when a node receives a packet, the forwarder’s coordinates are collected for the removal of redundant packets. If the node’s ID node and its sequence number are matched with the entries of packet queue, packet is dropped immediately dropped. Thus forbidding the forwarding of redundant packets in the network. Holding time calculation is important for the suppression of the propagation of duplicate packets in the network and improving the packet delivery [24, 25]. The proposed protocol prolongs the holding time for up to second hop of the source node as the network architecture showing two hop nodes is given in Fig. 3. The parameters for the calculation include the distance of the forwarder from the edge of the transmission range of the source, the distance of the second hop forwarder from source node, the distance from sink to the next forwarder, and the distance from the virtual pipeline. The algorithm for routing table updation is given in Fig. 4.

5 Simulation Results 5.1 Simulation Setup For evaluating the efficiency of proposed Energy Efficient Void Avoiding Routing (EE-VAR), the results will be compared with two baseline protocols, DBR and WDFAD-DBR. The numbers of nodes in the network are varied from 100 to 300.

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Fig. 3 Network model Fig. 4 Algorithm for routing table updation

1: Deployment of relay and sink nodes 2: Broadcasting of the beacon message by the nodes 3: for nodes n nodes, perform 4: Look for two hop neighbors 5: if two hop neighbors exist then 6: Calculate the depth from the surface 7: Maintain a neighbor table 8: end if 9: Add or update entry in the table by using (NID,Nr, Dr, Counthop) 10: Δd = pkt.depth - noden.depth 11:ifnoden.depth

LR, SVM

Review data

Amazon.com

NB, SVM

Reviews

TripAdvisor.com (continued)

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Table 3 (continued) Ref. No.

Year

[82]

2014

Task

Algorithms used Data scope

Dataset/source

RF, SVM

Apondator

Review data

References 1. Liu B (2010) Sentiment analysis and subjectivity. In: Indurkhya N, Damerau FJ (eds) Handbook of natural language processing, 2nd edn. Taylor and Francis 2. Liu B (2009) Sentiment analysis and opinion mining. In: 5th Text analytics summit, Boston, June 1–2, 2009 3. Singh J, Singh G, Singh R (2016) A review of sentiment analysis techniques for opinionated web text, CSI Trans. ICT, 2016 4. Aydogan E, Akcayol MA (2016) A comprehensive survey for sentiment analysis tasks using machine learning techniques. In: International Symposium on INnovations in Intelligent SysTems and Applications 5. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain ShamsShams Eng J 5(4):1093–1113 6. Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst 89:14–46 7. Rushdi Saleh M, Martín-Valdivia MT, Montejo-Ráez A, Ureña- López LA (2011) Experiments with SVM to classify opinions in different domains. Exp Syst Appl 38(12):14799–14804 8. Xu T, Qinke P, Yinzhao C (2012) Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowl-Based Syst 35:279–289 9. Yu LC, Wu J, Chang PC, Chu HS (2013) Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowl-Based Syst 41:89–97 10. Hagenau M, Liebmann M, Neumann D (2013) Automated news reading: stock price prediction based on financial news using context-capturing features. Decis Supp Syst 55(3):685–697 11. Isa M, Piek V (2012) A lexicon model for deep sentiment analysis and opinion mining applications. Decis Support Syst 53:680–688 12. Martín-Valdivia MT, Martínez-Cámara E, Perea-Ortega JM, Ureña-López LA (2013) Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches. Exp Syst Appl 40(10) 3934–3942 13. Ortigosa-Hernández J, Rodríguez JD, Alzate L, Lucania M, Inza I, Lozano JA (2012) Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing 92:98–115 14. Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Tech 5(1):1–167 15. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 79–86 16. Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the association for computational linguistics (ACL), pp 115–124 17. Turney P (2005) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the association for computational linguistics (ACL), pp 417–424 18. Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of of the 12th international conference on World Wide Web (WWW), pp 519–528 19. Yu H, Hatzivassiloglou V (2003) Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)

Sentiment Analysis Using Learning Techniques

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20. Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international World Wide web conference (WWW-2005). ACM Press, pp 10–14 21. Kim S, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the international conference on computational linguistics (COLING) 22. Kamps J, Marx M, Mokken RJ, de Rijke M (2004) Using WordNet to measure semantic orientation of adjectives. In: Language resources and evaluation (LREC) 23. Hatzivassiloglou V, McKeown K (2004) Predicting the semantic orientation of adjectives. In: Proceedings of the Joint ACL/EACL conference, pp 174–181 24. Esuli A, Sebastiani, F (2005) Determining the semantic orientation of terms through gloss classification. In: Proceedings of CIKM-05, the ACM SIGIR conference on information and knowledge management, Bremen, DE 25. Day M, Lee C (2016) Deep learning for financial sentiment analysis on finance news providers, no. 1, pp 11271134 26. Vateekul and Koomsubha (2016) A study of sentiment analysis using deep learning techniques on Thai Twitter data 27. Zhou S, Chen Q, Wang X (2013) Active deep learning method for semi-supervised sentiment classification. Neurocomputing 120:536546 28. Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: IEEE Int. Conf. Acoust. Speech Signal Process., pp 85998603 29. Bengio S, Deng L, Larochelle H, Lee H, Salakhutdinov R (2013) Guest editors introduction: special section on learning deep architectures. IEEE Trans Pattern Anal Mach Intell 35(8):17951797 30. Arnold L, Rebecchi S, Chevallier S, Paugam-Moisy H (2011) An introduction to deep learning, Esann, no. April, p 12 31. Ouyang X, Zhou P, Li CH, Liu L (2015) Sentiment analysis using convolutional neural network, Comput. Inf. Technol. Ubiquitous Comput. Commun. Dependable, Auton. Secur. Comput. Pervasive Intell. Comput. (CIT/IUCC/DASC/PICOM), 2015 IEEE Int. Conf., pp 23592364 32. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space, Arxiv, no. 9, pp 112 33. Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences, Acl, pp 655665 34. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of 2014 Conf. Empir. Methods Nat. Lang. Process. (EMNLP 2014),pp 17461751 35. Mikolov T, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality, Nips, pp 19 36. Wu Z, Virtanen T, Kinnunen, T, Chng ES, Li H (2013) Exemplar-based unit selection for voice conversion utilizing temporal Information. In: Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, pp 30573061 37. Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proc. ACL, pp. 15561566 38. Piryani R, Madhavi D, Singh VK (2017) Analytical mapping of opinion mining and sentiment analysis research. Inf Process Manage 53(1):122–150. https://doi.org/10.1016/j.ipm. 2016.07.001 39. Hussein DMEDM (2016) A survey on sentiment analysis challenges. J King Saud University - Engineering Sciences, 34(4). doi:https://doi.org/10.1016/j.jksues.2016.04.002 40. Devika MD, Sunitha C, Ganesh A (2016) Sentiment analysis: a comparative study on different approaches. Procedia Computer Science 87:44–49 41. Kharde VA, Sonawane SS (2016) Sentiment analysis of twitter data: a survey of techniques. Int J Comput Appl 139(11):975–8887 42. Rajput Q, Haider S, Ghani S (2016) Lexicon-based sentiment analysis of teachers ’ evaluation. Hindawi Appl Comput Intell Soft Comput 6:2016

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43. Pradhan VM, Vala J, Balani P (2016) A survey on sentiment analysis algorithms for opinion mining. Int J Comp Appl 133(9):7–11. https://doi.org/10.1016/j.jksues.2016.04.002 44. Wang Z, Cui X, Gao L, Yin Q, Ke L, Zhang S (2016) A hybrid model of sentimental entity recognition on mobile social media. EURASIP J Wirel Commun Netw 2016(1):253. https:// doi.org/10.1186/s13638-016-0745-7 45. Jotheeswaran J, Kumaraswamy YS (2013) Opinion mining using decision tree based feature selection through Manhattan hierarchical cluster measure. J Theor Appl Inform Technol 58(1):72–80 46. Kaur J, Vashisht S (2012) Analysis and indentifying variation in human emotion through data mining. Int J Comp Technol Appl 133(9):121–126 47. Bhadane C, Dalal H, Doshi H (2015) Sentiment analysis: measuring opinions, Science Direct 48. Li W, Xu H (2013) Text-based emotion classification using emotion cause extraction, Elsevier 49. Bhadane C, Dalal H, Doshi H (2015) Sentiment analysis: measuring opinions. International conference on advanced computing technologies and applications (ICACTA2015). Procedia Computer Science 45:808–814 50. dos Santos CN, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts, Coling-2014, pp 6978 51. Gao K, Xu H, Wanga J (2015) A rule-based approach to emotion cause detection for Chinese micro-blogs, Elsevier 52. Smeureanu I, Bucur C (2012) Applying supervised opinion mining techniques on online user reviews. Informatica Economic˘a 16(2):81–91 53. Pang B, Lee L (2008) Opinion mining and sentiment analysis, foundations and trends in information retrieva l. 2:1–2 54. Nithya R, Maheswari D (2014) Sentiment analysis on unstructured review. In: International Conference in Intelligent Computing Applications (ICICA), pp 367–371 55. Fersini E, Messina E, Pozzi FA (2014) Sentiment analysis: Bayesian ensemble learning. Dec Supp Syst 68:26–38 56. Cheong M, Lee VCS (2011) A microblogging-based approach to terrorism informatics: exploration and chronicling civilian sentiment and response to terrorism events via Twitter. Inf Syst Front 13(1):45–59 57. Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. Processing 150(12):1–6 58. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1– 2):1–135 59. Nigam K, Lafferty J, McCallum A (1999) Using maximum entropy for text classification. In: IJCAI-99 workshop on machine learning for information filtering, vol 1, pp 61–67 60. Vinodhini G, Chandrasekaram RM (2012) Sentiment analysis and opinion mining: a survey. Int J Adv Res Comp Sci Softw Eng 2(6):28–35 61. Popescu AM, Etzioni O (2005) Extracting product features and opinions from reviews. In: Proceedings of international conference on human language technology and empirical methods in natural language processing, pp 339–346 62. Benamara F, Cesarano C, Reforgiato D (2006) Sentiment analysis: Adjectives and Adverbs are better than Adjectives Alone. In: Proceedings of international conference on Weblogs and social media, pp 1–7 63. Kaya M (2013) Sentiment analysis of Turkish political columns with transfer learning. Middle East Technical University, Diss 64. Çetin M, Amasyali MF (2013) Active learning for Turkish sentiment analysis. In: IEEE international symposium on innovations in intelligent systems and applications (INISTA), pp 1–4 65. Moraes R, Valiati JF, Gavião Neto WP (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Exp Sys Appl 40(2):621–633 66. Seker SE, Al-Naami K (2013) Sentimental analysis on Turkish blogs via ensemble classifier. In: Proceedings the international conference on data mining

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67. Rui H, Liu Y, Whinston A (2013) Whose and what chatter matters? The effect of tweets on movie sales. Decis Support Syst 55(4):863–870 68. Cârdei C, Manior F, Rebedea T (2013) Opinion mining for social media and news items in Romanian. In: 2nd international conference on systems and computer science (ICSCS), pp 240–245 69. Akba F, Uçan A, Sezer E, Sever H (2014) Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews. In: 8th European conference on data mining, vol 191, pp 180–184 70. Nizam H, Akın SS (2014) Sosyal medyada makine ö˘grenmesi ile duygu analizinde dengeli ve dengesiz veri setlerinin performanslarının kar¸sıla¸stırılması. XIX. Türkiye’de ˙Internet Konferansı, pp 1–6 71. Meral M. Diri B (2014) Sentiment analysis on Twitter. In: Signal processing and communications applications conference (SIU), pp 690–693 72. Tripathy A, Agrawal A, Rath SK (2015) Classification of sentimental reviews using machine learning techniques. Procedia Computer Science 57:821–829 73. Vinodhini G, Chandrasekaran RM (2015) A comparative performance evaluation of neural network based approach for sentiment classification of online reviews. J King Saud University - Comp Inform Sci 28(1):2–12 74. Shahana PH, Omman B (2015) Evaluation of features on sentimental analysis. Procedia Computer Science 46:1585–1592 75. Florian B, Schultze F, Strauch L (2015) Semantic search: sentiment analysis with machine learning algorithms on German news articles 76. Tian, F, Wu F, Chao KM, Zheng Q, Shah N, Lan T, Yue J (2015) A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews. Elect Comm Res Appl 77. Lee S, Choeh JY (2014) Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst Appl 41(6):3041–3046 78. Chen CC, De Tseng Y (2011) Quality evaluation of product reviews using an information quality framework. Decis Support Syst 50(4):755–768 79. Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498– 1512 80. Lin Y, Zhu T, Wu H, Zhang J, Wang X, Zhou A (2014) Towards online anti-opinion spam: spotting fake reviews from the review sequence. In: Proceedings of the 2014 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM, pp 261– 264, 2014 81. Ott M, Cardie C, Hancock JT (2013) Negative deceptive opinion spam. In: HLT-NAACL, pp 497–501 82. Costa H, Merschmann LHC, Barth F, Benevenuto F (2014) Pollution, badmouthing, and local marketing: the underground of location-based social networks. Inf Sci 279:123–137

Design of Novel Compact UWB Monopole Antenna on a Low-Cost Substrate P. Dalal

and S. K. Dhull

Abstract This paper, proposes and analyzes, a novel and compact ultra-wideband (UWB) monopole antenna. The antenna is designed on a 1.6 mm thick, low-cost flame retardant-4 (FR-4) substrate. The overall footprint of the antenna is 20 × 14 mm2 . Ansys High frequency structure simulator is employed to perform the simulations. The impedance bandwidth of the proposed antenna with S 11 < –10 dB, is from 3.1 to 11.3 GHz, with a fractional bandwidth of 113%. For the whole UWB bandwidth, the average gain of the antenna is approximately 3 dB, and the average radiation efficiency is above 95%. The far-field radiation pattern of the antenna is dumbbell-shaped in E-plane and Omnidirectional in H-plane. Keywords Monopole antenna · Ultra-wideband (UWB) · Low cost · Compact

1 Introduction The frequency band from 3.1 to 10.6 GHz was authorized by the Federal Communication Commission (FCC) in the year 2002, for unlicensed ultra-wideband (UWB) communications [1]. The UWB technology is aimed to provide short-range and highspeed, peer-to-peer indoor wireless data communication. The antenna placed at the transmitting and receiving site plays an important role in the successful deployment of this technology. But this technology demands antennas operating over a very large bandwidth of 7.5 GHz, which poses a significant challenge to antenna designers [2]. Because of the distinctive challenges posed, many researchers and antenna designers have been instigated to develop antennas for this technology. Other requirements of UWB antennas include low transmission power, low profile, compact size, and ease of incorporation with Monolithic Microwave Integrated Circuits (MMIC). To satisfy such requirements, many researchers have proposed printed monopole antennas (PMA) as a suitable candidate for this technology. This is because PMA offers a

P. Dalal (B) · S. K. Dhull Guru Jambheshwar University of Science and Technology, Hisar 125001, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_43

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lot of advantages, such as wide bandwidth, smaller size, low profile, easy to fabricate, low cost, omnidirectional radiation characteristics, and easy integration with MMIC. Over the last few years, different shapes of PMA designs have been proposed for UWB applications [3–12]. These antenna designs include circular disc monopole [3], triangular monopole [4], CPW fed tapered ring slot monopole [5], rectangular monopole with tapered feed [6], and many more. These monopole antennas have wide impedance bandwidth, easy to fabricate, and have acceptable radiation performance. But with the futuristic trend of miniaturization in communication systems, the demand for compact size antennas is increasing day by day. In this paper, a novel UWB monopole antenna on a low-cost FR4 substrate is proposed. The antenna is compact, with an overall footprint of 20 × 14 mm2 . The three-dimensional electromagnetic simulator software Ansys High frequency structure simulator (HFSS) is employed for design simulation. Different characteristics of the proposed antenna are investigated throughout the UWB frequency range, such as, return loss (S 11 ), real and imaginary impedance, gain, and radiation efficiency. E-plane and H-plane radiation patterns of the proposed antenna are studied at two dissimilar frequencies, at 4 and 8 GHz.

2 Antenna Design and Its Geometrical Parameters In this section, the design of the proposed monopole antenna is discussed. The geometry of the proposed antenna is illustrated in Fig. 1. A low-cost FR4 epoxy laminate material with height H = 1.6 mm and relative permittivity εr = 4.4, is chosen as the substrate for the antenna. From the geometry of the antenna, it is observed that the antenna is composed of a rectangular monopole of the size L 1 × W 1 . Then the rectangular monopole is tapered off until it connects to the 50  feedline of the antenna. The tapering helps in achieving good impedance matching of the monopole with the feedline for a wider range of frequencies, and hence wider bandwidth of the antenna. The partial ground plane of the monopole increases the bandwidth further. The various geometry parameters of the antenna are as follows: L 1 = 7.5 mm, L 2 = 5.5 mm, L f = 6 mm, L g = 3 mm, W1 = 14 mm, W f = 2 mm, g = 1 mm.

3 Results and Discussion The different simulated characteristics of the UWB monopole antenna proposed in the previous section are discussed in this section. For the study, Finite element method (FEM) based simulations are done using Ansys HFSS.

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Fig. 1 Geometry of proposed UWB monopole antenna a top view, b back view, c side view

3.1 Return Loss The return loss of an antenna is the measure of power reflected the source by the antenna. An antenna’s return loss should be less maintained below –10 dB for acceptable performance. This limiting value ensures that 90% of the input power is radiated by the antenna and 10% of the input power is reflected the source. The simulated return loss of the proposed antenna is presented in Fig. 2. From the figure,

Fig. 2 The plot of return loss (S 11 ) against frequency for the proposed antenna

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Gain(dB)

2 1 0 -1 -2 -3

2

3

4

5

6

7

8

9

10

11

12

Frequency (GHz)

Fig. 3 The plot of gain against frequency for the proposed antenna

it is observed that the –10 dB impedance bandwidth of the antenna is from 3.1 to 11.3 GHz, with a fractional bandwidth of about 113%. Thus, the proposed antenna covers the frequency band allocated by the FCC for the UWB applications.

3.2 Gain The graph of gain versus frequency for the proposed UWB monopole antenna is presented in Fig. 3. From the figure, it is observed that the gain of the proposed antenna is acceptable throughout the whole UWB band. The peak value of the gain is 3.2 dB around 9.5 GHz.

3.3 Real and Imaginary Impedance The plot of real impedance and imaginary impedance of the proposed monopole antenna with frequency is presented in Fig. 4. For the perfect impedance matching of the antenna with the source at the resonance frequency, the value of real impedance must be 50 , and the value of imaginary impedance must be 0 . From the figure, it is observed that the real impedance of the proposed monopole tries to maintain its value around 50 , and the imaginary impedance tries to maintain its value around 0 , for the whole bandwidth range of the UWB technology.

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real imag

70 60

Impedance Ω

50 40 30 20 10 0 -10 -20 -30

2

3

4

5

6

7

8

9

10

11

12

Frequency (GHz)

Fig. 4 The plot of real and imaginary impedance against frequency for the proposed antenna

Radia on Efficiency (%)

100 90 80 70 60 50 40

2

3

4

5

6

7

8

9

10

11

12

Frequency (GHz)

Fig. 5 The plot of radiation efficiency against frequency for the proposed antenna

3.4 Radiation Efficiency The plot of radiation efficiency versus frequency for the proposed monopole antenna is presented in Fig. 5. From the figure, it is observed that the value of radiation efficiency is above 90% for the whole UWB band. This value is considered good for the acceptable performance of the monopole antenna.

3.5 Radiation Pattern The radiation pattern of the proposed monopole antenna is studied at two different frequencies, at 4 and 8 GHz. This is done to authenticate the antenna’s radiation performance at both, the lower and the upper frequencies of the UWB band. The Eplane and the H-plane radiation characteristics of the suggested monopole antenna

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Fig. 6 The radiation pattern of the suggested antenna at 4 GHz a E-plane, b H-plane

at 4 GHz are respectively presented in Fig. 5a and 6b. From the figure, it is noticed that the E-plane radiation pattern is dumbbell-shaped, whereas the H-plane radiation pattern is Omnidirectional. Figure a and b, respectively present the radiation pattern of the suggested monopole antenna in the E-plane and the H-plane at 8 GHz. It is noticed from the figure, that the E-plane radiation pattern is slightly tilted at 8 GHz, as compared to the E-plane radiation pattern at 4 GHz. And the H-plane radiation pattern is slightly directional at 8 GHz, as compared to the H-plane radiation pattern at 4 GHz. Nevertheless, it can be concluded that the proposed antenna maintains its radiation performance throughout the UWB band (Fig. 7).

3.6 Comparison Table Table 1 compares the monopole antenna suggested in this paper, with other UWB PMAs already existing in the literature. From the comparison table, it can be concluded that as compared to other PMAs proposed in different references that are cited in the table, the suggested monopole antenna is compact, and covers the entire UWB band.

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Fig. 7 The radiation pattern of the suggested antenna at 8 GHz a E-plane, b H-plane

Table 1 Comparison of the proposed monopole antenna with the antenna existing in literature Frequency band (GHz)

Size mm2

Ref. No.

