Note: Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Role of AI in Cyber Security -- 1.1 Introduction -- 1.2 Need for Artificial Intelligence -- 1.3 Artificial Intelligence in Cyber Security -- 1.3.1 Multi-Layered Security System Design -- 1.3.2 Traditional Security Approach and AI -- 1.4 Related Work -- 1.4.1 Literature Review -- 1.4.2 Corollary -- 1.5 Proposed Work -- 1.5.1 System Architecture -- 1.5.2 Future Scope -- 1.6 Conclusion -- References -- 2 Privacy Preserving Using Data Mining -- 2.1 Introduction -- 2.2 Data Mining Techniques and Their Role in Classification and Detection -- 2.3 Clustering -- 2.4 Privacy Preserving Data Mining (PPDM) -- 2.5 Intrusion Detection Systems (IDS) -- 2.5.1 Types of IDS -- 2.6 Phishing Website Classification -- 2.7 Attacks by Mitigating Code Injection -- 2.7.1 Code Injection and Its Categories -- 2.8 Conclusion -- References -- 3 Role of Artificial Intelligence in Cyber Security and Security Framework -- 3.1 Introduction -- 3.2 AI for Cyber Security -- 3.3 Uses of Artificial Intelligence in Cyber Security -- 3.4 The Role of AI in Cyber Security -- 3.4.1 Simulated Intelligence Can Distinguish Digital Assaults -- 3.4.2 Computer-Based Intelligence Can Forestall Digital Assaults -- 3.4.3 Artificial Intelligence and Huge Scope Cyber Security -- 3.4.4 Challenges and Promises of Artificial Intelligence in Cyber Security -- 3.4.5 Present-Day Cyber Security and its Future with Simulated Intelligence -- 3.4.6 Improved Cyber Security with Computer-Based Intelligence and AI (ML) -- 3.4.7 AI Adopters Moving to Make a Move -- 3.5 AI Impacts on Cyber Security -- 3.6 The Positive Uses of AI Based for Cyber Security -- 3.7 Drawbacks and Restrictions of Using Computerized Reasoning For Digital Security -- 3.8 Solutions to Artificial Intelligence Confinements. , 3.9 Security Threats of Artificial Intelligence -- 3.10 Expanding Cyber Security Threats with Artificial Consciousness -- 3.11 Artificial Intelligence in Cybersecurity - Current Use-Cases and Capabilities -- 3.11.1 AI for System Danger Distinguishing Proof -- 3.11.2 The Common Fit for Artificial Consciousness in Cyber Security -- 3.11.3 Artificial Intelligence for System Danger ID -- 3.11.4 Artificial Intelligence Email Observing -- 3.11.5 Simulated Intelligence for Battling Artificial Intelligence Dangers -- 3.11.6 The Fate of Computer-Based Intelligence in Cyber Security -- 3.12 How to Improve Cyber Security for Artificial Intelligence -- 3.13 Conclusion -- References -- 4 Botnet Detection Using Artificial Intelligence -- 4.1 Introduction to Botnet -- 4.2 Botnet Detection -- 4.2.1 Host-Centred Detection (HCD) -- 4.2.2 Honey Nets-Based Detection (HNBD) -- 4.2.3 Network-Based Detection (NBD) -- 4.3 Botnet Architecture -- 4.3.1 Federal Model -- 4.3.2 Devolved Model -- 4.3.3 Cross Model -- 4.4 Detection of Botnet -- 4.4.1 Perspective of Botnet Detection -- 4.4.2 Detection (Disclosure) Technique -- 4.4.3 Region of Tracing -- 4.5 Machine Learning -- 4.5.1 Machine Learning Characteristics -- 4.6 A Machine Learning Approach of Botnet Detection -- 4.7 Methods of Machine Learning Used in Botnet Exposure -- 4.7.1 Supervised (Administrated) Learning -- 4.7.2 Unsupervised Learning -- 4.8 