Artificial intelligence (AI) Books

Book SynopsisSince antiquity, philosophers and engineers have tried to take life's measure by reproducing it. This title collects seventeen essays from distinguished scholars in several fields. It is intended for historians and philosophers of science and technology, scientists and engineers working in artificial life and intelligence, and others.

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Book SynopsisWill the use of artificial intelligence, algorithms, and smart machines be the end of journalism as we know itor its savior? Francesco Marconi, who has led the development of the Associated Press and Wall Street Journal's use of AI in journalism, offers a new perspective on the potential of these technologies.Trade ReviewFrancesco Marconi has it right. Artificial intelligence will augment—not automate—the news industry. Human judgment will be enhanced, not replaced. When you finish this book you do not fear the AI future in newsrooms. You have the tools to wonder what we will soon be able to do. -- Jay Rosen, New York UniversityOne part history, one part management strategy, and one part vision, Newsmakers provides readers with a detailed roadmap of how journalism workflows and content will change through the AI inspired process of iterative journalism. The result will be coverage that adjusts to readers’ information needs in real-time and increases the scale and scope of reporting. -- Jay Hamilton, Stanford UniversityIf you are a newsmaker or anyone interested in the future of journalism then this book is the perfect guide. Marconi is at the cutting edge of using AI technologies in the newsroom and one of the most intelligent strategic analysts of how they can help journalism survive and thrive in our radically changing digital media age. From news gathering to connecting to audiences he shows the plethora of opportunities and challenges presented by this complex set of tools and systems. If you are excited by or fearful of the prospect of the 'robot' age of news, this book will give you the facts and ideas to grapple with this rapidly evolving field. -- Charlie Beckett, London School of EconomicsNewsmakers explores human-machine collaboration in the future of news. Marconi offers a practical perspective of how journalists can be directly involved in training, testing and deploying algorithms. A valuable guide for journalists who want to stay in the drivers’ seat of making news and leverage emerging AI-powered tools to unlock new storytelling opportunities. -- Deb Roy, Massachusetts Institute of TechnologyMarconi’s book will help journalists start thinking about some of the exciting ways AI can improve and streamline their work and how to implement these new technologies in the newsroom. If we are going to create a sustainable future for journalism, this is exactly what we need to be thinking about: putting audience needs and rapid iteration at the center of everything we do. -- Carrie Brown, City University of New YorkIn an era when machine learning is being applied to optimize decisions in countless other industries, Newsmakers examines the richness and complexity of using these tools to make dynamic, personalized, and impactful choices about the stories we offer our readers. For Marconi, it is not journalism automated by computation, but rather journalism augmented. Machine learning changes the ways a reporter sees the world around her, pieces a story together, and builds an audience in a complex information ecosystem. Newsmakers presents a view of journalism that is explicitly iterative, experimental and collaborative. -- Mark Hansen, Columbia UniversityA must read for all journalists and media scholars, this book provides a clear pathway for understanding AI in the newsmaking process. * Journalism and Mass Communication Quarterly *Table of ContentsPrefaceWhat Is This Book About, According to AI? Introduction: Technology Moves Faster Than Journalistic Standards1. The Problem: A Journalistic Model in Transition2. Enablers: The AI Technologies Driving Journalistic Change3. Workflow: A Scalable Process for Newsroom TransformationConclusionAcknowledgmentsNotesBibliographyIndex

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Book SynopsisWill the use of artificial intelligence, algorithms, and smart machines be the end of journalism as we know it—or its savior? Francesco Marconi, who has led the development of the Associated Press and Wall Street Journal’s use of AI in journalism, offers a new perspective on the potential of these technologies.Trade ReviewFrancesco Marconi has it right. Artificial intelligence will augment—not automate—the news industry. Human judgment will be enhanced, not replaced. When you finish this book you do not fear the AI future in newsrooms. You have the tools to wonder what we will soon be able to do. -- Jay Rosen, New York UniversityOne part history, one part management strategy, and one part vision, Newsmakers provides readers with a detailed roadmap of how journalism workflows and content will change through the AI inspired process of iterative journalism. The result will be coverage that adjusts to readers’ information needs in real-time and increases the scale and scope of reporting. -- Jay Hamilton, Stanford UniversityIf you are a newsmaker or anyone interested in the future of journalism then this book is the perfect guide. Marconi is at the cutting edge of using AI technologies in the newsroom and one of the most intelligent strategic analysts of how they can help journalism survive and thrive in our radically changing digital media age. From news gathering to connecting to audiences he shows the plethora of opportunities and challenges presented by this complex set of tools and systems. If you are excited by or fearful of the prospect of the 'robot' age of news, this book will give you the facts and ideas to grapple with this rapidly evolving field. -- Charlie Beckett, London School of EconomicsNewsmakers explores human-machine collaboration in the future of news. Marconi offers a practical perspective of how journalists can be directly involved in training, testing and deploying algorithms. A valuable guide for journalists who want to stay in the drivers’ seat of making news and leverage emerging AI-powered tools to unlock new storytelling opportunities. -- Deb Roy, Massachusetts Institute of TechnologyMarconi’s book will help journalists start thinking about some of the exciting ways AI can improve and streamline their work and how to implement these new technologies in the newsroom. If we are going to create a sustainable future for journalism, this is exactly what we need to be thinking about: putting audience needs and rapid iteration at the center of everything we do. -- Carrie Brown, City University of New YorkIn an era when machine learning is being applied to optimize decisions in countless other industries, Newsmakers examines the richness and complexity of using these tools to make dynamic, personalized, and impactful choices about the stories we offer our readers. For Marconi, it is not journalism automated by computation, but rather journalism augmented. Machine learning changes the ways a reporter sees the world around her, pieces a story together, and builds an audience in a complex information ecosystem. Newsmakers presents a view of journalism that is explicitly iterative, experimental and collaborative. -- Mark Hansen, Columbia UniversityA must read for all journalists and media scholars, this book provides a clear pathway for understanding AI in the newsmaking process. * Journalism and Mass Communication Quarterly *Table of ContentsPrefaceWhat Is This Book About, According to AI? Introduction: Technology Moves Faster Than Journalistic Standards1. The Problem: A Journalistic Model in Transition2. Enablers: The AI Technologies Driving Journalistic Change3. Workflow: A Scalable Process for Newsroom TransformationConclusionAcknowledgmentsNotesBibliographyIndex

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Book SynopsisAn insightful, engaging tour by a noted Silicon Valley insider of how accelerating developments in Artificial Intelligence will transform the way we live and workSelected as one of the 10 best science and technology books of 2015 by The Economist After billions of dollars and fifty years of effort, researchers are finally cracking the code on artificial intelligence. As society stands on the cusp of unprecedented change, Jerry Kaplan unpacks the latest advances in robotics, machine learning, and perception powering systems that rival or exceed human capabilities. Driverless cars, robotic helpers, and intelligent agents that promote our interests have the potential to usher in a new age of affluence and leisure but as Kaplan warns, the transition may be protracted and brutal unless we address the two great scourges of the modern developed world: volatile labor markets and income inequality. He proposes innovative, free-market adjustments to our economic system and social policies to aTrade Review"Glimmers with originality and verve. . . . Others have raised these issues but Mr. Kaplan is unique in devising solutions."—Economist"A reminder that AI systems don’t need red laser eyes to be dangerous."—John Gilbey, Times Higher Education Supplement"Kaplan also sidesteps the usual arguments of techno-optimism and dystopia, preferring to go for pragmatic solutions to a shrinking pool of jobs."—Emma Jacobs, Financial Times"Well worth reading, especially by anybody who wants to go painlessly from a standing start to a pretty thorough grounding in a debate that’s only going to intensify in the years ahead."—James Walton, The Guardian"An intriguing, insightful and well-written look at how modern artificial intelligence, powering algorithms and robots, threatens jobs and may increase wealth inequalities, by a Silicon Valley entrepreneur and AI expert."—The Economist, "Books of the Year""Kaplan gives a fascinating insight into this world we are moving into . . . reveals, in an informative and engaging way, the issues we need to be aware of in this fascinating area of technological advancement."—Jonathan Stevens, Legal Practice Management"Artificial intelligence will transform how we live and work. But how we use AI is up to us. We are lucky to have as gifted and experienced a thinker as Jerry Kaplan to guide us as we navigate through this new age."—John Doerr, Partner at Kleiner Perkins Caufield & Byers"Soon, Jerry Kaplan suggests from his perch at Stanford’s AI Lab, 'synthetic intellects' and 'forged laborers' are going to start changing the world in unpredictable ways. How can we make sure the benefits they deliver are broadly distributed? In this candid and informed take on the coming AI revolution—and how we might mitigate its problematic aspects—Jerry will have you thinking long into the night about a future that’s just around the corner."—Reid Hoffman, co-founder/chairman of LinkedIn and co-author of the #1 NYT bestseller The Alliance: Managing Talent in the Networked Age"In a world where the popular perception of Artificial Intelligence is often driven by Hollywood fiction, it's refreshing to read such a realistic and insightful analysis to help inform public discourse about this important technology.”—Ron Moore, producer and screenwriter for Star Trek and Battlestar Galactica"AI is creating enormous wealth, but there's no economic law that everyone will share in this bounty. As Jerry Kaplan masterfully explains, the great challenge is to harness these new technologies to deliver shared prosperity."—Erik Brynjolfsson, co-author of The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies"A compelling, prophetic, and timely book from a leading technology thinker, Humans Need Not Apply is a must-read for entrepreneurs, scientists, policymakers, and anyone concerned about the promise and peril of artificially intelligent machines."—Fei-Fei Li, Director, Stanford Artificial Intelligence Lab

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Book SynopsisVectors and Tensors.- The Geometry of Deformation.- The Transmission of Force.- Elastic Constitutive Theory.- Boundary Value Problems in Elasticity.- The Ritz Method of Approximation.- The Linear Theory of Beams.- The Linear Theory of Plates.- Energy Principles and Static Stability.- Fundamental Concepts in Static Stability.- The Planar Buckling of Beams.- Numerical Computation for Nonlinear Problems.Table of ContentsVectors and Tensors.- The Geometry of Deformation.- The Transmission of Force.- Elastic Constitutive Theory.- Boundary Value Problems in Elasticity.- The Ritz Method of Approximation.- The Linear Theory of Beams.- The Linear Theory of Plates.- Energy Principles and Static Stability.- Fundamental Concepts in Static Stability.- The Planar Buckling of Beams.- Numerical Computation for Nonlinear Problems.

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Book SynopsisLotfi Zadeh, the father of fuzzy logic, coined the phrase computing with words (CWW) to describe a methodology in which the computation objects are words drawn from a natural language.Table of ContentsPreface. 1 Introduction. 1.1 Perceptual Computing. 1.2 Examples. 1.3 Historical Origins of Perceptual Computing. 1.4 How to Validate the Perceptual Computer. 1.5 The Choice of Fuzzy Set Models for the Per-C. 1.6 Keeping the Per-C as Simple as Possible. 1.7 Coverage of the Book. 1.8 High-Level Synopses of Technical Details. References. 2 Interval Type-2 Fuzzy Sets. 2.1 A Brief Review of Type-1 Fuzzy Sets. 2.2 Introduction to Interval Type-2 Fuzzy Sets. 2.3 Definitions. 2.4 Wavy-Slice Representation Theorem. 2.5 Set-Theoretic Operations. 2.6 Centroid of an IT2 FS. 2.7 KM Algorithms. 2.8 Cardinality and Average Cardinality of an IT2 FS. 2.9 Final Remark. Appendix 2A. Derivation of the Union of Two IT2 FSs. Appendix 2B. Enhanced KM (EKM) Algorithms. References. 3 Encoding: From a Word to a Model—The Codebook. 3.1 Introduction. 3.2 Person FOU Approach for a Group of Subjects. 3.3 Collecting Interval End-Point Data. 3.4 Interval End-Points Approach. 3.5 Interval Approach. 3.6 Hedges. Appendix 3A. Methods for Eliciting T1 MF Information From Subjects. Appendix 3B. Derivation of Reasonable Interval Test. References. 4 Decoding: From FOUs to a Recommendation. 4.1 Introduction. 4.2 Similarity Measure Used as a Decoder. 4.3 Ranking Method Used as a Decoder. 4.4 Classifier Used as a Decoder. 5 Novel Weighted Averages as a CWW Engine. 5.1 Introduction. 5.2 Novel Weighted Averages. 5.3 Interval Weighted Average. 5.4 Fuzzy Weighted Average. 5.5 Linguistic Weighted Average. 5.6 A Special Case of the LWA. 5.7 Fuzzy Extensions of Ordered Weighted Averages. 6 IF–THEN Rules as a CWW Engine—Perceptual Reasoning. 6.1 Introduction. 6.2 A Brief Overview of Interval Type-2 Fuzzy Logic Systems. 6.3 Perceptual Reasoning: Computations. 6.4 Perceptual Reasoning: Properties. 7 Assisting in Making Investment Choices—Investment Judgment Advisor (IJA). 7.1 Introduction. 7.2 Encoder for the IJA. 7.3 Reduction of the Codebooks to User-Friendly Codebooks. 7.4 CWW Engine for the IJA. 7.5 Decoder for the IJA. 7.6 Examples. 7.7 Interactive Software for the IJA. 7.8 Conclusions. References. 8 Assisting in Making Social Judgments—Social Judgment Advisor (SJA). 8.1 Introduction. 8.2 Design an SJA. 8.3 Using an SJA. 8.4 Discussion. 8.5 Conclusions. References. 9 Assisting in Hierarchical Decision Making—Procurement Judgment Advisor (PJA). 9.1 Introduction. 9.2 Missile Evaluation Problem Statement. 9.3 Per-C for Missile Evaluation: Design. 9.4 Per-C for Missile Evaluation: Examples. 9.5 Comparison with Previous Approaches. 9.6 Conclusions. Appendix 9A: Some Hierarchical Multicriteria Decision-Making Applications. References. 10 Assisting in Hierarchical and Distributed Decision Making— Journal Publication Judgment Advisor (JPJA)/ 10.1 Introduction. 10.2 The Journal Publication Judgment Advisor (JPJA). 10.3 Per-C for the JPJA. 10.4 Examples. 10.4.5 Complete Reviews. 10.5 Conclusions. Reference. 11 Conclusions. 11.1 Perceptual Computing Methodology. 11.2 Proposed Guidelines for Calling Something CWW. Index.

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Book SynopsisThe computer can do more than show us pretty pictures. [It] canoptimize, backtest, prove or disprove old theories, eliminate thebad ones and make the good ones better. Cybernetic TradingStrategies explores new ways to use the computer and finds ways tomake a valuable machine even more valuable. --from the Foreword byJohn J. Murphy. Until recently, the computer has been used almost exclusively as acharting and data-gathering tool. But as traders and analysts havequickly discovered, its capabilities are far more vast. Now, inthis groundbreaking new book, Murray Ruggiero, a leading authorityon cybernetic trading systems, unlocks their incredible potentialand provides an in-depth look at the growing impact of advancedtechnologies on intermarket analysis. A unique resource, CyberneticTrading Strategies provides specific instructions and applicationson how to develop tradable market timing systems using neuralnetworks, fuzzy logic, genetic algorithms, chaos theory, andmachine inductTable of ContentsPartial table of contents: CLASSICAL MARKET PREDICTION. Classical Intermarket Analysis as a Predictive Tool. Seasonal Trading. Trading Using Technical Analysis. The Commitment of Traders Report. STATISTICALLY BASED MARKET PREDICTION. A Trader's Guide to Statistical Analysis. Cycle-Based Trading. Using Statistical Analysis to Develop Intelligent Exits. Using System Feedback to Improve Trading System Performance. MAKING SUBJECTIVE METHODS MECHANICAL. How to Make Subjective Methods Mechanical. Building the Wave. TRADING SYSTEM DEVELOPMENT AND TESTING. Developing a Trading System. Testing, Evaluating, and Trading a Mechanical TradingSystem. USING ADVANCED TECHNOLOGIES TO DEVELOP TRADING STRATEGIES. Developing a Neural Network Based on Standard Rule-BasedSystems. Using Genetic Algorithms for Trading Applications. References and Readings. Index.

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Book SynopsisWith a strong emphasis on applications of intelligent control, this extremely accessible book covers the fundamentals, methodologies, architectures and algorithms of automatic control systems.Table of ContentsFundamental Techniques for Intelligent Control. Learning Strategies and Algorithms. System Modeling and Estimation. Dynamic Controls. Optimization Control Techniques. Multivariate Statistics and Quality Control. Fault Detection and Diagnosis. Appendix. Bibliography. Index.

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Book SynopsisExploring the chasm between the tyranny of surveillance and the ideal of privacy, this book traces the origins of personal data collection in digital technologies including artificial intelligence (AI) embedded in social network sites, search engines, mobile apps, the web, and email.

