{"product_id":"algorithms-for-smart-world-technologies-a-comprehensive-guide-to-applications-in-ai-iot-and-automation-for-electrical-and-computer-engineers-hardback-9781119823612","title":"Algorithms for Smart World Technologies; A Comprehensive Guide to Applications in AI, IoT and Automation for Electrical and Computer Engineers (Hardback) 9781119823612","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAlgorithms for Smart World Technologies\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Comprehensive Guide to Applications in AI, IoT and Automation for Electrical and Computer Engineers\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSuman Saha (Author), Shailendra Shukla (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119823612, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 17 March 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e272 pages\u003cbr\u003e25.9 x 18.5 x 2 cm, 0.703 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\u003eEnables readers to learn how to design and implement algorithms for efficient and secure smart technologies\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eAlgorithms for Smart World Technologies\u003c\/i\u003e explains the fundamentals of key algorithms and their application in a variety of use cases, covering the factors, assumptions, and models essential for the design of a real-world algorithm and discussing the importance of advanced algorithms in the use of modern world technologies such as AI, IoT, and Blockchain. \u003c\/p\u003e\n\u003cp\u003eEach chapter is written to provide a self-contained treatment of one major topic. Collectively, the chapters have been designed and carefully integrated to be entirely complementary with respect to definitions, terminology, and notation. Chapters are divided into three parts—complexities, paradigms, and recent applications—and at the beginning of each part, a detailed introduction explaining each subject area is provided. The foundational subjects are supported by end-of-chapter exercises and case studies, while the application-focused chapters are supported by projects to give worked experience. \u003c\/p\u003e\n\u003cp\u003eWritten by two highly qualified authors in academia, sample topics covered in \u003ci\u003eAlgorithms for Smart World Technologies\u003c\/i\u003e include: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e Complexities, including complex systems and algorithms, measuring efficiency, types of systems, and types of complexities\u003c\/li\u003e\n\u003cli\u003e Ethics bounds, including algorithms ethics maps, algorithmic traceability, social ethics, environmental ethics, and parameters and thresholds\u003c\/li\u003e\n\u003cli\u003e Algorithmic paradigms, including the design of an algorithm, the divide and conquer algorithm, backtracking, exhaustive search, solvability, and reducibility\u003c\/li\u003e\n\u003cli\u003e Intelligent search algorithms, including solution space, uninformed, and informed search algorithms, evolutionary algorithms, and nature-inspired algorithms\u003c\/li\u003e\n\u003cli\u003e Smart transportation, including scheduling algorithms for vehicular traffic and opportunistic communication for VANET\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eWritten for developers and domain experts who want to explore the opportunities and challenges of designing and developing algorithms and protocols for Smart-world problems, \u003ci\u003eAlgorithms for Smart World Technologies\u003c\/i\u003e is an authoritative resource on the topic that provides both foundational knowledge and guidance on practical applications.