{"product_id":"artificial-intelligence-and-data-mining-approaches-in-security-frameworks-hardback-9781119760405","title":"Artificial Intelligence and Data Mining Approaches in Security Frameworks (Hardback) 9781119760405","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence and Data Mining Approaches in Security Frameworks\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\"\u003eNeeraj Bhargava (Edited by), N Bhargava (Author), Ritu Bhargava (Edited by), Pramod Singh Rathore (Edited by), Rashmi Agrawal (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119760405, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 24 August 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e320 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 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\u003cb\u003eARTIFICIAL INTELLIGENCE AND DATA MINING IN SECURITY FRAMEWORKS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eWritten and edited by a team of experts in the field, this outstanding new volume offers solutions to the problems of security, outlining the concepts behind allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. \u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eArtificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to artificial intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalized security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and data mining and several other computing technologies to deploy such a system in an effective manner. \u003c\/p\u003e\n\u003cp\u003eThis book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice. \u003c\/p\u003e\n\u003cp\u003eThis groundbreaking new volume presents these topics and trends, bridging the research gap on AI and data mining to enable wide-scale implementation. Whether for the veteran engineer or the student, this is a must-have for any library. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eThis groundbreaking new volume: \u003c\/b\u003e \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eClarifies the understanding of certain key mechanisms of technology helpful in the use of artificial intelligence and data mining in security frameworks \u003c\/li\u003e \u003cli\u003eCovers practical approaches to the problems engineers face in working in this field, focusing on the applications used every day \u003c\/li\u003e \u003cli\u003eContains numerous examples, offering critical solutions to engineers and scientists \u003c\/li\u003e \u003cli\u003ePresents these new applications of AI and data mining that are of prime importance to human civilization as a whole \u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Role of AI in Cyber Security 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNavani Siroya and Prof Manju Mandot\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Need for Artificial Intelligence 2\u003c\/p\u003e \u003cp\u003e1.3 Artificial Intelligence in Cyber Security 3\u003c\/p\u003e \u003cp\u003e1.3.1 Multi-Layered Security System Design 3\u003c\/p\u003e \u003cp\u003e1.3.2 Traditional Security Approach and AI 4\u003c\/p\u003e \u003cp\u003e1.4 Related Work 5\u003c\/p\u003e \u003cp\u003e1.4.1 Literature Review 5\u003c\/p\u003e \u003cp\u003e1.4.2 Corollary 6\u003c\/p\u003e \u003cp\u003e1.5 Proposed Work 6\u003c\/p\u003e \u003cp\u003e1.5.1 System Architecture 7\u003c\/p\u003e \u003cp\u003e1.5.2 Future Scope 7\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 7\u003c\/p\u003e \u003cp\u003eReferences 