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Book SynopsisArtificial Life is the brainchild of scientists who view self-replicating computer programs - such as computer viruses - as new forms of life. This book looks at the social and simulated worlds of Artificial Life - primarily at the Santa Fe Institute, a well-known center for studies in the sciences of complexity.Table of ContentsPreface to the Paperback Edition Acknowledgments Introduction I Simulation in Santa Fe 2 The Word for World Is Computer 3 Inside and Outside the Looking-Glass Worlds of Artificial Life 4 Concerning the Spiritual in Artificial Life 5 Artificial Life in a Worldwide Web Coda Notes References IndexX

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Book SynopsisLiving with Robots recounts a foundational shift in robotics, from artificial intelligence to artificial empathy, and foreshadows an inflection point in human evolution. As robots engage with people in socially meaningful ways, social robotics probes the nature of the human emotions that social robots are designed to emulate.Trade ReviewOffers insight into problems raised by advances in robotics and artificial intelligence that will be faced by future societies. Throughout the book, the authors provide a conceptual framework for thinking about possible scenarios of human–robot interactions, most extensively with regard to our relationships with social robots… Living with Robots will meet various expectations, uniting the intellectual depth of a carefully documented academic treatise with the pleasure of a casual page-turner. Those in search of cultural erudition are provided with myriad references to books and movies, and those with a taste for technical novelty are treated to fascinating descriptions of the most hi-tech social robots. -- Paula Quinon * Science *A thoughtful and engaging discussion about an emerging area in applied ethics—social robotics… A timely and well-written volume that addresses many contemporary and future moral questions regarding how we treat artificial intelligence. -- William Simkulet * Library Journal *A very substantial philosophical study. * Philosophie Magazine *One should not lose sight of the prospective and speculative aspect of the research and ideas of Dumouchel and Damiano. But their work is nevertheless remarkably profound and intelligent, and it provides us, as do all serious inquiries into robotics, with a better understanding of ourselves, especially the social aspect of our minds. Even if one might doubt that social robots could ever decipher the incredible complexity of our feelings and adapt to them, this project nevertheless represents a fascinating step, less in robotics itself than in the quest for the human mind to understand itself. * Le Temps *Living with Robots is a convincing reflection on the increasing presence of robots in society. Designed to operate in an environment shaped and occupied by humans, robots are the new actors in a technical, social, and cultural transformation. The book offers a distinctive and fruitful approach to social robotics through different theoretical frameworks, analyzing the implications of interactions between humans and robots, between humans via robots, and between robots themselves. -- Zaven Paré, Rio de Janeiro State UniversityLiving with Robots is a timely and fascinating examination of social robots that exist in the real world, have bodies, and interact with human beings. While addressing the practical functions of social robots, at its heart the book is deeply philosophical. The authors invite us to reflect on the nature of human beings, mind, and sociability, as well as the human–robot dynamics of emotional relationships. This gives rise to novel and important engagement with moral and political questions, from quality of life to military applications. -- Takanori Shibata, National Institute of Advanced Industrial Science and Technology[Dumouchel and Damiano’s] book takes us on a detailed tour of the philosophy of artificial intelligence (AI)—especially as it applies to robots intended to build social relationships with humanity. This is a work of serious scholarship, with arguments about identity, authority, autonomy and what is termed ‘artificial empathy’ presented with reference to a range of example systems. Kant, Descartes, Hobbes and other philosophical heavyweights get the exposure you might expect, but when set alongside the views of such disparate players as psychologist Jean Piaget and science-fiction writer Algis Budrys the analysis offers considerable breadth…If we are to build a robust, appropriate ethical structure around the next generation of technical development—some combination of deep learning, artificial intelligence, robotics and artificial empathy—we need to understand that managing the impact of these technologies is far too important to be left to those who are enthusiastically engaged in producing them. This book is both a comprehensive, engaging review of philosophical thought and a warning to anyone who thinks that the integration of robotics into our society is about technology alone. -- John Gilbey * Times Higher Education *

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Book SynopsisTrade Review"One of The Guardian’s Favourite Reads of 2017 as chosen by scientists"

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Book SynopsisAn exploration of the relationship between Marxist theory and Artificial Intelligence.Trade Review'Indispensable reading for all those who want to understand the relationship between artificial intelligence and capitalism' -- Christian Fuchs, author of 'Digital Demagogue: Authoritarian Capitalism in the Age of Trump and Twitter''A radical and provocative reading. The authors' criticisms of left accelerationism are timely and persuasive, their conclusion is bracing but necessary.' -- Sarah Kember, author of 'iMedia: The Gendering of Objects, Environments and Smart Materials''A fascinating and pioneering work that deploys Marx to understand contemporary AI capitalism. An exemplary contribution to understanding how machine learning is changing our world and transforming communist strategy' -- Nick Srnicek, author of 'Platform Capitalism''A disturbing but essential addition to the rapidly growing literature on the risks posed by capitalist-conceived AI' -- Morning StarTable of ContentsSeries Preface Acknowledgements Introduction: AI-Capital 1. Means of Cognition 2. Automating the Social Factory 3. Perfect Machines, Inhuman Labour Conclusion: Communist AI Notes Bibliography Index

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Book SynopsisUsing examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge representation, knowledge acquisition, and reasoning methodologies Chaotic analysis of biomedical time series Table of ContentsPreface. Acknowledgments. Overview. NEURAL NETWORKS. Foundations of Neural Networks. Classes of Neural Networks. Classification Networks and Learning. Supervised Learning. Unsupervised Learning. Design Issues. Comparative Analysis. Validation and Evaluation. ARTIFICIAL INTELLIGENCE. Foundation of Computer-Assisted Decision Making. Knowledge Representation. Knowledge Acquisition. Reasoning Methodologies. Validation and Evaluation. ALTERNATIVE APPROACHES. Genetic Algorithms. Probabilistic Systems. Fuzzy Systems. Hybrid Systems. HyperMerge, a Hybird Expert System. Future Perspectives. Index. About the Authors.

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Book SynopsisAddresses emerging biotechnologies with prodigious potential to benefit humankind but that are also fraught with ethical consequences. James Bradley guides discussions of the thorny issues resulting from the development of new biotechnologies. He also highlights the responsibilities of scientists to conduct research in an ethical manner.Trade Review“Building further on his remarkable scholarly work, James Bradley once again observes and dissects modern science and modern life in ways that challenge any kind of reader: student, scholar, research scientist, and most especially political decision makers. His interdisciplinary approach to studying the implications of biotechnology is the most accessible and useful, yet profound, of any academic work in this vast field. With characteristic good humor and patience, he confronts the fundamental issues within not only life sciences but moral and political philosophy as well. This is a necessary, although uncomfortable, wake-up call for humankind generally." - Timothy P. Terrel, Emory University School of Law and author of The Dimensions of Legal Reasoning: Developing Analytical Acuity from Law School to Law PracticeTable of Contents Abbreviations and Acronyms Preface Acknowledgments Chapter 1: Cells, Molecules, Genes, and Nature Chapter 2: Embryos, Stem Cells, Genetic Enhancement, Genomics, and Synthetic Biology Chapter 3: Genetically Engineered Organisms Chapter 4: CRISPR and Life's Future Chapter 5: Nanotechnology, Life, and Nanoethics Chapter 6: Brains, Minds, and Neuroethics Chapter 7: Robots and Roboethics Chapter 8: Responsibilities and Living Well with Modern Biotechnologies Chapter 9: The Urgency of Now Appendix 1: The Central Dogma of Biology, CRISPR, and Gene Drive Appendix 2: Tools for Neuroscience and Clinical Neurology Appendix 3: Sources of Scientific Information for Non-Scientists References Index

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Book SynopsisTable of ContentsContents: Forward: Curtis E. A. Karnow Part I Introduction to Law and Artificial Intelligence 1. Towards a Law of Artificial Intelligence Woodrow Barfield 2. Accelerating AI John O. McGinnis 3. Finding the Right Balance in Artificial Intelligence and Law L. Thorne McCarty 4. Learning Algorithms and Discrimination Nizan Packin and Yafit Lev-Aretz 5. The Principal Japanese AI and Robot Strategy and Research Toward Establishing Basic Principles Fumio Shimpo Part II Regulation of Artificial Intelligence 6. Artificial Intelligence and Private Law Shawn Bayern 7. Regulation of Artificial Intelligence John Frank Weaver 8. Legal Personhood in the Age of Artificially Intelligent Robots Robert van den Hoven van Genderen 9. Autonomous Driving: Regulatory Challenges Raised by Artificial Decision-Making and Tragic Choices Antje von Ungern-Sternberg Part III Fundamental Rights and Constitutional Law Issues 10. Artificial Intelligence and Privacy- AI Enters the House Through the Cloud Ronald Leenes and Silvia De Conca 11. Future Privacy: A Real Right to Privacy for Artificial Intelligence S. J. Blodgett-Ford 12. Artificial Intelligence and the First Amendment Toni M. Massaro and Helen Norton 13. Data Algorithms and Privacy in Surveillance: On Stages, Numbers, and the Human Factor Arno R. Lodder and Ronald P. Loui 14. The Impact of AI on Criminal Law, and its Twofold Procedures Ugo Pagallo and Serena Quattrocolo Patrt IV Intellectual Property 15. The Law of Artificial Intelligence Intellectual Property Jeremy A. Cubert and Richard G. A. Bone 16. Kinematically Abstract Claims in Surgical Robotics Patents Andrew Chin 17. Artificial Intelligence and the Patent System: Can a New Tool Render a Once Patentable Idea Obvious? William Samore 18. Thinking Machines and Patent Law Liza Vertinsky 19. Artificial Intelligence and the Creative Industry: New Challenges for the EU Paradigm for Art and Technology by Autonomous Creation Madeleine de Cock Buning Part V Applications of Artificial Intelligence 20. Free Movement of Algorithms: Artificially Intelligent Persons Conquer the European Union’s Internal Market Thomas Burri 21. The Artificially Intelligent Internet of Things and Article 2 of the Uniform Commercial Code Stacy-Ann Elvy 22. Artificial Intelligence and Robotics, the Workplace, and Workplace-Related Law Isabelle Wildhaber 23. Robotics Law 1.0: On Social System Design for Artificial Intelligence Yueh-Hsuan Weng 24. Antitrust, Algorithmic Pricing and Tacit Collusion Maurice E. Stucke and Ariel Ezrachi 25. Robots in the Boardroom: Artificial Intelligence and Corporate Law Florian Möslein Index

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Book SynopsisTrade Review‘Gloor (MIT) draws on AI technology to research and analyze employee happiness and performance. He first clarifies fundamental concepts and the personal and social network structures within work environments. Essentially, he redefines aspects of organizational behavior and psychology and relates them to well-known business concepts like collaboration, and also to relatively unique ideas such as entanglement. The second half of the book moves into slightly more technical discussions of measurement, collection, analysis, data visualization, and the AI/KM tools that can be leveraged, but it never strays far from the fundamental and ultimate concept of personal and organizational well-being. Along the way, Gloor presents interesting, timely, and practical supporting materials and reinforces his concepts with quotes from Dostoevsky all the way to Freddie Mercury. Gloor's book is highly readable, interesting, and at times entertaining, enabling the reader to quickly grasp his concepts. The subject matter is also very timely in the face of the recent pandemic and the transition, in many cases, to a partial virtual working environment.’ -- A. Pantelides, Choice MagazineTable of ContentsContents: 1. Introduction to happimetrics: leveraging artificial intelligence to untangle the surprising link between ethics, happiness and business success PART I PRINCIPLES OF GROUPFLOW 2. What is groupflow? 3. The influence of morality on emotions 4. Building blocks of happiness 5. Virtual tribes 6. Beeflow, antflow and leechflow 7. Entanglement is more than collaboration 8. Creating entangled collaborative innovation networks 9. Virtual mirroring 10. Steps to entanglement PART II MEASURING GROUPFLOW 11. AI makes emotions measurable by aggregating the wisdom of the crowd 12. AI-based interaction analysis between humans (and other living creatures) 13. Measuring social network structure 14. Measuring emotions 15. Measuring moral values from facial expressions 16. Measuring moral values from email 17. Measuring influence through quoting novel words 18. Measuring entanglement 19. Measuring tribes 20. Building a social compass Epilogue: from collective intelligence to collective wisdom References Index

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Book SynopsisThe ever-evolving marketing technologies now include the extensive use of advanced AI with important implications for the decision making processes of both marketers and consumers. This detailed and insightful book rigorously examines the role of heuristics and marketersâ decision making within the industryâs growing utilisation of AI.Trade Review‘Professor Guercini’s new book on the automation of marketing offers a unique and insightful glimpse at the future of marketing by helping to answer the question of how human and AI decision making can be integrated together to create an effective marketing strategy.’ -- Brandon Randolph-Seng, Texas A&M University, US‘Professor Guercini makes a fresh and comprehensive contribution to finding the proper role for decision making heuristics in automated marketing. A must-read for those who do not want to just repeat platitudes about biased human behaviour and perfectly accurate AI in modern business, but search for realistic and transparent solutions.’ -- Konstantinos Katsikopoulos, University of Southampton, UKTable of ContentsContents: 1 Introduction to Marketing Automation and Decision Making 2 Decision making based on heuristics in the marketing literature 3 Consumers’ heuristics and marketer as choice architect 4 A set of rules for the marketer’s adaptive toolbox 5 Marketing automation emergence and evolution 6 Artificial intelligence and marketer’s decisions in marketing automation 7 Marketing automation and heuristics in marketers’ experience 8 Conclusion and implications: Marketing Automation and Decision Making Index

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Book SynopsisThis book explores how AI and Generative AI (GenAI) are shaping contemporary societies. It examines the mechanisms through which they influence politics and governance, and sectors such as education and business, with a focus on their alignment with the Sustainable Development Goals (SDGs).

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Book SynopsisThis insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android. It offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition

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Book SynopsisThe Fundamental Science in Computer Science Is the Science of Thought For the first time, the collective genius of the great 18th-century German cognitive philosopher-scientists Immanuel Kant, Georg Wilhelm Friedrich Hegel, and Arthur Schopenhauer have been integrated into modern 21st-century computer science. In contrast to the languishing mainstream of Artificial Intelligence, this book takes the human thought system as its model, resulting in an entirely different approach. This book presents the architecture of a thoroughly and broadly educated human mind as translated into modern software engineering design terms. The result is The Autonomous System, based on dynamic logic and the architecture of the human mind. With its human-like intelligence, it is capable of rational thought, reasoning, and an understanding of itself and its tasks. A system of thoughts must always have an architectural structure. Arthur Schopenhauer, Table of ContentsPreface xiii Introduction xix 1. The Architecture of the Autonomous System 1 1.1 Introduction, 1 1.2 The System Constellation, 1 1.3 System Constellation Architectural Overview, 3 1.4 The Constellation Architecture, 5 1.5 The Software Systems Comprising the Constellation, 8 2. The Architectural Methodology 22 2.1 Articulation of the Requirements and Design, 23 2.2 System Development and Integration Testing, 30 2.3 Phase I: The Idea, 33 2.4 Making Rational Judgments, 36 2.5 Phase II: The Concept, 38 2.6 Using JPL-STD-D-4000 for System Requirements, 39 3. The Architecture of the Will System 41 3.1 The Search for Truth, 41 3.2 The Nature of the Will, 45 3.3 Das Ding an Sich, 45 3.4 The Will as a System, 49 3.5 The Architecture of the Will System, 51 3.6 The Interfaces of the Will System, 53 3.7 The Subsystems of the Will System, 54 4. The Architecture of the Reason System 62 4.1 The Reason and Ethics, 62 4.2 The Nature of the Reason, 64 4.3 The Reason as a System, 65 4.4 The Architecture of the Reason System, 65 4.5 The External Interfaces of the Reason, 67 4.6 The Subsystems of the Reason, 68 5. The Architecture of the Intellect System 74 5.1 The Intellect as a System, 74 5.2 The Nature of the Intellect, 77 5.3 The Intellect as a System, 79 5.4 The Subsystems of the Intellect System, 80 5.5 The External Interfaces of the Intellect System, 81 6. The Architecture of the Presentation System 83 6.1 The Presentation System, 84 6.2 The Presentation as a System, 86 6.3 The Subsystems of the Presentation, 86 7. The Architecture of the Understanding System 90 7.1 The Understanding as a System, 92 7.2 The External Interfaces of the Understanding, 94 8. The Architecture of the Sensory System 98 8.1 The Sensory System, 98 8.2 The Architecture of the Sensory System, 100 8.3 The Phenomenon Subsystem, 101 8.4 A Historical Perspective on Languages, 104 8.5 The Workings of the Noumenon, 105 9. The Architecture of the Decision System 107 9.1 The Process of Decision Making, 107 9.2 Understanding the Decision Process, 111 9.3 The Decision as a System, 113 9.4 The Subsystems of the Decision System, 114 9.5 The Interfaces of the Decision System, 121 9.6 The Building of Preferences, 121 10. The Architecture of the Thought System 124 10.1 The "Movers" of the Thought Process, 125 10.2 The Pursuit of Thinking, 127 10.3 The Nexus Cogitationis, 128 10.4 The Subsystems of the Thought System, 130 10.5 Initialization Process of the Autonomous System, 136 Epilogue 142 Endnotes 144 Index 155