Substrate, dielectric constant, substrate height in mm

[4]

FR-4, 4.7, 1

4–10

60 × 20

[5]

Rogers RO4003, 3.38, 0.762

3.1–12

66.1 × 44

[6]

Teflon, 2.65, 0.8

2.75–16.2

30 × 8

[7]

FR-4, 4.4, 1.6

2.6–13.04

25 × 25

[8]

FR-4, 4.4, 1.6

3.1–10.6

27 × 30.5

[9]

FR-4, 4.4, 1.5

2.9–19.2

40 × 40

[10]

FR-4, 4.4, 1.6

2.4–11.2

30 × 30

[11]

FR-4, 4.4, 1.6

3.1–10.6

20 × 33

[12]

Rogers 4350, 3.66, 0.508

2.39–13.78

36 × 23

Proposed

FR-4, 4.4, 1.6

3.1–11.3

20 × 14

4 Conclusion A compact PMA, designed on a low-cost FR4 substrate is proposed in this research paper. The proposed antenna performance is evaluated by studying different characteristics such as return loss, gain, radiation efficiency, and radiation pattern are studied. Ansys HFSS is used to perform all the simulations. From the simulation results, it is observed that the proposed antenna maintains acceptable performance throughout the entire UWB band.

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References 1. Yang L, Giannakis GB (2004) Ultra-wideband communications: an idea whose time has come. IEEE Signal Process Mag 21(6):26–54 2. Malik J, Patnaik A, Kartikeyan MV (2018) Compact antennas for high data rate communication, 1st edn. Springer, Cham 3. Liang, J, Chiau CC, Chen X, Parini CG (2005) Study of a printed circular disc monopole antenna for UWB systems. IEEE Trans Antennas Propagn 53(11), 3500–3504 (2005) 4. Lin CC, Kan YC, Kuo LC, Chuang HR (2005) A planar triangular monopole antenna for UWB communication. IEEE Microw Wireless Compon Lett 15(10):624–626 5. Ma TG, Tseng CH (2006) An ultrawideband coplanar waveguide-fed tapered ring slot antenna. IEEE Trans Antennas Propagn 54(4):1105–1110 6. Wu Q, Jin R, Geng J, Ding M (2008) Printed omni-directional UWB monopole antenna with very compact size. IEEE Trans Antennas Propagn 56(3):896–899 7. Gautam AK, Yadav S, Kanaujia BK (2013) A CPW-fed compact UWB microstrip antenna. IEEE Antennas Wireless Propag Lett 12:151–154 8. Cai Y, Yang H, Cai L (2014) Wideband monopole antenna with three band-notched characteristics. IEEE Antennas Wireless Propag Lett 13:607–610 9. Boutejdar A, Abd Ellatif W (2016) A novel compact UWB monopole antenna with enhanced bandwidth using triangular defected microstrip structure and stepped cut technique. Microw Opt Technol Lett 58:1514–1519 10. Dwivedi RP, Khan Z, Kommuri UK (2020) UWB circular cross slot AMC design for radiation improvement of UWB antenna. Int J Electron Commun (AEU) 117:1–8 11. Singh C, Kumawat G (2020) A compact rectangular ultra-wideband microstrip patch antenna with double band notch feature at Wi-Max and WLAN. Wireless Pers Commun 114:2063–2077 12. Ma Z, Jiang Y (2020) L-shaped slot-loaded stepped-impedance microstrip structure UWB antenna. Micromachines 11:828

Multi-Objective Genetic Algorithm for Job Planning in Cloud Environment Neha Dutta and Pardeep Cheema

Abstract In Cloud environments, scheduling decisions must be made in the shortest time because there are many users competing for resources and time. Optimization measures are used when making scheduling decisions and represent the objectives of the scheduling process. Better resource utilization, a higher amount of effectively accomplished jobs and minimum reply time are the key constraints for optimization. The events are stated by the cost of objective function which measures the worth of computed solution and associates it with dissimilar solution. Here objective role is defined with the help of constraints and criteria. The suggested job planning methodology centred on Multi-Objective Genetic procedures in cloud computing settings uses an enhanced cost-based planning set of rules for building resourceful mapping of jobs to existing resources in cloud computing. This scheduling algorithm achieves the minimization of both assets cost and working performance. Keywords Standard genetic algorithm · Multi-objective genetic algorithm · Evolutionary algorithms · Virtual machine · Artificial Bee colony

1 Introduction 1.1 Cloud Computing Cloud computing offers pooled assets, data, programs and other devices to the consumer’s necessity at particular time is an on-demand facility. It’s usually used in the example of internet. The entire internet can be seen as a cloud. Cost and functioning expenses can be changed by means of cloud computing. Cloud computing facilities can be used from dissimilar and extensive assets, rather than faraway servers or native technologies. Cloud computing, in general, comprises of a group of scattered servers acknowledged as masters, providing required facilities and assets to dissimilar users acknowledged as clients with scalability and trustworthiness of datacenter. N. Dutta (B) · P. Cheema Acet Eternal University Baru Sahib, Baru Sahib, Himachal Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_44

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Fig. 1 A cloud is used in the network diagrams to depict the internet [2]

Facilities may be of software resources (e.g. Software as a Service, SaaS) or physical resources (e.g. Platform as a Service, PaaS) or hardware/infrastructure (e.g. Hardware as a Service, HaaS or Infrastructure as a Service, IaaS). Amazon EC2 (Amazon Elastic Compute Cloud) is an example of Cloud Computing services [1] (Fig. 1).

1.2 Task Scheduling in Cloud Computing It is a set of instructions to govern the order of jobs to be executed by a server. Job planning is a very challenging assignment in cloud computing because it is parallel and distributed design. The job completion time determination is difficult in cloud because the job may be distributed between more than one Virtual machine. Job planning and delivery of resources are two main problems in grid and cloud computing. Cloud computing is evolving tool in IT field. The planning of the cloud facilities to the customers by service sources impacts the budget and profit of these computing prototypes. Though, there are several procedures given by many academics for job planning. In 2008, a heuristic scheme to plan bag-of-tasks (jobs with small finishing time and not reliant) in a cloud is offered so that the amount of virtual machinery to perform all the jobs within the budget is less. In 2009, Dikaiakos et al. recognized the notion of organization of distributed internet computing as public usefulness and

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talked some major difficulties and available prospects about the utilization, effective actions and usage of cloud computing set-ups [3]. In 2009, Sudha and Jayarani suggested a proficient two-level job planner (customer centric meta-task planner for collection of assets and system centric VM schedular for posting jobs) in cloud computing environment established on QoS [4]. In 2010, Ge and Wei offered a novel job planner that takes the planning judgment through assessing the complete collection of jobs in a job line. A genetic process is considered as the optimization technique for a novel job planner that delivers improved makespan and better load balancing than FIFO and delays planning [5]. In 2010, an ideal programming strategy established on linear programming; to subcontract target constraint jobs in a hybrid cloud setting is projected. In 2011, Sandeep Tayal suggested a procedure built on Fuzzy-GA optimization to assess the whole collection of jobs in a job line on base of the forecast of finishing time of jobs allocated to certain workstations and creates the planning judgment [6]. In 2011, Zhao et al. Sakurai offered a DRR (Deadline, Reliability, Resourceaware) planning procedure, which programs the jobs such that all the works can be completed before the deadline, guaranteeing the reliability and minimization of assets [7]. In 2011, Sindhu and Mukherjee proposed two algorithms for cloud computing environment and compared them with default policy of Cloudsim toolkit while considering computational complexity of jobs [8]. Refer to the cloud computing framework MapReduce that Google presented. Each unit consists of an independent master task scheduling node and the slave task assigned node which derives from a cluster node under the jurisdiction of each master node in Cloud environment. The master node is answerable for planning all the jobs, monitoring their implementation, re-run the failed task, or disposing of errors. The slave node is only accountable for the implementation of the jobs allocated by the master node. Upon receipt of the assignment of the master node, the slave node starts to find a suitable computing node. First, the slave node discovers the rest of its own computing resources, if the rest of resources are sufficient to meet the user’s calculative requirement; the slave node allocates its own computing resources; if resources have been exhausted or insufficient to meet the minimum computing requirements of user’s task, the master node begins to search for other suitable cloud computing resources. Cloud computing uses mature virtualization technology to map the resources to the virtual machine layer, implementing the user’s task, so the work planning of cloud environment succeeds at the application layer and virtual resources layer. Scheduling is mapping tasks and resources according to a certain principle of optimal goal. Cloud computing simplifies the matching of tasks and resources, the required resources from a virtual machine, the procedure of examining resource package to the method of examining the virtual machine. Cloud computing resources allocation is an important portion of cloud computing technology, its efficiency directly affects performance of the whole cloud computing environment. The main purpose of planning is to program jobs to the compliant virtual machines in agreement with adjustable time, which in fact includes discovery of the appropriate order in which jobs can be fulfilled

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under contract sensibleness constraint. Here genetic simulated annealing algorithm is selected for scheduling [9]. Task scheduling is very important in Cloud Computing in order to achieve the optimal solution. Task scheduling is the job of start and finish times of the dissimilar tasks that are subject to certain constraints. The constraints can be resource constraints or time constraints. Task scheduling is a very important part of cloud computing. Task scheduling helps to maximize resource utilization and lessen the completion time by allocating the burden on different processors. It has two types: static scheduling and dynamic scheduling. Several algorithms are proposed for scheduling mechanisms. Scheduling mechanism is very important to improve resource utilization. The basic process of task scheduling is presented in Fig. 2. In Cloud computing there is a queue of different tasks, each task has different priority. Scheduler checks the precedence of every task and allocates the tasks to different processors according to their priority. Job Planning of cloud computing talks about to report the computing jobs to resource pooling between dissimilar resource consumers agreeing to certain instructions of resource use under specified cloud settings. At present-day, there is not a uniform normal for work planning in cloud computing. Resource management and work planning are the key skills of cloud computing that show a vibrant part in effectual cloud resource management. Meta-heuristics customize the operations of supporting heuristics to come up with higher quality results expeditiously, optimizing each performance and price whereas considering no uniformity of virtual machines. Various heuristic and Meta-heuristic Techniques can be used [10]:

Fig. 2 Task scheduling [9]

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• • • • • • •

595

Ant Colony Optimization (ACO). Genetic Algorithmic (GA). Particle Swarm Optimization (PSO). League Championship Algorithmic (LCA). Simulated Annealing. Tabu Search. Threshold Accepting.

2 Task Scheduling Using Genetic Algorithm The Genetic Procedure is adaptable methodology for the assignment planning issue. A Genetic procedure is a hunt investigative that copies the procedure of normal evolution. This heuristic is routinely used to create helpful answers for advancement and search issues [11]. Genetic procedures fit into the class of evolutionary procedures, which create resolutions for optimization difficulties using skills motivated by normal progression, such as inheritance, mutation, selection and crossover. But, the correct representation of probable resolutions is critical to guarantee that the mutation of any couple (i.e. chromosome) will outcome in a new usable and expressive individual for the problem. An output plan of jobs is an arrangement list of population (named chromosomes), which translate applicant resolutions to an optimization problem, grows toward improved results. Time minimization will give profit to service provider and less care cost to the resources. It will too offer profit to cloud’s service customers as their submission will be fulfilled at a reduced price [11]. Genetic Procedure is centred on the biological theory of population generation. The common structure of Standard Genetic algorithm (SGA) is labelled as (Fig. 3). In 2015 Jena suggested job planning using a multi-objective genetic algorithm to improve energy and completion time. The author compared proposed procedure with present job planning procedures and reported that proposed procedure delivers optimal balance outcomes for multiple goals [12]. In 2016 Nagaraju and Saritha offered the multi-objective genetic procedure. A single point crossover prototype is engaged for the generation of fresh population. Mutation procedure is conceded by arbitrarily altering the bit locations in the chromosomes [13]. In 2017 Mahmood and Khan projected a greedy and a genetic algorithm with an adaptive choice of appropriate crossover and mutation actions (called as AGA) to assign and plan real-time jobs with priority control on heterogamous virtual machines [14]. In 2018 Srichandan et al. discovered the job planning procedure using a hybrid methodology, which pools necessary features of two of the most commonly used biologically-inspired heuristic procedures, the genetic process and the bacterial foraging procedures in the cloud [15].

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Create a Population of Chromosomes

Determine the Fitness of Each Individual

>100 Generations

Next Generation

Select Next Generation

Display Results

Perform Reproduction using Crossover

Perform Mutation Fig. 3 Standard genetic algorithm (SGA) [11]

3 Proposed Work The proposed task scheduling approach centred on Multi-Objective Genetic algorithm in cloud environment makes use of an enhanced cost-based planning procedure for creating competent mapping of jobs to access resources in cloud. This scheduling algorithm achieves the minimization of both resource cost and computation performance. The proposed work is compared with existing improved ABC algorithm [16]. (a) (b)

Input—Required parameters are Cloudlets and Virtual machines are taken from the user. Output—Scheduling of Cloudlets on Virtual machines with the minimum Cost and Makespan. Proposed Algorithm. Multi-Objective Genetic Algorithm (MOGA).

• Generate an initial population of individuals with randomly generated individuals. • Evaluate the fitness of all individuals. • Archive building with best schedule (Non-dominate sorting).

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• • • • • • •

597

While end state does not happen do Select appropriate individuals for replica with smallest completing time. Crossover between individuals by two-point crossover. Mutate individuals by simple substitution operator. Assess the fitness of the improved individuals having appropriate fitness. Make a new population. End while

Fitness Function A fitness function is used to measure the quality of the individuals in the population according to the given optimization objective. The fitness function can be different for different cases. In some cases, the fitness function can be based on deadline, while in cases it can be based on budget constraints. In the proposed work there are two objectives: • Objective 1: Makespan (Total time to complete the schedule) • Objective 2: Cost (Total cost of the schedule to complete jobs on resources according to makespan) The fitness function is applied to these two above objectives (Fig. 4).

II Initialize Population Size (N)

Evaluate Objective Functions

Rank Population

Selection

Crossover

Mutation Report Final Population and Stop Evaluate Objective Function

No

Yes Stopping Criteria Reached?

Select N Individuals

Fig. 4 Multi-objective genetic algorithm (MOGA)

Combine Parent and Child Population, Rank Population

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4 Results and Discussion Here the objective is to analyze the performance of genetic algorithm in minimizing the makespan and cost of the processor and to find the best scheduling of jobs on the resources, MOGA (Multi-Objective genetic algorithm) is implemented on Intel core i3 machine with 500 GB HDD, 4 GB RAM on Windows 7 OS, Java language version 1.7 and simulation work is performed using CloudSim3.0 toolkit on Netbeans IDE 8.01. A worthy job planning procedure is that which leads to enhanced resource use, small average Makespan and healthier system output. Makespan denotes the accomplishment time of all cloudlets in the array. To express the problem we assume Jobs (J1, J2 and J3 … Jn) run on Resources (R1, R2, R3 … Rn). Our goal is to reduce the Makespan and Cost. The speed of CPUs is stated in MIPS (Million instructions per second) and size of the job can be stated as number of commands to be performed. Each processor is assigned varying processing power and respective cost in Indian rupees. Our objective is to compute the Makespan (completion time of tasks) and the corresponding Cost of output schedules from the proposed algorithm and compare the result with existing improved ABC algorithm [16] (Tables 1 and 2).

Table 1 GA parameters

Table 2 List of resources

Parameter

Value

No. of resources

6

No. of jobs

25–100

Population size

5

No. of iterations

1000

Crossover type

Two-point crossover

Crossover probability

0.5

Mutation type

Simple swap

Mutation probability

0.015

Selection method

Tournament

Termination condition

No. of iterations

Resource Id

Processor capacity (Mips)

R1

120

R2

131

R3

153

R4

296

R5

126

R6

210

Multi-Objective Genetic Algorithm for Job Planning in Cloud … Table 3 Best scheduling of jobs on resources at pop generation = 1000

Jobs

Resources

J1

R2

J2

R2

J3

R2

J4

R2

J5

R2

J6

R2

J7

R2

J8

R2

J9

R2

J10

R2

J11

R2

J12

R2

J13

R2

J14

R2

J15

R2

J16

R2

J17

R2

J18

R2

J19

R2

J20

R2

J21

R2

J22

R2

J23

R2

J24

R2

J25

R2

599

Output: After getting the values of Cost and Makespan at 1000th generation then we have the best schedule. The Best Scheduling at Population Generation = 1000 are (Tables 3 and 4): Comparison of Improved ABC Algorithm with MOGA. The results are compared for processing time and processing cost for various numbers of Cloudlets namely 25, 50, 75 and 100. Table 5 compares MOGA scheduling algorithm with Improved ABC scheduling algorithm [16] on the basis of cost spent for processing the tasks. From Table 5 it can be seen that for MOGA scheduling the processing cost spent to complete tasks is very less when compared with the processing cost spent to complete the tasks with ABC algorithm. Table 6 compares Improved ABC scheduling algorithm with MOGA (Multi-objective Genetic Algorithm) on the basis of time taken for completion of the tasks. From Table 6 it can be seen that for MOGA scheduling the time taken to complete tasks is

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Table 4 Cloudlets executed on virtual machines Cloudlet ID

STATUS

Data center ID

VM ID

Start Time

Finish Time

1

SUCCESS

3

1

9.8

0.2

10

3

SUCCESS

3

1

9.8

0.2

10

5

SUCCESS

3

1

9.8

0.2

10

7

SUCCESS

3

1

9.8

0.2

10

9

SUCCESS

3

1

9.8

0.2

10

11

SUCCESS

3

1

9.8

0.2

10

13

SUCCESS

3

1

9.8

0.2

10

15

SUCCESS

3

1

9.8

0.2

10

17

SUCCESS

3

1

9.8

0.2

10

19

SUCCESS

3

1

9.8

0.2

10

21

SUCCESS

3

1

9.8

0.2

10

23

SUCCESS

3

1

9.8

0.2

10

0

SUCCESS

2

0

10.61

0.2

10.81

2

SUCCESS

2

0

10.61

0.2

10.81

4

SUCCESS

2

0

10.61

0.2

10.81

6

SUCCESS

2

0

10.61

0.2

10.81

8

SUCCESS

2

0

10.61

0.2

10.81

10

SUCCESS

2

0

10.61

0.2

10.81

12

SUCCESS

2

0

10.61

0.2

10.81

14

SUCCESS

2

0

10.61

0.2

10.81

16

SUCCESS

2

0

10.61

0.2

10.81

18

SUCCESS

2

0

10.61

0.2

10.81

20

SUCCESS

2

0

10.61

0.2

10.81

22

SUCCESS

2

0

10.61

0.2

10.81

24

SUCCESS

2

0

10.61

0.2

10.81

Table 5 Simulation of processing cost for improved ABC algorithms [16] and MOGA (multi-objective genetic algorithm)

No. of cloudlets

Time

Improved ABC algorithm (Processing cost in Rs.)

MOGA (Processing cost in Rs.)

25

72.34

53.12

50

374.01

105.13

75

402.61

141.53

100

543.32

203.17

Multi-Objective Genetic Algorithm for Job Planning in Cloud …

601

Table 6 Simulation of processing time for improved ABC algorithms [16] and MOGA (multiobjective genetic algorithm) No. of cloudlets

Improved ABC algorithm (Processing time in seconds)

MOGA (Processing time in seconds)

25

133.1

10.81

50

234.01

21.02

75

378.51

28.3

100

487.5

40.63

Fig. 5 ABC algorithms versus MOGA cost

very less when compared with the time taken to complete the tasks with Improved ABC Algorithm. From the results, we can conclude that the MOGA scheduling algorithm is better than improved ABC scheduling algorithm (Figs. 5 and 6).

5 Conclusion and Future Scope The proposed approach leads to better results in a multi-objective genetic algorithm in Cloud Computing environment for scheduling of jobs on the processors. The fitness is established to boost the establishment of solutions to attain both the cost and makespan minimization. The performance of MOGA (on minimization of cost and makespan of the resources) with the variation of its control parameters is evaluated. Increasing the Population generation and making population size constant enables the genetic algorithm to obtain minimum Cost and Makespan which results in better scheduling. From the comparison of completion time taken and processing

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Fig. 6 ABC algorithms versus MOGA processing time

cost spent for MOGA scheduling algorithm and improved ABC scheduling algorithm, concluded that the MOGA scheduling algorithm is better than improved ABC scheduling algorithm. In future, study has to be done to select the correct parameter. It is planned to extend the proposed work for, dynamic scheduling of both dependent and independent tasks considering different objectives. It is planned to compare the simulated results with other scheduling algorithms also. It can also be extended by considering the objective like maximizing the processor utilization. This simulation will be tested on the realworld scenario. The simulation is tested for reliability of fitness.

