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Artificial Intelligence and Natural Algorithms Edited by Rijwan Khan Department of Computer Science and Engineering ABES Institute of Technology, Ghaziabad (U.P.), India Pawan Kumar Sharma Department of Applied Science Dronacharya Group of Institutions, Gr. Noida (U.P.), India Sugam Sharma Senior Systems Analyst Center for Survey Statistics & Methodology, Iowa State University, USA & Santosh Kumar Department of Mathematics, College of Natural and Applied Sciences, University of Dar es Salaam, Tanzania Artificial Intelligence and Natural Algorithms Editors: Rijwan Khan, Pawan Kumar Sharma, Sugam Sharma and Santosh Kumar ISBN (Online): 978-981-5036-09-1 ISBN (Print): 978-981-5036-10-7 ISBN (Paperback): 978-981-5036-11-4 © 2022, Bentham Books imprint. Published by Bentham Science Publishers Pte. Ltd. Singapore. All Rights Reserved. First published in 2022. BSP-EB-PRO-9789815036091-TP-364-TC-18-PD-20220923 BENTHAM SCIENCE PUBLISHERS LTD. 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Ltd. 80 Robinson Road #02-00 Singapore 068898 Singapore Email: [email protected] BSP-EB-PRO-9789815036091-TP-364-TC-18-PD-20220923 CONTENTS PREFACE ................................................................................................................................................ i LIST OF CONTRIBUTORS .................................................................................................................. ii CHAPTER 1 DATA COMPUTATION: AWARENESS, ARCHITECTURE AND APPLICATIONS ..................................................................................................................................... 1 Vani Kansal and Sunil K. Singh INTRODUCTION .......................................................................................................................... 2 SURVEY STRATEGIES ............................................................................................................... 3 Big Data .................................................................................................................................. 4 Cloud Computing .................................................................................................................... 5 Pervasive Computing .............................................................................................................. 6 Reconfigurable Computing ..................................................................................................... 6 Green Computing .................................................................................................................... 6 EMBEDDED COMPUTING ......................................................................................................... 7 Parallel Computing ................................................................................................................. 7 Fog Computing ....................................................................................................................... 7 Internet of Things and Computing Technology ...................................................................... 8 Blockchain .............................................................................................................................. 9 NGS-Throughput .................................................................................................................... 11 Digital Image Processing ........................................................................................................ 11 E-commerce ............................................................................................................................ 13 Healthcare Informatics and Clinical Research ........................................................................ 13 SURVEY OUTCOMES .................................................................................................................. 14 DATA COMPUTING CHALLENGES ........................................................................................ 16 RELIABLE INDUSTRY 4.0 BASED ON MACHINE LEARNING AND IOT FOR ANALYZING .................................................................................................................................. 17 CONCLUSION ............................................................................................................................... 18 CONSENT FOR PUBLICATION ................................................................................................ 18 CONFLICT OF INTEREST ......................................................................................................... 18 ACKNOWLEDGEMENT ............................................................................................................. 18 REFERENCES ............................................................................................................................... 18 CHAPTER 2 DIFFERENT TECHNIQUES OF DATA FUSION IN INTERNET OF THINGS (IOT) ......................................................................................................................................................... 24 Harsh Pratap Singh, Bhaskar Singh, Rashmi Singh and Vaseem Naiyer INTRODUCTION .......................................................................................................................... 24 Accumulating and Sending Information ................................................................................. 25 Receiving and Acting on Information ..................................................................................... 25 Doing Both .............................................................................................................................. 26 Key Challenges of IoT ............................................................................................................ 26 DATA FUSION ARCHTECHTURE ............................................................................................ 27 Centralized Fusion Architecture ............................................................................................. 28 Distributed Fusion Architecture .............................................................................................. 29 Hybrid Fusion Architecture .................................................................................................... 30 LITERATURE REVIEW .............................................................................................................. 31 MULTI-SENSOR DATA FUSION ............................................................................................... 33 Fuzzy Logic-Based Data Fusion ............................................................................................. 34 Bayesian-based Technique ...................................................................................................... 34 Markov Process-based Technique .......................................................................................... 35 Demspter-Shafer Theory Based Technique ............................................................................ 35 Thresholding Techniques and Others ..................................................................................... 36 APPLICATION OF IOT ............................................................................................................... 36 Smart Environment ................................................................................................................. 37 Health Care ............................................................................................................................. 38 IoT in Agriculture ................................................................................................................... 39 Associated Industry ................................................................................................................. 