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Book SynopsisProvides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basis functiTable of ContentsAcknowledgments xi 1. Introduction to Computational Intelligence 1 1.1 Welcome to Computational Intelligence 1 1.2 What Makes This Book Special 1 1.3 What This Book Covers 2 1.4 How to Use This Book 2 1.5 Final Thoughts Before You Get Started 3 PART I NEURAL NETWORKS 5 2. Introduction and Single-Layer Neural Networks 7 2.1 Short History of Neural Networks 9 2.2 Rosenblatt’s Neuron 10 2.3 Perceptron Training Algorithm 13 2.4 The Perceptron Convergence Theorem 23 2.5 Computer Experiment Using Perceptrons 25 2.6 Activation Functions 28 Exercises 30 3. Multilayer Neural Networks and Backpropagation 35 3.1 Universal Approximation Theory 35 3.2 The Backpropagation Training Algorithm 37 3.3 Batch Learning and Online Learning 45 3.4 Cross-Validation and Generalization 47 3.5 Computer Experiment Using Backpropagation 53 Exercises 56 4. Radial-Basis Function Networks 61 4.1 Radial-Basis Functions 61 4.2 The Interpolation Problem 62 4.3 Training Algorithms For Radial-Basis Function Networks 64 4.4 Universal Approximation 69 4.5 Kernel Regression 70 Exercises 75 5. Recurrent Neural Networks 77 5.1 The Hopfield Network 77 5.2 The Grossberg Network 81 5.3 Cellular Neural Networks 88 5.4 Neurodynamics and Optimization 91 5.5 Stability Analysis of Recurrent Neural Networks 93 Exercises 99 PART II FUZZY SET THEORY AND FUZZY LOGIC 101 6. Basic Fuzzy Set Theory 103 6.1 Introduction 103 6.2 A Brief History 107 6.3 Fuzzy Membership Functions and Operators 108 6.4 Alpha-Cuts, The Decomposition Theorem, and The Extension Principle 117 6.5 Compensatory Operators 120 6.6 Conclusions 124 Exercises 124 7. Fuzzy Relations and Fuzzy Logic Inference 127 7.1 Introduction 127 7.2 Fuzzy Relations and Propositions 128 7.3 Fuzzy Logic Inference 131 7.4 Fuzzy Logic For Real-Valued Inputs 135 7.5 Where Do The Rules Come From? 138 7.6 Chapter Summary 142 Exercises 143 8. Fuzzy Clustering and Classification 147 8.1 Introduction to Fuzzy Clustering 147 8.2 Fuzzy c-Means 155 8.3 An Extension of The Fuzzy c-Means 167 8.4 Possibilistic c-Means 169 8.5 Fuzzy Classifiers: Fuzzy k-Nearest Neighbors 174 8.6 Chapter Summary 179 Exercises 180 9. Fuzzy Measures and Fuzzy Integrals 183 9.1 Fuzzy Measures 183 9.2 Fuzzy Integrals 188 9.3 Training The Fuzzy Integrals 191 9.4 Summary and Final Thoughts 203 Exercises 203 PART III EVOLUTIONARY COMPUTATION 207 10. Evolutionary Computation 209 10.1 Basic Ideas and Fundamentals 209 10.2 Evolutionary Algorithms: Generate and Test 216 10.3 Representation, Search, and Selection Operators 221 10.4 Major Research and Application Areas 223 10.5 Summary 225 Exercises 225 11. Evolutionary Optimization 227 11.1 Global Numerical Optimization 229 11.2 Combinatorial Optimization 233 11.3 Some Mathematical Considerations 238 11.4 Constraint Handling 255 11.5 Self-Adaptation 258 11.6 Summary 264 Exercises 265 12. Evolutionary Learning and Problem Solving 269 12.1 Evolving Parameters of A Regression Equation 270 12.2 Evolving The Structure and Parameters of Input–Output Systems 274 12.3 Evolving Clusters 292 12.4 Evolutionary Classification Models 298 12.5 Evolutionary Control Systems 307 12.6 Evolutionary Games 314 12.7 Summary 320 Exercises 321 13. Collective Intelligence and Other Extensions of Evolutionary Computation 323 13.1 Particle Swarm Optimization 323 13.2 Differential Evolution 326 13.3 Ant Colony Optimization 329 13.4 Evolvable Hardware 331 13.5 Interactive Evolutionary Computation 333 13.6 Multicriteria Evolutionary Optimization 335 13.7 Summary 340 Exercises 340 References 343 Index 361

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Book SynopsisAn authoritative guide to computer simulation grounded in a multi-disciplinary approach for solving complex problems Simulation and Computational Red Teaming for Problem Solvingoffers a review of computer simulation that is grounded in a multi-disciplinary approach. The authors present the theoretical foundations of simulation and modeling paradigms from the perspective of an analyst. The book provides the fundamental background information needed for designing and developing consistent and useful simulations. In addition to this basic information, the authors explore several advanced topics. The book's advanced topics demonstrate how modern artificial intelligence and computational intelligence concepts and techniques can be combined with various simulation paradigms for solving complex and critical problems. Authors examine the concept of Computational Red Teaming to reveal how the combined fundamentals and advanced techniques are used successfully for solving and testing complex real-world problems. This important book: Demonstrates how computer simulation and Computational Red Teaming support each other for solving complex problems Describes the main approaches to modeling real-world phenomena and embedding these models into computer simulations Explores how a number of advanced artificial intelligence and computational intelligence concepts are used in conjunction with the fundamental aspects of simulation Written for researchers and students in the computational modelling and data analysis fields,Simulation and Computational Red Teaming for Problem Solvingcovers the foundation and the standard elements of the process of building a simulation and explores the simulation topic with a modern research approach.Table of ContentsPreface xi List of Figures xv List of Tables xxv Part I On Problem Solving, Computational Red Teaming, and Simulation 1 1. Problem Solving, Simulation, and Computational Red Teaming 3 1.1 Introduction 3 1.2 Problem Solving 4 1.3 Computational Red Teaming and Self-‘Verification and Validation’ 8 2. Introduction to Fundamentals of Simulation 11 2.1 Introduction 11 2.2 System 14 2.3 Concepts in Simulation 17 2.4 Simulation Types 21 2.5 Tools for Simulation 23 2.6 Conclusion 24 Part II Before Simulation Starts 25 3. The Simulation Process 27 3.1 Introduction 27 3.2 Define the System and its Environment 27 3.3 Build a Model 29 3.4 Encode a Simulator 30 3.5 Design Sampling Mechanisms 32 3.6 Run Simulator Under Different Samples 33 3.7 Summarise Results 33 3.8 Make a Recommendation 34 3.9 An Evolutionary Approach 35 3.10 A Battle Simulation by Lanchester Square Law 35 4. Simulation Worldview and Conflict Resolution 57 4.1 Simulation Worldview 57 4.2 Simultaneous Events and Conflicts in Simulation 64 4.3 Priority Queue and Binary Heap 68 4.4 Conclusion 72 5. The Language of Abstraction and Representation 73 5.1 Introduction 73 5.2 Informal Representation 75 5.3 Semi-formal Representation 76 5.4 Formal Representation 82 5.5 Finite-state Machine 86 5.6 Ant in Maze Modelled by Finite-state Machine 89 5.7 Conclusion 99 6. Experimental Design 101 6.1 Introduction 101 6.2 Factor Screening 103 6.3 Metamodel and Response Surface 113 6.4 Input Sampling 116 6.5 Output Analysis 117 6.6 Conclusion 120 Part III Simulation Methodologies 121 7. Discrete Event Simulation 123 7.1 Discrete Event Systems 123 7.2 Discrete Event Simulation 126 7.3 Conclusion 142 8. Discrete Time Simulation 143 8.1 Introduction 143 8.2 Discrete Time System and Modelling 145 8.3 Sample Path 148 8.4 Discrete Time Simulation and Discrete Event Simulation 149 8.5 A Case Study: Car-following Model 151 8.6 Conclusion 154 9. Continuous Simulation 157 9.1 Continuous System 157 9.2 Continuous Simulation 159 9.3 Numerical Solution Techniques for Continuous Simulation 164 9.4 System Dynamics Approach 172 9.5 Combined Discrete–continuous Simulation 174 9.6 Conclusion 176 10. Agent-based Simulation 179 10.1 Introduction 179 10.2 Agent-based Simulation 181 10.3 Examples of Agent-based Simulation 185 10.4 Conclusion 194 Part IV Simulation and Computational Red Teaming Systems 197 11. Knowledge Acquisition 199 11.1 Introduction 199 11.2 Agent-enabled Knowledge Acquisition: Core Processes 202 11.3 Human Agents 203 11.4 Human-inspired Agents 208 11.5 Machine Agents 211 11.6 Summary Discussion and Perspectives on Knowledge Acquisition 215 12. Computational Intelligence 219 12.1 Introduction 219 12.2 Evolutionary Computation 223 12.3 Artificial Neural Networks 232 12.4 Conclusion 239 13. Computational Red Teaming 241 13.1 Introduction 241 13.2 Computational Red Teaming: The Challenge Loop 242 13.3 Computational Red Teaming Objects 243 13.4 Computational Red Teaming Purposes 244 13.5 Objectives of Red Teaming Exercises in Computational Red Teaming Purposes 245 13.6 Discovering Biases 246 13.7 Computational Red Teaming Lifecycle: A Systematic Approach to Red Teaming Exercises 247 13.8 Conclusion 251 Part V Simulation and Computational Red Teaming Applications 253 14. Computational Red Teaming for Battlefield Management 255 14.1 Introduction 255 14.2 Battlefield Management Simulation 256 14.3 Conclusion 261 15. Computational Red Teaming for Air Traffic Management 263 15.1 Introduction 263 15.2 Air Traffic Simulation 263 15.3 A Human-in-the-loop Application 270 15.4 Conclusion 271 16. Computational Red Teaming Application for Skill-based Performance Assessment 273 16.1 Introduction 273 16.2 Cognitive Task Analysis-based Skill Modelling and Assessment Methodology 274 16.3 Sudoku and Human Players 276 16.4 Sudoku and Computational Solvers 280 16.5 The Proposed Skill-based Computational Solver 283 16.6 Discussion of Simulation Results 293 16.7 Conclusions 300 17. Computational Red Teaming for Driver Assessment 301 17.1 Introduction 301 17.2 Background on Cognitive Agents 303 17.3 The Society of Mind Agent 306 17.4 Society of Mind Agents in an Artificial Environment 312 17.5 Case Study 325 17.6 Conclusion 330 18. Computational Red Teaming for Trusted Autonomous Systems 333 18.1 Introduction 333 18.2 Trust for Influence and Shaping 334 18.3 The Model 335 18.4 Experiment Design and Parameter Settings 342 18.5 Results and Discussion 344 18.6 Conclusion 347 A. Probability and Statistics in Simulation 349 A.1 Foundation of Probability and Statistics 349 A.2 Useful Distributions 369 A.3 Mathematical Characteristics of Random Variables 390 A.4 Conclusion 396 B Sampling and Random Numbers 397 B.1 Introduction 397 B.2 Random Number Generator 400 B.3 Testing Random Number Generators 408 B.4 Approaches to Generating Random Variates 413 B.5 Generating Random Variates 416 B.6 Monte Carlo Method 423 B.7 Conclusion 432 Bibliography 435 Index 459