Problems with Existing Botnet Detection Systems -- 4.9 Extensive Botnet Detection System (EBDS) -- 4.10 Conclusion -- References -- 5 Spam Filtering Using AI -- 5.1 Introduction -- 5.1.1 What is SPAM? -- 5.1.2 Purpose of Spamming -- 5.1.3 Spam Filters Inputs and Outputs -- 5.2 Content-Based Spam Filtering Techniques -- 5.2.1 Previous Likeness-Based Filters -- 5.2.2 Case-Based Reasoning Filters -- 5.2.3 Ontology-Based E-Mail Filters -- 5.2.4 Machine-Learning Models. , 5.3 Machine Learning-Based Filtering -- 5.3.1 Linear Classifiers -- 5.3.2 Naïve Bayes Filtering -- 5.3.3 Support Vector Machines -- 5.3.4 Neural Networks and Fuzzy Logics-Based Filtering -- 5.4 Performance Analysis -- 5.5 Conclusion -- References -- 6 Artificial Intelligence in the Cyber Security Environment -- 6.1 Introduction -- 6.2 Digital Protection and Security Correspondences Arrangements -- 6.2.1 Operation Safety and Event Response -- 6.2.2 AI2 -- 6.3 Black Tracking -- 6.3.1 Web Security -- 6.4 Spark Cognition Deep Military -- 6.5 The Process of Detecting Threats -- 6.6 Vectra Cognito Networks -- 6.7 Conclusion -- References -- 7 Privacy in Multi-Tenancy Frameworks Using AI -- 7.1 Introduction -- 7.2 Framework of Multi-Tenancy -- 7.3 Privacy and Security in Multi-Tenant Base System Using AI -- 7.4 Related Work -- 7.5 Conclusion -- References -- 8 Biometric Facial Detection and Recognition Based on ILPB and SVM -- 8.1 Introduction -- 8.1.1 Biometric -- 8.1.2 Categories of Biometric -- 8.1.3 Significance and Scope -- 8.1.4 Biometric Face Recognition -- 8.1.5 Related Work -- 8.1.6 Main Contribution -- 8.1.7 Novelty Discussion -- 8.2 The Proposed Methodolgy -- 8.2.1 Face Detection Using Haar Algorithm -- 8.2.2 Feature Extraction Using ILBP -- 8.2.3 Dataset -- 8.2.4 Classification Using SVM -- 8.3 Experimental Results -- 8.3.1 Face Detection -- 8.3.2 Feature Extraction -- 8.3.3 Recognize Face Image -- 8.4 Conclusion -- References -- 9 Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using -- 9.1 Introduction -- 9.2 Inspection System for Defect Detection -- 9.3 Defect Recognition Methodology -- 9.4 Health Care MGPS Inspection -- 9.5 Conclusion -- References -- 10 Fuzzy Approach for Designing Security Framework -- 10.1 Introduction -- 10.2 Fuzzy Set. , 10.3 Planning for a Rule-Based Expert System for Cyber Security -- 10.3.1 Level 1: Defining Cyber Security Expert System Variables -- 10.3.2 Level 2: Information Gathering for Cyber Terrorism -- 10.3.3 Level 3: System Design -- 10.3.4 Level 4: Rule-Based Model -- 10.4 Digital Security -- 10.4.1 Cyber-Threats -- 10.4.2 Cyber Fault -- 10.4.3 Different Types of Security Services -- 10.5 Improvement of Cyber Security System (Advance) -- 10.5.1 Structure -- 10.5.2 Cyber Terrorism for Information/Data Collection -- 10.6 Conclusions -- References -- 11 Threat Analysis Using Data Mining Technique -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Data Mining Methods in Favor of Cyber-Attack Detection -- 11.4 Process of Cyber-Attack Detection Based on Data Mining -- 11.5 Conclusion -- References -- 12 Intrusion Detection Using Data Mining -- 12.1 Introduction -- 12.2 Essential Concept -- 12.2.1 Intrusion Detection System -- 12.2.2 Categorization of IDS -- 12.3 Detection Program -- 12.3.1 Misuse Detection -- 12.4 Decision Tree -- 12.4.1 Classification and Regression Tree (CART) -- 12.4.2 Iterative Dichotomise 3 (ID3) -- 12.4.3 C 4.5 -- 12.5 Data Mining Model for Detecting the Attacks -- 12.5.1 Framework of the Technique -- 12.6 Conclusion -- References -- 13 A Maize Crop Yield Optimization and Healthcare Monitoring Framework Using Firefly Algorithm through IoT -- 13.1 Introduction -- 13.2 Literature Survey -- 13.3 Experimental Framework -- 13.4 Healthcare Monitoring -- 13.5 Results and Discussion -- 13.6 Conclusion -- References -- 14 Vision-Based Gesture Recognition: A Critical Review -- 14.1 Introduction -- 14.2 Issues in Vision-Based Gesture Recognition -- 14.2.1 Based on Gestures -- 14.2.2 Based on Performance -- 14.2.3 Based on Background -- 14.3 Step-by-Step Process in Vision-Based -- 14.3.1 Sensing -- 14.3.2 Preprocessing -- 14.3.3 Feature Extraction. , 14.4 Classification -- 14.5 Literature Review -- 14.6 Conclusion -- References -- 15 SPAM Filtering Using Artificial Intelligence -- 15.1 Introduction -- 15.2 Architecture of Email Servers and Email Processing Stages -- 15.2.1 Architecture Email Spam Filtering -- 15.2.2 Email Spam Filtering Process -- 15.2.3 Freely Available Email Spam Collection -- 15.3 Execution Evaluation Measures -- 15.4 Classification Machine Learning Technique for Email Spam -- 15.4.1 Flock Technique Clustering -- 15.4.2 Naïve Bayes Classifier -- 15.4.3 Neural Network -- 15.4.4 Firefly Algorithm -- 15.4.5 Fuzzy Set Classifiers -- 15.4.6 Support Vector Machine -- 15.4.7 Decision Tree -- 15.4.8 Ensemble Classifiers -- 15.4.9 Random Forests (RF) -- 15.5 Conclusion -- References -- About the Editors -- Index -- EULA.

Note: Intro -- Preface -- Contents -- About the Editors -- Internet of Things and Web Services for Handling Pandemic Challenges -- 1 Introduction -- 1.1 IoT Process to Combat the Covid-19 Pandemic -- 2 IoT for Handling the Pandemic Challenges -- 2.1 Literature Survey -- 2.2 IoT in Personal Medical Devices -- 2.3 In Smart Home IoT -- 2.4 In IT Sector -- 2.5 Challenges of IoT in the Wake of COVID-19 -- 2.6 Uses of IoT for COVID-19 Pandemic -- 2.7 Practices Involved in IoT for Tracking COVID-19 Patients -- 2.8 Global Technological Advancements to Resolve COVID-19 Cases Rapidly -- 3 Web Services for Handling COVID-19 Pandemic Challenges -- 3.1 Accessing COVID-19 Data -- 3.2 Contact Tracing -- 3.3 Support Work from Home Activities -- 3.4 Overcome Challenges in Critical Sectors -- 3.5 Adoption of Microservices Technology -- 4 Conclusion -- References -- Corona Thwack: Socio-Economic Impact of Covid-19 Pandemic in India -- 1 Introduction -- 1.1 Coronavirus: A Bird's Eye View -- 1.2 Covid-19 Acquisition -- 1.3 Covid-19 Complications -- 1.4 Safety Measures -- 2 Pathogenic Diagnosis -- 3 Corona Cure -- 3.1 Treatment -- 3.2 Vaccine: The Ray of Hope -- 4 Covid-19 Pandemic in India -- 4.1 Cataclysm -- 4.2 History -- 4.3 Initiatives -- 4.4 Creating History -- 5 Socio-Economic Impact of Covid-19 in India -- 5.1 Social Impact -- 5.2 Economic Impact -- 6 Conclusion -- References -- Mathematical Modeling on Double Quarantine Process in the Spread and Stability of COVID-19 -- 1 Introduction -- 2 Mathematical Model -- 2.1 Model Analysis -- 2.2 Calculation of R0 2 from SIQ1 R Model -- 2.3 Stability Analysis for SIQ1 R Model -- 2.4 Calculation of Basic Reproduction Number from SQH IQ1 R Model -- 2.5 Stability Analysis for SQH IQ1 R Model -- 2.6 Analysis of Global Stability at Endemic Equilibrium -- 3 Discussion of the Result -- 4 Conclusion -- References. , A Study and Novel AI/ML-Based Framework to Detect COVID-19 Virus Using Smartphone Embedded Sensors -- 1 Introduction -- 2 Smart Phones to Detect COVID-19 -- 3 Combating Covid-19 with Artificial Intelligence/Machine Learning -- 4 Proposed Smartphone Based Framework -- 5 Conclusion -- References -- Transmission Modelling on COVID-19 Pandemic and Its Challenges -- 1 Introduction -- 1.1 Nomenclature -- 2 Model Formations and Basic Assumptions -- 2.1 Mathematical Model -- 2.2 Boundedness and Positivity of the Model -- 3 Stability Analysis and Calculation of the Basic Reproduction Number -- 3.1 Calculation of Basic Reproduction Number -- 3.2 Global Stability -- 4 Discussion of the Result -- 5 Conclusion -- References -- Effect of COVID-19 Pandemic on Mental Health: An Under-Realized Sociological Enigma -- 1 COVID-19 Pandemic Effect on Mental Health: Introduction -- 2 Common Psychological Problems Post COVID-19 -- 2.1 Among General Population -- 2.2 Among COVID-19 Patients -- 2.3 Among Close Relatives and Neighbors -- 2.4 Among Healthcare Workers -- 2.5 Among Geriatric Patients with Co-morbidities -- 3 Psychological Imbalance in Home Quarantine -- 3.1 Some Major Risk Factors of Stressor in Quarantine -- 3.2 How to Minimize the Consequence of Home Quarantine -- 4 Psychological Imbalance in Hospital Quarantine -- 4.1 Psychological Problem During Hospital Quarantine Can Be Categories into Two-Part [22] -- 4.2 Exacerbation of Preexisting Psychiatric Conditions -- 4.3 There is Three Risk Factor Related to Exacerbation of Preexisting Psychiatric Condition -- 4.4 How to Minimize the Negative Psychological Effect of Hospital Quarantine -- 5 Psychological Approach to Assess Mental Health Post COVID-19 -- 5.1 Relevance of Yoga on COVID-19 -- 5.2 Use of Digital Platforms -- 5.3 Shielding Measures in Psychiatric Hospitals. , 5.4 Rehabilitation of Mental Health in Times of COVID-19 -- 5.5 Coping Mental Health Issues in the Wake of COVID-19 Pandemic -- 6 Steps to Mitigate Pandemic in Future -- 6.1 Awareness of Situation -- 6.2 Eliminating Sparks of Pandemic -- 6.3 Communication of Risk -- 6.4 Scaling up of Potentials -- 7 Conclusion -- References -- Predicting the COVID-19 Outspread in Andhra Pradesh Using Hybrid Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Deep Learning Techniques -- 3 Methodology -- 3.1 Deep Learning Classifiers -- 3.2 Data Pre-processing -- 3.3 Feature Selection -- 4 Performance Measures -- 4.1 Experiment Setup -- 4.2 Performance Metrics -- 4.3 Performance Results -- 5 Conclusion -- References -- Mental Health Decline During Corona Virus Outbreak -- 1 Introduction -- 1.1 Worldwide Impact -- 1.2 Effect in India -- 1.3 Mental Effects of the Pandemic/Lockdown -- 2 Investigation of Indian Population (Survey Findings) -- 2.1 Review Methodology and Demographics -- 2.2 Vulnerability of Duration -- 