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003eAcronyms xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction xxv\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Complexities of Smart Algorithms 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Complexities 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Complex Systems and Algorithms 4\u003c\/p\u003e \u003cp\u003e1.2 Complex Systems 4\u003c\/p\u003e \u003cp\u003e1.2.1 Key Features of Complex Systems 4\u003c\/p\u003e \u003cp\u003e1.2.2 Examples of Complex Systems 5\u003c\/p\u003e \u003cp\u003e1.2.3 The Role of Algorithms in Complex Systems 5\u003c\/p\u003e \u003cp\u003e1.2.4 Modeling and Simulation 5\u003c\/p\u003e \u003cp\u003e1.2.5 Data Analysis 5\u003c\/p\u003e \u003cp\u003e1.2.6 Optimization 5\u003c\/p\u003e \u003cp\u003e1.2.7 Machine Learning 5\u003c\/p\u003e \u003cp\u003e1.2.8 Challenges and Opportunities in Algorithmic Design 5\u003c\/p\u003e \u003cp\u003e1.2.9 Future Directions 5\u003c\/p\u003e \u003cp\u003e1.3 Efficiency Metrics for Complex Systems 6\u003c\/p\u003e \u003cp\u003e1.3.1 Challenges in Measuring Efficiency 6\u003c\/p\u003e \u003cp\u003e1.3.2 Techniques for Measuring Efficiency 7\u003c\/p\u003e \u003cp\u003e1.3.3 Defining Efficiency in Complex Systems: A Holistic Approach 7\u003c\/p\u003e \u003cp\u003e1.3.4 The Path Forward: Toward a Unified Framework 8\u003c\/p\u003e \u003cp\u003e1.4 Applications of Complexity 8\u003c\/p\u003e \u003cp\u003e1.4.1 Complexity in Practice 8\u003c\/p\u003e \u003cp\u003e1.4.2 Complexity Management 8\u003c\/p\u003e \u003cp\u003e1.4.3 Complexity Economics 8\u003c\/p\u003e \u003cp\u003e1.4.4 Complexity and Education 9\u003c\/p\u003e \u003cp\u003e1.4.5 Complexity and Modeling 9\u003c\/p\u003e \u003cp\u003e1.4.6 Complexity and Chaos Theory 9\u003c\/p\u003e \u003cp\u003e1.4.7 Complexity and Network Science 9\u003c\/p\u003e \u003cp\u003e1.4.8 Complexity and Future Research Directions 10\u003c\/p\u003e \u003cp\u003e1.5 Types of Complexities 10\u003c\/p\u003e \u003cp\u003e1.6 Exercises 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Computational Complexity 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Computability 13\u003c\/p\u003e \u003cp\u003e2.2 Computational Models 15\u003c\/p\u003e \u003cp\u003e2.3 Complexity Classes 16\u003c\/p\u003e \u003cp\u003e2.4 Probabilistic Complexity 17\u003c\/p\u003e \u003cp\u003e2.4.1 The BPP Complexity Class 17\u003c\/p\u003e \u003cp\u003e2.4.2 Examples of Probabilistic Complexity 17\u003c\/p\u003e \u003cp\u003e2.4.3 BPP: Efficient Probabilistic Computation 17\u003c\/p\u003e \u003cp\u003e2.4.4 Future Directions in Probabilistic Complexity 18\u003c\/p\u003e \u003cp\u003e2.5 Quantum Complexity 18\u003c\/p\u003e \u003cp\u003e2.5.1 BQP: Power and Intrigue 18\u003c\/p\u003e \u003cp\u003e2.5.2 The P, NP, and BQP 19\u003c\/p\u003e \u003cp\u003e2.5.3 BQP: Efficient Quantum Computation 19\u003c\/p\u003e \u003cp\u003e2.5.4 Future Directions in Quantum Complexity 19\u003c\/p\u003e \u003cp\u003e2.6 Exercises 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Communication Complexity 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Deterministic Communication 24\u003c\/p\u003e \u003cp\u003e3.2 Deterministic Communication Complexity 24\u003c\/p\u003e \u003cp\u003e3.3 Nondeterministic Communication 26\u003c\/p\u003e \u003cp\u003e3.4 Nondeterministic Communication Complexity 26\u003c\/p\u003e \u003cp\u003e3.5 Randomized Communication Complexity 27\u003c\/p\u003e \u003cp\u003e3.5.1 Approximate Rank 28\u003c\/p\u003e \u003cp\u003e3.6 Exercises 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Data Complexity 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Algorithmic Information Theory 32\u003c\/p\u003e \u003cp\u003e4.1.1 Philosophy of Mathematics: Randomness Within Mathematics 32\u003c\/p\u003e \u003cp\u003e4.1.2 Philosophy of Probability: Understanding Randomness of Individual Sequences 32\u003c\/p\u003e \u003cp\u003e4.2 Occam’s Razor and Inductive Inference 33\u003c\/p\u003e \u003cp\u003e4.3 Philosophy of Information 33\u003c\/p\u003e \u003cp\u003e4.4 Lessons for the Philosophy of Information 34\u003c\/p\u003e \u003cp\u003e4.5 Kolmogorov Complexity: Measuring Randomness 35\u003c\/p\u003e \u003cp\u003e4.5.1 Defining Descriptions and Complexity 35\u003c\/p\u003e \u003cp\u003e4.5.2 Compression and Invariance 35\u003c\/p\u003e \u003cp\u003e4.5.3 Randomness and Compressibility 36\u003c\/p\u003e \u003cp\u003e4.5.4 Connection to Gödel’s Theorem 36\u003c\/p\u003e \u003cp\u003e4.6 VC Dimension: Measuring Model Complexity 36\u003c\/p\u003e \u003cp\u003e4.6.1 VC Dimension of Set Families 36\u003c\/p\u003e \u003cp\u003e4.6.2 VC Dimension of Classification Models 36\u003c\/p\u003e \u003cp\u003e4.7 Rademacher Complexity 37\u003c\/p\u003e \u003cp\u003e4.7.1 Rademacher Complexity of a Set 37\u003c\/p\u003e \u003cp\u003e4.7.2 Rademacher Complexity of a Function Class 37\u003c\/p\u003e \u003cp\u003e4.7.3 Example 37\u003c\/p\u003e \u003cp\u003e4.7.4 Generalization Bound 38\u003c\/p\u003e \u003cp\u003e4.7.5 Using Rademacher Complexity 38\u003c\/p\u003e \u003cp\u003e4.7.6 Representativeness of a Sample 38\u003c\/p\u003e \u003cp\u003e4.8 Exercises 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Risk Measures 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Addressing Algorithmic Bias 41\u003c\/p\u003e \u003cp\u003e5.2 Risk Measures 42\u003c\/p\u003e \u003cp\u003e5.3 Algorithmic Fairness Measures 43\u003c\/p\u003e \u003cp\u003e5.4 Risks in Algorithmic Monoculture 44\u003c\/p\u003e \u003cp\u003e5.5 Green Efficiency 45\u003c\/p\u003e \u003cp\u003e5.5.1 Green Internet Technologies 46\u003c\/p\u003e \u003cp\u003e5.5.2 Green RFID Tags 46\u003c\/p\u003e \u003cp\u003e5.5.3 Green Wireless Sensor Networks 47\u003c\/p\u003e \u003cp\u003e5.5.4 Green Cloud Computing 47\u003c\/p\u003e \u003cp\u003e5.5.5 Green Data Centers 47\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 47\u003c\/p\u003e \u003cp\u003e5.7 Exercises 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Ethics and Algorithmic Boundaries 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 49\u003c\/p\u003e \u003cp\u003e6.2 Objectives 50\u003c\/p\u003e \u003cp\u003e6.3 Algorithmic Decision-making 50\u003c\/p\u003e \u003cp\u003e6.3.1 Background 50\u003c\/p\u003e \u003cp\u003e6.3.2 Algorithmic Decision-making in Public Discourse 51\u003c\/p\u003e \u003cp\u003e6.3.3 Ethical Challenges in Algorithmic Decision-making 51\u003c\/p\u003e \u003cp\u003e6.3.4 ml and Autonomous Decision-making 51\u003c\/p\u003e \u003cp\u003e6.4 Algorithmic Morality 52\u003c\/p\u003e \u003cp\u003e6.4.1 Artificial Life and Emerging Ethical Behavior 52\u003c\/p\u003e \u003cp\u003e6.4.2 Unbiased Learning Machines 52\u003c\/p\u003e \u003cp\u003e6.4.3 Associative Learning and Moral Training 53\u003c\/p\u003e \u003cp\u003e6.4.4 Ethical Risks of Learning Systems 53\u003c\/p\u003e \u003cp\u003e6.5 Ethics as a Service 53\u003c\/p\u003e \u003cp\u003e6.5.1 Service Model Analogies for Ethical Governance 53\u003c\/p\u003e \u003cp\u003e6.5.2 Implementing the Ethics-as-a-Service Model 54\u003c\/p\u003e \u003cp\u003e6.5.3 Case Study: Digital Catapult Pilot 54\u003c\/p\u003e \u003cp\u003e6.5.4 Future Research Directions 54\u003c\/p\u003e \u003cp\u003e6.6 Current Discussions and Future Research Directions 54\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 55\u003c\/p\u003e \u003cp\u003e6.8 Exercises 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Algorithmic Paradigms for Smart World Technologies 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Introduction to Paradigms of Smart Algorithms 