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Book SynopsisA comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal Data Processing contains a review of the most recent applications of data processing. The Editors and contributors ? noted experts on the topic ? offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-Table of ContentsList of contributors xv Series Preface xix Preface xxi About the Companion Website xxv 1 Introduction 1Soham Sarkar, Abhishek Basu, and Siddhartha Bhattacharyya 1.1 Areas of Application for Multimodal Signal 1 1.1.1 Implementation of the Copyright Protection Scheme 1 1.1.2 Saliency Map Inspired Digital Video Watermarking 1 1.1.3 Saliency Map Generation Using an Intelligent Algorithm 2 1.1.4 Brain Tumor Detection Using Multi-Objective Optimization 2 1.1.5 Hyperspectral Image Classification Using CNN 2 1.1.6 Object Detection for Self-Driving Cars 2 1.1.7 Cognitive Radio 2 1.2 Recent Challenges 2 References 3 2 Progressive Performance of Watermarking Using Spread Spectrum Modulation 5Arunothpol Debnath, Anirban Saha, Tirtha Sankar Das, Abhishek Basu, and Avik Chattopadhyay 2.1 Introduction 5 2.2 Types of Watermarking Schemes 9 2.3 Performance Evaluation Parameters of a Digital Watermarking Scheme 10 2.4 Strategies for Designing the Watermarking Algorithm 11 2.4.1 Balance of Performance Evaluation Parameters and Choice of Mathematical Tool 11 2.4.2 Importance of the Key in the Algorithm 13 2.4.3 Spread Spectrum Watermarking 13 2.4.4 Choice of Sub-band 14 2.5 Embedding and Detection of a Watermark Using the Spread Spectrum Technique 15 2.5.1 General Model of Spread Spectrum Watermarking 15 2.5.2 Watermark Embedding 17 2.5.3 Watermark Extraction 18 2.6 Results and Discussion 18 2.6.1 Imperceptibility Results for Standard Test Images 20 2.6.2 Robustness Results for Standard Test Images 20 2.6.3 Imperceptibility Results for Randomly Chosen Test Images 22 2.6.4 Robustness Results for Randomly Chosen Test Images 22 2.6.5 Discussion of Security and the key 24 2.7 Conclusion 31 References 36 3 Secured Digital Watermarking Technique and FPGA Implementation 41Ranit Karmakar, Zinia Haque, Tirtha Sankar Das, and Rajeev Kamal 3.1 Introduction 41 3.1.1 Steganography 41 3.1.2 Cryptography 42 3.1.3 Difference between Steganography and Cryptography 43 3.1.4 Covert Channels 43 3.1.5 Fingerprinting 43 3.1.6 Digital Watermarking 43 3.1.6.1 Categories of Digital Watermarking 44 3.1.6.2 Watermarking Techniques 45 3.1.6.3 Characteristics of Digital Watermarking 47 3.1.6.4 Different Types of Watermarking Applications 48 3.1.6.5 Types of Signal Processing Attacks 48 3.1.6.6 Performance Evaluation Metrics 49 3.2 Summary 50 3.3 Literary Survey 50 3.4 System Implementation 51 3.4.1 Encoder 52 3.4.2 Decoder 53 3.4.3 Hardware Realization 53 3.5 Results and Discussion 55 3.6 Conclusion 57 References 64 4 Intelligent Image Watermarking for Copyright Protection 69Subhrajit Sinha Roy, Abhishek Basu, and Avik Chattopadhyay 4.1 Introduction 69 4.2 Literature Survey 72 4.3 Intelligent Techniques for Image Watermarking 75 4.3.1 Saliency Map Generation 75 4.3.2 Image Clustering 77 4.4 Proposed Methodology 78 4.4.1 Watermark Insertion 78 4.4.2 Watermark Detection 81 4.5 Results and Discussion 82 4.5.1 System Response for Watermark Insertion and Extraction 83 4.5.2 Quantitative Analysis of the Proposed Watermarking Scheme 85 4.6 Conclusion 90 References 92 5 Video Summarization Using a Dense Captioning (DenseCap) Model 97Sourav Das, Anup Kumar Kolya, and Arindam Kundu 5.1 Introduction 97 5.2 Literature Review 98 5.3 Our Approach 101 5.4 Implementation 102 5.5 Implementation Details 108 5.6 Result 110 5.7 Limitations 127 5.8 Conclusions and Future Work 127 References 127 6 A Method of Fully Autonomous Driving in Self-Driving Cars Based on Machine Learning and Deep Learning 131Harinandan Tunga, Rounak Saha, and Samarjit Kar 6.1 Introduction 131 6.2 Models of Self-Driving Cars 131 6.2.1 Prior Models and Concepts 132 6.2.2 Concept of the Self-Driving Car 133 6.2.3 Structural Mechanism 134 6.2.4 Algorithm for theWorking Procedure 134 6.3 Machine Learning Algorithms 135 6.3.1 Decision Matrix Algorithms 135 6.3.2 Regression Algorithms 135 6.3.3 Pattern Recognition Algorithms 135 6.3.4 Clustering Algorithms 137 6.3.5 Support Vector Machines 137 6.3.6 Adaptive Boosting 138 6.3.7 TextonBoost 139 6.3.8 Scale-Invariant Feature Transform 140 6.3.9 Simultaneous Localization and Mapping 140 6.3.10 Algorithmic Implementation Model 141 6.4 Implementing a Neural Network in a Self-Driving Car 142 6.5 Training and Testing 142 6.6 Working Procedure and Corresponding Result Analysis 143 6.6.1 Detection of Lanes 143 6.7 Preparation-Level Decision Making 146 6.8 Using the Convolutional Neural Network 147 6.9 Reinforcement Learning Stage 147 6.10 Hardware Used in Self-Driving Cars 148 6.10.1 LIDAR 148 6.10.2 Vision-Based Cameras 149 6.10.3 Radar 150 6.10.4 Ultrasonic Sensors 150 6.10.5 Multi-Domain Controller (MDC) 150 6.10.6 Wheel-Speed Sensors 150 6.10.7 Graphics Processing Unit (GPU) 151 6.11 Problems and Solutions for SDC 151 6.11.1 Sensor Disjoining 151 6.11.2 Perception Call Failure 152 6.11.3 Component and Sensor Failure 152 6.11.4 Snow 152 6.11.5 Solutions 152 6.12 Future Developments in Self-Driving Cars 153 6.12.1 Safer Transportation 153 6.12.2 Safer Transportation Provided by the Car 153 6.12.3 Eliminating Traffic Jams 153 6.12.4 Fuel Efficiency and the Environment 154 6.12.5 Economic Development 154 6.13 Future Evolution of Autonomous Vehicles 154 6.14 Conclusion 155 References 155 7 The Problem of Interoperability of Fusion Sensory Data from the Internet of Things 157Doaa Mohey Eldin, Aboul Ella Hassanien, and Ehab E. Hassanein 7.1 Introduction 157 7.2 Internet of Things 158 7.2.1 Advantages of the IoT 159 7.2.2 Challenges Facing Automated Adoption of Smart Sensors in the IoT 159 7.3 Data Fusion for IoT Devices 160 7.3.1 The Data Fusion Architecture 160 7.3.2 Data Fusion Models 161 7.3.3 Data Fusion Challenges 161 7.4 Multi-Modal Data Fusion for IoT Devices 161 7.4.1 Data Mining in Sensor Fusion 162 7.4.2 Sensor Fusion Algorithms 163 7.4.2.1 Central Limit Theorem 163 7.4.2.2 Kalman Filter 163 7.4.2.3 Bayesian Networks 164 7.4.2.4 Dempster-Shafer 164 7.4.2.5 Deep Learning Algorithms 165 7.4.2.6 A Comparative Study of Sensor Fusion Algorithms 168 7.5 A Comparative Study of Sensor Fusion Algorithms 170 7.6 The Proposed Multimodal Architecture for Data Fusion 175 7.7 Conclusion and Research Trends 176 References 177 8 Implementation of Fast, Adaptive, Optimized Blind Channel Estimation for Multimodal MIMO-OFDM Systems Using MFPA 183Shovon Nandi, Narendra Nath Pathak, and Arnab Nandi 8.1 Introduction 183 8.2 Literature Survey 185 8.3 STBC-MIMO-OFDM Systems for Fast Blind Channel Estimation 187 8.3.1 Proposed Methodology 187 8.3.2 OFDM-Based MIMO 188 8.3.3 STBC-OFDM Coding 188 8.3.4 Signal Detection 189 8.3.5 Multicarrier Modulation (MCM) 189 8.3.6 Cyclic Prefix (CP) 190 8.3.7 Multiple Carrier-Code Division Multiple Access (MC-CDMA) 191 8.3.8 Modified Flower Pollination Algorithm (MFPA) 192 8.3.9 Steps in the Modified Flower Pollination Algorithm 192 8.4 Characterization of Blind Channel Estimation 193 8.5 Performance Metrics and Methods 195 8.5.1 Normalized Mean Square Error (NMSE) 195 8.5.2 Mean Square Error (MSE) 196 8.6 Results and Discussion 196 8.7 Relative Study of Performance Parameters 198 8.8 Future Work 201 References 201 9 Spectrum Sensing for Cognitive Radio Using a Filter Bank Approach 205Srijibendu Bagchi and Jawad Yaseen Siddiqui 9.1 Introduction 205 9.1.1 Dynamic Exclusive Use Model 206 9.1.2 Open Sharing Model 206 9.1.3 Hierarchical Access Model 206 9.2 Cognitive Radio 207 9.3 Some Applications of Cognitive Radio 208 9.4 Cognitive Spectrum Access Models 209 9.5 Functions of Cognitive Radio 210 9.6 Cognitive Cycle 211 9.7 Spectrum Sensing and Related Issues 211 9.8 Spectrum Sensing Techniques 213 9.9 Spectrum Sensing in Wireless Standards 216 9.10 Proposed Detection Technique 218 9.11 Numerical Results 221 9.12 Discussion 222 9.13 Conclusion 223 References 223 10 Singularity Expansion Method in Radar Multimodal Signal Processing and Antenna Characterization 231Nandan Bhattacharyya and Jawad Y. Siddiqui 10.1 Introduction 231 10.2 Singularities in Radar Echo Signals 232 10.3 Extraction of Natural Frequencies 233 10.3.1 Cauchy Method 233 10.3.2 Matrix Pencil Method 233 10.4 SEM for Target Identification in Radar 234 10.5 Case Studies 236 10.5.1 Singularity Extraction from the Scattering Response of a Circular Loop 236 10.5.2 Singularity Extraction from the Scattering Response of a Sphere 237 10.5.3 Singularity Extraction from the Response of a Disc 238 10.5.4 Result Comparison with Existing Work 239 10.6 Singularity Expansion Method in Antennas 239 10.6.1 Use of SEM in UWB Antenna Characterization 240 10.6.2 SEM for Determining Printed Circuit Antenna Propagation Characteristics 241 10.6.3 Method of Extracting the Physical Poles from Antenna Responses 241 10.6.3.1 Optimal Time Window for Physical Pole Extraction 241 10.6.3.2 Discarding Low-Energy Singularities 242 10.6.3.3 Robustness to Signal-to-Noise Ratio (SNR) 243 10.7 Other Applications 243 10.8 Conclusion 243 References 243 11 Conclusion 249Soham Sarkar, Abhishek Basu, and Siddhartha Bhattacharyya References 250 Index 253

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Book SynopsisAn industry-specific guide to the applications of Advanced Analytics and AI to the banking industry Artificial Intelligence (AI) technologies help organisations to get smarter and more effective over time ultimately responding to, learning from and interacting with human voices. It is predicted that by 2025, half of all businesses will be using these intelligent, self-learning systems. Across its entire breadth and depth, the banking industry is at the forefront of investigating Advanced Analytics and AI technology for use in a broad range of applications, such as customer analytics and providing wealth advice for clients. AI and the Future of Banking provides new and established banking industry professionals with the essential information on the implications of data and analytics on their roles, responsibilities and personal career development. Unlike existing books on the subject which tend to be overly technical and complex, this accessible, reader-friendly guide is designed toTable of ContentsAcknowledgements xv About the Author xvi Introduction xvii Chapter 1 Prologue: Why Banking? 1 Summary 1 Introduction 2 What is Banking? 4 What Do We Mean by ‘Money’ Today? 7 A Cashless Society Increasingly Emerges 8 Key Banking Functions 10 Future Jobs in Banking 14 Conclusion 17 References 18 Chapter 2 Imperatives in Banking 21 Summary 21 Introduction 21 Strategy and Imperatives 23 Strategy 23 Business Imperatives 24 Current Imperatives in Banking 24 Comparable Imperatives in the Retail Industry 26 Comparable Imperatives in the Telecom Industry 27 Comparable Imperatives in the Healthcare Industry 29 Future Imperatives in Banking 30 Greater Customer Centricity 30 Becoming Truly Digital 31 Completely Accepting Technological Change 33 Reimagining Banking 33 Reinventing Risk Management 35 Conclusion 36 References 37 Chapter 3 Data and Analytics Primer 39 Summary 39 Introduction 39 Data Management and Analytics 42 Data Management 42 The Hierarchy of Analytics 43 ‘Next-Generation’ Cognitive Analytics 45 Extracting Value from Data 46 The Importance of Location Analytics 50 Conclusion 51 References 52 Chapter 4 Key Elements of Banking Analytics 55 Summary 55 Introduction 56 Office of Finance Management 56 Performance Management and Integrated Decision Making 58 The Key Elements of Banking Performance Management 59 Customer Analytics 61 Customer Insight 61 Credit Ratings 65 Branch-Specific Campaigns 66 Impact of Social Media Campaigns 67 Relationship Pricing 69 Client Servicing 71 Risk Management 73 Risk Scenario Analytics 74 Fraud Detection 75 Regulatory Compliance 76 Risk Management and AI 76 Operational Efficiency 77 IT Cost Transparency 78 Branch Performance Management 78 Contact Centre Service 80 Payments Monitoring 82 Mortgage Tracking 83 Sales, Compensation and Commission Management 83 Financial Markets Risk and Trade Monitoring 84 Analytics in Portfolio Management 85 Derivative Markets 86 Conclusion 87 References 89 Chapter 5 Machine Learning, AI and ‘Apps’ 93 Summary 93 Introduction 93 Theory and Practice of Machine Learning 95 Apps and Their Usage 96 Data Visualisation 101 Voice Recognition and Voice Assistants 102 Visual and Facial Recognition 104 Thumbprint Recognition 106 Palm Vein Recognition 107 Wealth Management Systems and Apps 107 The Biometric Moral Argument 109 Conclusion 111 References 112 Chapter 6 AI and the Importance of Brand in Banking 115 Summary 115 Introduction 116 Brand Value and Equity in Banking 117 Millennial and Gen Y Brand Expectations from Bank Brands 119 Branding Expectations of Generation Z 120 Branding Expectations of Generation X 121 Branding and Customer Experience Interlocked 123 Branding and Human-Centred Design 124 To Brand or to Debrand? 125 Banks Will Use AI to Become Lifestyle Managers 128 Consumption and Credit Smoothing 128 Conclusion 130 References 130 Chapter 7 AI Leadership and Employee Transformation 133 Summary 133 Introduction 133 Leadership in an AI-Infused Age 136 Augmented Leadership 137 Analytically Infused Leadership 138 A New Approach to Leadership: ‘Trust but Verify’ 140 Attributes of AI-Infused Leaders 141 Leadership Training for the Future 143 ‘Digital Future of Banking Requires a New Leadership Model’ 145 Zen and Leadership in Banking 146 Functional Change and Role Transformation 147 The Evolution of the Banking Employee 149 A Banking Employee Persona in 2050 149 Conclusion 151 References 152 Chapter 8 The Bank of the Future 155 Summary 155 Introduction 156 Branch Makeover 156 The Emergence of the Café Bank 159 Millennials Not Happy with Dealing Only with Robots 160 Virtual Reality: Banking and Gaming Converge 161 Universal Banking and Beyond 163 Universal Banking in the United States 164 Banks as the Catalyst for Change: ‘Peer to Peer’ 164 Payment Processes Become More Customer Centric 166 Five Scenarios for the ‘Bank of the Future’ 167 The Full-Service Bank 168 The Digital Bank 169 The Disaggregated Bank 170 The Conversational Bank 173 The Collaborative Bank 174 Transformation of the Investment Bank 175 Conclusion 178 References 179 Chapter 9 Open Banking and Blockchain 183 Summary 183 Introduction 184 Setting the Stage: Open Banking 184 Interlock between AI and Open Banking 186 Blockchain: Setting the Stage 186 Interlock between Blockchain and AI 188 Blockchain in Banking 188 Interbank Market 189 Forex Market 190 Investment Banks and Blockchain 191 Blockchain in Indian Banking 193 Blockchain and Open Banking in Africa 196 Conclusion 199 References 201 Chapter 10 Innovation and Implementation 203 Summary 203 Introduction 203 New Roles and Responsibilities 204 Bootcamps, Hackathons, Innovation Labs, and Other Devices 205 Implementation 207 Innovation or Adaptation? 208 The Use of ‘Design Thinking’ 210 Finding Capital to Innovate: One Example 211 Fintech for Banking 212 Blockages to Innovation 216 Conclusion 218 References 218 Chapter 11 Cybercrime and IT Resilience 221 Summary 221 Introduction 222 Cybercrime in the Context of Operational Risk 224 The Internationalisation of Cybercrime 225 Cyber Security Toolkits 226 Cyber Risk Management Apps 227 Broader Cyber Issues for Banking 227 Safeguarding the Bank of the Future: New Cyber Security Threats 228 Responding to Cyber Attack 230 Cyber Readiness 231 New Cyber Roles, New Cyber Responsibilities 233 AI Fraud Detection in Banking 234 Advanced Analytics in Fraud Detection 235 AI and Anomaly Detection 236 Fraudulent Use of Data 236 Cyber and the Law 238 Conclusion 239 References 239 Chapter 12 Epilogue 243 Appendix: Fintech in Banking 247 Index 271