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Privacy Preserving Using Data Mining 11\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChitra Jalota and Dr. Rashmi Agrawal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 11\u003c\/p\u003e \u003cp\u003e2.2 Data Mining Techniques and Their Role in Classification and Detection 14\u003c\/p\u003e \u003cp\u003e2.3 Clustering 19\u003c\/p\u003e \u003cp\u003e2.4 Privacy Preserving Data Mining (PPDM) 21\u003c\/p\u003e \u003cp\u003e2.5 Intrusion Detection Systems (IDS) 22\u003c\/p\u003e \u003cp\u003e2.5.1 Types of IDS 23\u003c\/p\u003e \u003cp\u003e2.5.1.1 Network-Based IDS 23\u003c\/p\u003e \u003cp\u003e2.5.1.2 Host-Based IDS 24\u003c\/p\u003e \u003cp\u003e2.5.1.3 Hybrid IDS 25\u003c\/p\u003e \u003cp\u003e2.6 Phishing Website Classification 26\u003c\/p\u003e \u003cp\u003e2.7 Attacks by Mitigating Code Injection 27\u003c\/p\u003e \u003cp\u003e2.7.1 Code Injection and Its Categories 27\u003c\/p\u003e \u003cp\u003e2.8 Conclusion 28\u003c\/p\u003e \u003cp\u003eReferences 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Role of Artificial Intelligence in Cyber Security and Security Framework 33\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShweta Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 34\u003c\/p\u003e \u003cp\u003e3.2 AI for Cyber Security 36\u003c\/p\u003e \u003cp\u003e3.3 Uses of Artificial Intelligence in Cyber Security 38\u003c\/p\u003e \u003cp\u003e3.4 The Role of AI in Cyber Security 40\u003c\/p\u003e \u003cp\u003e3.4.1 Simulated Intelligence Can Distinguish Digital Assaults 41\u003c\/p\u003e \u003cp\u003e3.4.2 Computer-Based Intelligence Can Forestall Digital Assaults 42\u003c\/p\u003e \u003cp\u003e3.4.3 Artificial Intelligence and Huge Scope Cyber Security 42\u003c\/p\u003e \u003cp\u003e3.4.4 Challenges and Promises of Artificial Intelligence in Cyber Security 43\u003c\/p\u003e \u003cp\u003e3.4.5 Present-Day Cyber Security and its Future with Simulated Intelligence 44\u003c\/p\u003e \u003cp\u003e3.4.6 Improved Cyber Security with Computer-Based Intelligence and AI (ML) 45\u003c\/p\u003e \u003cp\u003e3.4.7 AI Adopters Moving to Make a Move 45\u003c\/p\u003e \u003cp\u003e3.5 AI Impacts on Cyber Security 46\u003c\/p\u003e \u003cp\u003e3.6 The Positive Uses of AI Based for Cyber Security 48\u003c\/p\u003e \u003cp\u003e3.7 Drawbacks and Restrictions of Using Computerized Reasoning For Digital Security 49\u003c\/p\u003e \u003cp\u003e3.8 Solutions to Artificial Intelligence Confinements 50\u003c\/p\u003e \u003cp\u003e3.9 Security Threats of Artificial Intelligence 51\u003c\/p\u003e \u003cp\u003e3.10 Expanding Cyber Security Threats with Artificial Consciousness 52\u003c\/p\u003e \u003cp\u003e3.11 Artificial Intelligence in Cybersecurity – Current Use-Cases and Capabilities 55\u003c\/p\u003e \u003cp\u003e3.11.1 AI for System Danger Distinguishing Proof 56\u003c\/p\u003e \u003cp\u003e3.11.2 The Common Fit for Artificial Consciousness in Cyber Security 56\u003c\/p\u003e \u003cp\u003e3.11.3 Artificial Intelligence for System Danger ID 57\u003c\/p\u003e \u003cp\u003e3.11.4 Artificial Intelligence Email Observing 58\u003c\/p\u003e \u003cp\u003e3.11.5 Simulated Intelligence for Battling Artificial Intelligence Dangers 58\u003c\/p\u003e \u003cp\u003e3.11.6 The Fate of Computer-Based Intelligence in Cyber Security 59\u003c\/p\u003e \u003cp\u003e3.12 How to Improve Cyber Security for Artificial Intelligence 60\u003c\/p\u003e \u003cp\u003e3.13 Conclusion 61\u003c\/p\u003e \u003cp\u003eReferences 