References 1. Randles M, Lamb D, Taleb-Bendiab A (2010) A comparative study into distributed load balancing algorithms for cloud computing. In: IEEE 24th international conference on advanced information networking and applications workshops, pp 551–556 2. Velte AT, Velte TJ, Elsenpeter R (2010) Cloud computing a practical approach, TATA McGrawHILL edn, pp 3–11 3. Dikaiakos M, katsaros D, Mehra P, Vakali A (2009) Cloud computing: distributed internet computing for IT and scientific research. IEEE Trans Internet Comput 13(5):10–13 4. Sadhasivam S, Nagaveni N (2009) Design and implementation of an efficient two-level scheduler for cloud computing environment. In: International conference on advances in recent technologies in communication and computing, IEEE, pp 884–886 5. Van den Bossche R, Vanmechelen K, Broeckhove J (2010) Cost optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: IEEE international conference on cloud computing, Miami, pp 228–235 6. Ge Y, Wei G (2010) GA-based task scheduler for the cloud computing systems. In: IEEE international conference on web information systems and mining, pp 181–186 7. Zhao L, Ren Y, Sakurai K (2011) A resource minimizing scheduling algorithm with ensuring the deadline and reliability in heterogeneous systems. In: International conference on advance information networking and applications, IEEE, pp 275–282

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8. Sindhu S, Mukherjee S (2011) Efficient task scheduling algorithms for cloud computing environment. In: International conference on high performance architecture and grid computing, vol 169, pp 79–83 9. Guo G, Ting-Iei H, Shuai G (2010) Genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In: IEEE international conference on intelligent computing and integrated systems (ICISS), Guilin, pp 60–63 10. Sandhu SK, Kumar A (2017) A survey on meta-heuristic scheduling optimization techniques in cloud computing environment. Int J Recent Innov Trends Comput Commun 5(6):486–492 11. Sureshbabu GNK, Srivatsa SK (2014) A review of load balancing algorithms for cloud computing. Int J Eng Comput Sci 3(9):8297–8302 12. Jena RK (2015) Task scheduling in cloud environment using multi-objective genetic algorithm. In: 5th international workshop on computer science and engineering, WCSE 13. Nagaraju D, Saritha V (2017) An evolutionary multi-objective approach for resource scheduling in mobile cloud computing. Int J Intell Eng Syst 10(1):12–21 14. Mahmood A, Khan SA (2017) Hard real-time task scheduling in cloud computing using an adaptive genetic algorithm. Computers 6(15):1–20 15. Srichandan S, Ashok Kumar T, Bibhudatta S (2018) Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Comput Inf J 3:210–230 16. Selvarani S, Sadhasivam GS (2011) Improved cost-based algorithm for task scheduling in cloud computing, IEEE, pp 1–5 17. Tayal S (2011) Tasks scheduling optimization for the cloud computing systems. Int J Adv Eng Sci Technol 5(2):111–115

Facial Expression Recognition Using Convolutional Neural Network Vandana and Nikhil Marriwala

Abstract Facial expression conveys the emotional state of human beings. Facial expressions are a common form of non-verbal communication that helps to transfer necessary information or data from one person to another. However, in today’s world with increasing demand for artificial intelligence, recognition of facial expressions is a challenging task in solving problems related to artificial intelligence, machine learning, and computer vision. In this paper, we present an approach that helps to classify different types of facial expressions using Convolutional Neural Network (CNN) algorithm. The proposed model is a Neural Network architecture that is based on sharing of weights and optimizing parameters using CNN algorithm. Two Models are designed using this algorithm which is named Simple CNN and Improved CNN models having different convolution layers. Architecture designs of these two models are different from each other. The input of our system is grayscale images which consist of expressions of different faces. Using Input as grayscale images, both CNN models are trained and parameters optimized in neural network. Output of system is seven common facial expressions such as happy, anger, sad, surprise, fear, disgust, and neutral. To achieve better experimental results of designed model, graph of loss and accuracy is plotted for both of the above models. The overall simulation results prove that Improved CNN model improves the accuracy of facial expression recognition as compared to Simple CNN model. As result, analysis of confusion matrix is also obtained for Simple CNN and Improved CNN model. Keywords Facial expression recognition · Convolution neural network · Simple CNN and improved CNN · Confusion matrix

Vandana (B) University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India N. Marriwala Electronics and Communication Engineering Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_45

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1 Introduction 1.1 Related Work Human beings communicate through verbal as well as through body movement and gestures (non-verbal), to convey necessary information and to interact with each other. Facial Expression Recognition System is a technology that automatically detects facial expressions in real time. Nowadays, facial expression recognition is a beneficial artificial intelligence technology in human–computer interaction, automobile driving, biometric system, customer feedback system, market research, market assistance, video games, and video testing, computer vision problems, online distance education, online medicines, and healthcare services, safe driving monitoring, lie detector, predictive learning [1] and other fields [2]. There are many literature surveys and researches related to face detection techniques, image preprocessing techniques, feature extraction mechanisms, and some other techniques used for expression classification, but development of an automatic system with an improved accuracy that helps to achieve facial expression recognition system is a challenging and difficult task [3]. Some of the most commonly used techniques are Local Binary Pattern (LBP), Sparse Support Vector Machine (SSVM) [4], Support Vector Regression [5], LeNet and ResNet [3], STM-ExpLet [6], k-means Clustering algorithm, fuzzy C-means Clustering algorithm, Support Vector Machines (SVM), Maximum Entropy, and Deep learning algorithms include Long-Short term Memory (LSTM), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), Artificial Neural Network (ANN), Merged Convolution Neural Network [3], Adaboost, Extreme Learning Machine (ELM), Bayesian Networks and the multilevel Hidden Markov Model (HMM) [7]. These approaches are used for facial expression recognition systems and emotions classification systems.

2 Architecture of Proposed CNN Model In this paper, the proposed model is a Convolution Neural Network which uses back-propagation algorithm for extracting parameters and optimizing them with new parameters using convolution operation (dot product) [7]. Convolution Neural Network helps to improve the accuracy of facial expression recognition system. CNN algorithm includes a system that is based on shared weights, which uses multi-layers to successively extract higher level parameters from the input images [2]. CNN is a model used for extracting desired parameters and is also used for image classification [8]. In this paper, two models, first model Simple CNN model and other Improved CNN model [9] are designed using CNN algorithm to compare accuracy and performance of the models. Improved CNN model has a greater number of layers than CNN model. Therefore, Improved CNN is called an improved version of CNN

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607

Fig. 1 Seven facial expressions

model. The proposed method aims to classify emotions and recognize facial expressions. Therefore, the method is divided into some steps: FER-2013 Database, Image pre-processing, Feature extraction, Facial expression classification, and recognition (Fig. 1). Facial expression dataset or FER is the basic condition for recognition of Facial expression. FER-2013 is an open-source database provided by Kaggle. It has 35,887 grayscale images of (48 * 48)-pixel size facial expressions. Each grayscale image is divided into seven emotion labels. Facial Expressions are classified as follows: 0 = Angry, 1 = Disgust, 2 = Fear, 3 = Happy, 4 = Sad, 5 = Surprise, and 6 = Neutral. The original data consists in form of arrays in.csv format with grayscale value for each pixel. This data is converted into images and hence divided into multiple sets which are training, validation, and test sets. There are 28,709 images for training purposes, 3589 images for validation set, and 3589 images for testing set. In the total set, there are 4953, 547, 5121, 8989, 6077, 4002, 6198 images of the seven kinds of expressions respectively [10]. From the total set, 3995, 436, 4097, 7215, 4830, 3171, 4965 images are used for only training purpose [11]. Training of model is done in python language using keras with TensorFlow backend. This database is used to conduct training, validation, and testing on models in 80:10:10 ratio. Some of the commonly known datasets are: FER-2013 dataset, CK + dataset or CohnKanade dataset, JAFFE dataset [4], RAF database [2, 3], CMU and NIST dataset [1], and EURECOM Kinect face dataset [12], other datasets [13]. Image Pre-processing step involves improvement of image that removes undesired distortions, unnecessary information from input. Facial Expression image pre-processing mainly consists of face detection and location, rotation rectification system [14], normalization function, scale normalization, Gray level Equalization [7]. Feature extraction is the main step, its purpose is to extract relevant parameters from input layer. LBP is a feature extraction technique. It creates new parameters from the original features and the new reduced set of parameters collects most of the information which helps to classify the facial expressions in the next step. Facial expressions classify into seven types of emotion labels [2]. The structure of Facial Expression Recognition is shown in Fig. 2. The Convolution Neural Network is a composition of multiple layers, it is a feedforward technique [7] that can easily extract desired parameters from input and optimize next network parameters by using a back-propagation algorithm. It is a feedforward network that works heavily by using convolution operations (dot operations) [15]. This process consists of five layers of convolution and three layers of max

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Fig. 2 Structure of facial expression recognition

pooling in a neural network. These layers are input layer, convolution layer, pooling layer, fully connected layer, and output layer. A Neural Network is designed when these layers connect with each other. In addition, some other layers such as Dropout layer, Activation function, Flatten Layer, Batch Normalization are also necessary for designing CNN architecture [6, 16]. The edge detecting filters are used in convolution layers [1]. The Layers of network are described as Input Layer is the first layer in CNN architecture which contains grayscale images dataset in the form of arrays. Size of Input layer is 46 * 46 * 1. A filter of size 5 * 5 or 3 * 3 or 1 * 1 applies to input layer and then convolution operation is performed on each parameter of input layer. Convolution Layer is the feature extracting layer in CNN model which extracts basic parameters of the image. Convolution operation is performed in one convolution layer and a filter is applied on the image to perform convolution again for next feature extraction. The process of convolution will repeat again and again until all features extract from the whole image. After convolution operations, normalization between 0 and 1 is done in layer. ReLu as activation function is used after this layer. Now, Max pooling layer performs downsampling functions [16] in layer, and filter of size 2 * 2 is used. Adam-Optimizer is used after this layer [17]. Max pooling layer reduces size of layer. Fully Connected Layer contains number of neurons and

Facial Expression Recognition Using Convolutional Neural Network

609

Fig. 3 (a) Architecture of convolution neural network. (b) Convolution operation

weights either 256 or 512 or 1024 neurons are used in fully connected layer [16]. This layer is mainly responsible for connection of neurons of one layer to neurons of another layer in a Convolution Neural Network architecture [16]. Output Layer is the final layer that consists of labels as result of CNN model architecture [6, 7]. The Architecture of Convolution Neural Network is shown in Fig. 3 as: Experimental Results The Simple CNN model architecture is basically composed of a mixture of convolutional layers, fully connected layers, max pooling layer, Flatten layer Batch Normalization layer, dropout layer, activation function, and loss function [15]. The design idea of first model is to connect two convolution layers with two fully connected layers [3] and some other layers such as Batch Normalization function, activation function, Max pooling layer, Flatten layer, and dropout layer [9, 15]. Therefore, Simple CNN model is designed and the layers of CNN model are shown in Table 1. According to experimental results obtained from Simple CNN model, the model gave accuracy after training is 54% for 50 number epochs and the execution time after training is very less. The execution time for CNN model is 2 min 42 s. For Second model, the idea of designing is to increase number of layers for experimental purposes in network architecture of CNN. The idea is to connect eight convolutional layers with eight fully connected layers [3] and some other layers such as max pooling layer, Batch Normalization layer, dropout layer, activation function, and loss function. After the addition of convolution layers and fully connected layers

610 Table 1 CNN model layers

Vandana and N. Marriwala Input layer

Output layer

Weights

Convolution layer

46 * 46 * 32

320

Batch normalization

46 * 46 * 32

128

Activation function

46 * 46 * 32

0

Max pooling layer

23 * 23 * 32

0

Dropout layer

23 * 23 * 32

0

Convolution layer

21 * 21 * 64

18,496

Batch normalization

21 * 21 * 64

256

Activation function

21 * 21 * 64

0

Max pooling layer

10 * 10 * 64

0

Dropout layer

10 * 10 * 64

0

Flatten layer

6400

0

Fully connected layer

256

1,638,656

Batch normalization

256

1024

Activation function

256

0

Dropout layer

256

0

Fully connected layer

256

65,792

Batch normalization

256

1024

Activation function

256

0

Dropout layer

256

0

Fully connected layer

7

1799

Accuracy of simple CNN model

54%

[15], the Improved CNN architecture is designed [9] and the Layers of Improved CNN architecture are shown in Table 2. According to experimental results obtained from Improved CNN Model, the best Improved model gave Accuracy after training is 62% for 50 number epochs and the execution time after training is more as compared to Simple CNN model. The execution time for Improved CNN model is 7 min 30 s. Improved CNN is an improved version of Simple CNN model. It can be noticed from the above experimental results, that the accuracy of CNN model increases with an increase in number of convolutional, fully connected, and other layers in the model. A confusion matrix is obtained from the analysis of above experimental results and it is designed to make a comparison between both of the models [3]. A Confusion matrix is a type of matrix plotted between true value of emotion and predicted value of emotion [17]. As Improved CNN model has a greater number of convolutions, fully connected and other layers and more parameters therefore, accuracy of predicting facial expressions is higher in Improved CNN model. With an increase in number of layers, accuracy increases. It is clearly shown in Figs. 4 and 5 confusion matrix, that happy expression is highly predicted expression in Simple CNN model of value 528 as well as in Improved CNN model of value 712. Prediction of happy expression

Facial Expression Recognition Using Convolutional Neural Network Table 2 Improved CNN model layers

Input layer

611 Output layer

Weights

Convolution layer

46 * 46 * 64

640

Batch normalization

46 * 46 * 64

256

Activation function

46 * 46 * 64

0

Max pooling layer

23 * 23 * 64

0

Dropout layer

23 * 23 * 64

0

Convolution layer

19 * 19 * 128 204,928

Batch normalization

19 * 19 * 128 512

Activation function

19 * 19 * 128 0

Max pooling layer

9 * 9 * 128

Dropout layer

9 * 9 * 128

0

Convolution layer

7 * 7 * 128

147,584

Batch normalization

7 * 7 * 128

512

Activation function

7 * 7 * 128

0

Max pooling layer

3 * 3 * 128

0

0

Dropout layer

3 * 3 * 128

0

Convolution layer

1 * 1 * 128

147,584

Batch normalization

1 * 1 * 128

512

Activation function

1 * 1 * 128

0

Max pooling layer

1 * 1 * 128

0

Dropout layer

1 * 1 * 128

0

Convolution layer

1 * 1 * 512

66,048

Batch normalization

1 * 1 * 512

2048

Activation function

1 * 1 * 512

0

Max pooling layer

1 * 1 * 512

0

Dropout layer

1 * 1 * 512

0

Convolution layer

1 * 1 * 512

262,656

Batch normalization

1 * 1 * 512

2048

Activation function

1 * 1 * 512

0

Max pooling layer

1 * 1 * 512

0

Dropout layer

1 * 1 * 512

0

Convolution layer

1 * 1 * 1024

525,312

Batch normalization

1 * 1 * 1024

4096

Activation function

1 * 1 * 1024

0

Max pooling layer

1 * 1 * 1024

0

Dropout layer

1 * 1 * 1024

0

Convolution layer

1 * 1 * 1024

1,049,600

Batch normalization

1 * 1 * 1024

4096 (continued)

612 Table 2 (continued)

Vandana and N. Marriwala Input layer

Output layer

Weights

Activation function

1 * 1 * 1024

0

Max pooling layer

1 * 1 * 1024

0

Dropout layer

1 * 1 * 1024

0

Flatten layer

1024

0

Fully connected layer

256

262,400

Batch normalization

256

1024

Activation function

256

0

Dropout layer

256

0

Fully connected layer

256

65,792

Batch normalization

256

1024

Activation function

256

0

Dropout layer

256

0

Fully connected layer

512

131,584

Batch normalization

512

2048

Activation function

512

0

Dropout layer

512

0

Fully connected layer

512

262,656

Batch normalization

512

2048

Activation function

512

0

Dropout layer

512

0

Fully connected layer

512

262,656

Batch normalization

512

2048

Activation function

512

0

Dropout layer

512

0

Fully connected layer

512

262,656

Batch Normalization

512

2048

Activation function

512

0

Dropout layer

512

0

Fully connected layer

1024

525,312

Batch normalization

1024

4096

Activation function

1024

0

Dropout layer

1024

0

Fully connected layer

1024

1,049,600

Batch normalization

1024

4096

Activation function

1024

0

Dropout layer

1024

0

Fully connected layer

7

7175

Accuracy of improved CNN model 62%

Facial Expression Recognition Using Convolutional Neural Network

613

Fig. 4 Confusion matrix of simple CNN model

Fig. 5 Confusion matrix of improved CNN model

means that CNN algorithm can easily predict happy expression as compared to any other expressions from FER-2013 database [3]. It is interesting to observe that, for Prediction of Disgust expression value is 22 for Simple CNN and 20 value for Improved CNN which means disgust expression is the least predicted expression. The expressions such as Sad, Surprise, Neutral in Simple CNN and Improved CNN model are likely to be confused by confusion matrix because these expressions have almost equivalent values as shown in Figs. 4 and 5 which means CNN algorithm

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Fig. 6 Comparison between simple CNN and improved CNN

confused in Sad, Surprise, and Neutral expressions. Also, prediction of Neutral, Sad expressions in Improved CNN model is higher than prediction in Simple CNN model. In confusion matrix, it is also observed that the prediction of Angry, Fear expressions is higher in Simple CNN as compared to Improved CNN which means Simple CNN has more accuracy of prediction for some expressions because, for some expressions, it is not important that going in deeper layers will provide better features in CNN network architecture. Therefore, no improvement was observed in some features in experimental results [3]. Also, Angry and Fear expressions are the most suitable expressions in confusion matrix for both the models as shown in Figs.4 and 5 [15, 17]. Figure 6 shows the comparison of expressions between two models. Both the models Simple CNN and Improved CNN predict expressions such as happy, surprise, neutral, sad, anger, fear, disgust. It is noticed from Fig. 5 that Improved CNN model improves parameters as well as predicts expressions accurately except for some expressions such as Anger, Fear. Happy expression is the most predicted expression and disgust is the least predicted expression from the neural network (Table 3). A graph of loss and accuracy is plotted for both of the models as shown in Figs.7 and 8 to compare performance of Simple CNN model and Improved CNN model [3]. In Figs. 7a and 8a a graph is plotted between loss and no. of epochs. In Figs. 7b and 8b, a graph is plotted between accuracy and no. of epochs. The total no. of epochs is 50 in both cases. It is observed from above plotted graphs that Simple CNN model has better learning rate than Improved CNN that means CNN model has fast learning ability than Improved CNN model. Loss of Simple CNN model is very less as compared to Improved CNN. Accuracy of Simple CNN model has high learning features parameters therefore, it quickly reaches its peak value and then

Facial Expression Recognition Using Convolutional Neural Network Table 3 Comparison between CNN and improved CNN model

615

Method

Accuracy (%)

Time taken

SIMPLE CNN MODEL (2 layers)

54

2 min. 42 s

IMPROVED CNN MODEL (4 layers)

57

3 min. 26 s

IMPROVED CNN MODEL (6 layers)

58

5 min. 12 s

IMPROVED CNN MODEL (8 layers)

62

7 min. 30 s

Fig. 7 Plotted graph of simple CNN model where a graph plots between loss in training and no. of epochs used during training and b graph plots between accuracy obtained in training and no. of epochs used during training

saturates very easily. As result, Simple CNN model learns facial faster as compared to Improved CNN model [3].

3 Conclusion and Future Work In this research paper, we designed an efficient CNN algorithm for facial expression recognition system. Two models are designed based on this algorithm which is Simple CNN and Improved CNN. These models are designed to compare the main effect of addition of number of layers on accuracy and performance of CNN model. However, the experimental results showed that Improved CNN model is capable of learning facial features and improving facial expression recognition system but Simple CNN model learns features faster as compared to Improved CNN. The time required for

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Fig. 8 Plotted graph of improved CNN model where a graph plots between loss in training and no. of epochs used during training and b graph plots between accuracy obtained in training and no. of epochs used during training

training is also very less in case of Simple CNN model as compared to Improved CNN. Both of the models obtain good detection results which means they are suitable for facial expression recognition systems. As result, Happy expression is easily detected expression by both of the CNN models and Disgust expression is the least detected expression. These results describe that Improved CNN approach is a better model as well as an easy approach to designing neural networks for facial recognition systems. In the future, we will want to expand our model to predict expressions in real time. We will also want to predict the accuracy of each expression in percentage using CNN algorithm and also work on comparing our model with different models for facial expression recognition in real time. Furthermore, our work will also focus on simplifying the algorithm used for neural network architecture and to increase the speed of CNN algorithm. In future, we will also want to work on applications and designing techniques of Convolution Neural Network.