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Book SynopsisTable of ContentsPreface xv Acknowledgments xvii Part 1 Introduction and Theory 1 1 Alternative Data: The Lay of the Land 3 1.1 Introduction 3 1.2 What is “Alternative Data”? 5 1.3 Segmentation of Alternative Data 7 1.4 The Many Vs of Big Data 9 1.5 Why Alternative Data? 11 1.6 Who is Using Alternative Data? 15 1.7 Capacity of a Strategy and Alternative Data 16 1.8 Alternative Data Dimensions 19 1.9 Who Are the Alternative Data Vendors? 23 1.10 Usage of Alternative Datasets on the Buy Side 24 1.11 Conclusion 26 2 The Value of Alternative Data 27 2.1 Introduction 27 2.2 The Decay of Investment Value 27 2.3 Data Markets 29 2.4 The Monetary Value of Data (Part I) 31 2.4.1 Cost Value 34 2.4.2 Market Value 34 2.4.3 Economic Value 35 2.5 Evaluating (Alternative) Data Strategies with and without Backtesting 35 2.5.1 Systematic Investors 36 2.5.2 Discretionary Investors 38 2.5.3 Risk Managers 39 2.6 The Monetary Value of Data (Part II) 39 2.6.1 The Buyer’s Perspective 40 2.6.2 The Seller’s Perspective 41 2.7 The Advantages of Maturing Alternative Datasets 45 2.8 Summary 46 3 Alternative Data Risks and Challenges 47 3.1 Legal Aspects of Data 47 3.2 Risks of Using Alternative Data 50 3.3 Challenges of Using Alternative Data 51 3.3.1 Entity Matching 52 3.3.2 Missing Data 54 3.3.3 Structuring the Data 55 3.3.4 Treatment of Outliers 56 3.4 Aggregating the Data 57 3.5 Summary 58 4 Machine Learning Techniques 59 4.1 Introduction 59 4.2 Machine Learning: Definitions and Techniques 60 4.2.1 Bias, Variance, and Noise 60 4.2.2 Cross-Validation 61 4.2.3 Introducing Machine Learning 62 4.2.4 Popular Supervised Machine Learning Techniques 64 4.2.5 Clustering-Based Unsupervised Machine Learning Techniques 70 4.2.6 Other Unsupervised Machine Learning Techniques 71 4.2.7 Machine Learning Libraries 71 4.2.8 Neutral Networks and Deep Learning 72 4.2.9 Gaussian Processes 80 4.3 Which Technique to Choose? 82 4.4 Assumptions and Limitations of the Machine Learning Techniques 84 4.4.1 Causality 84 4.4.2 Non-stationarity 85 4.4.3 Restricted Information Set 86 4.4.4 The Algorithm Choice 86 4.5 Structuring Images 87 4.5.1 Features and Feature Detection Algorithms 87 4.5.2 Deep Learning and CNNs for Image Classification 89 4.5.3 Augmenting Satellite Image Data with Other Datasets 90 4.5.4 Imaging Tools 91 4.6 Natural Language Processing (NLP) 91 4.6.1 What is Natural Language Processing (NLP)? 91 4.6.2 Normalization 93 4.6.3 Creating Word Embeddings: Bag-of-Words 94 4.6.4 Creating Word Embeddings: Word2vec and Beyond 94 4.6.5 Sentiment Analysis and NLP Tasks as Classification Problems 96 4.6.6 Topic Modeling 96 4.6.7 Various Challenges in NLP 97 4.6.8 Different Languages and Different Texts 98 4.6.9 Speech in NLP 99 4.6.10 NLP Tools 100 4.7 Summary 102 5 The Processes behind the Use of Alternative Data 105 5.1 Introduction 105 5.2 Steps in the Alternative Data Journey 106 5.2.1 Step 1. Set up a Vision and Strategy 106 5.2.2 Step 2. Identify the Appropriate Datasets 107 5.2.3 Step 3. Perform Due Diligence on Vendors 108 5.2.4 Step 4. Pre-assess Risks 109 5.2.5 Step 5. Pre-assess the Existence of Signals 109 5.2.6 Step 6. Data Onboarding 110 5.2.7 Step 7. Data Preprocessing 110 5.2.8 Step 8. Signal Extraction 111 5.2.9 Step 9. Implementation (or Deployment in Production) 112 5.2.10 Maintenance Process 113 5.3 Structuring Teams to Use Alternative Data 114 5.4 Data Vendors 116 5.5 Summary 118 6 Factor Investing 119 6.1 Introduction 119 6.1.1 The CAPM 119 6.2 Factor Models 120 6.2.1 The Arbitrage Pricing Theory 122 6.2.2 The Fama-French 3-Factor Model 123 6.2.3 The Carhart Model 124 6.2.4 Other Approaches (Data Mining) 125 6.3 The Difference between Cross-Sectional and Time Series Trading Approaches 126 6.4 Why Factor Investing? 126 6.5 Smart Beta Indices Using Alternative Data Inputs 127 6.6 ESG Factors 128 6.7 Direct and Indirect Prediction 129 6.8 Summary 132 Part 2 Practical Applications 133 7 Missing Data: Background 135 7.1 Introduction 135 7.2 Missing Data Classification 136 7.2.1 Missing Data Treatments 137 7.3 Literature Overview of Missing Data Treatments 139 7.3.1 Luengo et al. (2012) 139 7.3.2 Garcia-Laencina et al. (2010) 143 7.3.3 Grzymala-Busse et al. (2000) 146 7.3.4 Zou et al. (2005) 147 7.3.5 Jerez et al. (2010) 147 7.3.6 Farhangfar et al. (2008) 148 7.3.7 Kang et al. (2013) 149 7.4 Summary 149 8 Missing Data: Case Studies 151 8.1 Introduction 151 8.2 Case Study: Imputing Missing Values in Multivariate Credit Default Swap Time Series 152 8.2.1 Missing Data Classification 153 8.2.2 Imputation Metrics 154 8.2.3 CDS Data and Test Data Generation 154 8.2.4 Multiple Imputation Methods 157 8.2.5 Deterministic and EOF-Based Techniques 160 8.2.6 Results 164 8.3 Case Study: Satellite Images 173 8.4 Summary 176 8.5 Appendix: General Description of the MICE Procedure 178 8.6 Appendix: Software Libraries Used in This Chapter 179 9 Outliers (Anomalies) 181 9.1 Introduction 181 9.2 Outliers Definition, Classification, and Approaches to Detection 182 9.3 Temporal Structure 183 9.4 Global Versus Local Outliers, Point Anomalies, and Micro-Clusters 184 9.5 Outlier Detection Problem Setup 184 9.6 Comparative Evaluation of Outlier Detection Algorithms 185 9.7 Approaches to Outlier Explanation 189 9.7.1 Micenkova et al. 189 9.7.2 Duan et al. 191 9.7.3 Angiulli et al. 192 9.8 Case Study: Outlier Detection on Fed Communications Index 194 9.9 Summary 201 9.10 Appendix 202 9.10.1 Model-Based Techniques 202 9.10.2 Distance-Based Techniques 202 9.10.3 Density-Based Techniques 203 9.10.4 Heuristics-Based Approaches 203 10 Automotive Fundamental Data 205 10.1 Introduction 205 10.2 Data 206 10.3 Approach 1: Indirect Approach 211 10.3.1 The Steps Followed 212 10.3.2 Stage 1 213 10.4 Approach 2: Direct Approach 223 10.4.1 The Data 223 10.4.2 Factor Generation 224 10.4.3 Factor Performance 225 10.4.4 Detailed Factor Results 229 10.5 Gaussian Processes Example 238 10.6 Summary 239 10.7 Appendix 240 10.7.1 List of Companies 240 10.7.2 Description of Financial Statement Items 241 10.7.3 Ratios Used 242 10.7.4 IHS Markit Data Features 243 10.7.5 Reporting Delays by Country 244 11 Surveys and Crowdsourced Data 245 11.1 Introduction 245 11.2 Survey Data as Alternative Data 245 11.3 The Data 247 11.4 The Product 247 11.5 Case Studies 249 11.5.1 Case Study: Company Event Study (Pooled Survey) 249 11.5.2 Case Study: Oil and Gas Production (Q&A Survey) 252 11.6 Some Technical Considerations on Surveys 254 11.7 Crowdsourcing Analyst Estimates Survey 255 11.8 Alpha Capture Data 256 11.9 Summary 256 11.10 Appendix 256 12 Purchasing Managers’ Index 259 12.1 Introduction 259 12.2 PMI Performance 261 12.3 Nowcasting GDP Growth 262 12.4 Impacts on Financial Markets 263 12.5 Summary 266 13 Satellite Imagery and Aerial Photography 267 13.1 Introduction 267 13.2 Forecasting US Export Growth 269 13.3 Car Counts and Earnings Per Share for Retailers 271 13.4 Measuring Chinese PMI Manufacturing with Satellite Data 277 13.5 Summary 280 14 Location Data 283 14.1 Introduction 283 14.2 Shipping Data to Track Crude Oil Supplies 283 14.3 Mobile Phone Location Data to Understand Retail Activity 287 14.3.1 Trading REIT ETF Using Mobile Phone Location Data 288 14.3.2 Estimating Earnings per Share with Mobile Phone Location Data 291 14.4 Taxi Ride Data and New York Fed Meetings 295 14.5 Corporate Jet Location Data and M&A 296 14.6 Summary 298 15 Text Web Social Media and News 299 15.1 Introduction 299 15.2 Collecting Web Data 299 15.3 Social Media 300 15.3.1 Hedonometer Index 302 15.3.2 Using Twitter Data to Help Forecast US Change in Nonfarm Payrolls 305 15.3.3 Twitter Data to Forecast Stock Market Reaction to FOMC 308 15.3.4 Liquidity and Sentiment from Social Media 309 15.4 News 309 15.4.1 Machine-Readable News to Trade FX and Understand FX Volatility 310 15.4.2 Federal Reserve Communications and US Treasury Yields 316 15.5 Other Web Sources 320 15.5.1 Measuring Consumer Price Inflation 321 15.6 Summary 322 16 Investor Attention 323 16.1 Introduction 323 16.2 Readership of Payrolls to Measure Investor Attention 323 16.3 Google Trends Data to Measure Market Themes 325 16.4 Investopedia Search Data to Measure Investor Anxiety 328 16.5 Using Wikipedia to Understand Price Action in Cryptocurrencies 330 16.6 Online Attention for Countries to Inform EMFX Trading 330 16.7 Summary 333 17 Consumer Transactions 335 17.1 Introduction 335 17.2 Credit and Debit Card Transaction Data 336 17.3 Consumer Receipts 337 17.4 Summary 340 18 Government, Industrial, and Corporate Data 341 18.1 Introduction 341 18.2 Using Innovation Measures to Trade Equities 342 18.3 Quantifying Currency Crisis Risk 344 18.4 Modeling Central Bank Intervention in Currency Markets 346 18.5 Summary 348 19 Market Data 351 19.1 Introduction 351 19.2 Relationship between Institutional FX Flow Data and FX Spot 351 19.3 Understanding Liquidity Using High-Frequency FX Data 355 19.4 Summary 357 20 Alternative Data in Private Markets 359 20.1 Introduction 359 20.2 Defining Private Equity and Venture Capital Firms 360 20.3 Private Equity Datasets 362 20.4 Understanding the Performance of Private Firms 363 20.5 Summary 364 Conclusions 365 Some Last Words 365 References 367 About the Authors 373 Index 375

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Book SynopsisHarness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sectiTable of ContentsIntroduction xix Part 1 Fundamentals of Machine Learning 1 Chapter 1 Introduction to Machine Learning 3 What is Machine Learning? 4 Tools Commonly Used by Data Scientists 4 Common Terminology 5 Real-World Applications of Machine Learning 7 Types of Machine Learning Systems 8 Supervised Learning 9 Unsupervised Learning 10 Semisupervised Learning 11 Reinforcement Learning 11 Batch Learning 12 Incremental Learning 12 Instance-Based Learning 13 Model-Based Learning 13 Common Machine Learning Algorithms 13 Linear Regression 14 Support Vector Machines 15 Logistic Regression 19 Decision Trees 21 Artificial Neural Networks 23 Sources of Machine Learning Datasets 24 Scikit-learn Datasets 24 AWS Public Datasets 27 Kaggle.com Datasets 27 UCI Machine Learning Repository 27 Summary 28 Chapter 2 The Machine-Learning Approach 29 The Traditional Rule-Based Approach 29 A Machine-Learning System 33 Picking Input Features 34 Preparing the Training and Test Set 39 Picking a Machine-Learning Algorithm 40 Evaluating Model Performance 41 The Machine-Learning Process 44 Data Collection and Preprocessing 44 Preparation of Training, Test, and Validation Datasets 44 Model Building 45 Model Evaluation 45 Model Tuning 45 Model Deployment 46 Summary 46 Chapter 3 Data Exploration and Preprocessing 47 Data Preprocessing Techniques 47 Obtaining an Overview of the Data 47 Handling Missing Values 57 Creating New Features 60 Transforming Numeric Features 62 One-Hot Encoding Categorical Features 64 Selecting Training Features 65 Correlation 65 Principal Component Analysis 68 Recursive Feature Elimination 70 Summary 71 Chapter 4 Implementing Machine Learning on Mobile Apps 73 Device-Based vs Server-Based Approaches 73 Apple’s Machine Learning Frameworks and Tools 75 Task-Level Frameworks 75 Model-Level Frameworks 76 Format Converters 76 Transfer Learning Tools 77 Third-Party Machine-Learning Frameworks and Tools 78 Summary 79 Part 2 Machine Learning with CoreML, CreateML, and TuriCreate 81 Chapter 5 Object Detection Using Pre- trained Models 83 What is Object Detection? 83 A Brief Introduction to Artificial Neural Networks 86 Downloading the ResNet50 Model 92 Creating the iOS Project 92 Creating the User Interface 95 Updating Privacy Settings 100 Using the Resnet50 Model in the iOS Project 100 Summary 109 Chapter 6 Creating an Image Classifier with the Create ML App 111 Introduction to the Create ML App 112 Creating the Image Classification Model with the Create ML App 113 Creating the iOS Project 117 Creating the User Interface 118 Updating Privacy Settings 122 Using the Core ML Model in the iOS Project 123 Summary 132 Chapter 7 Creating a Tabular Classifier with Create ML 135 Preparing the Dataset for the Create ML App 135 Creating the Tabular Classification Model with the Create ML App 143 Creating the iOS Project 147 Creating the User Interface 148 Using the Classification Model in the iOS Project 156 Testing the App 172 Summary 173 Chapter 8 Creating a Decision Tree Classifier r 175 Decision Tree Recap 175 Examining the Dataset 176 Creating Training and Test Datasets 180 Creating the Decision Tree Classification Model with Scikit-learn 181 Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format 186 Creating the iOS Project 187 Creating the User Interface 188 Using the Scikit-learn Decision Tree Classifier Model in the iOS Project 193 Testing the App 201 Summary 202 Chapter 9 Creating a Logistic Regression Model Using Scikit-learn and Core ML 203 Examining the Dataset 203 Creating a Training and Test Dataset 208 Creating the Logistic Regression Model with Scikit-learn 210 Using Core ML Tools to Convert the Scikit-learn Model to the Core ML Format 216 Creating the iOS Project 218 Creating the User Interface 219 Using the Scikit-learn Model in the iOS Project 225 Testing the App 232 Summary 233 Chapter 10 Building a Deep Convolutional Neural Network with Keras 235 Introduction to the Inception Family of Deep Convolutional Neural Networks 236 GoogLeNet (aka Inception-v1) 236 Inception-v2 and Inception-v3 238 Inception-v4 and Inception-ResNet 239 A Brief Introduction to Keras 244 Implementing Inception-v4 with the Keras Functional API 246 Training the Inception-v4 Model 259 Exporting the Keras Inception-v4 Model to the Core ML Format 269 Creating the iOS Project 270 Creating the User Interface 271 Updating Privacy Settings 276 Using the Inception-v4 Model in the iOS Project 277 Summary 286 Appendix A Anaconda and Jupyter Notebook Setup 287 Installing the Anaconda Distribution 287 Creating a Conda Python Environment 288 Installing Python Packages 291 Installing Jupyter Notebook 293 Summary 296 Appendix B Introduction to NumPy and Pandas 297 NumPy 297 Creating NumPy Arrays 297 Modifying Arrays 301 Indexing and Slicing 304 Pandas 305 Creating Series and Dataframes 305 Getting Dataframe Information 307 Selecting Data 311 Summary 313 Index 315

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Book SynopsisTable of ContentsIntroduction ix Part One The Truth About Personality 1Making sense of human behavior in an unpredictable world 1: The High Cost of Not Understanding People 3 2: The Ingredients of a Unique Personality 17 3: The Biggest Challenges to Successful Communication 22 4: How to Understand Anyone’s Personality 30 5: The Personality Map 42 Part Two Read Your Own User Manual 51Understanding your personality and how to harness it 6: Personality Differences Shape Every Relationship 53 7: How to Find Your Personality Type 56 8: Take a Personality Assessment 59 9: The 16 Personality Types 61 10: How I Fired (and then Rehired) Myself 139 Part Three How Personality AI Works 149Understanding the technology driving the personality revolution 11: Why the Soft Skills are the Hardest 151 12: Before Personality AI: Flying Blind 156 13: How AI is Already Impacting You 158 14: A GPS for Communicating with People 161 15: Under the Hood of Personality AI 166 16: Using Personality AI to Understand Anyone 193 Part Four Communicate Better 197Using personality insights for every conversation 17: Communicating with Ignorance versus Empathy 199 18: Adapting to Different Personality Types 201 19: Personality AI for Email Outreach 204 20: Personality AI for Sales Meetings 220 21: Personality AI for Difficult Conversations 235 22: Personality AI for More Situations 244 Part Five Lead Better 247Using personality profiles to build teams with chemistry 23: Understanding the Dynamics of Your Team 249 24: Facilitating One-on-One Chemistry between Others 259 25: Creating Chemistry within an Entire Group 270 26: Becoming an Empathy-Driven Leader 277 Part Six Predict Responsibly 299Understanding the proper, ethical use of Personality AI 27: How to Properly Use Personality Data 301 28: Restoring Empathy in a Hyper-Skeptical World 311 Acknowledgments 313 About the Authors 315 Index 317