2.3 Dread of Contracting Covid-19 -- 2.4 Dread of Job Steadiness/Layoff -- 2.5 Impact of Covid-19 Related Stress on Lifestyle -- 2.6 Lockdown Fatigue -- 3 Effect of Prolonged Lockdown on Stress Levels -- 3.1 Fall in Work-Life Balance -- 3.2 Postponement of Exams -- 3.3 Pay Cut/Job Loss -- 3.4 Net Impact of Stress Through the Lockdown -- 3.5 Post Unlocks Fears -- 3.6 Net Impact on Emotions at the Beginning of Lockdown -- 3.7 City Wise Comparison -- 4 Net Change in Stress Levels in Top Cities, in Comparison to the Whole Country -- 4.1 Top 5 Coping Mechanisms to Deal with Stress During Lockdown -- 4.2 Meditations by Government and Other Organizations -- 4.3 Utilization Surge on Therapy Platforms-27 -- 5 Sample Case Studies -- 5.1 Case 1-Difficulty in Adjusting to Life at Home [21] -- 5.2 Case 2-Compulsive Cleaning. , 5.3 Case 3-Irrational Fear -- 5.4 Case 4-Violent Behavior Toward Parents -- 5.5 Recommendations -- 5.6 Management Strategies -- 6 Conclusion -- References -- Social Challenges and Consequences of COVID-19 -- 1 Introduction -- 2 Total Lockdown -- 3 India's Overstretched Healthcare System -- 4 The Human Face of COVID-19 -- 5 Themes from the Story of the Patient -- 6 The Indigenous Communities -- 7 Developmental Sport and Stability -- 8 The Effect on the Economy -- 9 Challenges in Socio-Culture -- 10 Urban Migrant Workers Plight -- 11 Dissemination of Misinformation -- 12 The Way Forward -- 13 Social Unrest Management -- 14 Completion -- References -- Economic Impact and Measures of Corona Regime -- 1 Introduction -- 2 Beginning of Economic Slowdown -- 3 International Trade Plunged as the Virus Spread and Projections by Various Agencies -- 4 Growth in Trade of Medical Products -- 5 Impact on Major Economies During the Period -- 6 Turbulence in Global Financial Markets -- 7 Impact of COVID-19 on Indian Economy -- 8 Policy Responses Taken so Far -- 9 Some of the Immediate Measures Included -- 10 How the Story Looks Like Now? -- 11 Debt Sustainability Issues -- 12 Rising Unemployment -- 13 China Leading the New Growth Phases -- 14 Inequality of Income -- 15 Slowdown of Globalization -- 16 Conclusion -- References -- Modeling the Impact of Various Treatment and Prevention Tact's on COVID-19 Worldwide -- 1 Introduction -- 1.1 Bats-Hosts-Reservoir-People -- 1.2 SIR Model -- 1.3 SEIR Model -- 2 Formulation of Mathematical Model -- 3 Basic Reproduction Number -- 4 Summary and Conclusion -- References -- Understanding Emotional Health Sustainability Amidst COVID-19 Imposed Lockdown -- 1 Introduction -- 2 Related Works -- 3 Data and Methodology -- 3.1 Dataset -- 3.2 Two-Way Emotion Characterization -- 3.3 Internet Portal -- 4 Results and Discussion. , 5 Reflections -- References -- Industry 4.0 Technologies and Their Applications in Fighting COVID-19 -- 1 Introduction -- 1.1 Industry 4.0 Overview -- 1.2 Industry 4.0 Technologies -- 2 COVID-19 Challenges -- 2.1 Review of Literature -- 2.2 COVID-19 Challenges in Lockdown Phase -- 2.3 After Lockdown: Unlock 5.0 -- 3 Industry 4.0 Applications in COVID-19 Challenges -- 3.1 Face Mask Detection System for COVID-19 -- 3.2 COVID-19 Self-test Software -- 3.3 Keep Track COVID-19 Positive Patient -- 3.4 Social Distancing Analysis System for COVID-19 -- 3.5 Care of Old Age -- 3.6 COVID-19 Data Analysis -- 4 Conclusion -- References -- Internet of Medical Things (IoMT) Enabled TeleCOVID System for Diagnosis of COVID-19 Patients -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Proposed Design -- 4 Results and Analysis -- 4.1 Results and Analysis of Activity Detection System -- 4.2 Results of the TeleCOVID Diagnostic System -- 5 Conclusion -- References.