40 Smart Retail ............................................................................................................................ 40 Smart Energy and Smart Grid ................................................................................................. 41 Traffic Monitoring .................................................................................................................. 41 Smart Parking .......................................................................................................................... 41 CONCLUSION ............................................................................................................................... 41 CONSENT FOR PUBLICATION ................................................................................................ 42 CONFLICT OF INTEREST ......................................................................................................... 42 ACKNOWLEDGEMENTS ........................................................................................................... 42 REFERENCES ............................................................................................................................... 42 CHAPTER 3 ROLE OF ARTIFICIAL INTELLIGENCE IN MEDICINE AND HEALTH CARE ........................................................................................................................................................ 45 Upasana Pandey and Arvinda Kushwaha INTRODUCTION .......................................................................................................................... 45 RECENT APPLICATIONS OF AI IN MEDICINE AND HEALTH CARE ........................... 46 Diagnosis of Disease and Prediction ....................................................................................... 46 In Reduction of Complications ............................................................................................... 47 Taking Care of Patients Under Treatment .............................................................................. 47 In Assisting to Improve the Success Ratio of Treatment ........................................................ 47 Living Assistance .................................................................................................................... 48 Biomedical Information Processing ........................................................................................ 48 AI in Biomedical Research ..................................................................................................... 49 AI in Medical Imaging ............................................................................................................ 49 LATEST AI TECHNIQUES IN MEDICAL SCIENCES ........................................................... 50 EFFECTS OF USAGE OF AI TECHNIQUES ........................................................................... 52 Fast and Accurate Diagnostics Reduce the Mortality Rate .................................................... 52 Reduce Errors Related to Human Fatigue .............................................................................. 52 Decrease in Medical Cost ....................................................................................................... 53 AREA OF CONCERNS ................................................................................................................. 53 Care of Old Age People .......................................................................................................... 53 Replacement of Humans with AI Techniques ........................................................................ 53 Data Collection and its Security ............................................................................................. 54 RECENTLY USED AI-BASED MEDICAL TOOLS ................................................................. 54 CONCLUSION ............................................................................................................................... 55 CONSENT OF PUBLICATION ................................................................................................... 56 CONFLICT OF INTEREST ......................................................................................................... 56 ACKNOWLEDGEMENTS ........................................................................................................... 56 REFERENCES ............................................................................................................................... 56 CHAPTER 4 THREAT DETECTION AND REPORTING SYSTEM ........................................... 64 Devika Bihani, Saransh Sharma and Harshit Jain INTRODUCTION .......................................................................................................................... 64 RELATED WORK ......................................................................................................................... 65 PROPOSED METHOD ................................................................................................................. 67 Weapon Detection ................................................................................................................... 68 Violence Detection .................................................................................................................. 69 Medical Emergency Detection ................................................................................................ 70 DATASET & PSEUDOCODE ...................................................................................................... 71 PSEUDOCODE ............................................................................................................................... 72 CONCLUSION ............................................................................................................................... 73 CURRENT & FUTURE DEVELOPMENTS .............................................................................. 73 CONSENT FOR PUBLICATION ................................................................................................ 74 CONFLICT OF INTEREST ......................................................................................................... 74 ACKNOWLEDGEMENTS ........................................................................................................... 74 REFERENCES ............................................................................................................................... 74 CHAPTER 5 OFFBEAT LOAD BALANCING MACHINE LEARNING BASED ALGORITHM FOR JOB SCHEDULING ...................................................................................................................... 76 Anand Singh Rajawat, Kanishk Barhanpurkar and Romil Rawat INTRODUCTION .......................................................................................................................... 76 RELATED WORK ......................................................................................................................... 78 PROPOSED WORK ...................................................................................................................... 79 HYBRID APPROACH ................................................................................................................... 80 PRODUCE POPULATION (PP) .................................................................................................. 81 FITNESS FUNCTION (FF) ........................................................................................................... 81 NATIVE PREEMINENT (NP) ...................................................................................................... 81 CROSSWAY ................................................................................................................................... 82 UPDATE GLOBAL PREEMINENT ............................................................................................ 