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Botnet Detection Using Artificial Intelligence 65\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAstha Parihar and Prof. Neeraj Bhargava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction to Botnet 66\u003c\/p\u003e \u003cp\u003e4.2 Botnet Detection 67\u003c\/p\u003e \u003cp\u003e4.2.1 Host-Centred Detection (HCD) 68\u003c\/p\u003e \u003cp\u003e4.2.2 Honey Nets-Based Detection (HNBD) 69\u003c\/p\u003e \u003cp\u003e4.2.3 Network-Based Detection (NBD) 69\u003c\/p\u003e \u003cp\u003e4.3 Botnet Architecture 69\u003c\/p\u003e \u003cp\u003e4.3.1 Federal Model 70\u003c\/p\u003e \u003cp\u003e4.3.1.1 IBN-Based Protocol 71\u003c\/p\u003e \u003cp\u003e4.3.1.2 HTTP-Based Botnets 71\u003c\/p\u003e \u003cp\u003e4.3.2 Devolved Model 71\u003c\/p\u003e \u003cp\u003e4.3.3 Cross Model 72\u003c\/p\u003e \u003cp\u003e4.4 Detection of Botnet 73\u003c\/p\u003e \u003cp\u003e4.4.1 Perspective of Botnet Detection 73\u003c\/p\u003e \u003cp\u003e4.4.2 Detection (Disclosure) Technique 73\u003c\/p\u003e \u003cp\u003e4.4.3 Region of Tracing 74\u003c\/p\u003e \u003cp\u003e4.5 Machine Learning 74\u003c\/p\u003e \u003cp\u003e4.5.1 Machine Learning Characteristics 74\u003c\/p\u003e \u003cp\u003e4.6 A Machine Learning Approach of Botnet Detection 75\u003c\/p\u003e \u003cp\u003e4.7 Methods of Machine Learning Used in Botnet Exposure 76\u003c\/p\u003e \u003cp\u003e4.7.1 Supervised (Administrated) Learning 76\u003c\/p\u003e \u003cp\u003e4.7.1.1 Appearance of Supervised Learning 77\u003c\/p\u003e \u003cp\u003e4.7.2 Unsupervised Learning 78\u003c\/p\u003e \u003cp\u003e4.7.2.1 Role of Unsupervised Learning 79\u003c\/p\u003e \u003cp\u003e4.8 Problems with Existing Botnet Detection Systems 80\u003c\/p\u003e \u003cp\u003e4.9 Extensive Botnet Detection System (EBDS) 81\u003c\/p\u003e \u003cp\u003e4.10 Conclusion 83\u003c\/p\u003e \u003cp\u003eReferences 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Spam Filtering Using AI 87\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYojna Khandelwal and Dr. Ritu Bhargava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 87\u003c\/p\u003e \u003cp\u003e5.1.1 What is SPAM? 87\u003c\/p\u003e \u003cp\u003e5.1.2 Purpose of Spamming 88\u003c\/p\u003e \u003cp\u003e5.1.3 Spam Filters Inputs and Outputs 88\u003c\/p\u003e \u003cp\u003e5.2 Content-Based Spam Filtering Techniques 89\u003c\/p\u003e \u003cp\u003e5.2.1 Previous Likeness–Based Filters 89\u003c\/p\u003e \u003cp\u003e5.2.2 Case-Based Reasoning Filters 89\u003c\/p\u003e \u003cp\u003e5.2.3 Ontology-Based E-Mail Filters 90\u003c\/p\u003e \u003cp\u003e5.2.4 Machine-Learning Models 90\u003c\/p\u003e \u003cp\u003e5.2.4.1 Supervised Learning 90\u003c\/p\u003e \u003cp\u003e5.2.4.2 Unsupervised Learning 90\u003c\/p\u003e \u003cp\u003e5.2.4.3 Reinforcement Learning 91\u003c\/p\u003e \u003cp\u003e5.3 Machine Learning–Based Filtering 91\u003c\/p\u003e \u003cp\u003e5.3.1 Linear Classifiers 91\u003c\/p\u003e \u003cp\u003e5.3.2 Naïve Bayes Filtering 92\u003c\/p\u003e \u003cp\u003e5.3.3 Support Vector Machines 94\u003c\/p\u003e \u003cp\u003e5.3.4 Neural Networks and Fuzzy Logics–Based Filtering 94\u003c\/p\u003e \u003cp\u003e5.4 Performance Analysis 97\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 97\u003c\/p\u003e \u003cp\u003eReferences 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Artificial Intelligence in the Cyber Security Environment 101\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJaya Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 