Artificial Intelligence and Natural Algorithms

383 Pages·2022·22.931 MB·English

by  Rijwan Khan, Pawan Kumar Sharma, Sugam Sharma, Santosh Kumar

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
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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