{"product_id":"ai-based-advanced-optimization-techniques-for-edge-computing-hardback-9781394287031","title":"AI-Based Advanced Optimization Techniques for Edge Computing (Hardback) 9781394287031","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAI-Based Advanced Optimization Techniques for Edge Computing\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eMohit Kumar (Edited by), Kumar (Author), Gautam Srivastava (Edited by), Ashutosh Kumar Singh (Edited by), Kalka Dubey (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394287031, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 21 July 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e22.9 x 15.2 x 2.9 cm, 0.871 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe book offers cutting-edge insights into AI-driven optimization algorithms and their crucial role in enhancing real-time applications within fog and Edge IoT networks and addresses current challenges and future opportunities in this rapidly evolving field.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThis book focuses on artificial intelligence-induced adaptive optimization algorithms in fog and Edge IoT networks. Artificial intelligence, fog, and edge computing, together with IoT, are the next generation of paradigms offering services to people to improve existing services for real-time applications. Over the past few years, there has been rigorous growth in AI-based optimization algorithms and Edge and IoT paradigms. However, despite several applications and advancements, there are still some limitations and challenges to address including security, adaptive, complex, and heterogeneous IoT networks, protocols, intelligent offloading decisions, latency, energy consumption, service allocation, and network lifetime. \u003c\/p\u003e\n\u003cp\u003eThis volume aims to encourage industry professionals to initiate a set of architectural strategies to solve open research computation challenges. The authors achieve this by defining and exploring emerging trends in advanced optimization algorithms, AI techniques, and fog and Edge technologies for IoT applications. Solutions are also proposed to reduce the latency of real-time applications and improve other quality of service parameters using adaptive optimization algorithms in fog and Edge paradigms. \u003c\/p\u003e\n\u003cp\u003eThe book provides information on the full potential of IoT-based intelligent computing paradigms for the development of suitable conceptual and technological solutions using adaptive optimization techniques when faced with challenges. Additionally, it presents in-depth discussions in emerging interdisciplinary themes and applications reflecting the advancements in optimization algorithms and their usage in computing paradigms. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearchers, industrial engineers, and graduate\/post-graduate students in software engineering, computer science, electronic and electrical engineering, data analysts, and security professionals working in the fields of intelligent computing paradigms and similar areas.