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction to Smart Paradigms 60\u003c\/p\u003e \u003cp\u003e7.2 Important Algorithms in Smart Paradigms 60\u003c\/p\u003e \u003cp\u003e7.2.1 ML Algorithms 60\u003c\/p\u003e \u003cp\u003e7.2.2 Optimization Algorithms 60\u003c\/p\u003e \u003cp\u003e7.2.3 IoT and Distributed Algorithms 61\u003c\/p\u003e \u003cp\u003e7.3 Roadmap for Future Advancements 61\u003c\/p\u003e \u003cp\u003e7.3.1 Enhancing Scalability 61\u003c\/p\u003e \u003cp\u003e7.3.2 Data Privacy and Security 61\u003c\/p\u003e \u003cp\u003e7.3.3 Autonomous and Intelligent Decision-making 61\u003c\/p\u003e \u003cp\u003e7.3.4 Green Computing and Energy Efficiency 61\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Optimization Algorithms 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Constrained Optimization: Optimization with Limitations 63\u003c\/p\u003e \u003cp\u003e8.2 Convex Optimization: Finding the Global Minimum 64\u003c\/p\u003e \u003cp\u003e8.3 Solving Linear Equations 65\u003c\/p\u003e \u003cp\u003e8.3.1 Steepest Descent: Gradient-based Minimization 66\u003c\/p\u003e \u003cp\u003e8.3.2 Improving Convergence 66\u003c\/p\u003e \u003cp\u003e8.3.3 Preconditioning with Trees 67\u003c\/p\u003e \u003cp\u003e8.4 Linear Programming Duality 67\u003c\/p\u003e \u003cp\u003e8.4.1 Complementary Slackness 68\u003c\/p\u003e \u003cp\u003e8.4.2 Congestion Minimization 69\u003c\/p\u003e \u003cp\u003e8.4.3 Maximum Weight Matching 69\u003c\/p\u003e \u003cp\u003e8.4.4 Games and Strategic Solutions 69\u003c\/p\u003e \u003cp\u003e8.4.5 The Minimax Theorem 70\u003c\/p\u003e \u003cp\u003e8.5 Network Problems 72\u003c\/p\u003e \u003cp\u003e8.5.1 Key Definitions 72\u003c\/p\u003e \u003cp\u003e8.5.2 The Minimum-cost Flow Problem 72\u003c\/p\u003e \u003cp\u003e8.5.3 The Transportation Problem 73\u003c\/p\u003e \u003cp\u003e8.5.4 The Maximum Flow Problem 79\u003c\/p\u003e \u003cp\u003e8.6 Exercises 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Decision-making Algorithms 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Markov Decision Process 85\u003c\/p\u003e \u003cp\u003e9.1.1 Discrete MDPs 86\u003c\/p\u003e \u003cp\u003e9.1.2 Nondiscrete MDPs: General Constructions 90\u003c\/p\u003e \u003cp\u003e9.1.3 Discrete State MDPs 92\u003c\/p\u003e \u003cp\u003e9.1.4 Classical Borel MDPs 92\u003c\/p\u003e \u003cp\u003e9.1.5 Assumptions for Borel MDPs 93\u003c\/p\u003e \u003cp\u003e9.1.6 Universally Measurable Borel MDPs 94\u003c\/p\u003e \u003cp\u003e9.1.7 Assumptions for Universally Measurable MDPs 94\u003c\/p\u003e \u003cp\u003e9.2 Reinforcement Learning 95\u003c\/p\u003e \u003cp\u003e9.3 Value Iteration 96\u003c\/p\u003e \u003cp\u003e9.4 Q-learning 97\u003c\/p\u003e \u003cp\u003e9.5 TD Learning 98\u003c\/p\u003e \u003cp\u003e9.6 Exercises 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Prediction Algorithms 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Regression 101\u003c\/p\u003e \u003cp\u003e10.1.1 Least Squares and Nearest-neighbor Methods 101\u003c\/p\u003e \u003cp\u003e10.1.2 Prediction Theory 102\u003c\/p\u003e \u003cp\u003e10.1.3 Curse of Dimensionality 102\u003c\/p\u003e \u003cp\u003e10.1.4 Learning as Function Approximation 102\u003c\/p\u003e \u003cp\u003e10.1.5 Key Formulas 102\u003c\/p\u003e \u003cp\u003e10.1.6 Linear Regression and Least Squares 103\u003c\/p\u003e \u003cp\u003e10.1.7 Variable Selection 104\u003c\/p\u003e \u003cp\u003e10.1.8 Best Subset Selection and Forward and Backward Stepwise Selection 104\u003c\/p\u003e \u003cp\u003e10.1.9 Smoothly Clipped Absolute Deviation 106\u003c\/p\u003e \u003cp\u003e10.1.10 Consistency and Oracle Property 106\u003c\/p\u003e \u003cp\u003e10.1.11 Selecting a Group of Variables 107\u003c\/p\u003e \u003cp\u003e10.1.12 Least Squares, Penalized