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Book SynopsisIn the fast-developing world of Industry 4.0, which combines Extended Reality (XR) technologies, such as Virtual Reality (VR) and Augmented Reality (AR), creating location aware applications to interact with smart objects and smart processes via Cloud Computing strategies enabled with Artificial Intelligence (AI) and the Internet of Things (IoT), factories and processes can be automated and machines can be enabled with self-monitoring capabilities. Smart objects are given the ability to analyze and communicate with each other and their human co-workers, delivering the opportunity for much smoother processes, and freeing up workers for other tasks. Industry 4.0 enabled smart objects can be monitored, designed, tested and controlled via their digital twins, and these processes and controls are visualized in VR/AR. The Industry 4.0 technologies provide powerful, largely unexplored application areas that will revolutionize the way we work, collaborate and live our lives. It is importantTable of ContentsList of Figures xi List of Tables xv Foreword xvii Introduction xix Preface xxiii Acknowledgments xxv Acronyms xxvii Part I Extended Reality Education 1 Mixed Reality Use in Higher Education: Results from an International Survey 3J. Riman, N. Winters, J. Zelenak, I. Yucel, J. G. Tromp 1.1 Introduction 4 1.2 Organizational Framework 4 1.3 Online Survey About MR Usage 5 1.4 Results 6 1.4.1 Use in Classrooms 8 1.4.2 Challenges 9 1.4.3 Examples of Research in Action 10 1.4.4 Hardware and Software for Use in Classrooms and Research 10 1.4.5 Challenges Described by Researcher Respondents 12 1.4.6 Anecdotal Responses about Challenges 12 1.5 Conclusion 13 References 15 2 Applying 3D VR Technology for Human Body Simulation to Teaching, Learning and Studying 17Le Van Chung, Gia Nhu Nguyen, Tung Sanh Nguyen, Tri Huu Nguyen, Dac-Nhuong Le 2.1 Introduction 18 2.2 Related Works 18 2.3 3D Human Body Simulation System 19 2.3.1 The Simulated Human Anatomy Systems 19 2.3.2 Simulated Activities and Movements 20 2.3.3 Evaluation of the System 23 2.4 Discussion of Future Work 25 2.5 Conclusion 26 References 26 Part II Internet of Things 3 A Safety Tracking and Sensor System for School Buses in Saudi Arabia 31Samah Abbas, Hajar Mohammed, Laila Almalki Maryam Hassan, Maram Meccawy 3.1 Introduction 32 3.2 Related Work 32 3.3 Data Gathering Phase 33 3.3.1 Questionnaire 34 3.3.2 Driver Interviews 35 3.4 The Proposed Safety Tracking and Sensor School Bus System 36 3.4.1 System Analysis and Design 37 3.4.2 User Interface Design 38 3.5 Testing and Results 41 3.6 Discussion and Limitation 42 3.7 Conclusions and Future Work 42 References 42 4 A Lightweight Encryption Algorithm Applied to a Quantized Speech Image for Secure IoT 45Mourad Talbi 4.1 Introduction 46 4.2 Applications of IoT 46 4.3 Security Challenges in IoT 47 4.4 Cryptographic Algorithms for IoT 47 4.5 The Proposed Algorithm 48 4.6 Experimental Setup 50 4.7 Results and Discussion 52 4.8 Conclusion 57 References 58 Part III Mobile Technology 5 The Impact of Social Media Adoption on Entrepreneurial Ecosystem 63Bodor Almotairy, Manal Abdullah, Rabeeh Abbasi 5.1 Introduction 64 5.2 Background 65 5.2.1 Small and Medium-Sized Enterprises (SMEs) 65 5.2.2 Social Media 65 5.2.3 Social Networks and Entrepreneurial Activities 66 5.3 Analysis Methodology 66 5.4 Understanding the Entrepreneurial Ecosystem 67 5.5 Social Media and Entrepreneurial Ecosystem 69 5.5.1 Social Media Platforms and Entrepreneurship 71 5.5.2 The Drivers of Social Media Adoption 71 5.5.3 The Motivations and Benefits for Entrepreneurs to Use Social Media 71 5.5.4 Entrepreneurship Activities Analysis Techniques in Social Media Networks 71 5.6 Research Gap and Recommended Solution 73 5.6.1 Research Gap 73 5.6.2 Recommended Solution 74 5.7 Conclusion 74 References 75 6 Human Factors for E-Health Training System: UX Testing for XR Anatomy Training App 81Zhushun Timothy Cai, Oliver Medonza, Kristen Ray, Chung Van Le, Damian Schofield, Jolanda Tromp 6.1 Introduction 82 6.2 Mobile Learning Applications 82 6.3 Ease of Use and Usability 82 6.3.1 Effectiveness 83 6.3.2 Efficiency 83 6.3.3 Satisfaction 83 6.4 Methods and Materials 86 6.5 Results 89 6.5.1 Task Completion Rate (TCR) 89 6.5.2 Time-on-Task (TOT) 90 6.5.3 After-Scenario Questionnaire (ASQ) 91 6.5.4 Post-Study System Usability Questionnaire (PSSUQ) 93 6.6 Conclusion 93 References 94 Part IV Towards Digital Twins and Robotics 7 Augmented Reality at Heritage Sites: Technological Advances and Embodied Spatially Minded Interactions 101Lesley Johnston, Romy Galloway, Jordan John Trench, Matthieu Poyade, Jolanda Tromp, Hoang Thi My 7.1 Introduction 102 7.2 Augmented Reality Devices 103 7.3 Detection and Tracking 105 7.4 Environmental Variation 106 7.5 Experiential and Embodied Interactions 109 7.6 User Experience and Presence in AR 114 7.7 Conclusion 115 References 116 8 TELECI Architecture for Machine Learning Algorithms Integration in an Existing LMS 121V. Zagorskis, A. Gorbunovs, A. Kapenieks 8.1 Introduction 122 8.2 TELECI Architecture 123 8.2.1 TELECI Interface to a Real LMS 123 8.2.2 First RS Steps in the TELECI System 124 8.2.3 Real Student Data for VS Model 125 8.2.4 TELECI Interface to VS Subsystem 126 8.2.5 TELECI Interface to AI Component 128 8.3 Implementing ML Technique 128 8.3.1 Organizational Activities 128 8.3.2 Data Processing 129 8.3.3 Computing and Networking Resources 130 8.3.4 Introduction to Algorithm 130 8.3.5 Calibration Experiment 132 8.4 Learners’ Activity Issues 133 8.5 Conclusion 136 References 137 Part V Big Data Analytics 9 Enterprise Innovation Management in Industry 4.0: Modeling Aspects 141V. Babenko 9.1 Introduction 142 9.2 Conceptual Model of Enterprise Innovation Process Management 144 9.3 Formation of Restrictions for Enterprise Innovation Management Processes 147 9.4 Formation of Quality Criteria for Assessing Implementation of Enterprise Innovation Management Processes 148 9.5 Statement of Optimization Task of Implementation of Enterprise Innovation Management Processes 148 9.6 Structural and Functional Model for Solving the Task of Dynamic 150 9.7 Formulation of the Task of Minimax Program Management of Innovation Processes at Enterprises 152 9.8 General Scheme for Solving the Task of Minimax Program Management of Innovation Processes at the Enterprises 154 9.9 Model of Multicriteria Optimization of Program Management of Innovation Processes 156 9.10 Conclusion 161 References 162 10 Using Simulation for Development of Automobile Gas Diesel Engine Systems and their Operational Control 165Mikhail G. Shatrov, Vladimir V. Sinyavski, Andrey Yu. Dunin, Ivan G. Shishlov, Sergei D. Skorodelov, Andrey L. Yakovenko 10.1 Introduction 166 10.2 Computer Modeling 167 10.3 Gas Diesel Engine Systems Developed 168 10.3.1 Electronic Engine Control System 168 10.3.2 Modular Gas Feed System 169 10.3.3 Common Rail Fuel System for Supply of the Ignition Portion of Diesel Fuel 169 10.4 Results and Discussion 172 10.4.1 Results of Diesel Fuel Supply System Simulation 172 10.4.2 Results of Engine Bed Tests 181 10.5 Conclusion 183 References 184 Part VI Towards Cognitive Computing 11 Classification of Concept Drift in Evolving Data Stream 189Mashail Althabiti and Manal Abdullah 11.1 Introduction 190 11.2 Data Mining 190 11.3 Data Stream Mining 191 11.3.1 Data Stream Challenges 191 11.3.2 Features of Data Stream Methods 193 11.4 Data Stream Sources 193 11.5 Data Stream Mining Components 193 11.5.1 Input 194 11.5.2 Estimators 194 11.6 Data Stream Classification and Concept Drift 194 11.6.1 Data Stream Classification 194 11.6.2 Concept Drift 194 11.6.3 Data Stream Classification Algorithms with Concept Drift 196 11.6.4 Single Classifier 196 11.6.5 Ensemble Classifiers 197 11.6.6 Output 200 11.7 Datasets 200 11.8 Evaluation Measures 200 11.9 Data Stream Mining Tools 201 11.10 Data Stream Mining Applications 202 11.11 Conclusion 202 References 202 12 Dynamical Mass Transfer Systems in Buslaev Contour Networks with Conflicts 207Marina Yashina, Alexander Tatashev, Ivan Kuteynikov 12.1 Introduction 208 12.2 Construction of Buslaev Contour Networks 210 12.3 Concept of Spectrum 211 12.4 One-Dimensional Contour Network Binary Chain of Contours 212 12.5 Two-Dimensional Contour Network-Chainmail 214 12.6 Random Process with Restrictions on the Contour with the Possibility of Particle Movement in Both Directions 218 12.7 Conclusion 218 References 219 13 Parallel Simulation and Visualization of Traffic Flows Using Cellular Automata Theory and QuasigasDynamic Approach 223Antonina Chechina, Natalia Churbanova, Pavel Sokolov, Marina Trapeznikova, Mikhail German, Alexey Ermakov, Obidzhon Bozorov 13.1 Introduction 224 13.2 The Original CA Model 224 13.3 The Slow-to-Start Version of the CA Model 225 13.4 Numerical Realization 225 13.5 Test Predictions for the CA Model 229 13.6 The QGD Approach to Traffic Flow Modeling 230 13.7 Parallel Implementation of the QGD Traffic Model 232 13.8 Test Predictions for the QGD Traffic Model 232 13.9 Conclusion 235 References 236

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Book SynopsisCOMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analyTable of ContentsList of Contributors xv Preface xxi Acknowledgments xxv Acronyms xxvii Part I Introduction 1 1 Overview of Network and Service Management 3Marco Mellia, Nur Zincir-Heywood, and Yixin Diao 1.1 Network and Service Management at Large 3 1.2 Data Collection and Monitoring Protocols 5 1.2.1 SNMP Protocol Family 5 1.2.2 Syslog Protocol 5 1.2.3 IP Flow Information eXport (IPFIX) 6 1.2.4 IP Performance Metrics (IPPM) 7 1.2.5 Routing Protocols and Monitoring Platforms 8 1.3 Network Configuration Protocol 9 1.3.1 Standard Configuration Protocols and Approaches 9 1.3.2 Proprietary Configuration Protocols 10 1.3.3 Integrated Platforms for Network Monitoring 10 1.4 Novel Solutions and Scenarios 12 1.4.1 Software-Defined Networking – SDN 12 1.4.2 Network Functions Virtualization –NFV 14 Bibliography 15 2 Overview of Artificial Intelligence and Machine Learning 19Nur Zincir-Heywood, Marco Mellia, and Yixin Diao 2.1 Overview 19 2.2 Learning Algorithms 20 2.2.1 Supervised Learning 21 2.2.2 Unsupervised Learning 22 2.2.3 Reinforcement Learning 23 2.3 Learning for Network and Service Management 24 Bibliography 26 Part II Management Models and Frameworks 33 3 Managing Virtualized Networks and Services with Machine Learning 35Raouf Boutaba, Nashid Shahriar, Mohammad A. Salahuddin, and Noura Limam 3.1 Introduction 35 3.2 Technology Overview 37 3.2.1 Virtualization of Network Functions 38 3.2.1.1 Resource Partitioning 38 3.2.1.2 Virtualized Network Functions 40 3.2.2 Link Virtualization 41 3.2.2.1 Physical Layer Partitioning 41 3.2.2.2 Virtualization at Higher Layers 42 3.2.3 Network Virtualization 42 3.2.4 Network Slicing 43 3.2.5 Management and Orchestration 44 3.3 State-of-the-Art 46 3.3.1 Network Virtualization 46 3.3.2 Network Functions Virtualization 49 3.3.2.1 Placement 49 3.3.2.2 Scaling 52 3.3.3 Network Slicing 55 3.3.3.1 Admission Control 55 3.3.3.2 Resource Allocation 56 3.4 Conclusion and Future Direction 59 3.4.1 Intelligent Monitoring 60 3.4.2 Seamless Operation and Maintenance 60 3.4.3 Dynamic Slice Orchestration 61 3.4.4 Automated Failure Management 61 3.4.5 Adaptation and Consolidation of Resources 61 3.4.6 Sensitivity to Heterogeneous Hardware 62 3.4.7 Securing Machine Learning 62 Bibliography 63 4 Self-Managed 5G Networks 69Jorge Martín-Pérez, Lina Magoula, Kiril Antevski, Carlos Guimarães, Jorge Baranda, Carla Fabiana Chiasserini, Andrea Sgambelluri, Chrysa Papagianni, Andrés García-Saavedra, Ricardo Martínez, Francesco Paolucci, Sokratis Barmpounakis, Luca Valcarenghi, Claudio EttoreCasetti, Xi Li, Carlos J. Bernardos, Danny De Vleeschauwer, Koen De Schepper, Panagiotis Kontopoulos, Nikolaos Koursioumpas, Corrado Puligheddu, Josep Mangues-Bafalluy, and Engin Zeydan 4.1 Introduction 69 4.2 Technology Overview 73 4.2.1 RAN Virtualization and Management 73 4.2.2 Network Function Virtualization 75 4.2.3 Data Plane Programmability 76 4.2.4 Programmable Optical Switches 77 4.2.5 Network Data Management 78 4.3 5G Management State-of-the-Art 80 4.3.1 RAN resource management 80 4.3.1.1 Context-Based Clustering and Profiling for User and Network Devices 80 4.3.1.2 Q-Learning Based RAN Resource Allocation 81 4.3.1.3 vrAIn: AI-Assisted Resource Orchestration for Virtualized Radio Access Networks 81 4.3.2 Service Orchestration 83 4.3.3 Data Plane Slicing and Programmable Traffic Management 85 4.3.4 Wavelength Allocation 86 4.3.5 Federation 88 4.4 Conclusions and Future Directions 89 Bibliography 92 5 AI in 5G Networks: Challenges and Use Cases 101Stanislav Lange, Susanna Schwarzmann, Marija Gaji´c, Thomas Zinner, and Frank A. Kraemer 5.1 Introduction 101 5.2 Background 103 5.2.1 ML in the Networking Context 103 5.2.2 ML in Virtualized Networks 104 5.2.3 ML for QoE Assessment and Management 104 5.3 Case Studies 105 5.3.1 QoE Estimation and Management 106 5.3.1.1 Main Challenges 107 5.3.1.2 Methodology 108 5.3.1.3 Results and Guidelines 109 5.3.2 Proactive VNF Deployment 110 5.3.2.1 Problem Statement and Main Challenges 111 5.3.2.2 Methodology 112 5.3.2.3 Evaluation Results and Guidelines 113 5.3.3 Multi-service, Multi-domain Interconnect 115 5.4 Conclusions and Future Directions 117 Bibliography 118 6 Machine Learning for Resource Allocation in Mobile Broadband Networks 123Sadeq B. Melhem, Arjun Kaushik, Hina Tabassum, and Uyen T. Nguyen 6.1 Introduction 123 6.2 ML in Wireless Networks 124 6.2.1 Supervised ML 124 6.2.1.1 Classification Techniques 125 6.2.1.2 Regression Techniques 125 6.2.2 Unsupervised ML 126 6.2.2.1 Clustering Techniques 126 6.2.2.2 Soft Clustering Techniques 127 6.2.3 Reinforcement Learning 127 6.2.4 Deep Learning 128 6.2.5 Summary 129 6.3 ML-Enabled Resource Allocation 129 6.3.1 Power Control 131 6.3.1.1 Overview 131 6.3.1.2 State-of-the-Art 131 6.3.1.3 Lessons Learnt 132 6.3.2 Scheduling 132 6.3.2.1 Overview 132 6.3.2.2 State-of-the-Art 132 6.3.2.3 Lessons Learnt 134 6.3.3 User Association 134 6.3.3.1 Overview 134 6.3.3.2 State-of-the-Art 136 6.3.3.3 Lessons Learnt 136 6.3.4 Spectrum Allocation 136 6.3.4.1 Overview 136 6.3.4.2 State-of-the-Art 138 6.3.4.3 Lessons Learnt 138 6.4 Conclusion and Future Directions 140 6.4.1 Transfer Learning 140 6.4.2 Imitation Learning 140 6.4.3 Federated-Edge Learning 141 6.4.4 Quantum Machine Learning 142 Bibliography 142 7 Reinforcement Learning for Service Function Chain Allocation in Fog Computing 147José Santos, Tim Wauters, Bruno Volckaert, and Filip De Turck 7.1 Introduction 147 7.2 Technology Overview 148 7.2.1 Fog Computing (FC) 149 7.2.2 Resource Provisioning 149 7.2.3 Service Function Chaining (SFC) 150 7.2.4 Micro-service Architecture 150 7.2.5 Reinforcement Learning (RL) 151 7.3 State-of-the-Art 152 7.3.1 Resource Allocation for Fog Computing 152 7.3.2 ML Techniques for Resource Allocation 153 7.3.3 RL Methods for Resource Allocation 154 7.4 A RL Approach for SFC Allocation in Fog Computing 155 7.4.1 Problem Formulation 155 7.4.2 Observation Space 156 7.4.3 Action Space 157 7.4.4 Reward Function 158 7.4.5 Agent 161 7.5 Evaluation Setup 162 7.5.1 Fog–Cloud Infrastructure 162 7.5.2 Environment Implementation 162 7.5.3 Environment Configuration 164 7.6 Results 165 7.6.1 Static Scenario 165 7.6.2 Dynamic Scenario 167 7.7 Conclusion and Future Direction 169 Bibliography 170 Part III Management Functions and Applications 175 8 Designing Algorithms for Data-Driven Network Management and Control: State-of-the-Art and Challenges 177Andreas Blenk, Patrick Kalmbach, Johannes Zerwas, and Stefan Schmid 8.1 Introduction 177 8.1.1 Contributions 179 8.1.2 Exemplary Network Use Case Study 179 8.2 Technology Overview 181 8.2.1 Data-Driven Network Optimization 181 8.2.2 Optimization Problems over Graphs 182 8.2.3 From Graphs to ML/AI Input 184 8.2.4 End-to-End Learning 187 8.3 Data-Driven Algorithm Design: State-of-the Art 188 8.3.1 Data-Driven Optimization in General 188 8.3.2 Data-Driven Network Optimization 190 8.3.3 Non-graph Related Problems 192 8.4 Future Direction 193 8.4.1 Data Production and Collection 193 8.4.2 ML and AI Advanced Algorithms for Network Management with Performance Guarantees 194 8.5 Summary 194 Acknowledgments 195 Bibliography 195 9 AI-Driven Performance Management in Data-Intensive Applications 199Ahmad Alnafessah, Gabriele Russo Russo, Valeria Cardellini, Giuliano Casale, and Francesco Lo Presti 9.1 Introduction 199 9.2 Data-Processing Frameworks 200 9.2.1 Apache Storm 200 9.2.2 Hadoop MapReduce 201 9.2.3 Apache Spark 202 9.2.4 Apache Flink 202 9.3 State-of-the-Art 203 9.3.1 Optimal Configuration 203 9.3.1.1 Traditional Approaches 203 9.3.1.2 AI Approaches 204 9.3.1.3 Example: AI-Based Optimal Configuration 206 9.3.2 Performance Anomaly Detection 207 9.3.2.1 Traditional Approaches 208 9.3.2.2 AI Approaches 208 9.3.2.3 Example: ANNs-Based Anomaly Detection 210 9.3.3 Load Prediction 211 9.3.3.1 Traditional Approaches 212 9.3.3.2 AI Approaches 212 9.3.4 Scaling Techniques 213 9.3.4.1 Traditional Approaches 213 9.3.4.2 AI Approaches 214 9.3.5 Example: RL-Based Auto-scaling Policies 214 9.4 Conclusion and Future Direction 216 Bibliography 217 10 Datacenter Traffic Optimization with Deep Reinforcement Learning 223Li Chen, Justinas Lingys, Kai Chen, and Xudong Liao 10.1 Introduction 223 10.2 Technology Overview 225 10.2.1 Deep Reinforcement Learning (DRL) 226 10.2.2 Applying ML to Networks 227 10.2.3 Traffic Optimization Approaches in Datacenter 229 10.2.4 Example: DRL for Flow Scheduling 230 10.2.4.1 Flow Scheduling Problem 230 10.2.4.2 DRL Formulation 230 10.2.4.3 DRL Algorithm 231 10.3 State-of-the-Art: AuTO Design 231 10.3.1 Problem Identified 231 10.3.2 Overview 232 10.3.3 Peripheral System 233 10.3.3.1 Enforcement Module 233 10.3.3.2 Monitoring Module 234 10.3.4 Central System 234 10.3.5 DRL Formulations and Solutions 235 10.3.5.1 Optimizing MLFQ Thresholds 235 10.3.5.2 Optimizing Long Flows 239 10.4 Implementation 239 10.4.1 Peripheral System 239 10.4.1.1 Monitoring Module (MM): 240 10.4.1.2 Enforcement Module (EM): 240 10.4.2 Central System 241 10.4.2.1 sRLA 241 10.4.2.2 lRLA 242 10.5 Experimental Results 242 10.5.1 Setting 243 10.5.2 Comparison Targets 244 10.5.3 Experiments 244 10.5.3.1 Homogeneous Traffic 244 10.5.3.2 Spatially Heterogeneous Traffic 245 10.5.3.3 Temporally and Spatially Heterogeneous Traffic 246 10.5.4 Deep Dive 247 10.5.4.1 Optimizing MLFQ Thresholds using DRL 247 10.5.4.2 Optimizing Long Flows using DRL 248 10.5.4.3 System Overhead 249 10.6 Conclusion and Future Directions 251 Bibliography 253 11 The New Abnormal: Network Anomalies in the AI Era 261Francesca Soro, Thomas Favale, Danilo Giordano, Luca Vassio, Zied Ben Houidi, and Idilio Drago 11.1 Introduction 261 11.2 Definitions and Classic Approaches 262 11.2.1 Definitions 263 11.2.2 Anomaly Detection: A Taxonomy 263 11.2.3 Problem Characteristics 264 11.2.4 Classic Approaches 266 11.3 AI and Anomaly Detection 267 11.3.1 Methodology 267 11.3.2 Deep Neural Networks 268 11.3.3 Representation Learning 270 11.3.4 Autoencoders 271 11.3.5 Generative Adversarial Networks 272 11.3.6 Reinforcement Learning 274 11.3.7 Summary and Takeaways 275 11.4 Technology Overview 277 11.4.1 Production-Ready Tools 277 11.4.2 Research Alternatives 279 11.4.3 Summary and Takeaways 280 11.5 Conclusions and Future Directions 282 Bibliography 283 12 Automated Orchestration of Security Chains Driven by Process Learning 289Nicolas Schnepf, Rémi Badonnel, Abdelkader Lahmadi, and Stephan Merz 12.1 Introduction 289 12.2 RelatedWork 290 12.2.1 Chains of Security Functions 291 12.2.2 Formal Verification of Networking Policies 292 12.3 Background 294 12.3.1 Flow-Based Detection of Attacks 294 12.3.2 Programming SDN Controllers 295 12.4 Orchestration of Security Chains 296 12.5 Learning Network Interactions 298 12.6 Synthesizing Security Chains 301 12.7 Verifying Correctness of Chains 306 12.7.1 Packet Routing 306 12.7.2 Shadowing Freedom and Consistency 306 12.8 Optimizing Security Chains 308 12.9 Performance Evaluation 311 12.9.1 Complexity of Security Chains 312 12.9.2 Response Times 313 12.9.3 Accuracy of Security Chains 313 12.9.4 Overhead Incurred by Deploying Security Chains 314 12.10 Conclusions 315 Bibliography 316 13 Architectures for Blockchain-IoT Integration 321Sina Rafati Niya, Eryk Schiller, and Burkhard Stiller 13.1 Introduction 321 13.1.1 Blockchain Basics 323 13.1.2 Internet-of-Things (IoT) Basics 324 13.2 Blockchain-IoT Integration (BIoT) 325 13.2.1 BIoT Potentials 326 13.2.2 BIoT Use Cases 328 13.2.3 BIoT Challenges 329 13.2.3.1 Scalability 332 13.2.3.2 Security 333 13.2.3.3 Energy Efficiency 334 13.2.3.4 Manageability 335 13.3 BIoT Architectures 335 13.3.1 Cloud, Fog, and Edge-Based Architectures 337 13.3.2 Software-Defined Architectures 337 13.3.3 A Potential Standard BIoT Architecture 338 13.4 Summary and Considerations 341 Bibliography 342 Index 345