102\u003c\/p\u003e \u003cp\u003e6.2 Digital Protection and Security Correspondences Arrangements 104\u003c\/p\u003e \u003cp\u003e6.2.1 Operation Safety and Event Response 105\u003c\/p\u003e \u003cp\u003e6.2.2 AI2 105\u003c\/p\u003e \u003cp\u003e6.2.2.1 CylanceProtect 105\u003c\/p\u003e \u003cp\u003e6.3 Black Tracking 106\u003c\/p\u003e \u003cp\u003e6.3.1 Web Security 107\u003c\/p\u003e \u003cp\u003e6.3.1.1 Amazon Macie 108\u003c\/p\u003e \u003cp\u003e6.4 Spark Cognition Deep Military 110\u003c\/p\u003e \u003cp\u003e6.5 The Process of Detecting Threats 111\u003c\/p\u003e \u003cp\u003e6.6 Vectra Cognito Networks 112\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 115\u003c\/p\u003e \u003cp\u003eReferences 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Privacy in Multi-Tenancy Frameworks Using AI 119\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShweta Solanki\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 119\u003c\/p\u003e \u003cp\u003e7.2 Framework of Multi-Tenancy 120\u003c\/p\u003e \u003cp\u003e7.3 Privacy and Security in Multi-Tenant Base System Using AI 122\u003c\/p\u003e \u003cp\u003e7.4 Related Work 125\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 125\u003c\/p\u003e \u003cp\u003eReferences 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Biometric Facial Detection and Recognition Based on ILPB and SVM 129\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShubhi Srivastava, Ankit Kumar and Shiv Prakash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 129\u003c\/p\u003e \u003cp\u003e8.1.1 Biometric 131\u003c\/p\u003e \u003cp\u003e8.1.2 Categories of Biometric 131\u003c\/p\u003e \u003cp\u003e8.1.2.1 Advantages of Biometric 132\u003c\/p\u003e \u003cp\u003e8.1.3 Significance and Scope 132\u003c\/p\u003e \u003cp\u003e8.1.4 Biometric Face Recognition 132\u003c\/p\u003e \u003cp\u003e8.1.5 Related Work 136\u003c\/p\u003e \u003cp\u003e8.1.6 Main Contribution 136\u003c\/p\u003e \u003cp\u003e8.1.7 Novelty Discussion 137\u003c\/p\u003e \u003cp\u003e8.2 The Proposed Methodolgy 139\u003c\/p\u003e \u003cp\u003e8.2.1 Face Detection Using Haar Algorithm 139\u003c\/p\u003e \u003cp\u003e8.2.2 Feature Extraction Using ILBP 141\u003c\/p\u003e \u003cp\u003e8.2.3 Dataset 143\u003c\/p\u003e \u003cp\u003e8.2.4 Classification Using SVM 143\u003c\/p\u003e \u003cp\u003e8.3 Experimental Results 145\u003c\/p\u003e \u003cp\u003e8.3.1 Face Detection 146\u003c\/p\u003e \u003cp\u003e8.3.2 Feature Extraction 146\u003c\/p\u003e \u003cp\u003e8.3.3 Recognize Face Image 147\u003c\/p\u003e \u003cp\u003e8.4 Conclusion 151\u003c\/p\u003e \u003cp\u003eReferences 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using ANN and IoT 155\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eS K Rajesh Kanna, O. Pandithurai, N. Anand, P. Sethuramalingam and Abdul Munaf\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 156\u003c\/p\u003e \u003cp\u003e9.2 Inspection System for Defect Detection 158\u003c\/p\u003e \u003cp\u003e9.3 Defect Recognition Methodology 162\u003c\/p\u003e \u003cp\u003e9.4 Health Care MGPS Inspection 165\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 168\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Fuzzy Approach for Designing Security Framework 173\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKapil Chauhan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 173\u003c\/p\u003e \u003cp\u003e10.2 Fuzzy Set 177\u003c\/p\u003e \u003cp\u003e10.3 