Likelihood, and Bayesian Inference 107\u003c\/p\u003e \u003cp\u003e10.2 Classifications 108\u003c\/p\u003e \u003cp\u003e10.2.1 Issues with Linear Regression Approach 108\u003c\/p\u003e \u003cp\u003e10.2.2 Linear Discriminant Analysis 108\u003c\/p\u003e \u003cp\u003e10.2.3 Reduced-rank LDA 109\u003c\/p\u003e \u003cp\u003e10.2.4 Comparison Between Logistic Regression and LDA 110\u003c\/p\u003e \u003cp\u003e10.2.5 Piecewise Polynomial Functions 111\u003c\/p\u003e \u003cp\u003e10.2.6 Smoothing Splines 111\u003c\/p\u003e \u003cp\u003e10.2.7 Choosing Smoothing Parameters 112\u003c\/p\u003e \u003cp\u003e10.2 8 Hilbert Space 113\u003c\/p\u003e \u003cp\u003e10.2.9 Generalized Additive Models 114\u003c\/p\u003e \u003cp\u003e10.2.10 Fitting GAMs 115\u003c\/p\u003e \u003cp\u003e10.2.11 Illustration: Predicting Email Spam 115\u003c\/p\u003e \u003cp\u003e10.2.12 Tree-based Regression and Classification 116\u003c\/p\u003e \u003cp\u003e10.2.13 Regression Trees 116\u003c\/p\u003e \u003cp\u003e10.2.14 Classification Trees 116\u003c\/p\u003e \u003cp\u003e10.2.15 Challenges in Tree-based Methods 117\u003c\/p\u003e \u003cp\u003e10.2.16 Illustrative Example: Spam Prediction 117\u003c\/p\u003e \u003cp\u003e10.2.17 Hierarchical Mixtures of Experts and Missing Values 118\u003c\/p\u003e \u003cp\u003e10.2.18 One-dimensional Kernel Smoothers 118\u003c\/p\u003e \u003cp\u003e10.2.19 Considerations in Kernel Smoothing 119\u003c\/p\u003e \u003cp\u003e10.2.20 Local Regression and Local Likelihood Method 119\u003c\/p\u003e \u003cp\u003e10.2.21 Selecting the Width of the Kernel 120\u003c\/p\u003e \u003cp\u003e10.2.22 Structured Kernels and Local Likelihood Methods 120\u003c\/p\u003e \u003cp\u003e10.2.23 Kernel Density Estimation 120\u003c\/p\u003e \u003cp\u003e10.2.24 Application to Classification 121\u003c\/p\u003e \u003cp\u003e10.2.25 Mixture Models 122\u003c\/p\u003e \u003cp\u003e10.3 Model Complexity 122\u003c\/p\u003e \u003cp\u003e10.3.1 Bia-variance Decomposition 123\u003c\/p\u003e \u003cp\u003e10.3.2 Estimate the Errors 124\u003c\/p\u003e \u003cp\u003e10.3.3 Cross-validation 125\u003c\/p\u003e \u003cp\u003e10.3.4 Bootstrap 126\u003c\/p\u003e \u003cp\u003e10.3.5 The EM Algorithm 126\u003c\/p\u003e \u003cp\u003e10.3.6 Two Other Interpretations of EM Algorithm 127\u003c\/p\u003e \u003cp\u003e10.4 Bayesian Algorithms 129\u003c\/p\u003e \u003cp\u003e10.4.1 Variational Bayes 129\u003c\/p\u003e \u003cp\u003e10.4.2 The Key Identity 129\u003c\/p\u003e \u003cp\u003e10.4.3 Variational Inference 130\u003c\/p\u003e \u003cp\u003e10.4.4 Improvements and Variants 131\u003c\/p\u003e \u003cp\u003e10.4.5 Approximate Bayesian Computation 131\u003c\/p\u003e \u003cp\u003e10.4.6 The Discrete Version 131\u003c\/p\u003e \u003cp\u003e10.4.7 The Continuous Version 131\u003c\/p\u003e \u003cp\u003e10.4.8 Issues 132\u003c\/p\u003e \u003cp\u003e10.5 Neural Networks 132\u003c\/p\u003e \u003cp\u003e10.5.1 Fitting Neural Networks 132\u003c\/p\u003e \u003cp\u003e10.5.2 Some Issues with Neural Networks 133\u003c\/p\u003e \u003cp\u003e10.6 Support Vector Machines 134\u003c\/p\u003e \u003cp\u003e10.6.1 Separating Hyperplane 134\u003c\/p\u003e \u003cp\u003e10.6.2 Support Vectors 134\u003c\/p\u003e \u003cp\u003e10.7 Cluster Analysis 135\u003c\/p\u003e \u003cp\u003e10.7.1 Clustering Algorithms: Combinatorial 136\u003c\/p\u003e \u003cp\u003e10.7.2 Clustering Algorithms: k-means 136\u003c\/p\u003e \u003cp\u003e10.7.3 Clustering Algorithms: Hierarchical Clustering 136\u003c\/p\u003e \u003cp\u003e10.7.4 Principal Components, Curves, and Surfaces 137\u003c\/p\u003e \u003cp\u003e10.7.5 Procrustes Transform and Shape Averaging 138\u003c\/p\u003e \u003cp\u003e10.7.6 Factor Model and Independent Component Analysis 139\u003c\/p\u003e \u003cp\u003e10.7.7 