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgement xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Navigating Next-Generation Network Architecture: Unleashing the Power of SDN, NFV, NS, and AI Convergence 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMonika Dubey, Snehlata, Ashutosh Kumar Singh, Richa Mishra and Mohit Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Revolutionizing Infrastructure with SDN, NFV, and NS 4\u003c\/p\u003e \u003cp\u003e1.2.1 SDN: Definition and Architecture 6\u003c\/p\u003e \u003cp\u003e1.2.2 NFV: Definition and Architecture 9\u003c\/p\u003e \u003cp\u003e1.2.3 NS: Conceptual Abstractions 11\u003c\/p\u003e \u003cp\u003e1.3 Realizing NS Potential with SDN and NFV 13\u003c\/p\u003e \u003cp\u003e1.4 Artificial Intelligence: Pivotal Role in Networking Transformation 15\u003c\/p\u003e \u003cp\u003e1.4.1 Supervised Learning 16\u003c\/p\u003e \u003cp\u003e1.4.2 Unsupervised Learning 18\u003c\/p\u003e \u003cp\u003e1.4.3 Reinforcement Learning 18\u003c\/p\u003e \u003cp\u003e1.4.4 Deep Learning 21\u003c\/p\u003e \u003cp\u003e1.5 Navigating Challenges and Solutions 23\u003c\/p\u003e \u003cp\u003e1.5.1 Performance Issues in Network Structure 23\u003c\/p\u003e \u003cp\u003e1.5.2 Management and Orchestration Issues 24\u003c\/p\u003e \u003cp\u003e1.5.3 Security and Privacy 24\u003c\/p\u003e \u003cp\u003e1.5.4 New Business Models 25\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 26\u003c\/p\u003e \u003cp\u003eDisclosure Statement 26\u003c\/p\u003e \u003cp\u003eReferences 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 OctoEdge: An Octopus-Inspired Adaptive Edge Computing Architecture 35\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSashi Tarun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 36\u003c\/p\u003e \u003cp\u003e2.1.1 Edge Computing as Resource Manager 36\u003c\/p\u003e \u003cp\u003e2.1.2 Edge Computing Hurdles 37\u003c\/p\u003e \u003cp\u003e2.1.3 Edge Computing and the Need for Adaptability 38\u003c\/p\u003e \u003cp\u003e2.2 Problem Statement 39\u003c\/p\u003e \u003cp\u003e2.3 Motivations 40\u003c\/p\u003e \u003cp\u003e2.4 Related Work 41\u003c\/p\u003e \u003cp\u003e2.5 OctoEdge Proposed Architecture 45\u003c\/p\u003e \u003cp\u003e2.5.1 OctoEdge Working Principles 48\u003c\/p\u003e \u003cp\u003e2.5.2 Benefits of OctoEdge 49\u003c\/p\u003e \u003cp\u003e2.6 OctoEdge Architecture Functional Components 53\u003c\/p\u003e \u003cp\u003e2.7 Results and Discussion 59\u003c\/p\u003e \u003cp\u003e2.8 OctoEdge Architecture: Scope and Scientific Merits 60\u003c\/p\u003e \u003cp\u003e2.9 Use Cases and Applications 64\u003c\/p\u003e \u003cp\u003e2.10 Challenges and Future Directions 68\u003c\/p\u003e \u003cp\u003e2.11 Conclusion 68\u003c\/p\u003e \u003cp\u003eReferences 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Development of Optimized Machine Learning Oriented Models 71\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRatnesh Kumar Dubey, Dilip Kumar Choubey and Shubha Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 72\u003c\/p\u003e \u003cp\u003e3.1.1 NSL-KDD Dataset 75\u003c\/p\u003e \u003cp\u003e3.2 Literature Review 76\u003c\/p\u003e \u003cp\u003e3.3 Problem Definition 78\u003c\/p\u003e \u003cp\u003e3.4 Proposed Work 80\u003c\/p\u003e \u003cp\u003e3.4.1 Machine Learning 82\u003c\/p\u003e \u003cp\u003e3.5 Experimental Analysis 86\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 90\u003c\/p\u003e \u003cp\u003e3.7 Future Scope 91\u003c\/p\u003e \u003cp\u003eReferences 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Leveraging Multimodal Data and Deep Learning for Enhanced Stock Market Prediction 93\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePinky Gangwani and Vikas Panthi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 94\u003c\/p\u003e \u003cp\u003e4.1.1 Motivation and Contribution 96\u003c\/p\u003e \u003cp\u003e4.1.2 Rationale for Selecting the Methods 98\u003c\/p\u003e \u003cp\u003e4.2 Literature Review 