Planning for a Rule-Based Expert System for Cyber Security 185\u003c\/p\u003e \u003cp\u003e10.3.1 Level 1: Defining Cyber Security Expert System Variables 185\u003c\/p\u003e \u003cp\u003e10.3.2 Level 2: Information Gathering for Cyber Terrorism 185\u003c\/p\u003e \u003cp\u003e10.3.3 Level 3: System Design 186\u003c\/p\u003e \u003cp\u003e10.3.4 Level 4: Rule-Based Model 187\u003c\/p\u003e \u003cp\u003e10.4 Digital Security 188\u003c\/p\u003e \u003cp\u003e10.4.1 Cyber-Threats 188\u003c\/p\u003e \u003cp\u003e10.4.2 Cyber Fault 188\u003c\/p\u003e \u003cp\u003e10.4.3 Different Types of Security Services 189\u003c\/p\u003e \u003cp\u003e10.5 Improvement of Cyber Security System (Advance) 190\u003c\/p\u003e \u003cp\u003e10.5.1 Structure 190\u003c\/p\u003e \u003cp\u003e10.5.2 Cyber Terrorism for Information\/Data Collection 191\u003c\/p\u003e \u003cp\u003e10.6 Conclusions 191\u003c\/p\u003e \u003cp\u003eReferences 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Threat Analysis Using Data Mining Technique 197\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRiddhi Panchal and Binod Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 198\u003c\/p\u003e \u003cp\u003e11.2 Related Work 199\u003c\/p\u003e \u003cp\u003e11.3 Data Mining Methods in Favor of Cyber-Attack Detection 201\u003c\/p\u003e \u003cp\u003e11.4 Process of Cyber-Attack Detection Based on Data Mining 204\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 205\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Intrusion Detection Using Data Mining 209\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAstha Parihar and Pramod Singh Rathore\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 209\u003c\/p\u003e \u003cp\u003e12.2 Essential Concept 210\u003c\/p\u003e \u003cp\u003e12.2.1 Intrusion Detection System 211\u003c\/p\u003e \u003cp\u003e12.2.2 Categorization of IDS 212\u003c\/p\u003e \u003cp\u003e12.2.2.1 Web Intrusion Detection System (WIDS) 213\u003c\/p\u003e \u003cp\u003e12.2.2.2 Host Intrusion Detection System (HIDS) 214\u003c\/p\u003e \u003cp\u003e12.2.2.3 Custom-Based Intrusion Detection System (CIDS) 215\u003c\/p\u003e \u003cp\u003e12.2.2.4 Application Protocol-Based Intrusion Detection System (APIDS) 215\u003c\/p\u003e \u003cp\u003e12.2.2.5 Hybrid Intrusion Detection System 216\u003c\/p\u003e \u003cp\u003e12.3 Detection Program 216\u003c\/p\u003e \u003cp\u003e12.3.1 Misuse Detection 217\u003c\/p\u003e \u003cp\u003e12.3.1.1 Expert System 217\u003c\/p\u003e \u003cp\u003e12.3.1.2 Stamp Analysis 218\u003c\/p\u003e \u003cp\u003e12.3.1.3 Data Mining 220\u003c\/p\u003e \u003cp\u003e12.4 Decision Tree 221\u003c\/p\u003e \u003cp\u003e12.4.1 Classification and Regression Tree (CART) 222\u003c\/p\u003e \u003cp\u003e12.4.2 Iterative Dichotomise 3 (ID3) 222\u003c\/p\u003e \u003cp\u003e12.4.3 C 4.5 223\u003c\/p\u003e \u003cp\u003e12.5 Data Mining Model for Detecting the Attacks 223\u003c\/p\u003e \u003cp\u003e12.5.1 Framework of the Technique 224\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 226\u003c\/p\u003e \u003cp\u003eReferences 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 A Maize Crop Yield Optimization and Healthcare Monitoring Framework Using Firefly Algorithm through IoT 229\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eS K Rajesh Kanna, V. Nagaraju, D. Jayashree, Abdul Munaf and M. Ashok\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 230\u003c\/p\u003e \u003cp\u003e13.2 Literature Survey 231\u003c\/p\u003e \u003cp\u003e13.3 