Independent Component Analysis 139\u003c\/p\u003e \u003cp\u003e10.7.8 Principal Curve and Multidimensional Scaling 139\u003c\/p\u003e \u003cp\u003e10.8 Graphical Models 140\u003c\/p\u003e \u003cp\u003e10.8.1 False Discovery Rate 140\u003c\/p\u003e \u003cp\u003e10.8.2 Markov Graphs and Gaussian Graphical Models 141\u003c\/p\u003e \u003cp\u003e10.8.3 Undirected Graphs for Discrete Variables 142\u003c\/p\u003e \u003cp\u003e10.8.4 Exponential Random Graphs 143\u003c\/p\u003e \u003cp\u003e10.8.5 Eigen-statistics of Sample Covariance Matrices 143\u003c\/p\u003e \u003cp\u003e10.8.6 Bulk Universality: Marchenko–Pastur Law (or Quartercircle Law) 143\u003c\/p\u003e \u003cp\u003e10.8.7 Edge Universality: Tracy–Widom Law 144\u003c\/p\u003e \u003cp\u003e10.9 Exercises 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Secure Algorithms 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Low-power Cryptography 148\u003c\/p\u003e \u003cp\u003e11.2 Secret-key Cryptography 148\u003c\/p\u003e \u003cp\u003e11.3 Public-key Cryptography 148\u003c\/p\u003e \u003cp\u003e11.3.1 Key Exchange Protocol 149\u003c\/p\u003e \u003cp\u003e11.3.2 Trapdoor Functions 150\u003c\/p\u003e \u003cp\u003e11.3.3 md 5 154\u003c\/p\u003e \u003cp\u003e11.3.4 Secure Sockets Layer 155\u003c\/p\u003e \u003cp\u003e11.3.5 Blockchain 156\u003c\/p\u003e \u003cp\u003e11.3.6 Digital Signature 157\u003c\/p\u003e \u003cp\u003e11.4 Exercises 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Smart World Applications 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Introduction to Smart World Applications 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Interesting Applications 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Smart Education 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Examples of Smart Education Tools 167\u003c\/p\u003e \u003cp\u003e13.2 Personalized Learning 168\u003c\/p\u003e \u003cp\u003e13.2.1 Key Algorithms 168\u003c\/p\u003e \u003cp\u003e13.2.2 Application Example 169\u003c\/p\u003e \u003cp\u003e13.3 Intelligent Content Delivery 169\u003c\/p\u003e \u003cp\u003e13.3.1 Key Algorithms 169\u003c\/p\u003e \u003cp\u003e13.3.2 Application Example 170\u003c\/p\u003e \u003cp\u003e13.4 Learning Analytics and Insights 170\u003c\/p\u003e \u003cp\u003e13.4.1 Key Algorithms 170\u003c\/p\u003e \u003cp\u003e13.4.2 Application Example 170\u003c\/p\u003e \u003cp\u003e13.5 Data Analytics in Education 170\u003c\/p\u003e \u003cp\u003e13.5.1 Key Algorithms 170\u003c\/p\u003e \u003cp\u003e13.5.2 Application Example 171\u003c\/p\u003e \u003cp\u003e13.6 AI Tutors and Assistants in Education 171\u003c\/p\u003e \u003cp\u003e13.6.1 Key Algorithms 171\u003c\/p\u003e \u003cp\u003e13.6.2 Application Example 171\u003c\/p\u003e \u003cp\u003e13.7 Assessment and Feedback in Education 171\u003c\/p\u003e \u003cp\u003e13.7.1 Key Algorithms 172\u003c\/p\u003e \u003cp\u003e13.7.2 Application Example 172\u003c\/p\u003e \u003cp\u003e13.8 Assessment and Feedback in Education 172\u003c\/p\u003e \u003cp\u003e13.8.1 Key Algorithms 172\u003c\/p\u003e \u003cp\u003e13.8.2 Application Example 172\u003c\/p\u003e \u003cp\u003e13.9 Collaborative Learning 173\u003c\/p\u003e \u003cp\u003e13.9.1 Key Algorithms 173\u003c\/p\u003e \u003cp\u003e13.9.2 Application Example 173\u003c\/p\u003e \u003cp\u003e13.10 Exercises 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Smart World Algorithms in Healthcare 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Patient Flow Scheduling and Capacity Planning 175\u003c\/p\u003e \u003cp\u003e14.1.1 Queueing Theory 176\u003c\/p\u003e \u003cp\u003e14.1.2 Simulation Algorithms 176\u003c\/p\u003e \u003cp\u003e14.1.3 Linear Programming 176\u003c\/p\u003e \u003cp\u003e14.2 Drug Packaging in the Healthcare Industry 