100\u003c\/p\u003e \u003cp\u003e4.3 Proposed Design of an Efficient Model that Leverages Multimodal Data and Deep Learning for Enhanced Stock Market Prediction 107\u003c\/p\u003e \u003cp\u003e4.3.1 Discussion on Selection Criteria 114\u003c\/p\u003e \u003cp\u003e4.4 Statistical Analysis and Comparison 116\u003c\/p\u003e \u003cp\u003e4.5 Acknowledging Limitations and Potential Challenges 122\u003c\/p\u003e \u003cp\u003e4.6 Mitigation Strategies and Future Directions 123\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 124\u003c\/p\u003e \u003cp\u003e4.8 Future Scope 125\u003c\/p\u003e \u003cp\u003eReferences 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Context Dependent Sentiments Analysis Using Machine Learning 129\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMahima Shanker Pandey, Bihari Nandan Pandey, Abhishek Singh, Ashish Kumar Mishra and Brijesh Pandey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 130\u003c\/p\u003e \u003cp\u003e5.1.1 Motivation 131\u003c\/p\u003e \u003cp\u003e5.2 Literature Review 131\u003c\/p\u003e \u003cp\u003e5.2.1 Text Sentiment 132\u003c\/p\u003e \u003cp\u003e5.2.2 Audio Sentiment 132\u003c\/p\u003e \u003cp\u003e5.2.3 Video Sentiment 133\u003c\/p\u003e \u003cp\u003e5.3 Methodology 135\u003c\/p\u003e \u003cp\u003e5.3.1 System Architecture 135\u003c\/p\u003e \u003cp\u003e5.4 Proposed Model 137\u003c\/p\u003e \u003cp\u003e5.4.1 Proposed Algorithm 137\u003c\/p\u003e \u003cp\u003e5.4.2 Data Set Sources 138\u003c\/p\u003e \u003cp\u003e5.4.3 Text Sentiment 140\u003c\/p\u003e \u003cp\u003e5.4.4 Audio Sentiment 141\u003c\/p\u003e \u003cp\u003e5.4.5 Video Sentiment 142\u003c\/p\u003e \u003cp\u003e5.5 Implementations and Results 142\u003c\/p\u003e \u003cp\u003e5.5.1 Results 142\u003c\/p\u003e \u003cp\u003e5.5.2 Text Sentiment 143\u003c\/p\u003e \u003cp\u003e5.5.3 Audio Sentiment 144\u003c\/p\u003e \u003cp\u003e5.5.4 Video Sentiment 146\u003c\/p\u003e \u003cp\u003e5.5.5 Applications 149\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 149\u003c\/p\u003e \u003cp\u003eReferences 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Thyroid Cancer Prediction Using Optimizations 153\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSwati Sharma, Vijay Kumar Sharma, Punit Mittal, Pradeep Pant and Nitin Rakesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 154\u003c\/p\u003e \u003cp\u003e6.2 Background and Related Work 155\u003c\/p\u003e \u003cp\u003e6.3 Proposed Methodology 160\u003c\/p\u003e \u003cp\u003e6.4 Architecture 165\u003c\/p\u003e \u003cp\u003e6.5 Materials and Methods 169\u003c\/p\u003e \u003cp\u003e6.6 Results and Discussion 171\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 175\u003c\/p\u003e \u003cp\u003eReferences 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 An LSTM-Oriented Approach for Next Word Prediction Using Deep Learning 181\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNidhi Shukla, Ashutosh Kumar Singh, Vijay Kumar Dwivedi, Pallavi Shukla, Jeetesh Srivastava and Vivek Srivastava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 182\u003c\/p\u003e \u003cp\u003e7.2 Related Work 184\u003c\/p\u003e \u003cp\u003e7.3 Design and Implementation 186\u003c\/p\u003e \u003cp\u003e7.3.1 Background 186\u003c\/p\u003e \u003cp\u003e7.4 Proposed Model Architecture 190\u003c\/p\u003e \u003cp\u003e7.4.1 Experimental Setup 192\u003c\/p\u003e \u003cp\u003e7.4.2 Dataset Specification 192\u003c\/p\u003e \u003cp\u003e7.5 Results and Discussions 193\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 