References 1. Mehendale N (2020) Facial emotion recognition using convolutional neural networks (FERC). SN Appl Sci 2(3):1–8. https://doi.org/10.1007/s42452-020-2234-1 2. Shi M, Xu L, Chen X (2020) A novel facial expression intelligent recognition method using improved convolutional neural network. IEEE Access 8:57606–57614. https://doi.org/10.1109/ ACCESS.2020.2982286 3. Ucar A (2017) Deep convolutional neural networks for facial expression recognition. In: Proceedings—2017 IEEE international conference on innovations in intelligent systems and applications INISTA 2017, pp 371–375. https://doi.org/10.1109/INISTA.2017.8001188

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4. Wu C, Chai L, Yang J, Sheng Y (2019) Facial expression recognition using convolutional neural network on graphs. In: Chinese control conference CCC, vol 2019, pp 7572–7576. https://doi. org/10.23919/ChiCC.2019.8866311 5. Yang Y, Key C, Sun Y, Key C (2017) Facial expression recognition based on arousal-valence emotion model and deep learning method. pp 59–62. https://doi.org/10.1109/ICCTEC.2017. 00022 6. Jung H et al (2015) Development of deep learning-based facial expression recognition system. In: 2015 Frontiers of computer vision, FCV 2015, pp 2–5. https://doi.org/10.1109/FCV.2015. 7103729 7. Zhang H, Jolfaei A, Alazab M (2019) A face emotion recognition method using convolutional neural network and image edge computing. IEEE Access 7:159081–159089. https://doi.org/ 10.1109/ACCESS.2019.2949741 8. Network CN, Wang Y, Li Y, Song Y, Rong X (2019) Facial expression recognition based on random (1):1–16 9. Xu Q, Zhao N (2020) A facial expression recognition algorithm based on CNN and LBP feature. In: Proceedings—2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC), pp 2304–2308. https://doi.org/10.1109/ITNEC48623. 2020.9084763 10. Zahara L, Musa P, Prasetyo Wibowo E, Karim I, Bahri Musa S (2020) The facial emotion recognition (FER-2013) dataset for prediction system of micro-expressions face using the convolutional neural network (CNN) algorithm based Raspberry Pi. In: 2020 Fifth international conference on informatics and computing (ICIC), March 2021. https://doi.org/10.1109/ICIC50 835.2020.9288560 11. Zhou N, Liang R, Shi W (2021) A lightweight convolutional neural network for real-time facial expression detection. IEEE Access 9:5573–5584. https://doi.org/10.1109/ACCESS.2020.304 6715 12. Ijjina EP, Mohan CK (2014) Facial expression recognition using kinect depth sensor and convolutional neural networks. In: Proceedings—2014 13th international conference on machine learning and applications ICMLA 2014, pp 392–396. https://doi.org/10.1109/ICMLA.2014.70 13. Kim JH, Kim BG, Roy PP, Jeong DM (2019) Efficient facial expression recognition algorithm based on hierarchical deep neural network structure. IEEE Access 7:41273–41285. https://doi. org/10.1109/ACCESS.2019.2907327 14. Yang B, Cao J, Ni R, Zhang Y (2017) Facial expression recognition using weighted mixture deep neural network based on double-channel facial images. IEEE Access 6(8):4630–4640. https://doi.org/10.1109/ACCESS.2017.2784096 15. Pranav E, Kamal S, Satheesh Chandran C, Supriya MH (2020) Facial emotion recognition using deep convolutional neural network. In: 2020 6th international conference on advanced computing and communication systems (ICACCS), pp 317–320. https://doi.org/10.1109/ICA CCS48705.2020.9074302 16. Kumar GAR, Kumar RK, Sanyal G (2018) Facial emotion analysis using deep convolution neural network. In: Proceedings—2017 international conference on signal processing and communication (ICSPC), vol 2018-Janua, pp 369–374. https://doi.org/10.1109/CSPC.2017. 8305872 17. Videla LS, Kumar PMA (2020) Facial expression classification using vanilla convolution neural network. In: 2020 7th international conference on smart structures and systems (ICSSS), pp 2–6. https://doi.org/10.1109/ICSSS49621.2020.9202053

FPGA Implementation of Elliptic Curve Point Multiplication Over Galois Field S. Hemambujavalli, P. Nirmal Kumar, Deepa Jose, and S. Anthoniraj

Abstract Safeguarding data from unauthorized access became a major issue in various aspects of daily life. To secure this, Elliptic curve cryptography plays a vital role. Elliptic curve cryptography (ECC) processor was built using a binary field in this project. The Montgomery ladder, as well as unified point addition algorithm, are considered in EC Point Multiplication. The ECC Processor’s conclusive operations are ECPM to generate a public key. The elliptical curve forms a group point addition, to reduce the operation and minimize the area size in the processor. The proposed designs are simulated and synthesized in VIVADO 2018 tool. Keywords Point multiplication · Galois binary field · Elliptic curve cryptography (ECC) · Coordinates

1 Introduction Cryptography is the technique to hide our data and secure it from unauthorized persons. It involves mathematical techniques that give the protection services like confidentiality, integrity, authentication, and non-repudiation. Various data or photographs from various sources are broadcast through the network, including online data exchange, medical details, military confidential details, and record storage systems, among other things. Highly confidential data is present. Elliptical curve cryptography (ECC) is often known as asymmetric cryptography, and it is used to protect this. The strength of ECC is that the keys are shorter. As compared to S. Hemambujavalli · P. Nirmal Kumar Department of ECE, CEG, Anna University, Chennai, Tamil Nadu 600025, India D. Jose (B) Department of ECE, KCG College of Technology, Rajiv Gandhi Salai, Chennai, Tamil Nadu 600097, India e-mail: [email protected] S. Anthoniraj Department of ECE, MVJ College of Engineering, Whitefield Salai, Bangalore 560067, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_46

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other cryptosystems, it has lower storage needs and faster field arithmetic operations, making it a better option for integrating asymmetric cryptography in resource constrained systems, which are often used in IoT devices, including such wireless sensors, and achieving high throughput. Innumerable public key cyphers, including ElGamal cryptosystem, Diffie and Hellman 1976, RSA and the Elliptical Curve Crypt, depend on algorithms from algebra, computer arithmetic, and number theory. Several public key cryptosystems make extensive use of the modular square, modular arithmetic operations, and modular multiplication over finite fields prime field and binary field. Among the techniques proposed in the literature, RSA and ECC have attracted a great deal of practical attention due to high level of securities involved. RSA requires a greater key length than ECC. A restricted environment seems to be a computational organisation that contains many diverse elements and restricted capacities of the underlying computational devices. These restrictions are imposed by the device’s computing power, connectivity range. Constrained structure refer to deployments of IoT, wireless technologies Traditional application, Smartphones applications, and newly found modern uses of elliptic curves are all using ECC. Miller [1] and Koblitz [2] were the first to use elliptic curves in cryptography. Several standardization bodies, including ANSI (ANSI 1999), NIST (NIST 1999), and ISO (ISO/IEC 2009), have adopted standards for ECC during the last two decades. Security problems will undoubtedly play a significant role in modern computer and communication systems; it is widely acknowledged [1–5]. Based on elliptic curves the implementation of either binary or prime field can be chosen. This paper implements the ECPM over the binary field GF (2233) and GF (2163). ECPM is a technique used to produce the key by multiplying a private key and the base point. The elliptical curve is used to create a group point addition, which helps to reduce the operation and reduce the processor’s area size. Modular multiplication is a widely used algorithm that is interleaved modular multiplication. This specifies the no. of. Clock cycles are necessary to complete the multiplication process. The ECPM level is compared using the unified point addition and Montgomery ladder algorithms [6–9].

2 Analytical Background The Weierstrass form of equations is applied in the form of binary. Y 2 + x y = x 3 + ax 2 + b

  a and b belongs to GF 2m

(1)

Mainly three operations can be done. Firstly, point addition it adding the one point of curve P with another point Q to produce point R (i.e. P + Q = R).secondly point doubling, it elliptic curve point P added itself to obtain R which is doubled of P. For

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point multiplication are multiply the point P with the integer k to obtain Q = kP. The affine coordinates into projective coordinates are used for conversions. Cryptography is usually carried out on the Galois binary field or the Galois prime field. The multiplication algorithm performance decides the area utilization of the processor and also demands the optimization of the area. In this, the simple XOR gates are implemented for the finite field arithmetic by this avoid the carry generation so by this reduce the resource utilization. In this, the simple XOR gates are implemented for the finite field arithmetic by this avoid the carry generation so by this reduce the resource utilization.

2.1 Group Operations In point addition, Fig. 1 consideration the EC’s both points P and Q, as well as the straight line that must be drawn between them. The line crosses an EC also at other points [−R]. It obtains a point [R] when it reflects a point with its respective x-axis. It results in P + Q = R (PA). Let P = (X 1 , Y 1 ), Q = (X 2 , Y 2 ) thus p not equivalent to ± Q, while P + Q = (X 3 , Y 3 ). Then (X 3 , Y 3 ) denotes R coordinates. Where

Fig. 1 Illustrates of point addition

X 3 = λ2 + λ + x 1 + x 2 + a

(2)

Y3 = λ(x1 + x3 ) + x3 + y1

(3)

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Fig. 2 Illustrates of point doubling

λ = (Y1 + Y2 )/(X 1 + X 2 )

(4)

In point doubling, Fig. 2 Draw one a tangent line at the point P on an EC. The elliptic curve intersects the line at a position −R. A point R is obtained by reflecting the position (−R) with appropriate x-axis. It effectively doubled the number of positions in the equation P = 2P. Thus 2P = (X 3 , Y 3 ). Where X 3 = λ2 + λ + a = x12 + b/x12

(5)

Y3 = x12 + λx3 + x3

(6)

λ = x1 + y1 /x1

(7)

2.2 Coordinates Systems Affine is the most simple point representation scheme. Each point P ∈ E(GF(2m)) in this scheme is represented by a coordinate pair, which is usually (x, y). A point can be represented with only two values, and in some situations, a single coordinate is necessary. The negative aspect is that retrieving results in affine coordinates needs many field inversions while computing the group operation. Inversions in the binary

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field are resource-intensive operations that can be avoided in restricted environments. This problem is solved using projective coordinates. The triple-axis (X:Y:Z) of a coordinate denotes a projective point P ∈ E(GF(2m)). The formulas for converting affine to projective coordinate are x => X/Z, y => Y /Z. The result is obtained without the use of an inversion operation in this method. An extra value is needed to describe a Z-axis. The affine point (x, y) could be used to derive the projective form (X, Y, Z) as follows: X => x, Y => y,

Z => 1

As follows, the projective form (X, Y, Z) could be reshaped into an affine point (x, y): x => X/Z , y => Y/Z

3 Hierarchy of ECC The implementation of elliptic curve cryptosystems constitutes complex crosssectoral research areas involving mathematics, electrical engineering, and computer science. In this Fig. 3, Field operations, elliptic curve group operations, and elliptic curve point multiplication are the three different levels of the ECC. • At the first stage of ECC, the Galois field arithmetic operation is found, which includes modular addition, subtraction, squaring, multiplication, and modular inversion. • The second stage of ECC is involved in group operations, which include point addition and point doubling to perform modular arithmetic operations. Fig. 3 Hierarchy of ECC

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• The third stage of ECC has an ECPM that sequentially combines a group and field operation. • ECPM is a time-consuming operation in the elliptic curve cryptosystem. It represents Q = kP for generating a public key. Q → public key P → elliptic curve’s base point K → secret key or private key The points over an elliptic curve are of the affine (x, y) nature since it is twodimensional curve. However, to simplify the computations, the used algorithm for ECP multiplication uses projective coordinates. These projective coordinates also contain a coordinate and are thus of the form (x, y, z).

4 Proposed Design 4.1 Binary Polynomial Algorithm This algorithm computes an X/Y mod f in the field of binary. In the scenario of elliptic over the binary field, where the degree m can be used to minimize an outcome of the operation with a degree exceeding m−1, polynomial arithmetic cannot be simplified. To more efficiency in polynomial reducer some implementation of operation on an elliptic curve it chose the irreducible polynomial as trinomial polynomials. For polynomial addition, it can be achieved by the XOR gates. Because there is no carry generation. For example, the addition of two numbers can be represented as A ⊕ B. Squaring is quite quicker than multiplies the two random polynomials because it is the linear operation for finite field. The binary form of a(x)2 is achieved by adding a zero’s between successive bits of a(X) is in binary form. These algorithms use the shift and add method, so they begin processing the multiplier b(x) with its LSB or either MSB.

4.2 ECC Processor It is designed to generate a public key with area-efficient Elliptic curve cryptography. It begins by obtaining a Q through the product of private key (k) with the reference points (P). The affine and projective coordinates are considered. Initially, it converts a P(X, Y ) to P(X, Y, Z); this conversion denotes the affine to projective convertor. After conversion X = x, Y = y, Z = 1. Then to yield Q(X, Y, Z) by computing the ECPM with P(X, Y, Z) and confidential key. It is accomplished by adding P with its own k−1 periods. Finally, it converts the Q(X, Y, Z) to Q(x, y). This convertor denotes projective to affine form. Figure 4 block diagram of public key generation.

FPGA Implementation of Elliptic Curve Point Multiplication …

X

625

X3

x3

x

y

Pre-process X->x, Y->y, Z->1

Y

Point multiplication Q=kP

Z

Y3

Post –process x=X/Z , y=Y/Z y3

Z3

Point doubling and point addition

Arithmetic unit ADD,MUL,SQUARER

Fig. 4 Public key generation

4.3 ECPM The methods for operating point multiplication on an elliptic curve are discussed in this section. These methods use the scalar k to compute the main ECC operation, which is kP, or elliptic curve point multiplication. The fundamental idea behind the kP method is to add a point P by k periods, whereas P is an elliptic curve point then k ∈ [1, n−1] is the integer. The binary algorithm is the easiest way to multiply points on an EC. An ECC Processor’s definitive action is ECPM. A position upon the elliptic curve is multiplied by this scalar. ECPM is a crucial mechanism in ECC systems. P(X, Y, Z) is the position upon a curve in projective coordinates, whereas represents a private or hidden key. By performing ECPM on the identified private key k and point P, a Q(X, Y, Z) public key is created. It performs, Q=kP Q is an elliptic curve point. It has been calculated by multiplying P by its own k−1 intervals. Q = P + P + P + ... + P

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Fig. 5 General steps to ECPM

Determine a point multiplication algorithm

To represent elliptic curves points determine the co-ordinates

Choose a finite field operations and select either prime or binary field

It is concerned with the number of group operations as well as the arithmetic operations. To make ECPM’s time complexity more manageable. Figure 5 represent the general steps to accelerate an Elliptic curve point multiplication. We have to follow these three steps Figure 6, shows the ECPM implemented the Montgomery ladder algorithm over binary fields.

4.3.1

Unified Point Addition

To prevent side-channel attacks, it performs PA and PD as similar modules in unified PA. This architecture has four stages, each of which has arithmetic modules connected in sequential order. Through this, it can achieve the least data path. In this input as P in projective coordinates and kit iteration from 0 to M and output as Q in projective coordinates. In ECPM, it operates on the bit pattern of the key for 233 bits over binary fields.  • Feed in: P(X, Y, Z), ki = 0M−1 2ki • Feed out: Q(X, Y, Z) Q1 ← P; While i reduce M−1 as 0 do Q2 ←P + P; if k i = 1 then Q3 ← Q2 + P;

FPGA Implementation of Elliptic Curve Point Multiplication … Fig. 6 Montgomery based ECPM

627

Montgomery ladder algorithm

Projective into affine co-ordinates vice versa

Design over a Binary field and modular multiplication, squaring, reduction, addition for field operations

Q1 ← Q3 ; else Q1 ← Q2 ; end if end for return Q1 ; 4.3.2

Montgomery Ladder for ECPM

To perform an ECPM, the proposed Montgomery ladder algorithm has been implemented. This simultaneously performs PA and PD group operations to make a key undetermined. The cumulative power used by the PD and PA at each iteration is the result of parallel behaviour. Thus result, jitter of PA has longer than PD because the PD modules accomplished prior to PA. The point addition module’s initial data is evaluated by the P, 2P. The module’s PD recalculation is determined by the (l−2)th k’s bit, whereas l denotes k’s length of bit. • Feed in: p(x, y) base point on curve, scalar (k i ) where i = (M−1) • Feed out: Q = kP Q0 = P Q1 =2P If (k i = 0)

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Q1 ← Q0 + Q1 Q0 ← 2Q0 else Q0 ← Q0 + Q1 Q1 ← 2Q1 end if Return Q0

5 Results The ECC Processor is designed 233-bit and 163-bit for generating a public key generation. The VIVADO 2018.2 version is used for the simulation of ECPM depends on Montgomery ladder and unified PA algorithms, which utilize the low area. Figure 7 depicts ECC’s group operations. It will be used to accelerate the ECPM which will result in a public key. P and Q are two inputs in (X, Y ) coordinates, with R being the output of PA and PD stages. Using the Vivado simulator the design simulation results are shown in Fig. 8. Here clock as 1, reset as 0, start as 1 are set. when operation as one bit it gets start point to add and point double operation works, remains it will be 0. The clock period is 1000 ps. The Montgomery ladder algorithm has been applied on an ECPM in this Fig. 9,

Fig. 7 Schematic of PA and PD

FPGA Implementation of Elliptic Curve Point Multiplication …

Fig. 8 PD and PA simulation

Fig. 9 Schematic of ECPM

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Fig. 10 Simulation of Montgomery point multiplication

which can be executed sequentially with PA as well as PD, following the binary bit sequence of the hidden key (k). This proposed design over a 163 bit are simulated and synthesized using Vivado simulator. Figure 10 shows the simulation results of an ECPM based on the Montgomery ladder algorithm which resulted in public key generation (Q). Clock period 1000 ps are determined. The proposed designs are implemented unified PA algorithm over an ECPM this shown in Fig. 11. Here the input was given as private key (k) and base point as (Xp, Yp) was forced a constant value for Xp and Yp, k. When clock is enabled signal is generated the XQ1 , YQ1 , XQ2 , YQ2 . When 233-bit are assigned for respective values and delay was 100 ps, the operation was completed it shows the done enabled. This simulation represents the generation of key in X, Y coordinates of Q (public key). The ECC modules of ECPM based on Montgomery ladder and unified PA algorithm has utilization resource has represented in Tables 1 and 2. Figures 12 and 13 shows the proposed designs of ECPM are implemented on both algorithms and compared the synthesized and utilization report performance of algorithms.

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Fig. 11 Unified PA of simulation

Table 1 Resource allocation of Montgomery ladder

Table 2 Utilized PA resource allocation

Resources

Estimation

Available

Utilization %

Lut

2293

303,600

0.75

Flip flop

1703

607,200

0.28

IO

820

600

136.67

BUFG

1

32

3.13

Resources

Estimation

Available

Utilization %

Lut

2588

303,600

Flip Flop

3066

607,200

Io

474

600

79.00

BUFG

1

32

3.13

0.85 0.50

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Fig. 12 Power estimation of Montgomery ladder

Fig. 13 Power estimation of unified PA

Acknowledgements This research work is part of the project work carried out by grant received from All India Council for Technical Education, New Delhi, India through the Research Promotion Project Scheme Grant File No. 8-79/FDC/RPS (POLICY-I)/2O19-2O.

References 1. Miller VS (1986) “Use of elliptic curves in cryptography” in Advances in cryptology—CRYPTO ’85 (Santa Barbara Calif. 1985), Springer, Berlin, pp 417–426 2. Koblitz N (1987) Elliptic curve cryptosystems. Math Comp 48(177):203–209 3. Islam MM, Hossain MS, Hasan MK, Shahjalal M, Jang YM (2019) FPGA implementation of high-speed area-efficient processor for elliptic curve point multiplication over prime field. IEEE Access 7:178811–178826 4. Javeed K, Wang X (2014) Radix-4 and radix-8 booth encoded interleaved modular multipliers over general Fp. In: Proceedings of 24th international conference on field programmable logic and applications (FPL), Munich, Germany, pp 1–6 5. Lara-Nino CA, Diaz-Perez A, Morales-Sandoval M (2018) Elliptic curve lightweight cryptography: a survey. IEEE Access 6:72514–72550

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6. Asif S, Kong Y (2017) Highly parallel modular multiplier for elliptic curve cryptography in residue number system. Circuits Syst Signal Process 36(3):1027–1051 7. Yang Y, Wu C, Li Z, Yang J (2016) Efficient FPGA implementation of modular multiplication based on Montgomery algorithm. Microprocessors Microsyst 47:209–215, Art 2016 8. Joseph GB, Devanathan R (2016) Design and analysis of online arithmetic operators for streaming data in FPGAs. Int J Appl Eng Res 11(3):1825–1834 9. Sivanantham S, Padmavathy M, Gopakumar G, Mallick PS, Perinbam JRP (2014) Enhancement of test data compression with multistage encoding. Integr VLSI J 47(4):499–509

Blockchain Usage in Theoretical Structure for Enlarging the Protection of Computerised Scholarly Data Preeti Sharma and V. K. Srivastava

Abstract All scholarly activities generate documents such as statements of results, testimonials, certificates, and transcripts, which are often held in manual file cabinets and relational databases. With the value of scholarly data, it has become critical to safeguard them against falsification and unintentional alteration. Despite the fact that relational databases have become the de facto norm for storing scholarly documents in recent years, the digitised contents are still under the control of a single administrator who has authoritative access to them as well as full rights and privileges. However, because of the centralised control of the system, authoritative access is likely to introduce instances of unauthorised record alteration. This paper provides a blockchain-based theoretical structure for the authentication of digitised scholarly documents with digital signatures. The storage, alteration, dissemination and verification of scholarly records would all be based on blockchain technology. Also, using this technology will notify all approved parties about the changes and they will be able to confirm or deny the status of any update on an earlier stored scholarly record by using its hash.