177\u003c\/p\u003e \u003cp\u003e14.2.1 Robotic Process Automation 177\u003c\/p\u003e \u003cp\u003e14.2.2 Optical Character Recognition 177\u003c\/p\u003e \u003cp\u003e14.2.3 Predictive Analytics 177\u003c\/p\u003e \u003cp\u003e14.3 Data Security of Smart Healthcare 178\u003c\/p\u003e \u003cp\u003e14.3.1 Encryption Algorithms 178\u003c\/p\u003e \u003cp\u003e14.3.2 Blockchain Technology 178\u003c\/p\u003e \u003cp\u003e14.3.3 Machine Learning for Anomaly Detection 178\u003c\/p\u003e \u003cp\u003e14.4 Automated Nutrition Monitoring System 178\u003c\/p\u003e \u003cp\u003e14.4.1 Dietary Assessment Algorithms 179\u003c\/p\u003e \u003cp\u003e14.4.2 Recommendation Systems 179\u003c\/p\u003e \u003cp\u003e14.4.3 Image Recognition 179\u003c\/p\u003e \u003cp\u003e14.5 Exercises 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Modern Approach Algorithms in Environmental and Energy I nfrastructure 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Crowdsensing for Urban Air Pollution Monitoring 181\u003c\/p\u003e \u003cp\u003e15.1.1 Algorithms 181\u003c\/p\u003e \u003cp\u003e15.1.2 Application Example 182\u003c\/p\u003e \u003cp\u003e15.2 Green Energy Scheduling for Demand Side Management 182\u003c\/p\u003e \u003cp\u003e15.2.1 Algorithms 182\u003c\/p\u003e \u003cp\u003e15.2.2 Application Example 183\u003c\/p\u003e \u003cp\u003e15.3 Smart Grid 183\u003c\/p\u003e \u003cp\u003e15.3.1 Application Example 184\u003c\/p\u003e \u003cp\u003e15.4 Smart Waste Management Systems 184\u003c\/p\u003e \u003cp\u003e15.4.1 Application Example 185\u003c\/p\u003e \u003cp\u003e15.5 Drone Monitoring 185\u003c\/p\u003e \u003cp\u003e15.5.1 Application Example 186\u003c\/p\u003e \u003cp\u003e15.6 Exercises 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Smart Agriculture 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Precision Farming 189\u003c\/p\u003e \u003cp\u003e16.1.1 Key Algorithms 190\u003c\/p\u003e \u003cp\u003e16.1.2 Application Example 190\u003c\/p\u003e \u003cp\u003e16.2 Soil Health Monitoring 191\u003c\/p\u003e \u003cp\u003e16.2.1 Key Algorithms 191\u003c\/p\u003e \u003cp\u003e16.2.2 Application Example 192\u003c\/p\u003e \u003cp\u003e16.3 Irrigation Management 192\u003c\/p\u003e \u003cp\u003e16.3.1 Application Example 193\u003c\/p\u003e \u003cp\u003e16.4 Crop Yield Prediction 193\u003c\/p\u003e \u003cp\u003e16.4.1 Key Algorithms 193\u003c\/p\u003e \u003cp\u003e16.4.2 Application Example 194\u003c\/p\u003e \u003cp\u003e16.5 Exercises 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Smart Transportation 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Smart Traffic Management 198\u003c\/p\u003e \u003cp\u003e17.1.1 Key Algorithms and Applications 198\u003c\/p\u003e \u003cp\u003e17.2 Intelligent Transportation Systems 199\u003c\/p\u003e \u003cp\u003e17.2.1 Key Algorithms and Applications 199\u003c\/p\u003e \u003cp\u003e17.3 Public Transportation 200\u003c\/p\u003e \u003cp\u003e17.3.1 Key Algorithms and Applications 200\u003c\/p\u003e \u003cp\u003e17.4 Smart Parking 201\u003c\/p\u003e \u003cp\u003e17.4.1 Key Algorithms and Applications 201\u003c\/p\u003e \u003cp\u003e17.5 Autonomous Vehicles 202\u003c\/p\u003e \u003cp\u003e17.5.1 Key Algorithms and Applications 202\u003c\/p\u003e \u003cp\u003e17.6 Infrastructure Monitoring and Maintenance 202\u003c\/p\u003e \u003cp\u003e17.6.1 Key Algorithms 202\u003c\/p\u003e \u003cp\u003e17.7 Electric and Connected Vehicles 203\u003c\/p\u003e \u003cp\u003e17.7.1 Key Algorithms and Applications 203\u003c\/p\u003e \u003cp\u003e17.8 Emergency Response 204\u003c\/p\u003e \u003cp\u003e17.8.1 Key Algorithms and Applications 204\u003c\/p\u003e \u003cp\u003e17.9 Exercises 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Information Technology and Society 