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Book SynopsisTable of ContentsIntroduction xvii Chapter 1 Healthcare IT and the Growing Need for AI Operations 1 A Brief History of AI and Healthcare 3 Healthcare IT Expansion and Growth 4 Data Overload 5 Digital Transformation of Healthcare 7 The Science of Healthcare Innovation 9 Artificial Intelligence in Healthcare 10 Healthcare IT Operations 14 AIOps Platform Strategy 18 Platform Types 19 Customer Experience and AIOps 20 AIOps Considerations and Goals 22 Summary 23 Chapter 2 AI Healthcare Operations (Clinical) 25 Clinical Impact of AIOps 26 Gaining a Competitive Edge with Intelligent Cloud, Data Analytics, and AI 27 Design and Innovation 29 AIOps for Healthcare Delivery 33 AIOps for Service Performance 38 Clinical AI, AIOps, and Future Platform Convergence 39 Security and Privacy 41 Why Security is Paramount in AIOps 41 HIPAA, PHI, and PII Protection 43 Summary 45 Chapter 3 AI Healthcare Operations (Operational Infrastructure) 47 Getting Started with AIOps 48 Strategy of AIOps Deployments 50 Creating a Scope 51 AIOps Platforms, Products, and Services Selection 54 AIOps Product Selection: General Topics 54 Product Review: AIOps Tool Splunk 57 Product Review: AIOps Tool ServiceNow 60 Product Review: AIOps Tool Dynatrace 64 Workflow and Event Management Design 67 Service Design with AIOps 67 Day-to-Day Operational Management 69 Summary 70 Chapter 4 Project Planning for AIOps 73 Project Planning Requirements 74 Assigning a Project Manager 75 Creating a Project Plan 77 Building the Project Plan 78 Planning a Healthcare System Project 83 Deploying AIOps 85 Deploying AIOps into the Environment 86 Configuring AIOps in the Environment 88 Summary 91 Chapter 5 Using AI for Metrics, Performance, and Reporting 93 System Performance Metrics 94 Information Technology Metrics 94 Using AI for Metrics, Performance, and Reporting 98 Strategy and Goals for AI Deployment 101 Benefits of Healthcare AIOps Service Performance Reporting 102 Developing Usable AIOps Metrics 104 Helpful Tools You Can Use 105 Gathering Usable Metrics 107 Using Dynatrace 108 Using Splunk 110 Using ServiceNow 117 Clinical and IT Metrics and Collective Actions 123 Usable Healthcare AIOps Dashboards 127 Summary 128 Chapter 6 AIOps and Automation in Healthcare Operations 131 Automation, Workflow, Process, and Intelligence Design 132 Designing the Framework for Automation 132 Understanding Automation 133 Improved User Experience 134 Designing Workflow and Process Engineering 135 Quality Control and Assurance 138 Foundational and Required Design Items 139 Configuring and Using AIOps Automation 146 Monitoring and Operating Event Management Services 148 Creating and Realizing Automation, ML, and AI 152 Automating Splunk and IT Service Analyzer 155 Splunk IT Service Intelligence 160 When Should You Use AI and ML? 162 Summary 163 Chapter 7 Cloud Operations and AIOps 165 Understanding the Cloud 166 Understanding Cloud Computing 166 Cloud as a Service 172 Hybrid Cloud Solutions 175 When You Should (and Shouldn’t) Consider the Cloud 178 Deploying to the Cloud 179 Conducting a Request for Proposal 182 Additional Deployment Options 184 Managing in the Cloud 186 Cloud Management and Monitoring Solutions 189 Summary 191 Chapter 8 The Future of Healthcare AI 193 The Dynamically Changing World of AI 194 The Future of AI 198 Artificial Intelligence and Healthcare Innovation 201 Big Data, DataOps, Analytics, and Informatics 201 Telehealth (Telemedicine) 204 Telehealth Innovations 206 Telehealth AI 209 Future Innovation Merging Clinical and IT Operations 212 The Future and Beyond 214 AIOps, the Cloud, and Security 218 Summary 218 Chapter 9 The Convergence of Healthcare AI Technology 221 Overview of Convergence 222 Systems Integration 225 Convergence of AI, HIT, and HIE 228 IoT and AI 230 IoT Management 237 AIOps Management and Security 239 Summary 245 Appendix Sample AIOps Use Cases and Examples 247 Index 259

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Book SynopsisTable of ContentsPreface xiii 1 IoT-Based Battery Management System for Hybrid Electric Vehicle 1P. Sivaraman and C. Sharmeela 1.1 Introduction 1 1.2 Battery Configurations 3 1.3 Types of Batteries for HEV and EV 5 1.4 Functional Blocks of BMS 6 1.4.1 Components of BMS System 7 1.5 IoT-Based Battery Monitoring System 11 References 14 2 A Noble Control Approach for Brushless Direct Current Motor Drive Using Artificial Intelligence for Optimum Operation of the Electric Vehicle 17Upama Das, Pabitra Kumar Biswas and Chiranjit Sain 2.1 Introduction 18 2.2 Introduction of Electric Vehicle 19 2.2.1 Historical Background of Electric Vehicle 19 2.2.2 Advantages of Electric Vehicle 20 2.2.2.1 Environmental 20 2.2.2.2 Mechanical 20 2.2.2.3 Energy Efficiency 20 2.2.2.4 Cost of Charging Electric Vehicles 21 2.2.2.5 The Grid Stabilization 21 2.2.2.6 Range 21 2.2.2.7 Heating of EVs 22 2.2.3 Artificial Intelligence 22 2.2.4 Basics of Artificial Intelligence 23 2.2.5 Advantages of Artificial Intelligence in Electric Vehicle 24 2.3 Brushless DC Motor 24 2.4 Mathematical Representation Brushless DC Motor 25 2.5 Closed-Loop Model of BLDC Motor Drive 30 2.5.1 P-I Controller & I-P Controller 31 2.6 PID Controller 32 2.7 Fuzzy Control 33 2.8 Auto-Tuning Type Fuzzy PID Controller 34 2.9 Genetic Algorithm 35 2.10 Artificial Neural Network-Based Controller 36 2.11 BLDC Motor Speed Controller With ANN-Based PID Controller 37 2.11.1 PID Controller-Based on Neuro Action 38 2.11.2 ANN-Based on PID Controller 38 2.12 Analysis of Different Speed Controllers 39 2.13 Conclusion 41 References 42 3 Optimization Techniques Used in Active Magnetic Bearing System for Electric Vehicles 49Suraj Gupta, Pabitra Kumar Biswas, Sukanta Debnath and Jonathan Laldingliana 3.1 Introduction 50 3.2 Basic Components of an Active Magnetic Bearing (AMB) 54 3.2.1 Electromagnet Actuator 54 3.2.2 Rotor 54 3.2.3 Controller 55 3.2.3.1 Position Controller 56 3.2.3.2 Current Controller 56 3.2.4 Sensors 56 3.2.4.1 Position Sensor 56 3.2.4.2 Current Sensor 57 3.2.5 Power Amplifier 57 3.3 Active Magnetic Bearing in Electric Vehicles System 58 3.4 Control Strategies of Active Magnetic Bearing for Electric Vehicles System 59 3.4.1 Fuzzy Logic Controller (FLC) 59 3.4.1.1 Designing of Fuzzy Logic Controller (FLC) Using MATLAB 60 3.4.2 Artificial Neural Network (ANN) 63 3.4.2.1 Artificial Neural Network Using MATLAB 63 3.4.3 Particle Swarm Optimization (PSO) 67 3.4.4 Particle Swarm Optimization (PSO) Algorithm 68 3.4.4.1 Implementation of Particle Swarm Optimization for Electric Vehicles System 70 3.5 Conclusion 71 References 72 4 Small-Signal Modelling Analysis of Three-Phase Power Converters for EV Applications 77Mohamed G. Hussien, Sanjeevikumar Padmanaban, Abd El-Wahab Hassan and Jens Bo Holm-Nielsen 4.1 Introduction 77 4.2 Overall System Modelling 79 4.2.1 PMSM Dynamic Model 79 4.2.2 VSI-Fed SPMSM Mathematical Model 80 4.3 Mathematical Analysis and Derivation of the Small-Signal Model 86 4.3.1 The Small-Signal Model of the System 86 4.3.2 Small-Signal Model Transfer Functions 87 4.3.3 Bode Diagram Verification 96 4.4 Conclusion 100 References 100 5 Energy Management of Hybrid Energy Storage System in PHEV With Various Driving Mode 103S. Arun Mozhi, S. Charles Raja, M. Saravanan and J. Jeslin Drusila Nesamalar 5.1 Introduction 104 5.1.1 Architecture of PHEV 104 5.1.2 Energy Storage System 105 5.2 Problem Description and Formulation 106 5.2.1 Problem Description 106 5.2.2 Objective 106 5.2.3 Problem Formulation 106 5.3 Modeling of HESS 107 5.4 Results and Discussion 108 5.4.1 Case 1: Gradual Acceleration of Vehicle 108 5.4.2 Case 2: Gradual Deceleration of Vehicle 109 5.4.3 Case 3: Unsystematic Acceleration and Deceleration of Vehicle 110 5.5 Conclusion 111 References 112 6 Reliability Approach for the Power Semiconductor Devices in EV Applications 115Krishnachaitanya, D., Chitra, A. and Biswas, S.S. 6.1 Introduction 115 6.2 Conventional Methods for Prediction of Reliability for Power Converters 116 6.3 Calculation Process of the Electronic Component 118 6.4 Reliability Prediction for MOSFETs 119 6.5 Example: Reliability Prediction for Power Semiconductor Device 121 6.6 Example: Reliability Prediction for Resistor 122 6.7 Conclusions 123 References 123 7 Modeling, Simulation and Analysis of Drive Cycles for PMSM-Based HEV With Optimal Battery Type 125Chitra, A., Srivastava, Shivam, Gupta, Anish, Sinha, Rishu, Biswas, S.S. and Vanishree, J. 7.1 Introduction 126 7.2 Modeling of Hybrid Electric Vehicle 127 7.2.1 Architectures Available for HEV 128 7.3 Series—Parallel Hybrid Architecture 129 7.4 Analysis With Different Drive Cycles 129 7.4.1 Acceleration Drive Cycle 130 7.4.1.1 For 30% State of Charge 130 7.4.1.2 For 60% State of Charge 131 7.4.1.3 For 90% State of Charge 131 7.5 Cruising Drive Cycle 132 7.6 Deceleration Drive Cycle 132 7.6.1 For 30% State of Charge 134 7.6.2 For 60% State of Charge 136 7.6.3 For 90% State of Charge 137 7.7 Analysis of Battery Types 139 7.8 Conclusion 140 References 141 8 Modified Firefly-Based Maximum Power Point Tracking Algorithm for PV Systems Under Partial Shading Conditions 143Chitra, A., Yogitha, G., Karthik Sivaramakrishnan, Razia Sultana, W. and Sanjeevikumar, P. 8.1 Introduction 143 8.2 System Block Diagram Specifications 146 8.3 Photovoltaic System Modeling 148 8.4 Boost Converter Design 150 8.5 Incremental Conductance Algorithm 152 8.6 Under Partial Shading Conditions 153 8.7 Firefly Algorithm 154 8.8 Implementation Procedure 156 8.9 Modified Firefly Logic 157 8.10 Results and Discussions 159 8.11 Conclusion 162 References 162 9 Induction Motor Control Schemes for Hybrid Electric Vehicles/Electric Vehicles 165Sarin, M.V., Chitra, A., Sanjeevikumar, P. and Venkadesan, A. 9.1 Introduction 166 9.2 Control Schemes of IM 167 9.2.1 Scalar Control 167 9.3 Vector Control 168 9.4 Modeling of Induction Machine 169 9.5 Controller Design 174 9.6 Simulations and Results 175 9.7 Conclusions 176 References 177 10 Intelligent Hybrid Battery Management System for Electric Vehicle 179Rajalakshmi, M. and Razia Sultana, W. 10.1 Introduction 179 10.2 Energy Storage System (ESS) 181 10.2.1 Lithium-Ion Batteries 183 10.2.1.1 Lithium Battery Challenges 183 10.2.2 Lithium–Ion Cell Modeling 184 10.2.3 Nickel-Metal Hydride Batteries 186 10.2.4 Lead-Acid Batteries 187 10.2.5 Ultracapacitors (UC) 187 10.2.5.1 Ultracapacitor Equivalent Circuit 187 10.2.6 Other Battery Technologies 189 10.3 Battery Management System 190 10.3.1 Need for BMS 191 10.3.2 BMS Components 192 10.3.3 BMS Architecture/Topology 193 10.3.4 SOC/SOH Determination 193 10.3.5 Cell Balancing Algorithms 197 10.3.6 Data Communication 197 10.3.7 The Logic and Safety Control 198 10.3.7.1 Power Up/Down Control 198 10.3.7.2 Charging and Discharging Control 199 10.4 Intelligent Battery Management System 199 10.4.1 Rule-Based Control 201 10.4.2 Optimization-Based Control 201 10.4.3 AI-Based Control 202 10.4.4 Traffic (Look Ahead Method)-Based Control 203 10.5 Conclusion 203 References 203 11 A Comprehensive Study on Various Topologies of Permanent Magnet Motor Drives for Electric Vehicles Application 207Chiranjit Sain, Atanu Banerjee and Pabitra Kumar Biswas 11.1 Introduction 208 11.2 Proposed Design Considerations of PMSM for Electric Vehicle 209 11.3 Impact of Digital Controllers 211 11.3.1 DSP-Based Digital Controller 212 11.3.2 FPGA-Based Digital Controller 212 11.4 Electric Vehicles Smart Infrastructure 212 11.5 Conclusion 214 References 215 12 A New Approach for Flux Computation Using Intelligent Technique for Direct Flux Oriented Control of Asynchronous Motor 219A. Venkadesan, K. Sedhuraman, S. Himavathi and A. Chitra 12.1 Introduction 220 12.2 Direct Field-Oriented Control of IM Drive 221 12.3 Conventional Flux Estimator 222 12.4 Rotor Flux Estimator Using CFBP-NN 223 12.5 Comparison of Proposed CFBP-NN With Existing CFBP-NN for Flux Estimation 224 12.6 Performance Study of Proposed CFBP-NN Using MATLAB/SIMULINK 225 12.7 Practical Implementation Aspects of CFBP-NN-Based Flux Estimator 229 12.8 Conclusion 231 References 231 13 A Review on Isolated DC–DC Converters Used in Renewable Power Generation Applications 233Ingilala Jagadeesh and V. Indragandhi 13.1 Introduction 233 13.2 Isolated DC–DC Converter for Electric Vehicle Applications 234 13.3 Three-Phase DC–DC Converter 238 13.4 Conclusion 238 References 239 14 Basics of Vector Control of Asynchronous Induction Motor and Introduction to Fuzzy Controller 241S.S. Biswas 14.1 Introduction 241 14.2 Dynamics of Separately Excited DC Machine 243 14.3 Clarke and Park Transforms 244 14.4 Model Explanation 251 14.5 Motor Parameters 252 14.6 PI Regulators Tuning 254 14.7 Future Scope to Include Fuzzy Control in Place of PI Controller 256 14.8 Conclusion 257 References 258 Index 259