Experimental Framework 232\u003c\/p\u003e \u003cp\u003e13.4 Healthcare Monitoring 237\u003c\/p\u003e \u003cp\u003e13.5 Results and Discussion 240\u003c\/p\u003e \u003cp\u003e13.6 Conclusion 242\u003c\/p\u003e \u003cp\u003eReferences 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Vision-Based Gesture Recognition: A Critical Review 247\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNeela Harish, Praveen, Prasanth, Aparna and Athaf\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 247\u003c\/p\u003e \u003cp\u003e14.2 Issues in Vision-Based Gesture Recognition 248\u003c\/p\u003e \u003cp\u003e14.2.1 Based on Gestures 249\u003c\/p\u003e \u003cp\u003e14.2.2 Based on Performance 249\u003c\/p\u003e \u003cp\u003e14.2.3 Based on Background 249\u003c\/p\u003e \u003cp\u003e14.3 Step-by-Step Process in Vision-Based 249\u003c\/p\u003e \u003cp\u003e14.3.1 Sensing 251\u003c\/p\u003e \u003cp\u003e14.3.2 Preprocessing 252\u003c\/p\u003e \u003cp\u003e14.3.3 Feature Extraction 252\u003c\/p\u003e \u003cp\u003e14.4 Classification 253\u003c\/p\u003e \u003cp\u003e14.5 Literature Review 254\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 258\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 SPAM Filtering Using Artificial Intelligence 261\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAbha Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 261\u003c\/p\u003e \u003cp\u003e15.2 Architecture of Email Servers and Email Processing Stages 265\u003c\/p\u003e \u003cp\u003e15.2.1 Architecture - Email Spam Filtering 265\u003c\/p\u003e \u003cp\u003e15.2.1.1 Spam Filter - Gmail 266\u003c\/p\u003e \u003cp\u003e15.2.1.2 Mail Filter Spam - Yahoo 266\u003c\/p\u003e \u003cp\u003e15.2.1.3 Email Spam Filter - Outlook 267\u003c\/p\u003e \u003cp\u003e15.2.2 Email Spam Filtering - Process 267\u003c\/p\u003e \u003cp\u003e15.2.2.1 Pre-Handling 268\u003c\/p\u003e \u003cp\u003e15.2.2.2 Taxation 268\u003c\/p\u003e \u003cp\u003e15.2.2.3 Election of Features 268\u003c\/p\u003e \u003cp\u003e15.2.3 Freely Available Email Spam Collection 269\u003c\/p\u003e \u003cp\u003e15.3 Execution Evaluation Measures 269\u003c\/p\u003e \u003cp\u003e15.4 Classification - Machine Learning Technique for Email Spam 275\u003c\/p\u003e \u003cp\u003e15.4.1 Flock Technique - Clustering 275\u003c\/p\u003e \u003cp\u003e15.4.2 Naïve Bayes Classifier 276\u003c\/p\u003e \u003cp\u003e15.4.3 Neural Network 279\u003c\/p\u003e \u003cp\u003e15.4.4 Firefly Algorithm 282\u003c\/p\u003e \u003cp\u003e15.4.5 Fuzzy Set Classifiers 283\u003c\/p\u003e \u003cp\u003e15.4.6 Support Vector Machine 284\u003c\/p\u003e \u003cp\u003e15.4.7 Decision Tree 286\u003c\/p\u003e \u003cp\u003e15.4.7.1 NBTree Classifier 286\u003c\/p\u003e \u003cp\u003e15.4.7.2 C4.5\/J48 Decision Tree Algorithm 287\u003c\/p\u003e \u003cp\u003e15.4.7.3 Logistic Version Tree Induction (LVT) 287\u003c\/p\u003e \u003cp\u003e15.4.8 Ensemble Classifiers 288\u003c\/p\u003e \u003cp\u003e15.4.9 Random Forests (RF) 289\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 290\u003c\/p\u003e \u003cp\u003eReferences 290\u003c\/p\u003e \u003cp\u003eIndex 295\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430915535128,"sku":"9781119760405","price":140.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119760405.jpg?v=1784765404","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/artificial-intelligence-and-data-mining-approaches-in-security-frameworks-hardback-9781119760405","provider":"Freshly Printed Books","version":"1.0","type":"link"}