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 207\u003c\/p\u003e \u003cp\u003e18.2 Missing Person Identification 207\u003c\/p\u003e \u003cp\u003e18.2.1 Key Algorithms 207\u003c\/p\u003e \u003cp\u003e18.2.2 Applications 208\u003c\/p\u003e \u003cp\u003e18.3 Social Contagions 208\u003c\/p\u003e \u003cp\u003e18.3.1 Key Algorithms 208\u003c\/p\u003e \u003cp\u003e18.3.2 Applications 208\u003c\/p\u003e \u003cp\u003e18.4 Disease Propagation 208\u003c\/p\u003e \u003cp\u003e18.4.1 Key Algorithms 208\u003c\/p\u003e \u003cp\u003e18.4.2 Applications 208\u003c\/p\u003e \u003cp\u003e18.5 Crime Monitoring 209\u003c\/p\u003e \u003cp\u003e18.5.1 Key Algorithms 209\u003c\/p\u003e \u003cp\u003e18.5.2 Applications 209\u003c\/p\u003e \u003cp\u003e18.6 Exercises 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Smart Government 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 E-government Services 211\u003c\/p\u003e \u003cp\u003e19.1.1 Key Algorithms 211\u003c\/p\u003e \u003cp\u003e19.1.2 Applications 212\u003c\/p\u003e \u003cp\u003e19.2 Smart Utilities 212\u003c\/p\u003e \u003cp\u003e19.2.1 Key Algorithms 212\u003c\/p\u003e \u003cp\u003e19.2.2 Applications 213\u003c\/p\u003e \u003cp\u003e19.3 Public Safety Enhancements 214\u003c\/p\u003e \u003cp\u003e19.3.1 Key Algorithms 214\u003c\/p\u003e \u003cp\u003e19.3.2 Applications 214\u003c\/p\u003e \u003cp\u003e19.4 Environmental Monitoring 214\u003c\/p\u003e \u003cp\u003e19.4.1 Key Algorithms 214\u003c\/p\u003e \u003cp\u003e19.4.2 Applications 215\u003c\/p\u003e \u003cp\u003e19.5 Exercises 215\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Disaster Management 217\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 218\u003c\/p\u003e \u003cp\u003e20.2 Postaccident Mine Communications and Tracking Systems 219\u003c\/p\u003e \u003cp\u003e20.2.1 Leaky-feeder System 219\u003c\/p\u003e \u003cp\u003e20.3 Data Mining for Disaster Information Management 220\u003c\/p\u003e \u003cp\u003e20.4 Algorithms for Smart Sensor Networks in Disaster Management 221\u003c\/p\u003e \u003cp\u003e20.4.1 RSSI-based Localization with Mobile Anchors 222\u003c\/p\u003e \u003cp\u003e20.5 Exercises 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Communication Algorithms 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Communication Algorithms for WSN 225\u003c\/p\u003e \u003cp\u003e21.1.1 Key Algorithms 226\u003c\/p\u003e \u003cp\u003e21.1.2 Applications 226\u003c\/p\u003e \u003cp\u003e21.2 Store-carry-forward Based Communication Algorithm for DTN 226\u003c\/p\u003e \u003cp\u003e21.2.1 Key Algorithms 227\u003c\/p\u003e \u003cp\u003e21.2.2 Applications 227\u003c\/p\u003e \u003cp\u003e21.3 Low Power-based Communication Algorithms for LLN 227\u003c\/p\u003e \u003cp\u003e21.3.1 Key Algorithms 227\u003c\/p\u003e \u003cp\u003e21.3.2 Applications 227\u003c\/p\u003e \u003cp\u003e21.4 Software-defined Networking Algorithms 227\u003c\/p\u003e \u003cp\u003e21.4.1 Key Algorithms 228\u003c\/p\u003e \u003cp\u003e21.4.2 Applications 228\u003c\/p\u003e \u003cp\u003e21.5 Peer-to-peer Network Algorithm 228\u003c\/p\u003e \u003cp\u003e21.5.1 Key Algorithms 228\u003c\/p\u003e \u003cp\u003e21.5.2 Applications 228\u003c\/p\u003e \u003cp\u003e21.6 Exercises 228\u003c\/p\u003e \u003cp\u003eReferences 231\u003c\/p\u003e \u003cp\u003eIndex 243\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52430973370648,"sku":"9781119823612","price":87.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119823612.jpg?v=1784766843","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/algorithms-for-smart-world-technologies-a-comprehensive-guide-to-applications-in-ai-iot-and-automation-for-electrical-and-computer-engineers-hardback-9781119823612","provider":"Freshly Printed Books","version":"1.0","type":"link"}