1 Introduction The consistency of processing and storage is critical to the protection of digitised scholarly documents. The stored records are used as legitimate contents in relation to the individual performances of students and processing typically comes before storage. Scholarly records storage has never been a difficult job. The difficult part of the process typically occurs when the initially stored contents are altered, corrupted, lost and/or destroyed as a result of a variety of factors such as intentional theft, natural disasters and system failure. Because of the sensitivity and importance of P. Sharma (B) Department of Computer Science, Baba Mastnath University, Rohtak, Haryana, India V. K. Srivastava Faculty of Engineering, Baba Mastnath University, Rohtak, Haryana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_47

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scholarly records in the overall framework, as well as their importance to student’s professional growth, more sophisticated but low-cost methods of keeping them safe in all circumstances have become increasingly important. The retrieval of transcripts and the authentication of certificates are two important aspects of the scholarly record-keeping process. Numerous investigations have focused on record handling, in [1–3]. For example, [2] proposed a web-based transcript processing method. However, this method ignored the different levels of authorisation that could be used to improve the protection of the stored transcripts, such as the risk of unauthorised material alteration. Similarly, [3] suggested a method for optimising the production of transcripts from students’ performance. The majority of these studies focused on the ease of access, with little or no information provided about the safe method of maintaining these scholarly records to ensure that unintended modification and distortion are fully avoided. Although there are some costs associated with the tradeoff between ease of access and protection, a protected archive of records ensures confidentiality, integrity and availability. This paper provides a structure for introducing safe and distributed access to scholarly records using blockchain technology, with no instances of unauthorised manipulation or distortion of records. The solution proposes the use of a blockchain-based digital signature scheme and timestamps to create a highly transparent framework for ensuring the protection of digitised scholarly records with low implementation and maintenance costs.

2 Review of Literature The preservation and retrieval of computerised scholarly documents necessitate the use of solid implementation strategies. Relational database management systems like Oracle, PostgreSQL SQL Server, or MySQL in Alwi Mohd Yunus and Bunawan [2], Atzori [3] are used to store computerised scholarly records. Most scholarly institutions and some production environments also use these databases in web-based or desktop scholarly information systems and it has become standard practice in most scholarly institutions and some production environments. Authenticity and reliability are major considerations of the trustworthiness of digital data, according to Mohanta and Jena [4]. Database protection for scholarly information systems filled with data from transcripts, certificates and statements was addressed in Cachin [5]. Table constraints and relationships, as well as role-based access control, were used in this implementation. One of the issues that prompted Ouaddah and Elkalam [1], Catalini and Gans [6] to work together is the lack of adequate protection and management of scholarly documents.

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But, their proposed solution lacked an explicit security aspect that addresses the issue of record manipulation and distortion due to human error. The various levels of authorisation for accessing the stored contents were not specified at the same time. Blockchain can be used to replace centralised control over scholarly documents. Blockchain has been commonly used for the development of decentralised currencies, self-extracting digital contracts and intelligent assets over the Internet in Kim and Choi [7], Casino and Dasaklis [8], Gupta and Kumar [9], Kamel and Kamel [10]. Loebbecke and Lueneborg [11] suggested a blockchain-based framework for storing a permanent distributed record of intellectual effort and reputational reward. According to Gupta and Kumar [9], Lauter [12] and Swan [13], blockchain can be used to create a database of distributed records. These distributed records are executed and exchanged among participating parties, who use the distributed consensus method to allow updates to the records. Updates are only included in distributed consensus if a majority of the involved parties consent and once an update is made, it cannot be undone by a single party. Casino and Dasaklis [8] and Swan [13] addressed how blockchain, as a replicated database of transactions (or records), allows for the creation of smart contracts. Massively scalable and profitable applications with a high level of security are developed. Kim and Choi [7], Kamel and Kamel [10] and Okikiola and Samuel [14] include additional examples of blockchain technology applications.

3 Blockchain Technology Decentralised control over transactions and data is provided by blockchain. This technology was originally developed for the bitcoin blockchain, but it has recently been applied to a variety of other fields. According to Raval in [13], blockchain technology is the future of massively scalable and profitable applications that are more versatile, open, distributed and resilient. Decentralised applications are a reflection of a cryptographically stored ledger, scarce-asset model that uses peer-to-peer network technology. Decentralised authentication of transactions, which are held in a quasianonymous network utilising some kind of storage known as public ledgers, has found application for blockchain technology. The use of decentralised peer-to-peer technology to validate transactions has been shown to provide clear confidentiality, data integrity and non-repudiation services [15]. For the involved parties, transactions over blockchain technology are authenticated using public-key cryptography, with the public key mapped to the participant’s account number and the private key displaying the participant’s ownership credentials. Digital signatures are used to ensure data confidentiality and non-repudiation. As shown in Fig. 1. In [16] it describes blockchain as a paradigm of state machine replication. State machine replication allows a service to keep track of any state that can be changed by client-initiated operations. Following that, outputs are created, which the clients can then verify.

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Fig. 1 Portrayal of a blockchain

A distributed protocol is used to enforce blockchain across Internet-connected nodes. This way, blockchain functions as a trusted computing service in both permissioned and permissionless modes. Participation in a permissionless blockchain is dependent on the ability to expend CPU cycles as well as show proof-of-works—a prerequisite that ensures that costly computations are performed in order to enable transactions on a blockchain. Despite its status as a public ledger, blockchain technology has the potential to become a globally decentralised record of digital assets, according to [17]. Each device or node connected to a blockchain architecture with a client that participates in invalidating and relaying transactions has a downloaded copy of the shared ledger or database. The information contained in the blockchain database is still up to date and contains all transaction records back to the point of origin. This ensures that a chain of linked records can be created that is open to all validating parties and where changes or improvements can only be made with the approval of a majority of them. The ability of blockchain to implement a trustless proof system for all transactions initiated and checked by all nodes linked to it demonstrates its disruptive existence. Users can trust the public blockchain database of records stored globally on decentralised nodes without including any trusted intermediaries for the trustless proof mechanism [12, 17–20]. Figure 2 explained that the Blockchain is a distributed and decentralised technology. The term “decentralisation” refers to the fact that the failure of a single node does not have an effect on the entire blockchain architecture. Also, the timestamped public database or ledger is maintained on several Internetconnected nodes to create a distributed environment that is freely available to all participants [13]. Blockchain’s peer-to-peer technology enables nodes to communicate directly with one another. Since any node has access to the blockchain record, this provision uses smart contracts to achieve decentralised consensus. The smart contract, which is a piece of code that is activated based on preprogrammed conditions, is stored on the blockchain, which eliminates the problem of a single point of failure.

Blockchain Usage in Theoretical Structure for Enlarging … Distributed

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Centralized

Fig. 2 Distributed and decentralised blockchain

The smart contract, according to [18], is a piece of code that is implemented on a blockchain by an Ethereum Virtual Machine (EVM). The code is then made available to the network participants to use. Turing-complete programming languages are used to generate EVM bytecode from smart contracts in order for them to be successfully executed. Solidity, Serpent, Mutan and LLL (Low-level Lisp-like) programming languages are Turing-complete programming languages used to create smart contracts in a blockchain. Smart contracts are often known by addresses and the data they receive is used to execute the code they contain [18].

4 The Projected Approach The proposed method makes use of blockchain technology’s distributed consensus to improve the security of stored data. Figure 2 Shows how blockchain technology can be used to create both centralised and distributed [13] scholarly records. All processed scholarly records are stored on a public blockchain in this approach. After that, a cryptographic hash function is applied to the stored records and the result is stored in a blockchain, with the created transaction being signed by the tertiary institution’s private key. As a result, the stored scholarly record has a high degree of confidentiality, as the blockchain cannot be changed without a majority consensus of the participating parties who act as validators [21]. As shown in Fig. 3, before changes can be made to an earlier version of the stored scholarly records, each validator on a connected node (represented as Node-1 … Node-n), in this case, a member of the institution with access to the scholarly records, must agree on an update, where possible. These updates are then distributed throughout the blockchain database, ensuring that all validators are aware of any subsequent record reviews. Furthermore, the tertiary institution may use blockchain’s digital signature scheme to add the signatures of approved validators, allowing changes to be based on a previously accepted condition.

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Fig. 3 Planned framework of project using blockchain

This requirement may be the minimum number of approved validators or signatures required to complete an update. The cryptographic hash function, as shown in Fig. 3, plays an important role in the security and validity of the stored scholarly documents. The use of distributed consensus instead of authoritative access improves security in this situation. This means that providing a single validator with full control over access to scholarly records makes it virtually impossible to carry out fraudulent activities. The timestamp associated with the digital signature may be used to check the authenticity of scholarly documents. When the cryptographic hash function is applied to the scholarly record to construct a transaction that is stored in a public blockchain, the timestamp is automatically generated. The stable hash algorithms (SHA-256) are the chosen hash function in this case, as they make the blockchain database immutable. The SHA-256 algorithm was chosen to ensure that all blocks in the blockchain are well developed and untampered with, making the blockchain unbreakable in its use [18, 22] as shown in Fig. 4. Due to the decentralised existence of a blockchain, the digitally signed transaction includes the date the scholarly record was deposited on the public blockchain and as such, the record is publicly verifiable by users. It is also possible to use an encryption algorithm to secure scholarly documents from public access when they are explicitly signed and stored on the blockchain using a well-structured data format rather than a cryptographic hash function. The encryption is based on public/private key cryptography, which allows for the creation of a public/private key pair for each record stored on the blockchain. The public key is distributed to those who need access to the public blockchain, while the validator uses the private key to access the blockchain database as required [17]. The key pair is generated using the Elliptic Curve Digital Signature Algorithm

Blockchain Usage in Theoretical Structure for Enlarging … Fig. 4 SHA 256 hash function

IV

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256 bits

c

Message (block 1)

c

Message (block 2)

c

Message (block n)

512 bits

Padding

Hash

(ECDSA) [23], which is based on elliptic curve mathematics. This asymmetric key can also be used to digitally sign a document to verify that a network member has approved an upgrade. This asymmetric key can also be used to digitally sign a document to verify that a network member has approved an upgrade. When the network participant’s private key is created, it can be added to the encrypted scholarly record and each user who needs access to the public blockchain uses a public key that is cycled through additional layers of encryption using SHA-256 [17]. Figure 5 shows how access to the public key is granted based on the agreement between a group of people. To avoid unauthorised access to scholarly documents, explicitly stored records on the public blockchain validator and users accessing the contents of the blockchain database are encrypted. Figure 4 shows how this works. ECDSA, which is primarily based on elliptic curve cryptography, allows for the generation of a relatively short encryption key, which is faster and thus requires less computing power, particularly when used on wireless devices with limited computing power, memory and battery life [24–26]. RIPEMD, meanwhile, is more secure against pre-image attacks and has the property of being one-way, involving irreversible identity data transformation [26–28].

5 Conclusion This paper presents an alternative approach to authoritative access to the scholarly records database. The approach addresses the use of blockchain technologies, such as distributed consensus for authorising changes to stored scholarly records, digital signature schemes and timestamps for signing and checking the validity of the stored

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Fig. 5 Cryptography done on records in public blockchain

scholarly record and encryption algorithms for ensuring the security of the stored contents. If implemented correctly, the proposed system has the ability to remove the common problems associated with the centralised database approach, which enables an administrator to have too much control over sensitive data. The timestamp created by the digital signing of the transaction formed from the cryptographic hash of the original scholarly record can be used to verify its authenticity in the future. As a result, unauthorised modification and falsification of scholarly documents will be eliminated, providing a higher level of protection at all times. Any intended updating of the digitised scholarly record is communicated to all stakeholders, who must verify such an update before it is enforced on a blockchain. Through the implementation of Blockchain, this added layer of authentication and accountability enhances the security of the computerised documents.

References 1. Ouaddah A, Elkalam AA (2017) FairAccess: a new blockchain-based access control framework for the internet of things 2. Yunus AM, Bunawan A-A (2016) Explaining the importance: proper academic records management. In: International conference on information science, technology, management, humanities and business, ITMAHuB, USA 3. Atzori M (2017) Blockchain technology and decentralized governance: is the state still necessary? J Governance Regul 4. Mohanta BK, Jena D (2019) Blockchain technology: a survey on applications and security privacy challenges. Elsevier 5. Cachin C (2016) Architecture of the hyperledger blockchain fabric, IBM Research

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6. Catalini C, Gans JS (2019) Some simple economics of the blockchain. National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA 02138 7. Kim DG, Choi J (2018) Performance improvement of distributed consensus algorithms for blockchain through suggestion and analysis of assessment items. J Soc Korea Ind Syst Eng 8. Casino F, Dasaklis TK (2019) A systematic literature review of blockchain-based applications: current status, classification and open issues. Elsevier, pp 55–81 9. Gupta EP, Kumar S (2014) A comparative analysis of SHA and MD5 algorithm architecture 10. Kamel I, Kamel K (2011) Toward protecting the integrity of relational databases, internet security (WorldCIS). World Congress IEEE 11. Loebbecke C, Lueneborg L (2018) Blockchain technology impacting the role of trust in transactions: reflections in the case of trading diamonds. In: Twenty-sixth european conference on information systems (ECIS 2018), Portsmouth, UK 12. Lauter K (2004) The advantages of elliptic curve cryptography for wireless security. IEEE, pp 62–67 13. Swan M (2015) Blockchain: blueprint for a new economy. “O’Reilly Media, Inc. 14. Okikiola MA, Samuel F (2016) Optimizing the processing of results and generation of transcripts in Nigerian universities through the implementation of a friendly and reliable web platform. IJIR 15. Raikwar M, Gligoroski D (2019) SoK of used cryptography in blockchain. IEEE Acess 7 16. Omogbhemhe M, Akpojaro J (2018) Development of centralized transcript processing system. Am Sci Res J Eng Technol Sci (ASRJETS) 17. Memon M, Hussain SS (2018) Blockchain beyond bitcoin: blockchain technology challenges and real-world applications. IEEE 18. Onwudebelu U, Fasola S (2013) Creating pathway for enhanching student collection of academic records—a new direction computer science and information technology 19. Qui Q, Xiong Q (2004) Research on elliptic curve crptography. IEEE, Xiamen, China 20. Ghosh R (2014) The techniques behind the electronic signature based upon cryptographic algorithms. Int J Adv Res Comput Sci 21. Underwood S (2016) Blockchain beyond bitcoin. Commun ACM 59 22. Sharples M (2016) The blockchain and kudos: a distributed system for educational record, reputation and reward. In: European conference on technology enhanced learning, Springer, Cham 23. https://www.maximintegrated.com/en/design/technical-documents/tutorials/5/5767.html 24. Kalra S, Sood SK (2011) Elliptic curve cryptography: current status and research challenges. Springer, pp 455–460 25. Cai W, Wang Z (2018) Decentralized applications: the blockchain-empowered software system. IEEE 26. Tang Y-F, Zhang Y-S (2009) Design and implementation of college student information management system based on web services. In: IEEE international symposium on IT in medicine and education, IEEE 27. Zeb A (2014) Enhancement in TLS authentication with RIPEMD-160. Int J 401–406 28. Zhao W (2021) Blockchain technology: principles and applications. IEEE

A Framework of a Filtering and Classification Techniques for Enhancing the Accuracy of a Hybrid CBIR System Bhawna Narwal, Shikha Bhardwaj, and Shefali Dhingra

Abstract In recent times, content-based image retrieval (CBIR) has gained more attention. CBIR is a technology for retrieving the features of an image from a large database that closely matches the given query image. This paper proposes a novel hybrid framework for content-based image retrieval to enhance the accuracy related issues. Here, Color moment is used as a filtering metric to select some relevant images from a broad database. Then, the features of the filtered images are extracted using Color moment and Gray Level Co-occurrence Matrix (GLCM) techniques. Various distance measure functions like Euclidean, Manhattan, Cosine is used to calculate similarity compatibility. Lastly, for increasing the efficiency of the developed hybrid CBIR system, an intelligent approach, i.e., an ELM classifier has been used. The average precision obtained on the Wang database is 96.25% which is higher as compared to many other classification techniques. Keywords Content-based image retrieval (CBIR) · Color moment · Gray level co-occurrence matrix GLCM · Extreme learning machine · Filtering

1 Introduction Frequently used image capturing devices like smartphones, cameras, closed-circuit television cameras, etc. generate large volumes of visual data [1]. Billions of interactive media information records are getting developed and shared on the internet, basically social media websites. This dangerous increment in multimedia information, particularly pictures and recordings, has contributed to the issue of searching and recovering the relevant data from the archive collection. Within the current era, the complexity of picture information has expanded exponentially. These days, due to advanced imaging techniques, numerous applications such as clinical imaging, inaccessible detecting, wrongdoing anticipation, instruction, interactive media, information mining, so on have been developed. These applications require computerized B. Narwal (B) · S. Bhardwaj · S. Dhingra Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_48

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pictures as a source for different forms like division, protest acknowledgment, and others. This huge amount of visual data uploaded by users from various geographic regions with varied languages have either metadata in diverse languages or no metadata associated with the images [1]. To recognize comparable sorts of pictures from such unstructured visual information is a difficult undertaking. Content-Based Image Retrieval (CBIR) framework can upgrade the comparable picture search ability, particularly for pictures having multilingual labeling and comments. Typically, an image recovery process is split into two forms: Text-based and Content-based. In the Text-Based Approach (index of images using Keywords) pictures are recovered on the premise of text annotations in a text-based recovery framework. But, it endures from many disadvantages like human explanation blunders and also the utilization of synonyms, homonyms, etc. lead to a wrong picture recovery [2]. The text-based method uses keyword descriptions as input and gets the required output in a similar format type of image. The best solution for overcoming these limits is CBIR mechanism. Image retrieval using the content is considered as one of the most successful and proficient ways of getting to visual information. This strategy depends on the picture content like shape, shading, also, surface rather than the explained text. The picture recovery approach utilized in the CBIR framework is very distinctive from the picture retrieval systems which utilize the meta information-based approach for picture recovery. The image retrieval system is based on a feature extraction technique. Features are extracted from the query image then a similarity measure is used to find the most similar image from a database [3]. The basic block diagram of CBIR is given in Fig. 1. Database Creation: Create or store a few picture databases to plan for own database for the testing reason or as an inbuilt database. Query Image Submission: The query picture is nothing but an input picture which is we giving to the framework as input and concurring to that images system will discover out comparable pictures.

Query Image

Image Collection

Feature Extraction

Feature Extraction

Query Image Feature

Feature Database

Similarity Calculation

Retrieved Images

Fig. 1 Block diagram of CBIR [4]

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Extraction of features: Extract the significant features from the query image and database image like color, texture, shape, etc. [4]. Feature Matching: Measurement of query picture content and database pictures and based on that, similar pictures are recovered by comparing input images with all the pictures from the database. Retrieval of Desired Images: Depending on the content of the picture functions, it recovers the desired images [4]. Image retrieval based on Color Features: Color is the most critical content within the color picture and it is the most broadly visual content. It highlights the extent of pixels of particular color within the picture. In color pictures normally RGB, YCbCr, HSV, HSI, etc., different color space models are used and different color descriptors like color coherence vector, color histogram, color moments, and color correlogram can be used [4]. The essential qualities of the visual content of a picture are the shading highlights. People can easily identify the visual content in the image [5]. Image retrieval based on Texture features: Texture is another critical feature that can be recovered based on image content. The surface is a vital highlight within the picture which is used in design acknowledgment [5]. In common, the term “texture” alludes to the surface characteristics and appearance of objects in a picture that results from the measure, shape, thickness, course of action, and extent of their elementary parts that are commonly known as features. These surface features are calculated from the statistical dissemination of observed combinations of pixel power at indicated positions relative to each other within the picture. It represents the spatial format of the gray level of pixels in a locale. The texture is categorized into two sorts that are statistical and structural. The main contributions to this work are: • To filter the dataset images using color extraction techniques. • To develop a hybrid system by using a combination of color and texture features by using remaining images after filtration. • To increase the efficiency of the system by using an advanced and intelligent technique. The remaining organization of this paper is given in the following way: Sect. 2 introduces some strategies related to CBIR examined in the literature. In Sect. 3, the proposed methodology is examined. Section 4 provides the details of the implementation and results of the proposed approach. Finally, the paper concludes with a little guidance on future work in Sect. 5.

2 Related Work This section explores the numerous and latest CBIR methods. Pavithra and Sharmila [2] created a cross breed framework for the CBIR framework to address the precision

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issues related to the conventional image retrieval framework. This system at first chooses related pictures from a huge database using color moment data. Singh and Batra [1] developed an Efficient Bi-layer Content-Based Image Retrieval System named BiCBIR where three primitive features namely color, surface, and shape have been considered. The closeness between inquiry pictures and dataset pictures is computed in two layers. The proposed BiCBIR framework is split into two modules. In the first module, attributes of the dataset pictures are extracted by using color, texture, and shape. The second module performs the recovery task which is further divided into two layers. A hybrid feature-based proficient CBIR system is proposed by Mistry et al. [6] that utilizes different distance measures. Spatial space features consisting of color auto-correlogram, color moments, HSV histogram features, and frequency space features like moments utilizing SWT, features utilizing Gabor wavelet change are utilized. Shao et al. [7] presented directed two-stage deep learning cross-modal retrieval which supports content to image and picture to content recovery. Hashing-based picture recovery framework is faster as compared to traditional CBIR frameworks but they are sub-standard in terms of accuracy. A CBIR system developed in several levels of the hierarchy has been developed by Pradhan et al. [8]. Here, adaptive tetrolet transform is employed to extract the textual information. Edge joint histogram and color channel correlation histogram have been used respectively to research shape and color features associated with an image. This technique is realized within the sort of a three-level hierarchical system where the very best feature among the three is depicted at every level of the hierarchy. Speed enhancement and speedup plot was proposed by Fadaei and Rashno [9] that is dependent on a stretch registered from a proficient mix of Zernike and Wavelet highlights. This span was registered for each question picture to eliminate all immaterial pictures from the information base. Most proper highlights among Zernike and Wavelet highlights were chosen to utilize PSO to have the highest decreased data set. Alsmadi [10] develop a novel likeness evaluation employing a meta-heuristic calculation called a Memetic Algorithm (MA) (hereditary calculation with the great storm) is accomplished between the highlights of the Query Image (QI) and the features of the database pictures. This work proposed a viable CBIR framework utilizing MA to retrieve images from databases. At that point, utilizing the MA-based closeness degree; pictures that are pertinent to the QI were recovered proficiently. Corel picture database was utilized. It has the ultimate capability to separate color, shape, and color, surface highlights. The results outperformed other CBIR systems in terms of accuracy and recall where normal exactness and recall rates were 0.882 and 0.7002 separately.