Note: Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 A Fundamental Overview of Different Algorithms and Performance Optimization for Swarm Intelligence -- 1.1 Introduction -- 1.2 Methodology of SI Framework -- 1.3 Composing With SI -- 1.4 Algorithms of the SI -- 1.5 Conclusion -- References -- 2 Introduction to IoT With Swarm Intelligence -- 2.1 Introduction -- 2.1.1 Literature Overview -- 2.2 Programming -- 2.2.1 Basic Programming -- 2.2.2 Prototyping -- 2.3 Data Generation -- 2.3.1 From Where the Data Comes? -- 2.3.2 Challenges of Excess Data -- 2.3.3 Where We Store Generated Data? -- 2.3.4 Cloud Computing and Fog Computing -- 2.4 Automation -- 2.4.1 What is Automation? -- 2.4.2 How Automation is Being Used? -- 2.5 Security of the Generated Data -- 2.5.1 Why We Need Security in Our Data? -- 2.5.2 What Types of Data is Being Generated? -- 2.5.3 Protecting Different Sector Working on the Principle of IoT -- 2.6 Swarm Intelligence -- 2.6.1 What is Swarm Intelligence? -- 2.6.2 Classification of Swarm Intelligence -- 2.6.3 Properties of a Swarm Intelligence System -- 2.7 Scope in Educational and Professional Sector -- 2.8 Conclusion -- References -- 3 Perspectives and Foundations of Swarm Intelligence and its Application -- 3.1 Introduction -- 3.2 Behavioral Phenomena of Living Beings and Inspired Algorithms -- 3.2.1 Bee Foraging -- 3.2.2 ABC Algorithm -- 3.2.3 Mating and Marriage -- 3.2.4 MBO Algorithm -- 3.2.5 Coakroach Behavior -- 3.3 Roach Infestation Optimization -- 3.3.1 Lampyridae Bioluminescence -- 3.3.2 GSO Algorithm -- 3.4 Conclusion -- References -- 4 Implication of IoT Components and Energy Management Monitoring -- 4.1 Introduction -- 4.2 IoT Components -- 4.3 IoT Energy Management -- 4.4 Implication of Energy Measurement for Monitoring -- 4.5 Execution of Industrial Energy Monitoring. , 4.6 Information Collection -- 4.7 Vitality Profiles Analysis -- 4.8 IoT-Based Smart Energy Management System -- 4.9 Smart Energy Management System -- 4.10 IoT-Based System for Intelligent Energy Management in Buildings -- 4.11 Smart Home for Energy Management Using IoT -- References -- 5 Distinct Algorithms for Swarm Intelligence in IoT -- 5.1 Introduction -- 5.2 Swarm Bird-Based Algorithms for IoT -- 5.2.1 Particle Swarm Optimization (PSO) -- 5.2.2 Cuckoo Search Algorithm -- 5.2.3 Bat Algorithm -- 5.3 Swarm Insect-Based Algorithm for IoT -- 5.3.1 Ant Colony Optimization -- 5.3.2 Artificial Bee Colony -- 5.3.3 Honey-Bee Mating Optimization -- 5.3.4 Firefly Algorithm -- 5.3.5 Glowworm Swarm Optimization -- References -- 6 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT -- 6.1 Introduction -- 6.2 Content Management System -- 6.3 Data Management and Mining -- 6.3.1 Data Life Cycle -- 6.3.2 Knowledge Discovery in Database -- 6.3.3 