82 RANDOM FOREST TRAINING ................................................................................................. 82 PROPOSED TRAINING ALGORITHM .................................................................................... 82 PROCEDURE ................................................................................................................................. 83 PROPOSED ALGORITHM .......................................................................................................... 84 IMPROVED GENETIC ALGORITHM WITH HYBRID ALGORITHM (HA (GA, KMC AND RF)) ......................................................................................................................................... 85 LOAD BALANCING UNDER CLOUD COMPUTING ENVIRONMENT ............................ 85 RELEVANT OPERATIONS OF GA ........................................................................................... 86 SIMULATION RESULT ANALYSIS .......................................................................................... 87 RESULT ANALYSIS ..................................................................................................................... 89 Conclusion and Future Work .................................................................................................. 91 FUTURE SCOPE ............................................................................................................................ 91 CONSENT OF PUBLICATION ................................................................................................... 91 CONFLICT OF INTEREST ......................................................................................................... 91 ACKNOWLEDGEMENTS ........................................................................................................... 91 REFERENCES ............................................................................................................................... 92 CHAPTER 6 A PATTERN OPTIMIZATION FOR NOVEL CLASS IN MULTI-CLASS MINER FOR STREAM DATA CLASSIFICATION .......................................................................... 94 Harsh Pratap Singh, Vinay Singh, Divakar Singh and Rashmi Singh INTRODUCTION .......................................................................................................................... 94 RELATED WORK FOR STREAM CLASSIFICATION .......................................................... 95 PROPOSED ALGORITHM FOR PATTERN CLASSIFICATION IN MCM ........................ 97 RESULT ANALYSIS ..................................................................................................................... 99 CONCLUSION ............................................................................................................................... 102 CONSENT FOR PUBLICATION ................................................................................................ 102 CONFLICT OF INTEREST ......................................................................................................... 102 ACKNOWLEDGEMENTS ........................................................................................................... 102 REFERENCES ............................................................................................................................... 102 CHAPTER 7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ON THE VERGE OF MAJOR SHIFT WITH OPPORTUNITIES AND CHALLENGES ................................................... 104 Nahid Sami and Asfia Aziz INTRODUCTION .......................................................................................................................... 104 Why AI in Healthcare ............................................................................................................. 106 AI TECHNIQUES IN HEALTHCARE ....................................................................................... 108 Machine Learning ................................................................................................................... 108 Support Vector Machine ......................................................................................................... 109 Neural Network ....................................................................................................................... 110 Deep Learning ......................................................................................................................... 111 Natural Language Processing ................................................................................................. 111 Opportunity and its Impact ..................................................................................................... 111 Diagnosis ................................................................................................................................. 112 Therapy ................................................................................................................................... 112 Drug Development and Research ........................................................................................... 113 Rehabilitation of Elderly ......................................................................................................... 113 The Future ............................................................................................................................... 113 Challenges and Limitations ..................................................................................................... 114 Digitization of Clinical Data ................................................................................................... 114 Privacy and Security ............................................................................................................... 114 Role of Stakeholder ................................................................................................................. 114 Facing the Causality ................................................................................................................ 114 Black Box Issue ...................................................................................................................... 115 CONCLUSION ............................................................................................................................... 115 CONSENT OF PUBLICATION ................................................................................................... 115 CONFLICT OF INTEREST ......................................................................................................... 115 ACKNOWLEDGEMENTS ........................................................................................................... 115 REFERENCES ............................................................................................................................... 115 CHAPTER 8 A REVIEW ON AUTOMATIC PLANT SPECIES RECOGNITION SYSTEM BY LEAF IMAGE USING MACHINE LEARNING IN INDIAN ECOLOGICAL SYSTEM ............. 118 Sugandha Chakraverti, Ashish Kumar Chakraverti, Jyoti Kumar, Piyush Bhushan Singh and Rakesh Ranjan INTRODUCTION .......................................................................................................................... 119 IMAGE PROCESSING ................................................................................................................. 121 A Typical Image-Based Plant Identification System (SATTI Et Al., 2013) .......................... 125 Image Acquisition ................................................................................................................... 125 Pre-processing ......................................................................................................................... 125 Feature Extraction ................................................................................................................... 