198\u003c\/p\u003e \u003cp\u003eReferences 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Churn Prediction in Social Networks Using Modified BiLSTM-CNN Model 203\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHimanshu Rai and Jyoti Kesarwani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 204\u003c\/p\u003e \u003cp\u003e8.2 Customer Behavior in Social Networks 209\u003c\/p\u003e \u003cp\u003e8.3 Proposed Methodology 218\u003c\/p\u003e \u003cp\u003e8.3.1 Churn Dataset Acquisition 218\u003c\/p\u003e \u003cp\u003e8.3.2 Data Preprocessing 220\u003c\/p\u003e \u003cp\u003e8.3.3 Proposed Model 220\u003c\/p\u003e \u003cp\u003e8.4 Result 221\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 225\u003c\/p\u003e \u003cp\u003eReferences 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Fog Computing Security Concerns in Healthcare Using IoT and Blockchain 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRuchi Mittal, Shikha Gupta and Shefali Arora\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 232\u003c\/p\u003e \u003cp\u003e9.1.1 Types of Security Concerns in Healthcare 236\u003c\/p\u003e \u003cp\u003e9.2 Related Work 239\u003c\/p\u003e \u003cp\u003e9.3 Open Questions and Research Challenges 241\u003c\/p\u003e \u003cp\u003e9.4 Problem Definition 242\u003c\/p\u003e \u003cp\u003e9.5 Objectives 242\u003c\/p\u003e \u003cp\u003e9.6 Research Methodology 243\u003c\/p\u003e \u003cp\u003e9.6.1 The Three-Tier Blockchain Design 243\u003c\/p\u003e \u003cp\u003e9.6.2 System Architecture 243\u003c\/p\u003e \u003cp\u003e9.6.3 Workflow in Different Scenarios 245\u003c\/p\u003e \u003cp\u003e9.7 Conclusion and Future Work 249\u003c\/p\u003e \u003cp\u003eReferences 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Smart Agriculture Revolution: Cloud and IoT-Based Solutions for Sustainable Crop Management and Precision Farming 253\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShrawan Kumar Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 255\u003c\/p\u003e \u003cp\u003e10.1.1 IoT in Agriculture 257\u003c\/p\u003e \u003cp\u003e10.1.2 Cloud Computing in Agriculture 259\u003c\/p\u003e \u003cp\u003e10.1.3 Precision Farming 263\u003c\/p\u003e \u003cp\u003e10.1.4 Sustainable Agricultural and Remote Sensing 265\u003c\/p\u003e \u003cp\u003e10.2 Data Analytics and Decision Support 267\u003c\/p\u003e \u003cp\u003e10.2.1 Remote Monitoring 269\u003c\/p\u003e \u003cp\u003e10.3 Challenges and Solutions Smart Agriculture 270\u003c\/p\u003e \u003cp\u003e10.3.1 (AI) Approach in Agriculture and Needs 270\u003c\/p\u003e \u003cp\u003e10.3.2 Needs of AI Farm 273\u003c\/p\u003e \u003cp\u003e10.3.3 Role of AI in Agriculture 274\u003c\/p\u003e \u003cp\u003e10.4 AI for Soybean (Glycine max) Crop 275\u003c\/p\u003e \u003cp\u003e10.4.1 Soybean Disease Image Acquisition and Pretreatment 276\u003c\/p\u003e \u003cp\u003e10.5 Result Discussion 281\u003c\/p\u003e \u003cp\u003e10.5.1 Emerging Trends and Technologies in Smart Agriculture 281\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 283\u003c\/p\u003e \u003cp\u003eReferences 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Greedy Particle Swarm Optimization Approach Using Leaky ReLU Function for Minimum Spanning Tree Problem 289\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAshish Kumar Singh and Anoj Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 290\u003c\/p\u003e \u003cp\u003e11.1.1 Goal 291\u003c\/p\u003e \u003cp\u003e11.1.2 Research Contribution are Below Listed 292\u003c\/p\u003e \u003cp\u003e11.2 Background 