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3 Proposed Methodology In this work, the development of an Intelligent and Hybrid CBIR System has been proposed. Initially, all dataset images are filtered out by the color moment technique. Then, the features of the selected images are extracted. For color feature extraction, the color moment technique has been used, and for texture feature extraction, the Gray Level Co-occurrence Matrix (GLCM) technique has been used. The combination of color and texture features makes a hybrid system. For increasing the efficiency and accuracy of the developed CBIR system, an intelligent approach based on an ELM classifier has been used. The basic architecture of the proposed system is given in Fig. 2. Color Feature Extraction Color characteristics have been extracted using a variety of techniques in feature extraction systems. The colors moment has been selected as a good adsorbent in capturing color features. It’s reliable, quick, flexible, and contains less space and time. The illumination and brightness of the images were determined by color moments. Mean, standard deviation, and skewness are the color moments used for this system to extract features on a color basis. Since color information is more sensitive to the human visual system than pixel values, color is the first option for extracting features. The Color moment can be computed for any color space model. This color descriptor is also scale and rotation invariant but it includes the spatial information from images [11]. Texture Feature Extraction GLCM develops a matrix with pixel distances and directions, then eliminates significant figures as surface features from the matrix. The best second-order statistical texture feature extraction technique is Gray Level Co-occurrence Matrix (GLCM) which has many dominant attributes like (1) Rotation invariance (2) Diverse applications (3) Simple implementation and fast performance (4) Numerous resultant

Dataset Images

Filter Images

Feature Extraction by Color Moment

Hybrid System + ELM as a Classifier

Fig. 2 The design structure of the proposed framework

Selected Images

Feature Extraction by GLCM

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(Haralick) parameters (5) Precise inter-pixel relationship [11]. It’s an empirical method for extracting texture features from images for retrieval and identification [11]. The GLCM selects feature vectors based on energy, contrast, entropy, correlation, as well as other factors. Ensemble Classifier The ensemble methods, also known as committee-based learning or learning multiple classifier systems train multiple hypotheses to solve the same problem. Each classifier in the ensemble is a decision tree classifier and is generated using a random selection of attributes at each node to determine the split. During classification, each tree votes, and the most popular class is returned. Plenty of group classifiers are degenerate into twofold types of methods. To build a distinctive ensemble model, merge numerous poor novices. Characteristics rely upon the selection of the algorithm. Numerous sorts of gathering classifiers are utilized: 1—Ensemble stowed trees: we utilize irregular woodland pack, with choice tree inexperienced persons, 2—Ensemble feature space discriminant: with discriminant beginners, we are using subspace. 3—Ensemble subspace K-nearest neighbors: we use shortest path trainers in a spatial domain [12]. ELM Classifier Many types of classifiers have been used based on image retrieval to acquire precise accuracy during the classification of the image database [13]. Extreme learning machine (ELM) is a new learning algorithm that is a single hidden layer feedforward neural network. ELM is based on empirical risk minimization theory and its learning process needs only a single iteration. The algorithm avoids multiple iterations and local minimization. ELM designates learning parameters such as weights, input nodes, and biases in an adequate way, containing no tuning [13]. Only the number of hidden nodes involved should be indicated, as the output is estimated factually by the independent variable. In comparison to several other learning algorithms, the ELM has a speedy learning rate, considerably better efficiency, or reduced management interference [13].

4 Experimental Setup and Results Matlab R2018a, 4 GB memory, and 64-bit windows are used for the experiments. The experiments are carried out by using the Wang dataset. Wang’s dataset provides 1000 photos categorized into ten categories: African Tribes, Sea, Buildings, Buses, Dinosaurs, Elephants, Flowers, Horses, Mountains, and Food. There must be 100 photographs for each group. Also, every image is either 256 × 384 or 384 × 256 pixels in length [14]. Some of the sample images from Wang dataset are given in Fig. 3.

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Fig. 3 Sample images from Wang’s database

Experimental Results The efficiency of any CBIR system can be measured using several performance measurements. However, the following are some common assessment measures are given in Eqs. (1)–(3). Precision (P) = Recall (R) =

Number of relevant images retrieved Total number of images retrieved

Number of relevant images retrieved Number of relevant images in the database

F − Measure = 2

Precision × Recall Precision + Recall

(1) (2) (3)

Different distance metrics are being used to acquire similarities between any query image and database images. To start comparing the pictures, the distance metric employs a distance measure. The narrower the distance between these two photos, the larger the resemblance among them. It is chosen primarily on the basis that has been used to display the image. For retrieval of identical items contributing to the query item, similarity computation between pair feature vectors is necessary. It’s compared with the results of an appropriate distance-based function [15]. Some of the utilized distance [16] metrics are given in Eqs. 4–6 and precision results in terms of varied distance metrics [17] as shown in Table 1.

652 Table 1 Average precision using three distance metrics

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Distance metric

Precision %

Wang

Euclidean

97.00

Wang

Manhattan

96.25

Wang

Cosine

97.50

disEuclidean

  n  =  (|Ii − Di |)2

(4)

i=1

disManhattan =

n 

|Ii − Di |

(5)

i=1

disCosine = 1 − cos θ =

X ·Y XY

(6)

Since filtering has been used in this paper to sort the database images. The color moment [18] has been used here as a filtering technique to filter the desired images. A threshold level has been selected and the mean value of the images above that threshold are selected and below it are rejected. Thus, filtering is obtained. The results of filtering in terms of Precision, Recall, and F-measure [19] both before and after filtering are given in Fig. 4. To check the accuracy of the developed hybrid CBIR system, four classifiers namely Support vector machine (SVM), K-nearest neighbor (KNN), Ensemble, and ELM have been used. Among all these classifiers, ELM obtains the highest accuracy. Since, Multi-class classification [20] is not possible with SVM, error related to outliers is present in K-NN, Ensemble is too complicated. ELM is based on Single layer feed-forward neural network and its training is very fast as compared to others. The results are given in Fig. 5. To check the retrieval accuracy, two separate Graphical user interface (GUI) has been designed for ELM and Ensemble classifier. Results of GUI are given in Figs. 6 and 7. It can be concluded from Figs. 6 and 7 the images belonging to the same native category, viz. African tribes are obtained for ELM classifier with the precision of 1 and for ensemble, the obtained precision is 0.9. Fig. 4 Precision, recall, and F-measure before and after filtering

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Fig. 5 Accuracy comparison of various classifiers

Fig. 6 Retrieved results from ELM classifier

5 Conclusion A CBIR system has been presented in this paper, which uses color and texture features to display query and dataset images. In this work, all the dataset images are filtered out by using the color moment technique. Color features are extracted by using the color moment technique and texture features are extracted by using the GLCM technique and both features are used to form a hybrid system. Then after that, a machine learning classifier is used to create multiple classes for large databases. The search area for retrieving relevant images from the query image is reduced as a result of this classification, and the system has become more accurate and quick.

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Fig. 7 Retrieved results from ensemble classifier

KNN, SVM, Ensemble, and ELM are four machine learning classifiers whose outputs are compared here. From the comparison of these classifiers, ELM has the highest accuracy. The average precision, recall, accuracy, and F-score were all used to assess the system’s results.

References 1. Singh S, Batra S (2020) An efficient bi-layer content-based image retrieval System. Multimedia Tools Appl 2. Pavithra LK, Sharmila TS (2017) An efficient framework for image retrieval using color, texture and edge features. Comput Electr Eng 1–14 3. Nazir A, Ashraf R, Hamdani T, Ali N (2018) Content-based image retrieval system by using HSV color histogram, discrete wavelet transform, and edge histogram descriptor. In: IEEE international conference on computing, mathematics and engineering technologies, pp 978-15386-1370-2 4. Dahake PA, Thakare SS (2018) Content-based image retrieval: a review. Int Res J Eng Technol 05:1095–1061 5. Varma N, Mathur A (2020) A survey on evaluation of similarity measures for content-based image retrieval using hybrid features. In: IEEE international conference on smart electronics and communication, pp 978-1-7281-5461-9 6. Mistry Y, Ingole DT, Ingole MD (2018) Content-based image retrieval using hybrid features and various distance metric. J Electr Syst Inf Technol 5:874–888 7. Shao J, Zhao Z, Su F (2019) Two-stage deep learning for supervised cross-modal retrieval. Multimedia Tools Appl 78:16615–16631 8. Pradhan J, Kumar S, Pal AK, Banka H (2018) A hierarchical CBIR framework using adaptive tetrolet transform and novel histograms from color and shape features. Digit Signal Process A Rev J 82:258–281

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9. Fadaei S, Rashno A (2020) Content-based image retrieval speedup based on optimized combination of wavelet and Zernike features using particle swarm optimization the algorithm”, Int J Eng 33(5):1000–1009 10. Alsmadi MK (2019) An efficient similarity measure for content-based image retrieval using the memetic algorithm. Egyptian J Basic Appl Sci 4:112–122 11. Bhardwaj S, Pandove G, Dahiya PK (2020) An extreme learning machine-relevance feedback framework for enhancing the accuracy of a hybrid image retrieval system. Int J Interact Multimedia Artif Intell 6(2) 12. Kavitha PK, Saraswathi PV (2019) Machine learning paradigm towards content-based image retrieval on high-resolution satellite images. Int J Innov Technol Explor Eng 9:2278–3075 13. Dhingra S, Bansal P (2019) A competent and novel approach of designing an intelligent image retrieval system. EAI Endorsed Trans Scalable Inf Syst 7(24) 14. Dhingra S, Bansal P (2020) Experimental analogy of different texture feature extraction techniques in image retrieval systems. Multimedia Tools Appl 79:27391–27406 15. Bhardwaj S, Pandove G, Dahiya PK (2020) A genesis of a meticulous fusion based color descriptor to analyze the supremacy between machine learning and deep learning. Int J Intell Syst Appl (IJISA) 12(2):21–33 16. Rajkumar R, Sudhamani MV (2019) Retrieval of images using combination of features as color, color moments and Hu moments. Adv Image Video Process 7(5):9–21 17. Dewan JH, Thepade SD (2020) Image retrieval using low level and local features contents: a comprehensive review. Appl Comput Intell Soft Comput 20 18. Ghosh N, Agrawal S, Motwani M (2019) A survey of feature extraction for content-based image retrieval system. In: International conference on recent advancement on computer and communication 19. Bellaa MT, Vasuki A (2019) An efficient image retrieval framework using fused information feature. Comput Electr Eng 46–60 20. Du A, Wang L, Qin J (2019) Image retrieval based on color and improved NMI texture feature. J Control Measure Electron Comput Commun 60(4):491–499

Microgrid Architecture with Hybrid Storage Elements and Its Control Strategies Burri Ankaiah, Sujo Oommen, and P. A. Harsha Vardhini

Abstract A portable group of microgrid architecture is suggested in this paper, which exists in the microgrid approach with hybrid storage factors with stability and control strategies. The proposed model hardware implementation involves generated array for photovoltaics, wind turbulence, fuel cell, circuit breakers, power electronics circuits and controllers. With this proposed model the use of energies like renewable operates more effectively in portable groups like domestic, commercial and agricultural. Energy sources for renewable with penetration improvement and also future grids the proposed model with hardware implantation plays an important role. In future grids storage system is very much important for the stability of grids for maintenance and also power quality improvement. The storage system reliability and for improving the efficiency improvement for various time responses the proposed model is the best solution. The proposed model of microgrid hardware implementation with hybrid storage factors and its control strategies is shown with results obtained from simulation with standard values of factors. Keywords Renewable energy systems · Hybrid storage systems · Microgrids · Proposed hardware model

1 Introduction In technology development very essential is electrical power at present world electrical power, the service of nature and supply of coherence with most vital for power segments are very significant. With the smart grid, the overall drawback can be overcome. The use of energy storage like renewable systems by controller utilization, communication systems and power electronics systems. The energy sources of sustainability like photovoltaic, wind turbulence and battery storage system are B. Ankaiah (B) · S. Oommen REVA University, Bengaluru, Karnataka, India P. A. Harsha Vardhini Vignan Institute of Technology and Science, Deshmukhi, Telangana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_49

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Fig. 1 Cumulative global solar photovoltaics installed capacity by region (2010–2030)

a part of smart grid, the control electronic circuits are deployed as in Figs. 1 and 2 and the results of a single phase grid are illustrated in Fig. 3. Smart grids like enormous economical not practical for the investigations like testing, design of small grid can be exhibit as a model. So that the likewise energy sources are limited. A microgrid that consists of renewable energy sources and inverters keep running with switching frequency in real-time applications [1, 2]. Various methods are regarded to accelerate real application time simulation analysis of model smart grid with switching operation frequency [3–7]. Communication network is fundamental to effectively use a large number of the elements of the smart grid-like distributed automated systems [8, 9]. Presentation of execution and network state, resource protection of energy, islanding and controlling. Along with these sources, additionally, various types of network communication to stimulate the electric power system at real application time is a tough challenge to model smart grid [10–13].

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Fig. 2 Single phase grid connected solar photovoltaic system

2 Microgrids The microgrid model as illustrated in Fig. 4 has had very good stability and reliability with its varying parameters dependency on power system for main. power system for main is used to transmission of the power from a source of major to minor sources with centralized control and transmission line which alerting can do with the minimum efficiency of the power supply electrical. The model of supply of microgrid as depicted in Fig. 5, which is collecting of renewable energy resource gives of a portable group like domestic, commercial and agricultural. Many consumers in 1990s in a few states had power cuts because of damage to power lines in urban areas. By implantation of microgrid or power grid, these kinds of problems can be rectified. In period of disturbances, the model microgrid separation from the distribution main system to isolate loads from these kinds of incidents it will give the continuous and stability of the power supply electricity without affecting the grid of main transmission.

3 Obtained Simulation Results The results obtained simulation is shown for two grids that is one connected mode and another islanding mode. The results obtained simulation circuit is shown in Figs. 6 and 7, The results obtained simulation shown in Figs. 8 and 9 AC current and AC voltage of the microgrid which contains photovoltaic, wind turbulence, fuel cell, critical load and non-critical load as shown. Output of grid connected mode and in islanding is shown by comparing these two we can illustrate that dc, ac bus voltage

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Fig. 3 Results of single phase grid connected solar photovoltaic system

and load currents remains constant. Initially, circuit breakers are open and it will be closed at their respective transition time, the transition time of PV is connected from 0 to 0.1 at this time circuit breakers is closed and PV output is connected to grid and similarly, transition of non-critical both loads, fuel cell, wind turbulence and critical load is 0.1–1 respectively. During islanding mode, assumed that the photovoltaic and wind turbulence cannot provide the required power to the loads, therefore load shedding is necessary to maintain the system stable. In islanding mode, they can give the most extreme power of 9.7 kW, the PV 7.7 kW and the wind turbine 12 kW. On the load side, the power utilization of each non-critical load and critical load is 17 kW and 13 kW, separately.

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Fig. 4 Systems-level microgrid simulation from simple one-line diagram

4 Proposed Model Hardware Implementation The proposed model hardware implementation of microgrid with generated array photovoltaic, battery storage system, wind turbulence as energy sources and with normal local electricity supply is done. The proposed model hardware implementation output is without grid mode because of the battery storage the voltage is considering of when battery storage voltage will discharge to less voltage within 10 V it switches to normal local electricity supply as shown in Fig. 10 and the battery storage voltage is also rectified into alternating current with the inverter model circuit and transformer model.

5 Conclusion This paper proposed a microgrid model of a portable group that includes the generated array photovoltaic, fuel cell, wind turbulence, circuit breakers power electronic circuits and controllers. This proposed microgrid model is stimulated with switching frequency. With this proposed model the use of energies like renewable operates more effectively in portable groups like domestic, commercial and agricultural. Energy sources for renewable with penetration improvement and also future grids the proposed model with hardware implantation plays an important role. In future grids storage system is very much important for the stability of grids for maintained and also power quality improvement. The storage system reliability and for improving the efficiency improvement for various time responses the proposed model is the best solution.

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Fig. 5 Microgrid simulation model

Fig. 6 Microgrid output of grid connected

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Fig. 7 Microgrid output of Islanding mode

Fig. 8 Photovoltaic AC output generation

Fig. 9 Wind AC output generation

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Fig. 10 Proposed model of hardware model grid connected

References 1. Geng Y, Yang K, Lai Z, Zheng P, Liu H, Deng R (2019) A novel low voltage ride through control method for current source grid-connected photovoltaic inverters. IEEE Access 7:51735–51748 2. Al-Shetwi AQ, Hannan MA, Jern KP, Alkahtani AA, PG Abas AE (2020) Power quality assessment of grid-connected PV system in compliance with the recent integration requirements. Electronics MDPI 3. Di Silvestre ML, Favuzza S, Sanseverino ER, Zizzo G, Ngoc TN, Pham M-H, Nguyen TG (2018) Technical rules for connecting PV systems to the distribution grid: a critical comparison of the Italian and Vietnamese frameworks. In: Proceedings of the 2018 IEEE international conference on environment and electrical engineering and 2018 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe), Palermo, Italy, pp 1–5 4. Tang C-Y, Kao L-H, Chen Y-M, Ou S-Y (2018) Dynamic power decoupling strategy for three-phase PV power systems under unbalanced grid voltages. IEEE Trans Sustain Energy 10:540–548 5. Hu Y, Yurkovich BJ (2011) Electrothermal battery modeling and identification for auto motive applications. J. Power Sources 196(1):449–457 6. Hossain E, Tür MR, Padmanaban S, Ay S, Khan I (2018) Analysis and mitigation of power quality issues in distributed generation systems using custom power devices. IEEE Access 6:16816–16833

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7. Sai Krishna A, Alekya P, Satya Anuradha M, Harsha Vardhini PA, Pankaj Kumar SR (2014) Design and development of embedded application software for interface processing unit (IPU). Int J Res Eng Technol (IJRET) 3(9):212–216 8. Villalva MG, Gazolli JR (2010) Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans Power Electron 25(12):2910–2918 9. Al-Shetwi AQ, Sujod MZ (2018) Grid-connected photovoltaic power plants: a review of the recent integration requirements in modern grid codes. Int J Energy Res 42:1849–1865 10. Hu Y, Yurkovich BJ (2010) Battery state of charge estimation in automotive applications using LPV techniques. In: Proceeding of the 2010 ACC, Baltimore, MD, USA, pp 5043–5049 11. Harsha Vardhini PA, Murali Mohan Babu Y, Krishna Veni A (2019) Industry parameters monitoring and controlling system based on embedded web server 6(2) 12. Hu Y, Yurkovich BJ (2009) A technique for dynamic battery model identification in automotive applications using linear parameter varying structures. IFAC Control Eng Pract 17(10):1190– 1201 13. Hu Y, Yurkovich BJ (2008) Modeled-based calibration for battery characterization in HEV applications. In: Proceeding of the ACC, Seattle, WA, USA, pp 318–325

A Robust System for Detection of Pneumonia Using Transfer Learning Apoorv Vats, Rashi Singh, Ramneek Kaur Khurana, and Shruti Jain

Abstract Pneumonia disease has been reported as severe adversity for causing a large number of deaths in children every year. Detecting pneumonia at the early stage is a bit difficult as symptoms are similar to cold or flu. However, early detection with innovative technologies can help in the detection of pneumonia which reduces the mortality rate. As the number of cases continues to rise, the expertise becomes difficult, and an alternative approach is needed. To counterpart this limitation, authors have proposed an expert system for the early detection of Pneumonia using X-ray images considering Neural Network and Convolution Neural Networks (CNN) that help in differentiating normal from the diseased. The CNN takes advantage of local coherence in the input to cut down on the number of weights. In this research, neural network techniques and various pre-trained models like VGG (16 and 19), InceptionV3, and MobileV2 have been used. DenseNet has been designed which overcome the limitation of the existing pre-trained models. The proposed network consisting Convolution Pooling block, dense block with transition layer series, Global Average pool, and a fully connected layer. The designed network results in 92.6% accuracy considering 20 epochs. The work has been compared with the existing state of the art technology and the proposed network shows remarkable results. Keywords Pneumonia · Detection · Expert system · Neural network · Deep learning

A. Vats · R. Singh Department of Computer Science Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh 173234, India R. K. Khurana Department of Biotechnology, Jaypee University of Information Technology, Solan, Himachal Pradesh 173234, India S. Jain (B) Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh 173234, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_50