126 Color Features ......................................................................................................................... 126 Shape Features ........................................................................................................................ 126 A). Geometric Features ................................................................................................. 126 B). Morphological Features .......................................................................................... 127 C). Tooth Features ........................................................................................................ 128 INDIAN PLANTS IMAGE DATA SETS ..................................................................................... 129 MACHINE LEARNING TECHNIQUES FOR LEAF RECOGNITION ................................. 131 DEVELOPMENTS OF AUTOMATIC SYSTEMS/MOBILE APPS FOR LEAF RECOGNITION ............................................................................................................................. 133 Plantifier .................................................................................................................................. 133 Garden ..................................................................................................................................... 133 PlantNet ................................................................................................................................... 134 iNaturalist ................................................................................................................................ 134 KEY ATTRIBUTES ....................................................................................................................... 134 FlowerChecker ........................................................................................................................ 134 Agrobase ................................................................................................................................. 135 LEAF RECOGNITION APP ......................................................................................................... 135 Methodology ........................................................................................................................... 136 Integration of the Front-End with the Backend ...................................................................... 136 Description .............................................................................................................................. 139 CONCLUSION ............................................................................................................................... 139 CONSENT FOR PUBLICATION ................................................................................................ 139 CONFLICT OF INTEREST ......................................................................................................... 139 ACKNOWLEDGEMENT ............................................................................................................. 139 REFERENCES ............................................................................................................................... 140 CHAPTER 9 RECOGNIZING RICE LEAVES DISORDERS BY APPLYING DEEP LEARNING .............................................................................................................................................. 142 Taranjeet Singh, Krishna Kumar, S. S. Bedi and Harshit Bhadwaj INTRODUCTION .......................................................................................................................... 142 PADDY DISEASES ........................................................................................................................ 145 DEEP LEARNING (DL) ................................................................................................................ 145 Pretrained Neural Network (PNN) .......................................................................................... 146 CONCLUDING REMARKS ......................................................................................................... 150 CONSENT OF PUBLICATION ................................................................................................... 150 CONFLICT OF INTEREST ......................................................................................................... 150 ACKNOWLEDGEMENTS ........................................................................................................... 150 REFERENCES ............................................................................................................................... 151 CHAPTER 10 SHALLOW CLOUD CLASSIFICATION USING DEEP LEARNING AND IMAGE SEGMENTATION ................................................................................................................... 153 Amreen Ahmad, Chanchal Kumar, Ajay Kumar Yadav and Agnik Guha INTRODUCTION .......................................................................................................................... 153 What are Shallow Clouds? ...................................................................................................... 153 Why is it Important to Study Shallow Clouds? ...................................................................... 154 Motivation for an Automated System for Cloud Classification ............................................. 154 Benefits ................................................................................................................................... 154 RELATED WORK ......................................................................................................................... 155 PROPOSED METHODOLOGY .................................................................................................. 156 Data Preprocessing .................................................................................................................. 156 Data Analysis .......................................................................................................................... 156 Model Used ............................................................................................................................. 156 UNet .............................................................................................................................. 156 Idea Behind UNet .......................................................................................................... 156 Architecture UNet ......................................................................................................... 156 UNet on ResNet34 Backbone: Residual Network ......................................................... 157 Residual Blocks ............................................................................................................. 157 Architecture ............................................................................................................................. 157 Cross Entropy .......................................................................................................................... 158 Dice Loss ................................................................................................................................ 159 RAdam Optima ....................................................................................................................... 159 Evaluation Metric .................................................................................................................... 159 DATA SET ...................................................................................................................................... 159 EXPERIMENTAL ANALYSIS .................................................................................................... 162