292\u003c\/p\u003e \u003cp\u003e11.2.1 Minimum Spanning Tree 294\u003c\/p\u003e \u003cp\u003e11.2.2 Particle Swarm Optimization 296\u003c\/p\u003e \u003cp\u003e11.2.3 Firefly Algorithm 297\u003c\/p\u003e \u003cp\u003e11.2.4 Leaky ReLU Activation Function 298\u003c\/p\u003e \u003cp\u003e11.3 Population-Based Proposed Optimization Approach 298\u003c\/p\u003e \u003cp\u003e11.3.1 Motivation 299\u003c\/p\u003e \u003cp\u003e11.3.2 Greedy Particle Swarm Optimization Using Leaky ReLU (LR-GPSO) 300\u003c\/p\u003e \u003cp\u003e11.4 Experimental Setup and Result Analysis of Proposed Work (LR-GPSO) 307\u003c\/p\u003e \u003cp\u003e11.4.1 Complexity 307\u003c\/p\u003e \u003cp\u003e11.4.2 Simulation Experiments 308\u003c\/p\u003e \u003cp\u003e11.4.3 Convergence Curve 311\u003c\/p\u003e \u003cp\u003e11.5 Conclusion and Future Work 313\u003c\/p\u003e \u003cp\u003eReferences 314\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 SDN Deployed Secure Application Design Framework for IoT Using Game Theory 317\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMadhukrishna Priyadarsini and Padmalochan Bera\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 318\u003c\/p\u003e \u003cp\u003e12.1.1 IoT Overview 318\u003c\/p\u003e \u003cp\u003e12.1.2 SDN Overview 319\u003c\/p\u003e \u003cp\u003e12.1.3 Game Theory Overview 321\u003c\/p\u003e \u003cp\u003e12.2 Background Study 322\u003c\/p\u003e \u003cp\u003e12.2.1 IoT Security Using SDN 322\u003c\/p\u003e \u003cp\u003e12.2.2 IoT Security Using Game Theory 323\u003c\/p\u003e \u003cp\u003e12.3 SDN-Deployed Design Framework for IoT Using Game-Theoretic Solutions 324\u003c\/p\u003e \u003cp\u003e12.3.1 Trust Verification 324\u003c\/p\u003e \u003cp\u003e12.4 Case Study: SDN Deployed Design Framework in Robot Manufacturing Industry 334\u003c\/p\u003e \u003cp\u003e12.4.1 Working Procedure of a Robot Manufacturing Industry 334\u003c\/p\u003e \u003cp\u003e12.4.2 Integration of SDN-Deployed Design Framework in Robot Manufacturing Industry 335\u003c\/p\u003e \u003cp\u003e12.4.3 Experimental Results 336\u003c\/p\u003e \u003cp\u003e12.5 Discussion 338\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 339\u003c\/p\u003e \u003cp\u003eReferences 339\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Framework for PLM in Industry 4.0 Based on Industrial Blockchain 341\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAli Zaheer Agha, Rajesh Kumar Shukla, Ratnesh Mishra and Ravi Shankar Shukla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 342\u003c\/p\u003e \u003cp\u003e13.1.1 What is Blockchain? 343\u003c\/p\u003e \u003cp\u003e13.1.2 Blockchain Technology’s Integration with Industry 4.0 343\u003c\/p\u003e \u003cp\u003e13.1.3 Blockchain Applications in Industry 4.0 343\u003c\/p\u003e \u003cp\u003e13.1.4 A Consensus Algorithm 344\u003c\/p\u003e \u003cp\u003e13.1.5 Product Lifecycle Management 345\u003c\/p\u003e \u003cp\u003e13.1.6 Benefits of Smart Contracts in Addressing PLM Challenges 347\u003c\/p\u003e \u003cp\u003e13.2 Related Work 348\u003c\/p\u003e \u003cp\u003e13.2.1 Product Lifecycle Management 349\u003c\/p\u003e \u003cp\u003e13.2.2 Industrial Blockchain 351\u003c\/p\u003e \u003cp\u003e13.2.3 The On-Chain vs. Off-Chain Principle 353\u003c\/p\u003e \u003cp\u003e13.3 The Recommended Architecture’s Methodology 354\u003c\/p\u003e \u003cp\u003e13.3.1 The Suggested Platform’s Architecture 354\u003c\/p\u003e \u003cp\u003e13.3.2 The Suggested Platform’s Technological Solution 358\u003c\/p\u003e \u003cp\u003e13.4 Key Services That are Suggested 360\u003c\/p\u003e \u003cp\u003e13.4.1 A Co-Creation Service Enabled by Blockchain 360\u003c\/p\u003e \u003cp\u003e13.4.2 Blockchain-Enabled QAT2 Service 363\u003c\/p\u003e \u003cp\u003e13.4.3 Proactive Upkeep Service Facilitated by Blockchain 364\u003c\/p\u003e \u003cp\u003e13.4.4 Smart Recycling Program Driven by Blockchain 365\u003c\/p\u003e \u003cp\u003e13.5 Modelling and Assessment 366\u003c\/p\u003e \u003cp\u003e13.5.1 Overview of the Investigation 366\u003c\/p\u003e \u003cp\u003e13.5.2 Experimental Evaluation and Comparison 368\u003c\/p\u003e \u003cp\u003e13.5.3 Discussion 372\u003c\/p\u003e \u003cp\u003e13.6 Conclusion and Future Work 373\u003c\/p\u003e \u003cp\u003eA Statement of Competing Interests 374\u003c\/p\u003e \u003cp\u003eReferences 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Machine Learning Enabled Smart Agriculture Classification Technique for Edge Devices Using Remote Sensing Platform 381\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePriyanka Gupta, Suraj Kumar Singh, Neetish Kumar and Bhavna Thakur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 382\u003c\/p\u003e \u003cp\u003e14.1 Introduction 382\u003c\/p\u003e \u003cp\u003e14.2 Related Works 384\u003c\/p\u003e \u003cp\u003e14.3 Methods and Dataset 386\u003c\/p\u003e \u003cp\u003e14.3.1 Research Area and Dataset 386\u003c\/p\u003e \u003cp\u003e14.3.2 Pre-Processing and Image Dataset 387\u003c\/p\u003e \u003cp\u003e14.3.3 Classifiers 390\u003c\/p\u003e \u003cp\u003e14.4 Proposed Algorithm 391\u003c\/p\u003e \u003cp\u003e14.5 Results and Discussions 392\u003c\/p\u003e \u003cp\u003e14.5.1 Classified Crop Map 394\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 395\u003c\/p\u003e \u003cp\u003eReferences 396\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 A Lightweight Intelligent Detection Approach for Interest Flooding Attack 401\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNaveen Kumar, Brijendra Pratap Singh and Rohit\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 402\u003c\/p\u003e \u003cp\u003e15.2 NDN Background 405\u003c\/p\u003e \u003cp\u003e15.2.1 NDN Architecture 405\u003c\/p\u003e \u003cp\u003e15.2.2 NDN Security 408\u003c\/p\u003e \u003cp\u003e15.3 Related Work 409\u003c\/p\u003e \u003cp\u003e15.4 IFA Feature Selection and Detection 411\u003c\/p\u003e \u003cp\u003e15.4.1 IFA Modelling 412\u003c\/p\u003e \u003cp\u003e15.4.2 Data Collection 413\u003c\/p\u003e \u003cp\u003e15.4.3 Balancing the Dataset 414\u003c\/p\u003e \u003cp\u003e15.4.4 Feature Selection 415\u003c\/p\u003e \u003cp\u003e15.4.5 Dimensionality Reduction 421\u003c\/p\u003e \u003cp\u003e15.4.6 Classification 424\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 428\u003c\/p\u003e \u003cp\u003eReferences 429\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 An Internet of Vehicles Model Architecture with Seven Layers 433\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSujata Negi Thakur, Manisha Koranga, Sandeep Abhishek, Richa Pandey and Mayurika Joshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 434\u003c\/p\u003e \u003cp\u003e16.2 Literature Review 435\u003c\/p\u003e \u003cp\u003e16.3 Proposed Architecture of Internet of Vehicles 439\u003c\/p\u003e \u003cp\u003e16.4 Applications, Characteristics, and Challenges of the Internet of Vehicles (IoV) 451\u003c\/p\u003e \u003cp\u003eConclusion 455\u003c\/p\u003e \u003cp\u003eReferences 455\u003c\/p\u003e \u003cp\u003eIndex 457\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" 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