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1 Introduction Pneumonia is an infection inflaming the air sacs in one or both lungs. The air sacs may fill with purulent material or fluid, causing difficulty in breathing, cough, chills, and fever. There is a vast difference between a cold virus that produces the usual cough, stuffiness, and sore throat. The mechanism of infection starts from the upper respiratory band and spreads further in the lower region and the process is known as pneumonitis. We can get either viral or bacterial pneumonia, but both can be life-threatening [1, 2]. A deep productive cough from the lungs is a symptom of Pneumonia. The lungs react to emotions of grief, fear, unhappiness, feeling unloved, the inability to handle all that is coming at once, etc. People today just don’t understand how dangerous pneumonia is. Medically it is a disease that causes inflammation in the parenchyma tissues of the lungs. The germs that cause pneumonia are communicable and contagious. Bacterial and viral pneumonia can spread through inhaled airborne droplets from sneezing and coughing. The certain risk factors that are associated with pneumonia are chronic obstructive pulmonary disease, asthma, diabetes, cystic fibrosis, heart failure, etc. People suffering from pneumonia experience a weak immune system, and feel chills, shortness of breath, chest pain, etc. [3]. There have been 100 strains recorded so far but only a few cause most infections [4]. According to data, 45% of infections are mixed infections, i.e., caused by bacteria and viruses both and it becomes difficult to isolate the causative agent even after attentive testing. According to 2016 data, 1.2 million cases of pneumonia are reported in children of the age group of 5 years and out of which 880,000 died. For its treatment influenza vaccines are preferred, and medications like amantadine and Zanamivir are used. Certain other factors like ceasing smoking, curbing indoor air pollution, and maintaining proper hygiene can help to relax the disease arriving conditions [5]. For diagnostic purposes, chest radiographs are used which were processed and a diagnostic system was framed using various machine learning techniques [6, 7] and deep learning techniques. Neural Network (NN) is an algorithm that is inspired by the neurons in the brain [8]. Neural networks are used for a special purpose, have weights, hidden layers, and supervised learning. The input features are fed directly into these layers, and they are multiplied with the weights contained in the hidden layers and finally get an output [9, 10]. The output obtained after the first iteration will have a significant difference from targets which is calculated through a function called loss function [11, 12]. A loss function results in how the output is different from the target. Based on loss, alteration in the weights of the layers can be done to decrease the loss because in the next iteration the output is nearer to the target. The weight adjustment over different iterations will be done to reduce the loss and the process is known as Back Propagation [13]. In some cases, training is not successful, or overtraining where the networks’ ability to generalize is weak are some disadvantages of neural networks. For many applications, we need to understand why the solution found works and how robust it is. To overcome the disadvantages of NN, Convolution neural network (CNN) a type of deep learning technique is employed

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[14, 15]. The CNN shares the same weights among its filter, thus there are fewer computations in comparison to NN. The increase in the number of Convlayers can be afforded in CNN which would not have been possible in the case of NNs due to the huge computations involved [16, 17]. CNNs are traditionally used for image processing. There are various existing pre-trained models in the literature. Hashmi et al. in 2020, has worked on developing a model for detection of pneumonia using digital chest X-ray images and thus reducing the workload of radiologists [18]. The approach used is supervised learning and transfer learning for hypertuning of the deep learning models to obtain higher accuracy. The proposed model outmatched all individual models. Ibrahim et al. in 2021, proposed a work where an alternative to RT-PCR tests have been provided for rapid screening, and for this chest, X-rays have been used [19]. This work uses a pre-trained AlexNet model to identify and classify into four categories namely standard CXR scans, non-COVID-19 viral Pneumonia, COVID-19, and bacterial Pneumonia). Jain et al. in 2020, has worked on creating a CNN model and detecting Pneumonia using X-rays and classifying them as Pneumonia and non-Pneumonia [20]. The first two models, i.e., the model containing two and three convolutional layers used in this work were CNNs that results in an accuracy of 85.26% and 92.31%. Shin et al. in 2016, has worked to explore three important aspects which are exploring and evaluating different CNN models, then the impact of the dataset and spatial scale was evaluated, and lastly transfer learning from pre-trained ImageNet was examined [21]. Garstka and Strzelecki in 2020, has worked on creating a self-constructed CNN to detect lung X-ray images and classify them into healthy and viral pneumonia in the area of medical image processing and accurate diagnosis [22]. The obtained accuracy was 85%. Kaushik et al. in 2020, has presented a model based on a CNN that can detect Pneumonic from chest X-rays [23]. The four models which have been worked on consist of one, two, three, and four layers respectively. Due to overfitting, fully connected layers dropout regularization was used in the second, third, and fourth models. The accuracy of all four models was 89.74%, 85.26%, 92.31%, and 91.67% respectively. This work has helped in the analysis of data and understanding. Varshni et al. in 2019, has proposed an automatic system to detect Pneumonia and for this CNNs have been used [24]. The paper has helped in understanding the role of pre-trained models in feature extraction for image datasets. Detection of pneumonia at the early stages is a bit difficult as the symptoms are the same as that of cold or flu. However, early detection with innovative technologies can help in early detection of pneumonia and can reduce the mortality rate. As the number of cases continues to rise, the expertise becomes difficult, and an alternative approach is needed. This paper proposes an expert system for the detection of pneumonia using X-ray images that differentiate diseased (pneumonia) and non-diseased. In this work, the authors have used neural networks and different pre-trained models of CNN for detection. The novelty of the paper lies in the design of DenseNet which is showing remarkable results. The training and validation accuracy and loss have been evaluated using mini-batch gradient descent optimization for the designed model. A comparison with the other state of the art techniques has also been done.

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This paper covers methodology steps for the detection of Pneumonia in Sect. 2, results using NN, CNN, and the proposed network is shown in Sect. 3. Also, a comparison has been done that is followed by concluding remarks and the future work.

2 Materials and Methodology The image dataset has been considered from the online dataset Kaggle [25]. A total of 5840 images were considered which were divided into training and testing datasets. The training dataset comprises 1341 images of non-disease people and 3875 images of Pneumonia affected people and the testing dataset contains 234 images of nonaffected people and 390 for people having Pneumonia. All the simulations were done in a system equipped with Intel i7 @2.6 GHz, 16 GB RAM using Jupyter Notebook. Most of the research present in the literature uses X-ray images for the classification of Pneumonia to have less accuracy. In this paper, two-class classification has been done using Neural Network, different CNN models, and DensNet to attain high accuracy. The results are compared on the basis of accuracy and loss. Figure 1 shows the methodology used in this paper. In this paper, authors have considered images from online datasets which were pre-processed. The images of the dataset are of different sizes but input size is only 224 × 224. A diagnostic system has been made using a Neural network. A neural network is a combination of nodes assembled tier wise that is usually trained backto-back in a supervised method using stochastic gradient descent algorithm. Neural networks learn in a hierarchical manner whereby every layer discovers additional attributes. Therefore, the depth of the network is determined by the number of such layers. The higher the layer count the deeper the network and hence the name “deep learning”. Deep learning refers to neural networks that have a very large number of layers stacked one over the other. A CNN is a distinctive type of deep neural network which uses flashing layers of pooling and convolutions; it contains trainable filter banks per layer. Every filter in a filter bank is known as kernel and it has a fixed receptive window scanned over a layer for computation of output [26]. The output map is sub-sampled using pooling technique to reduce sensitivity to distortions of stimuli in the upper layers [27]. Figure 2 shows the architecture of CNN. CNNs utilize spatial information so as to reduce the number of parameters and overall complexity while learning similar information. For small problems, this is unnecessary and can make CNNs prohibitively expensive to train. For larger problems, the complexity of other algorithms grows faster, making CNNs more viable. The first layer ConvLayer is accountable for confining the low-level functions along with color, edges, gradient orientation, etc. With delivered layers, the architecture adapts to the High-Level functions properly, giving us a network that has the wholesome information of images within the dataset. To reduce the overfitting of the dataset, Regularization is used. Regularization is a strategy that makes slight adjustments to the learning calculation with the end goal that the model sums up

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Fig. 1 Proposed methodology for the detection of pneumonia

better. This improves the model’s exhibition on the inconspicuous information too. In different words, while going towards the right, the unpredictability of the model increases. The estimation of the regularization coefficient has to be done to get a wellfitted model. The different regularizations in deep learning that enable decreasing overfitting are L1 and L2 regularization and dropout. L1 and L2 regularization are the most common kinds of regularization. Equation (1) represents the L2 while Eq. (2) represents L1 regularization techniques. Cost function = Loss +

λ  w2 2m

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Fig. 2 Basic architecture of CNN

Cost function = Loss +

λ  w 2m

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λ is the hyper-parameter or regularization parameter whose cost is optimized for better effects. In L2 regularization weight decays in the direction of 0 (but not precisely zero) while in L1 weights can be decreased to 0. Dropout is one of the common and frequently used regularization strategies. There are forms of results to the operation, one in which the convolved characteristic is reduced in dimensionality in comparison to the other, and the opposite in which the dimensionality elevates or remains identical. This is completed with the aid of making use of Valid Padding and Same Padding respectively. Padding with zero (0-padding) is done so that it fits or drops the part of the picture wherein the filter is no longer in shape. To introduce the non-linearity in the network, Rectified Linear Unit (ReLU) is used. If the spatial size of convolved features is diminishing, then the Pooling layer comes into the picture. The Pooling Layer and Convolutional Layer jointly structured the ith layer of a CNN. Over a progression of ages, the model is in a situation to recognize overwhelming and certain low-stage and the utilization of the Softmax Classification technique. There are different types of pre-trained layers. In this paper, the authors have used VGG16, VGG19, InceptionV3, and Mobile V2 layers. Based on existing layers, authors have designed a Dense Net layer consisting of convolution pooling block, dense block transition layer series, global average pool, and fully connected block. The accuracy and loss have been evaluated as a performance evaluation parameter. The proposed network results better than the existing networks and the work have also been compared to the other state of the art method.

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3 Results and Discussion In this paper, an expert system has been designed for the diagnosis of Pneumonia using neural networks and CNN. The training/validation accuracy and loss have been evaluated as performance parameters. Initially, the model is designed using a neural network. All the simulations were carried out in MATLAB and 85.74% accuracy has been achieved considering NN. The training is not successful while using NN. Due to which authors have considered designing the model using CNN. CNN has shown a lot of variants over the past few years that have led to significant developments in the field of Deep Learning [26, 27]. CNN often uses low image resolutions with 255 × 255 pixels because increasing the image size would increase the number of parameters of the network and hence it would take longer to train, and we require a larger dataset in order for it to work well. Another reason is that we as humans don’t necessarily need high-resolution images to do simple tasks such as what object is present in an image. Some of CNN’s most popular architectures that have shown significant improvement in error rates have been used in this work. (a)

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Residual Network (Resnet 50 and Resnet 50V2): The construction of ResNet50 can be described into four stages. All ResNet architecture performs the initial convolution and max-pooling using 7 × 7 and 3 × 3 kernel sizes respectively. When the stage changes, the channel width doubles while the size of the input is reduced by half. Finally, the network has an average pooling layer followed by a fully connected layer consisting of 1000 neurons. Deepening the ResNet won’t impact the accuracy much, at least won’t worsen it, but the inference will be much slower and can’t be used for real-time applications. However, for other networks which have no skip connections, deepening would mean more parameters and more overfitting on the training data. MobileNetV2: MobileNetV2 is a CNN architecture based on an inverted residual structure. These residual connections are located amid the layers of the bottleneck and the transitional layer uses lightweight depth-wise convolutions to filter features as a source of non-linearity. MobileNetV2 consists of 32 filters in a full convolution layer followed by 19 residual bottleneck layers. MobileNets are low-latency, small, low-power models to meet the resource constraints but they have linear bottlenecks and inverted residuals. Visual Geometry Group (VGG): One of the ways to improve deep neural networks can be provided by increasing their size. VGG16 and VGG 19are two types of VGG. VGG-16 has 13 constitutional and three fully connected layers along with ReLU activation function and used stacking layers and smallsized filters. The VGG19 consists of three additional convolution layers in comparison with VGG16 model. In VGG networks, the use of 3 × 3 convolutions with stride 1 providing an active field corresponding to 7 × 7 results in fewer factors to train. Both the networks are implemented, but Fig. 3 shows the training/validation loss and accuracy of VGG16 model.

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Fig. 3 Results of VGG16. a Training and validation loss, b training and validation accuracy

Alexnet and VGG are pretty much the same concepts, but VGG is deeper and has more parameters, as well as using only 3 × 3 filters. InceptionV3: InceptionV3 is a form of CNN architecture that has made several improvements including label smoothing. In InceptionV3 factorized convolution is done to reduce the computational efficiency because it reduces the number of parameters involved in the network. Smaller convolutions are used for faster training. Pooling operations are done so as to reduce the grid size. Lastly, an Auxiliary classifier has been used that uses small CNN embedded between layers at the time of training. All the concepts have been incorporated into the final architecture. The InceptionV3 is implemented, and the training/validation loss and accuracy are shown in Fig. 4. The difficulty in InceptionV3 is the use of Asymmetric convolutions. Considering all the pre-trained networks its advantages and limitations, the authors have designed Dense Net.

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Fig. 4 Results of InceptionV3. a Training and validation loss, b training and validation accuracy

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Proposed DenseNet: The designed DenseNet uses a convolution pooling layer followed by dense block transition layer series, global average pool, and fully connected layer. As the architecture of the design is very big, so authors are able to show only the first few blocks of the design. Figure 5 shows the architecture for the DenseNet model. For the proposed model, 942 and 32,767 pixels are used as width and height respectively and 32 as the bit depth is considered as the dimensions. The network is implemented, and the training/validation loss and accuracy are shown in Fig. 6. All the pre-trained models have been run for five epochs to 20 epochs. Table 1 tabulates the accuracy obtained for all the models which were implemented in this paper. From Table 1, it has been observed that maximum accuracy of 92.6% has been attained for 20 epochs using the proposed DenseNet model. 92.15%, 90.22%, 92.47% have been achieved using VGG16, Inception V3, and VGG19 layer respectively. Comparison with other state of the art techniques: Authors have compared the work with other state of the are methods and are tabulated in Table 2. Fig. 5 Proposed architecture of DenseNet (First few Blocks)

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(a)

(b)

Fig. 6 Results of DenseNet. a Training and validation loss, b training and validation accuracy

Table 1 Evaluation of performance parameters for different networks

Table 2 Comparison table with other state art of the technique

Model

Accuracy (5 epochs) (%)

Accuracy (20 epochs) (%)

Proposed DenseNet

92.15

92.60

VGG16

91.18

92.15

InceptionV3

89.74

90.22

VGG19

88.14

92.47

MobileNetV2

81.73

92.15

Neural network

85.74

Authors

Model

Accuracy (%)

Jain et al.[20]

VGG 16 net

87.28

Garstka and Strzelecki Self-constructed CNN 85 [22] Apoorv et al.

Proposed DenseNet

92.6

The results were compared with existing work, and it has been concluded that the proposed network results in 92.6% accuracy. The comparison based on accuracy resulted in 8.2% improvement with Garstka and Strzelecki [22] and 5.7% with Jain et al. [20] paper.

4 Conclusion and Future Work The detection of Pneumonia at the early stage is difficult. To overcome this problem, researchers have proposed an expert system for the detection of Pneumonia using

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neural network and deep learning techniques that helps doctors to diagnose the disease. The online dataset has been considered which was pre-processed and the two-class classification has been done. 85.75% accuracy has been obtained using neural network while 92.47% has been achieved using VGG 19 network. To achieve better accuracy, authors have proposed a Dense Net that uses a convolution pooling layer followed by dense block transition layer series, global average pool, and fully connected layer resulting in 92.6% accuracy. The work has been compared with other state of the art techniques which results in 8.2% improvement with Garstka and Strzelecki and 5.7% with Jain et al. in terms of accuracy. This work can be further improved by using a larger dataset. In the future, this model can be deployed so that it can be used by medical institutions and hospitals for Pneumonia detection in patients.

References 1. Healthcare, University of Utah. Pneumonia makes list for top 10 causes of death. Accessed on 31 December 2019; 2016 Available online: https://healthcare.utah.edu/the-scope/shows.php? shows=0_riw4wti7 2. WHO Pneumonia is the Leading Cause of Death in Children. Accessed on 31 December 2019; 2011 Available online: https://www.who.int/maternal_child_adolescent/news_events/ news/2011/pneumonia/en 3. World Health Organization (2001) Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children. World Health Organization, Geneva, Switzerland: 2001. Technical Report 4. Cherian T, Mulholland EK, Carlin JB, Ostensen H, Amin R, Campo MD, Greenberg D, Lagos R, Lucero M, Madhi SA et al (2005) Standardized interpretation of paediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies. Bull World Health Organ 83:353–359 5. Kallianos K, Mongan J, Antani S, Henry T, Taylor A, Abuya J, Kohli M (2019) How far have we come? Artificial intelligence for chest radiograph interpretation. Clin Radiol 74:338–345. https://doi.org/10.1016/j.crad.2018.12.015 6. Salau AO, Jain S (2019) Feature extraction: a survey of the types, techniques and applications. In: 5th international conference on signal processing and communication (ICSC-2019), Jaypee Institute of Information Technology, Noida (INDIA). 7. Bhusri S, Jain S, Virmani J (2016) Breast lesions classification using the amalagation of morphological and texture features. Int J Pharma BioSci (IJPBS) 7(2) B:617–624 8. Dhande G, Shaikh Z (2019) Analysis of epochs in environment based neural networks speech recognition system. In: 2019 3rd international conference on trends in electronics and informatics (ICOEI) 9. Kirti, Sohal H, Jain S (2020) Multistage classification of arrhythmia and atrial fibrillation on long-term heart rate variability. J Eng Sci Technol 15(2):1277–1295 10. Srivastava K, Malhotra G, Chauhan M, Jain S (2020) Design of novel hybrid model for detection of liver cancer. In: 2020 IEEE international conference on computing, power and communication technologies (GUCON), Greater Noida, India, pp 623–628 11. Wu X, Liu J (2009) A new early stopping algorithm for improving neural network generalization. In: 2009 second international conference on intelligent computation technology and automation. https://doi.org/10.1109/icicta.2009.11 12. Prashar N, Sood M, Jain S (2020) A novel cardiac arrhythmia processing using machine learning techniques. Int J Image Graphics 20(3):2050023

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13. Dogra J, Jain S, Sood M (2019) Glioma classification of MR brain tumor employing machine learning. Int J Innov Technol Explor Eng (IJITEE) 8(8):2676–2682 14. Sharma O (2019) Deep challenges associated with deep learning. In: 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon) 15. Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT press, Cambridge 16. Kanada Y (2016) Optimizing neural-network learning rate by using a genetic algorithm with per-epoch mutations. In: 2016 international joint conference on neural networks (IJCNN) 17. Bhardwaj C, Jain S, Sood M (2021) Deep learning based diabetic retinopathy severity grading system employing quadrant ensemble model. J Digital Imaging 18. Hashmi MF, Katiyar S, Keskar AG, Bokde ND, Geem ZW (2020) Efficient pneumonia detection in chest X-ray images using deep transfer learning. Diagnostics 10(6):417 19. Ibrahim AU, Ozsoz M, Serte S, Al-Turjman F, Yakoi PS (2021) Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cogn Comput 1–13 20. Jain R, Nagrath P, Kataria G, Kaushik VS, Hemanth DJ (2020) Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Measurement 165:108046 21. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298 22. Garstka J, Strzelecki M (2020) Pneumonia detection in X-ray chest images based on convolutional neural networks and data augmentation methods. In: 2020 signal processing: algorithms, architectures, arrangements, and applications (SPA), IEEE, pp 18–23 23. Kaushik VS, Nayyar A, Kataria G, Jain R (2020) Pneumonia detection using convolutional neural networks (CNNs). In: Proceedings of first international conference on computing, communications, and cyber-security (IC4S 2019), Springer, Singapore, pp 471–483 24. Varshni D, Thakral K, Agarwal L, Nijhawan R, Mittal A (2019) Pneumonia detection using CNN based feature extraction. In: 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT), IEEE, pp 1–7 25. Chest X-Ray Images (Pneumonia). https://www.kaggle.com/paultimothymooney/chest-xraypneumonia 26. Yaseen MU, Anjum A, Rana O, Antonopoulos N (2018) Deep learning hyper-parameter optimization for video analytics in clouds. IEEE Trans Syst Man Cybern: Syst 27. Bhardwaj C, Jain S, Sood M (2021) Transfer learning based robust automatic detection system for diabetic retinopathy grading. Neural Comput Appl

Reduce Overall Execution Cost (OEC) of Computation Offloading in Mobile Cloud Computing for 5G Network Using Nature Inspired Computing (NIC) Algorithms Voore Subba Rao and K. Srinivas Abstract Cloud computing utilizes mobile devices as resource computing for offloading computational tasks to servers. These mobile devices are not having that much efficient energy for uploading these heavy tasks because mobile devices are constrained in battery and life of battery is very short. The ubiquitous features of mobile devices act as resources of cloud computing for utilizing for offloading of computational tasks. This affects communication costs for cloud servers for offloading. These offloading problems should be optimized to reduce overall execution cost in Mobile Cloud Computing for 5G network. The objective of this paper is optimization of offloading of computational tasks through smart mobile devices to cloud servers by Nature Inspired Computing (NIC) algorithms. Optimize the process of services with NIC to maximize the performance offloading tasks, minimize energy consumption and minimize total execution cost. Keywords 5G network · Nature inspired computing · Offloading tasks · Mobile devices