Data Mining vs. Data Warehousing -- 6.3.4 Data Mining Techniques -- 6.3.5 Data Mining Technologies -- 6.3.6 Issues in Data Mining -- 6.4 Introduction to Internet of Things -- 6.5 Swarm Intelligence Techniques -- 6.5.1 Ant Colony Optimization -- 6.5.2 Particle Swarm Optimization -- 6.5.3 Differential Evolution -- 6.5.4 Standard Firefly Algorithm -- 6.5.5 Artificial Bee Colony -- 6.6 Chapter Summary -- References -- 7 Healthcare Data Analytics Using Swarm Intelligence -- 7.1 Introduction -- 7.1.1 Definition -- 7.2 Intelligent Agent -- 7.3 Background and Usage of AI Over Healthcare Domain -- 7.4 Application of AI Techniques in Healthcare -- 7.5 Benefits of Artificial Intelligence -- 7.6 Swarm Intelligence Model -- 7.7 Swarm Intelligence Capabilities -- 7.8 How the Swarm AI Technology Works -- 7.9 Swarm Algorithm -- 7.10 Ant Colony Optimization Algorithm. , 7.11 Particle Swarm Optimization -- 7.12 Concepts for Swarm Intelligence Algorithms -- 7.13 How Swarm AI is Useful in Healthcare -- 7.14 Benefits of Swarm AI -- 7.15 Impact of Swarm-Based Medicine -- 7.16 SI Limitations -- 7.17 Future of Swarm AI -- 7.18 Issues and Challenges -- 7.19 Conclusion -- References -- 8 Swarm Intelligence for Group Objects in Wireless Sensor Networks -- 8.1 Introduction -- 8.2 Algorithm -- 8.3 Mechanism and Rationale of the Work -- 8.3.1 Related Work -- 8.4 Network Energy Model -- 8.4.1 Network Model -- 8.5 PSO Grouping Issue -- 8.6 Proposed Method -- 8.6.1 Grouping Phase -- 8.6.2 Proposed Validation Record -- 8.6.3 Data Transmission Stage -- 8.7 Bunch Hub Refreshing Calculation Dependent on an Improved PSO -- 8.8 Other SI Models -- 8.9 An Automatic Clustering Algorithm Based on PSO -- 8.10 Steering Rule Based on Informed Algorithm -- 8.11 Routing Protocols Based on Meta-Heuristic Algorithm -- 8.12 Routing Protocols for Avoiding Energy Holes -- 8.13 System Model -- 8.13.1 Network Model -- 8.13.2 Power Model -- References -- 9 Swam Intelligence-Based Resources Optimization and Analyses and Managing Data in IoT With Data Mining Technologies -- 9.1 Introduction -- 9.1.1 Swarm Intelligence -- 9.2 IoT With Data Mining -- 9.2.1 Data from IoT -- 9.2.2 Data Mining With KDD -- 9.2.3 PSO With Data Mining -- 9.3 ACO and Data Mining -- 9.4 Challenges for ACO-Based Data Mining -- References -- 10 Data Management and Mining Technologies to Manage and Analyze Data in IoT -- 10.1 Introduction -- 10.2 Data Management -- 10.3 Data Lifecycle of IoT -- 10.4 Procedures to Implement IoT Data Management -- 10.5 Industrial Data Lifecycle -- 10.6 Industrial Data Management Framework of IoT -- 10.6.1 Physical Layer -- 10.6.2 Correspondence Layer -- 10.6.3 Middleware Layer -- 10.7 Data Mining -- 10.7.1 Functionalities of Data Mining. , 10.7.2 Classification -- 10.8 Clustering -- 10.9 Affiliation Analysis -- 10.10 Time Series Analysis -- References -- 11 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT -- 11.1 Introduction -- 11.2 Information Mining Functionalities -- 11.2.1 Classification -- 11.2.2 Clustering -- 11.3 Data Mining Using Ant Colony Optimization -- 11.3.1 Enormous Information Investigation -- 11.3.2 Data Grouping -- 11.4 Computing With Ant-Based -- 11.4.1 Biological Background -- 11.5 Related Work -- 11.6 Contributions -- 11.7 SI in Enormous Information Examination -- 11.7.1 Handling Enormous Measure of Information -- 11.7.2 Handling Multidimensional Information -- 11.8 Requirements and Characteristics of IoT Data -- 11.8.1 IoT Quick and Gushing Information -- 11.8.2 IoT Big Information -- 11.9 Conclusion -- References -- 12 Swarm Intelligence-Based Energy-Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications -- 12.1 Introduction -- 12.1.1 Scope of Work -- 12.1.2 Related Works -- 12.1.3 Challenges in WSNs -- 12.1.4 Major Highlights of the Chapter -- 12.2 SI-Based Clustering Techniques -- 12.2.1 Growth of SI Algorithms and Characteristics -- 12.2.2 Typical SI-Based Clustering Algorithms -- 12.2.3 Comparison of SI Algorithms and Applications -- 12.3 WSN SI Clustering Applications -- 12.3.1 WSN Services -- 12.3.2 Clustering Objectives for WSN Applications -- 12.3.3 SI Algorithms for WSN: Overview -- 12.3.4 The Commonly Applied SI-Based WSN Clusterings -- 12.4 Challenges and Future Direction -- 12.5 Conclusions -- References -- 13 Swarm Intelligence for Clustering in Wireless Sensor Networks -- 13.1 Introduction -- 13.2 Clustering in Wireless Sensor Networks -- 13.3 Use of Swarm Intelligence for Clustering in WSN -- 13.3.1 Mobile Agents: Properties and Behavior. , 13.3.2 Benefits of Using Mobile Agents -- 13.3.3 Swarm Intelligence-Based Clustering Approach -- 13.4 Conclusion -- References -- 14 Swarm Intelligence for Clustering in Wi-Fi Networks -- 14.1 Introduction -- 14.1.1 Wi-Fi Networks -- 14.1.2 Wi-Fi Networks Clustering -- 14.2 Power Conscious Fuzzy Clustering Algorithm (PCFCA) -- 14.2.1 Adequate Cluster Head Selection in PCFCA -- 14.2.2 Creation of Clusters -- 14.2.3 Execution Assessment of PCFCA -- 14.3 Vitality Collecting in Remote Sensor Systems -- 14.3.1 Power Utilization -- 14.3.2 Production of Energy -- 14.3.3 Power Cost -- 14.3.4 Performance Representation of EEHC -- 14.4 Adequate Power Circular Clustering Algorithm (APRC) -- 14.4.1 Case-Based Clustering in Wi-Fi Networks -- 14.4.2 Circular Clustering Outlook -- 14.4.3 Performance Representation of APRC -- 14.5 Modifying Scattered Clustering Algorithm (MSCA) -- 14.5.1 Equivalence Estimation in Data Sensing -- 14.5.2 Steps in Modifying Scattered Clustering Algorithm (MSCA) -- 14.5.3 Performance Evaluation of MSCA -- 14.6 Conclusion -- References -- 15 Support Vector in Healthcare Using SVM/PSO in Various Domains: A Review -- 15.1 Introduction -- 15.2 The Fundamental PSO -- 15.2.1 Algorithm for PSO -- 15.3 The Support Vector -- 15.3.1 SVM in Regression -- 15.3.2 SVM in Clustering -- 15.3.3 Partition Clustering -- 15.3.4 Hierarchical Clustering -- 15.3.5 Density-Based Clustering -- 15.3.6 PSO in Clustering -- 15.4 Conclusion -- References -- 16 IoT-Based Healthcare System to Monitor the Sensor's Data of MWBAN -- 16.1 Introduction -- 16.1.1 Combination of AI and IoT in Real Activities -- 16.2 Related Work -- 16.3 Proposed System -- 16.3.1 AI and IoT in Medical Field -- 16.3.2 IoT Features in Healthcare -- 16.3.3 Approach for Sensor's Status of Patient -- 16.4 System Model -- 16.4.1 Solution Based on Heuristic Iterative Method. , 16.5 Challenges of Cyber Security in Healthcare With IoT.