1 Introduction Cloud computing is a modern technology that provides web based resources, services via internet. It is a high-requirement infrastructure service available for clients all the time [1]. Mobile Cloud Computing (MCC) is an architecture a feature of the data storage and process that will be happened externally of the mobile device. The applications of mobile cloud move data to cloud servers through mobile devices. MCC is to minimize burden on mobile devices to store, process such a huge application and also minimize overall execution cost of application processing [2]. V. S. Rao (B) Department of Physics and Computer Science, Dayalbagh Educational Institute (DEI), Agra, India K. Srinivas Department of Electrical Engineering, Dayalbagh Educational Institute (DEI), Agra, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3_51

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Figure 1 shows architecture of Mobile Cloud Computing (MCC) contains three parts those are smartphones, wireless communication technology, cloud infrastructure. The cloud infrastructure contains data centres for storing, process applications. The data centres provide storage of data, process data and security of data in a cloud. Mobile Cloud Computing (MCC) is integrated with cloud, mobile technologies for wireless networks to fulfil computation works by mobile devices [3]. Increase of handheld devices like smart mobile phones, tablets, laptops inbuilt huge memory and high processing capabilities. Even these devices do not support for offloading of high computational processing of applications for cloud computing. Smart mobile devices are small in size, less battery life. The network bandwidth, storage capability is very less for computational heavy applications of cloud computing. Mobile Cloud Computing (MCC) is a new technology that offloads complex applications to the cloud servers through mobile devices to save energy as well as time [4]. Offloading is a process of offloading for computational processes in a remote server for remote execution and to extend the Smart Mobile Devices (SMD) capability via internet [5]. Nowadays the vast improvement of technology provides high processing power, huge storage capability as well as equipped with various sensors. Even these high acquiring of high capabilities of mobile devices are constrained with a limited battery and when compared with PCs the processing capability of mobile devices is very less for act as resource utilization of heavy computational offloading tasks in cloud servers in 5G network. And these mobile devices do not have that much capability for executing heavy applications and involves require high processing time as well as drain the battery power. Cloud computing enables mobile devices to offload computational tasks to cloud servers these servers are remotely located in someplace. These feature of mobile devices acts as resources for offloading tasks for cloud computing involved in Mobile Cloud Computing (MCC). This MCC allows computational tasks as well as data

Fig. 1 Architecture of mobile cloud computing (MCC)

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storage from mobile phones to servers in cloud (remotely). The offloading computational tasks from mobile phones to servers in cloud that effects minimize execution costs also saves energy of digital devices. The offloading heavy computational tasks on mobile devices are NP-hard. These mobile devices are not supported for executing long running and heavy processing applications for complex operations. The mobile devices are low storage battery capabilities need frequently require battery charging operations. Smartphones couldn’t be able to execute heavy applications and data for complex network environments because all smartphones are battery operated. The problems in 5G are considered network optimization problems. These problems set of inputs to provide an optimal output with predefined constraints. Many traditional algorithmic techniques such as convex optimization, linear programming and greedy algorithms have also been applied. When the complexity of the above problems becomes exponential because of the features like inherent ability of decentralization, scalability, self-evolution and collective behaviour for particular tasks, etc. of Nature Inspired Computing (NIC) algorithms have been successfully applied to model complex 5G network heavy applications.

2 Literature Survey In [6] authors proposed an offloading decision for mobile devices. The objective of this paper is to optimization of energy consumption of mobile devices as well as minimize execution time by Genetic Algorithm (GA). The experimental solutions shows nearby optimum solutions with a reasonable amount of time. In [7] authors proposed a design of intelligent computational offloading system that take decision for program upload from a digital phone to server of cloud in 5th generation mobile technology. This proposed method maximize saving of energy, time management. In [8] authors proposed a framework for computational offloading tasks by applying Cuckoo algorithm approach. This method is used to minimize the energy consumption on smartphone devices as well as increase the speed of computational tasks.

3 Modeling, Solving and Optimization of Offloading Problem Using Nature, Inspired Computing Nature Inspired Computing algorithms are the inspiration from nature. These are the model of solving computational problems efficiently. Due to bandwidth problems, increasing heterogeneity of devices, increasing network is a challenging task. For such a scenario find optimal solutions for a given time is not possible. But it can be

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possible to find near-optimal solutions in a given time by Nature Inspired Computing algorithms. These NIC algorithms are used to explore the exploitation or exploration method to maximize or minimize an objective. Swarm Intelligence (SI) algorithms are the features self-motivation, self-adaptation, collective behaviour of homogenous agents in nature and being able to solve complex problems [9]. Examples are Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO).

3.1 Genetic Algorithm (GA) Optimization of offloading problem using Genetic Algorithm is mean of offloading for represents a chromosome. Chromosomes contain genes. Each gene constitutes a mobile service. The length of a chromosome depends upon the number of genes it contains. Every gene represents the value of 0 or 1. The value 1 represents service is required to offload otherwise not. Reduce the execution time of offloading tasks, as well as minimize energy consumption using Genetic Algorithm [10]. Pseudo-code for Genetic Algorithm (GA)

3.2 Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO) is a swarm intelligence algorithm that is a metaheuristic algorithm for searching optimal solutions within search space of fitness function. Swarm Intelligence (SI) algorithms are group (swarm) of birds searching for food in a targeted area. These natural models are converted into computational models by best utilization of SI algorithm, i.e. Particle Swarm Optimization (PSO)

Reduce Overall Execution Cost (OEC) of Computation Offloading in Mobile …

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[11–13]. In Particle Swam Optimization the solutions have randomly generated these solutions are known as particles. These particles search for the best solutions and co-ordinately balance directions with velocities. The local and global searching for optimum results are mainly dependent and affected by particles directions and their velocities. The local search is particles Pbest , i.e. personnel best is particles personnel position towards search space. The global search is particles Gbest , i.e. global best is among all particles best position towards search space. The following equations are particles updating their positions as well as velocities as follows. Pseudo-code for Particle Swarm Optimization (PSO)

w = inertia weight that balance local search (exploitation), global search (exploration) rand() = random number ∈ [0, 1] c1, c2 = acceleration constants Pi = represents Pbest , i.e. personal best position for a particle in a swarm Pg = represents Gbest , i.e. global best position among all particles in a swarm

3.3 Biogeography Based Optimization BBO is a meta-heuristic algorithm that analyzes the species that will move one specific island to another specific island. In BBO every habitat has some value called as Habitat Suitability Index (HSI). And contains a parameter called as Suitability

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Index Variable (SVI). HSI of Island that is friendly to life is said to have a High Habitat Suitability Index (HSI). The features that classify habitable (i.e. fit to live in) are called SIVs. Features that correlate with HSI include rainfall, vegetation diversity, land area, temperature and others. In terms of habitability, SIV can be considered the independent variables of the island and HSI can be considered the dependent variables. BBO has some parameters, i.e. migration operator, mutation operator and elitism operator. To increase HSI value, Migration operator uses immigration and emigration. The mutation operator updates the solutions randomly. The elitism operator selects the best optimum solutions for the future. BBO has specific equalities with being found well known bio-inspired computing algorithms are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In Genetic Algorithm (GA), whether the parent is not fittest (not capable), according to these criteria, its child couldn’t survive for upcoming next generation because having low-probability, but whereas in Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO) solutions are capable for surviving in upcoming generations. In PSO, solutions are formed into same groups, whereas in BBO as well as GA, the solutions will not form into a group in cluster (is a group). In PSO solutions will be updated by velocity, in BBO solutions will be directly updated. So BBO will produce better optimum results compared with GA, PSO [14, 15]. Biogeography Based Optimization (BBO) Algorithm

Reduce Overall Execution Cost (OEC) of Computation Offloading in Mobile …

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Proposed BBO Algorithm

The fitness function for solving problems of optimizing to minimize execution time of mobile applications and also minimize energy consumption. The proposed method, the objective function for offloading technique is the sum of execution time for mobile service as well as use of energy of mobile device (MD). The objective function for calculating overall cost for execution. Overall_Execution_Costmobil_device = ω ∗ Execution_Costmobil_device + (1 − ω) ∗ Energy_consmobile_device

(1)

Execution_timemobile_device = overall execution time for MD for processing of an application Energy_consmobile_device = energy taken by MD ω = is inertia parameter have a value in range of 0–1 shows tradeoff in between execution time and energy consumption ω = 0.5, i.e. need to save device’s battery life. It’s a parameter of time and energy. Execution_Timemobil_device_L = local_execution_service/ processor_capacitymobile_device

(2)

will be computed by taken time, Execution_Timemobile_device Execution_Timemobile_device_L consider time for executing tasks locally mobile device. Execution_Timemobile_device_C is denoted for executing applications on remote m/c, servers in the cloud. The locally executed service is mentioned by local_execute_service and mobile device processor capacity is denoted by processor_capacitymobile_device .

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Execution_Timemobil_device_ C = ω − timecloud + inputi /data_trans_rate + Cloud_server/processor_capacitycloud + outputo /data_trans_rate

(3)

Execution_timemobile_device_C = the time duration wait for execution of task. data_trans_rate = require data transmission rate (calculate in kbps) will send/receive and input/output data for offload task to/from cloud through a mobile user cloud_server = workload of offloaded service input/output = data size of task processor_capacitycloud = processor capacity of cloud server Energy_consmobil_device_ L = Execution_Timemobile_device_ J ∗ Energy_cons_ratemobile_device

(4)

Energy consumes for the execution of application by two components are Energy_consmobile_device_L and Energy_cons_ratemobile_device_C. Energy_consmobile_device_L = energy consumption for local execution Energy_cons_ratemobile_device_C = rate of energy consumption of a mobile device while executing tasks in cloud. Execution_timemobile_device_C = inputi / data_trans_rateinput ∗ Energy_cons_ratemobile_device_up + output/data_trans_rateoutput ∗ Energy_cons_ratemobile_device_down

(5)

Energy consumption of send and receive about input and output data and information for a cloud offloading tasks is done by Energy_cons_ratemobile_device_up /Energy_cons_ratemobile_device_down For that …   Overall_Execution_Costmobile_device = ω ∗ Execution_Timemobile_device + (1 − ω) ⎛   ∗ ⎝− m Energy_costmobile_device_L

+





Energy_cons_ratemobile_device_C

c

(6)   m

locally

Energy_consmobile_device_L = denotes energy consumed by mobile devices

Reduce Overall Execution Cost (OEC) of Computation Offloading in Mobile …

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Energy_cons_ratemobile_device_C = denotes energy consumed by mobile c devices either on cloud. Assign fitness function to Nature Inspired Computing algorithm for optimum solutions for multiple iterations. For resulting every iteration in execution generating new population. These steps are continuous to get fitness values for the given population.

4 Results and Discussion Nature Inspired Computing algorithms GA, PSO, BBO for generating optimal offloading have been evaluated. The simulation software MATLAB was used for this proposed work. These algorithms are evaluated considering smartphones and laptops of different configurations. The experiments for optimization of energy consumption and minimizing application execution time by NIC algorithms for taking these parameters shows in Table form. Table1 provides parameters in detail of Nature Inspired Computing algorithms. Table2 provides the detailed configuration of smartphones and laptops for offloading experiments by Nature Inspired Computing algorithms. Figures 2 and 3 shows Overall Execution Cost (OEC) by GA, PSO, BBO for Table 1 Parameters of nature inspired computing algorithms NIS algorithms

Size of population

Generations

Control variables

GA

100

50

Mutation rate = 0.0001 Crossover rate = 0.95 Elitism count = 2

PSO

100

50

c1 = 0.5, c2 = 0.5, ω = 0.5

BBO

100

50

Appliances = 12, Migration = immigration/emigration Mutation = 0.1 Max. iterations = 5

Table 2 Specifications of devices for the experiment analysis

Equipment type

Mobile devices

Power (W)

Capacity (MIPS)

Smart phones

Rasberry Pi2

2

4744 at 1.6 GHz

Samsung Exynos 5250

4

14,000 at 20 GHz

i7 6950

140

9900 at 1.5 GHz

Intel Core i7 4770 K

84

13,374 at 3.9 GHz

Laptops

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Fig. 2 Raspberry Pi2—shows OEC by GA, PSO, BBO

Fig. 3 Samsung Exynos 5250—shows OEC by GA, PSO, BBO

mobile devices like Raspberry Pi2 and Samsung Exynos 5250. As per results, BBO algorithm provides the best optimal solution for the tasks. BBO provides the best performance results for overall execution cost. Figures 4 and 5 shows Overall Execution Cost (OEC) by GA, PSO, BBO for Laptops like i7 6950 and Core i7 4770 K. As per results, BBO algorithm provides the best optimal solution for the tasks. BBO provides the best performance results for overall execution cost.

5 Conclusion This paper focusing the importance of Nature Inspired Computing algorithms for optimum results for optimization of mobile computation offloading in Mobile Cloud

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Fig. 4 i7 6950 laptop—shows OEC by GA, PSO, BBO

Fig. 5 Intel corei7 4770 K laptop—shows OEC by GA, PSO, BBO

Computing tasks. The objective of this paper is to improve energy efficiency, minimize energy consumption by minimizing total execution cost for executing offloading tasks from mobile phones to cloud servers. By observing simulation results, it proved that compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) the Biogeography Based Optimization (BBO) algorithm is showing better performance results for getting near optimum results for minimum overall execution cost.

References 1. Mell P, Grance T (2011) The Nist definition of cloud computing 2. Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture,

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applications, and approaches. Wireless Commun Mobile Comput 13(18):1587–1611 3. https://en.wikipedia.org/wiki/Mobile_cloud_computing. Accessed 31 Dec 2016 4. Xia F, DingJie F, Xi J, Kong X, Yang LT, Ma J (2014) Phone2Cloud: exploiting computation offloading for energy saving on smartphones in mobile cloud computing. Inf Syst Front 16(1):95–111 5. Yang K, Ou S, Chen H-H (2008) On effective offloading services for resource-constrained mobile devices running heavier mobile internet applications. Commun Mag IEEE 46(1):56–63 6. Deng S et al (2014) Computation offloading for service workflow in mobile cloud computing. IEEE Transactions Parallel Distrib Syst 26(12):3317–3329 7. Khoda ME et al (2016) Efficient computation offloading decision in mobile cloud computing over 5G network. Mobile Netw Appl 21(5):777–792 8. Kemp R et al (2010) Cuckoo: a computation offloading framework for smartphones. In: International conference on mobile computing, applications, and services. Springer, Berlin, Heidelberg 9. Yang F et al (2017) Survey of swarm intelligence optimization algorithms. In: 2017 IEEE international conference on unmanned systems (ICUS), IEEE 10. Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27(6):17–26 11. Blum C, Li X (2008) Swarm intelligence in optimization. In: Swarm intelligence, Springer, Berlin, Heidelberg, pp 43–85 12. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948 13. Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Prob Eng 2015 14. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713 15. Wei Z et al (2020) An offloading strategy with soft time windows in mobile edge computing. Comput Commun 164:42–49

Author Index

A Ahmad, Sharik, 479. See 00 Amudhan, K., 223. See 00 Ankaiah, Burri, 657. See 00 Anthoniraj, S., 619. See 00 Aswin, V. R., 223. See 00

B Bala, Indu, 35. See 00 Balaji, S., 223. See 00 Batra, Amit, 109. See 00 Batra, Neera, 429. See 00 Bhardwaj, Shikha, 645. See 00 Bhavana, 415. See 00

C Chandra, Umesh, 479. See 00 Cheema, Pardeep, 591. See 00 Chhabra, Mohit, 297. See 00 Chugh, Himani, 267. See 00

Goyal, Sumeet, 281. See 00 Grewal, Surender K., 159. See 00 Gunavathi, K., 471. See 00 Gupta, Ankit, 21. See 00 Gupta, Anuj Kumar, 243. See 00 Gupta, Deepak, 95. See 00 Gupta, Monish, 521. See 00 Gupta, Parul, 365. See 00 Gupta, Sheifali, 267. See 00 Gupta, Swati, 543. See 00 Gupta, Vishal, 405, 521. See 00

H Harnal, Shilpi, 331. See 00 Harsha Priya, M., 129. See 00 Harsha Vardhini, P. A., 129, 657. See 00 Helenprabha, K., 167. See 00 Hemambujavalli, S., 619. See 00

D Dalal, P., 583. See 00 Dhingra, Shefali, 645. See 00 Dhull, S. K., 583. See 00 Dutta, Neha, 591. See 00

J Jacob, Minu Susan, 57. See 00 Jain, Anurag, 195. See 00 Jain, Ritika, 439. See 00 Jain, Shruti, 667. See 00 Jain, Stuti, 439. See 00 Jamwal, Shubhnandan S., 365. See 00 Jose, Deepa, 619. See 00

G Gagandeep, 179. See 00 Gopalsamy, Arunraj, 451. See 00 Goyal, Sonali, 429. See 00

K Karthikeyan, S., 1. See 00 Kathuria, A., 559. See 00 Kaur, Karamjit, 313. See 00

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marriwala et al. (eds.), Mobile Radio Communications and 5G Networks, Lecture Notes in Networks and Systems 339, https://doi.org/10.1007/978-981-16-7018-3

691

692 Kaur, Lakhwinder, 255. See 00 Kaur, Priya Pinder, 83. See 00 Khurana, Ramneek Kaur, 667. See 00 Krishnaveni, S., 129. See 00 Kumar, Akhilesh, 395. See 00 Kumar, Dinesh, 345. See 00 Kumar, Neeraj, 267. See 00 Kumar, Pawan, 345. See 00 Kumar, Rajneesh, 195, 297. See 00 Kumar, Velguri Suresh, 149. See 00 L Lakshmi, N., 379. See 00 M Mageshwari, R., 73. See 00 Mamatha, E., 533. See 00 Manisha, Chilumula, 137. See 00 Manju, S., 167. See 00 Marriwala, Nikhil, 521, 605. See 00 Mavi, Navneet Kaur, 255. See 00 Mishra, Brijesh, 395. See 00 Mishra, Ravi Dutt, 331. See 00 Mishra, Satanand, 211. See 00 N Narwal, Bhawna, 645. See 00 Nassa, Vinay Kumar, 281. See 00 Nirmal Kumar, P., 619. See 00 Nishanth, Adusumalli, 1. See 00 O Oommen, Sujo, 657. See 00 P Palta, Pankaj, 281. See 00 Pandey, Binay Kumar, 281. See 00 Pandey, Digvijay, 281. See 00 Pandey, Divya, 211. See 00 Parthiban, T., 379. See 00 Pathak, Monika, 243. See 00 Phukan, Arpan, 95. See 00 Pillai, Anuradha, 21. See 00 Ponraj, A., 379. See 00 Praveen, Annaa, 1. See 00 Punj, Deepika, 21. See 00 R Radha, B., 451, 511. See 00

Author Index Raheja, Neeraj, 415. See 00 Rajakarunakaran, S., 223. See 00 Rajdurai, P. S., 533. See 00 Rani, Manisha, 179. See 00 Rani, Pooja, 195. See 00 Rao, Voore Subba, 679. See 00 Ravi, T., 1. See 00 Reddy, C. S., 533. See 00 Reshmika, D., 379. See 00 Richa, 313. See 00

S Saini, Mehak, 159. See 00 Saini, Sanju, 119. See 00 Sakthivel, D., 511. See 00 Saravanan, Samyuktha, 471. See 00 Selvi Rajendran, P., 57. See 00 Sen, Vijay Singh, 365. See 00 Sethi, Hemant, 405. See 00 Sharduli, 109. See 00 Sharma, Ashish, 439. See 00 Sharma, Avinash, 479, 559. See 00 Sharma, Bhawna, 501. See 00 Sharma, Gaurav, 331. See 00 Sharma, Jyotika, 439. See 00 Sharma, Manvinder, 281. See 00 Sharma, Preeti, 635. See 00 Sharma, Raju, 243. See 00 Sharma, Sandhya, 267. See 00 Siddique, Shadab Azam, 395. See 00 Singh, Kritika, 395. See 00 Singh, Kulvinder, 109. See 00 Singh, N. P., 543. See 00 Singh, Priti, 313. See 00 Singh, Rashi, 667. See 00 Singh, Sukhdev, 83. See 00 Singh, Surinder Pal, 405. See 00 Soni, Sanjay Kumar, 395. See 00 Sreelatha, V., 533. See 00 Sri Kiruthika, G., 73. See 00 Srinivas, K., 679. See 00 Srivastava, Vikas, 35. See 00 Srivastava, V. K., 635. See 00 Suresh Kumar, Velguri, 137. See 00 Suwathi, T., 73. See 00

T Thomas, Tina Susan, 73. See 00 Tiwari, Garima, 119. See 00

Author Index V Vaid, Rohit, 501. See 00 Vandana, 605. See 00 Vats, Apoorv, 667. See 00 Venkatesh, R., 223. See 00 Venkatesh, Tadisetty Adithya, 149. See 00 Vignesh Saravanan, K., 223. See 00

693 Vijayabaskar, 1. See 00

Y Yadav, Dharminder, 479. See 00 Yadav, Shekhar, 211. See 00