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Book SynopsisINTELLIGENT CONNECTIVITY AI, IOT, AND 5G Explore the economics and technology of AI, IOT, and 5G integration Intelligent Connectivity: AI, IoT, and 5G delivers a comprehensive technological and economic analysis of intelligent connectivity and the integration of artificial intelligence, Internet of Things (IoT), and 5G. It covers a broad range of topics, including Machine-to-Machine (M2M) architectures, edge computing, cybersecurity, privacy, risk management, IoT architectures, and more. The book offers readers robust statistical data in the form of tables, schematic diagrams, and figures that provide a clear understanding of the topic, along with real-world examples of applications and services of intelligent connectivity in different sectors of the economy. Intelligent Connectivity describes key aspects of the digital transformation coming with the 4th industrial revolution that will touch on industries as disparate as transportation, educatioTable of ContentsPreface Acknowledgement Introduction 1 Technology Adoption and Emerging Trends 1.1 Introduction 1.2 Trends in Business technology 1.3 AI-Fueled Organizations 1.4 Connectivity of Tomorrow 1.5 Moving Beyond Marketing 1.6 Cloud Computing 1.7 Cybersecurity, Privacy, and Risk Management 1.8 Conclusion 2 Telecommunication Transformation and Intelligent Connectivity 2.1 Introduction 2.2 Cybersecurity Concerns in the 5G World 2.3 Positive Effects of Addressing Cybersecurity Challenges in 5G 2.4 Intelligent Connectivity Use-Cases 2.5 Industrial and Manufacturing Operations 2.6 Healthcare 2.7 Public Safety and Security 2.8 Conclusion 3 The Internet of Things (IoT): Potentials and the Future Trends 3.1 Introduction 3.2 Achieving the Future of IoT 3.3 Commercial Opportunities for IoT 3.4 The Industrial Internet of Things 3.5 Future Impact of IoT in Our Industry 3.6 Data Sharing in the IoT Environment 3.7 IoT Devises Environment Operation 3.8 Interoperability Issues of IoT 3.9 IoT-Cloud –Application 3.10 Regulation and Security Issues of IoT 3.11 Achieving IoT Innovations While Tackling Security and Regulation Issues 3.12 Future of IoT 3.13 Conclusion 4 The Wild Wonders of 5G Wireless Technology 4.1 Introduction 4.2 5G Architecture 4.3 5G Applications 4.4 5G Network Architecture 4.5 Security and Issues of 5G 4.6 IoT Devices in 5G Wireless 4.7 Big Data Analytics in 5G 4.8 AI Empowers a Wide Scope of Use Cases 4.9 Conclusion 5 Artificial Intelligence Technology 5.1 Introduction 5.2 Core Concepts of Artificial Intelligence 5.3 Machine Learning and Applications 5.4 Deep Learning 5.5 Neural Networks Follow a Natural Model 5.6 Classifications of Artificial Intelligence 5.7 Trends in Artificial Intelligence 5.8 Challenges of Artificial Intelligence 5.9 Funding Trends in Artificial Intelligence 5.10 Conclusion 6 AI, 5G, & IoT: Driving Forces Towards the Industry Technology Trends 6.1 Introduction 6.2 Fifth Generation of Network Technology 6.3 Internet of Things (IoT) 6.4 Industrial Internet of Things 6.5 IoT in Automotive 6.6 IoT in Agriculture 6.7 AI, IoT, and 5G Security 6.8 Conclusion 7 Intelligent Connectivity: A New Capabilities to Bring Complex Use Cases 7.1 Introduction 7.2 Machine-to-Machine Communication and the Internet of Things 7.3 Convergence of Internet of Things, Artificial Intelligence and 5G 7.4 Intelligent Connectivity Applications 7.5 Challenges and Risks of Intelligent Connectivity 7.6 Recommendations 7.7 Conclusion 8 IoT: Laws, Policies and Regulations 8.1 Introduction 8.2 Recently Published laws and Regulations 8.3 Developing Innovation and Growing the Internet of Things (DIGIT) Act 8.4 General View 8.5 Relaxation of laws by the Federal Aviation Administration's (FAA) 8.6 Supporting Innovation of Self Driving Cars by Allowing Policies 8.7 Recommendations 8.8 Conclusion 9 Artificial Intelligence and Blockchain 9.1 Introduction 9.2 Decentralized Intelligence 9.3 Applications 9.4 How Artificial Intelligence and Blockchain will Affect Society 9.5 How Augmented Reality Works 9.6 Mixed Reality 9.7 Virtual Reality 9.8 Key Components in a Virtual Reality System 9.9 Augmented Reality Uses 9.10 Applications of Virtual Reality in Business 9.11 The Future of Blockchain 9.12 Blockchain Applications 9.13 Blockchain and the Internet of Things 9.14 Law Coordination 9.15 Collaboration for Blockchain Success 10 Digital Twin Technology 10.1 Introduction 10.2 The Timeline and History of Digital Twin Technology 10.3 Technologies Employed in Digital Twin Models 10.4 The Dimension of Digital Twins Models 10.5 Digital Twin and Other Technologies 10.6 Digital Twin Technology Implementation 10.7 Benefits of Digital Twin 10.8 Application of Digital Twins 10.9 Challenges of Digital Twins 11 Artificial Intelligence, Big Data Analytics, and IoT 11.1 Introduction 11.2 Analytic 11.3 AI Technology in Big Data and IoT 11.4 AI Technology Applications and Use Cases 11.5 AI Technology Impact on the Vertical Market 11.6 AI in Big Data and IoT Market Analysis and Forecasts 11.7 Conclusion 12 Digital Transformation Trends in the Automotive Industry 12.1 Introduction 12.2 Evolution of Automotive Industry 12.3 Data-Driven Business Model and data monetization 12.4 Services of Data-Driven Business Model 12.5 Values of New Services in the New Automotive Industry 12.6 Conclusion 13 Wireless Sensors/IoT and Artificial Intelligence for Smart Grid and Smart Home 13.1 Introduction 13.2 Wireless Sensor Networks 13.3 Power Grid Impact 13.4 Benefits of Smart Grid 13.5 Internet of Things 13.6 Internet of Things on Smart Grid 13.7 Smart Grid and Artificial Intelligence 13.8 Smart Grid Programming 13.9 Conclusion 14 Artificial Intelligence, 5G and IoT: Security 14.1 Introduction 14.2 Understanding IoT 14.3 Artificial Intelligence 14.4 5G Network 14.5 Emerging Partnership of Artificial Intelligence, IoT, 5G, and Cybersecurity 14.6 Conclusion 15 Intelligent Connectivity and Agriculture 15.1 Introduction 15.2 The Potential of Wireless Sensors and IoT in Agriculture 15.3 IoT Sensory Technology with Traditional Farming 15.4 IoT Devices and Communication Techniques 15.5 IoT and all Crop Stages 15.6 Drone in Farming Applications 15.7 Conclusion 16 Applications of Artificial Intelligence, ML, and DL 16.1 Introduction 16.2 Building Artificial Intelligence Capabilities 16.3 What is Machine Learning? 16.4 Deep Learning 16.5 Machine Learning vs. Deep Learning Comparison 16.6 Feature Engineering 16.7 Application of Machine Learning 16.8 Applications of Deep learning 16.9 Future Trends 17 Big Data and Artificial Intelligence: Strategies for Leading Business Transformation 17.1 Introduction 17.2 Big Data 17.2 Machine Learning-Based Medical Systems 17.3 Artificial Intelligence for Stock Market Prediction 17.3.1 Application of Artificial Intelligence by Investors 17.4 Trends in AI and Big Data Technologies Drive Business Innovation 17.5 Driving Innovation Through Big Data 17.6 The Convergence of AI and Big Data 17.7 How AI and Big Data Will Combine to Create Business Innovation 17.8 AI and Big Data for Technological Innovation 17.9 AI and Production 17.10 AI and ML Operations Research 17.11 Collaboration Between Machines and Human 17.12 Generative Designs 17.13 Adapting to a Changing Market 17.14 Conclusion Index

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Book SynopsisTable of ContentsPreface xv 1 Introduction to Blockchain 1Akshay Mudgal 1.1 Introduction 1 1.1.1 Public Blockchain Architecture 5 1.1.2 Private Blockchain Architecture 5 1.1.3 Consortium Blockchain Architecture 5 1.2 The Privacy Challenges of Blockchain 6 1.3 De-Anonymization 8 1.3.1 Analysis of Network 9 1.3.2 Transaction Fingerprinting 9 1.3.3 DoS Attacks 9 1.3.4 Sybil Attacks 9 1.4 Transaction Pattern Exposure 10 1.4.1 Transaction Graph Analysis 10 1.4.2 AS-Level Deployment Analysis 10 1.5 Methodology: Identity Privacy Preservation 10 1.5.1 Mixing Services 10 1.5.2 Ring Signature 12 1.6 Decentralization Challenges Exist in Blockchain 14 1.7 Conclusion 15 1.8 Regulatory Challenges 16 1.9 Obstacles to Blockchain Regulation 16 1.10 The Current Regulatory Landscape 17 1.11 The Future of Blockchain Regulation 18 1.12 Business Model Challenges 19 1.12.1 Traditional Business Models 19 1.12.2 Manufacturer 19 1.12.3 Distributor 20 1.12.4 Retailer 20 1.12.5 Franchise 20 1.13 Utility Token Model 20 1.13.1 Right 21 1.13.2 Value Exchange 21 1.13.3 Toll 21 1.13.4 Function 21 1.13.5 Currency 22 1.13.6 Earning 22 1.14 Blockchain as a Service 22 1.15 Securities 23 1.16 Development Platforms 24 1.17 Scandals and Public Perceptions 25 1.17.1 Privacy Limitations 26 1.17.2 Lack of Regulations and Governance 26 1.17.3 Cost to Set Up 26 1.17.4 Huge Consumption of Energy 26 1.17.5 Public Perception 27 References 27 2 The Scope for Blockchain Ecosystem 29Manisha Suri 2.1 Introduction 30 2.2 Blockchain as Game Changer for Environment 32 2.3 Blockchain in Business Ecosystem 38 2.3.1 Business Ecosystem 39 2.3.1.1 What Is Business Model? 39 2.3.1.2 Business Model—Traditional 39 2.3.2 Are Blockchain Business Models Really Needed? 41 2.3.2.1 Blockchain Business Model 41 2.3.2.2 Model 1: Utility Token Model 41 2.3.2.3 Model 2: BaaS 43 2.3.2.4 Model 3: Securities 44 2.3.2.5 Model 4: Development Platforms 45 2.3.2.6 Model 5: Blockchain-Based Software Products 46 2.3.2.7 Model 6: Blockchain Professional Services 46 2.3.2.8 Model 7: Business Model—P2P 47 2.4 Is Blockchain Business Ecosystem Profitable? 48 2.5 How Do You “Design” a Business Ecosystem? 49 2.6 Redesigning Future With Blockchain 53 2.6.1 Is Earth Prepared for Blockchain? 53 2.7 Challenges and Opportunities 57 References 58 3 Business Use Cases of Blockchain Technology 59Vasudha Arora, Shweta Mongia, Sugandha Sharma and Shaveta Malik 3.1 Introduction to Cryptocurrency 60 3.2 What is a Bitcoin? 60 3.2.1 Bitcoin Transactions and Their Processing 62 3.2.2 Double Spending Problem 65 3.2.3 Bitcoin Mining 67 3.3 Bitcoin ICO 69 3.3.1 ICO Token 69 3.3.2 How to Participate in ICO 70 3.3.3 Types of Tokens 71 3.4 Advantages and Disadvantages of ICO 72 3.5 Merchant Acceptance of Bitcoin 73 References 75 4 Ethereum 77Shaveta Bhatia and S.S Tyagi 4.1 Introduction 78 4.2 Basic Features of Ethereum 78 4.3 Difference between Bitcoin and Ethereum 79 4.4 EVM (Ethereum Virtual Machine) 82 4.5 Gas 85 4.5.1 Gas Price Chart 85 4.6 Applications Built on the Basis of Ethereum 86 4.7 ETH 87 4.7.1 Why Users Want to Buy ETH? 87 4.7.2 How to Buy ETH? 88 4.7.3 Alternate Way to Buy ETH 88 4.7.4 Conversion of ETH to US Dollar 89 4.8 Smart Contracts 90 4.8.1 Government 90 4.8.2 Management 91 4.8.3 Benefits of Smart Contracts 91 4.8.4 Problems With Smart Contracts 92 4.8.5 Solution to Overcome This Problem 92 4.8.6 Languages to Build Smart Contracts 92 4.9 DApp (Decentralized Application or Smart Contract) 93 4.9.1 DApp in Ethereum 93 4.9.2 Applications of DApps 93 4.10 Conclusion 95 References 95 5 E-Wallet 97Ms. Vishawjyoti 5.1 Overview of Wallet Technology 97 5.2 Types of Wallet 98 5.2.1 Paper 98 5.2.2 Physical Bitcoins 99 5.2.3 Mobile 99 5.2.4 Web 100 5.2.5 Desktop 100 5.2.6 Hardware 100 5.2.7 Bank 101 5.3 Security of Bitcoin Wallets 101 5.4 Workings of Wallet Technology 101 5.5 Create HD Wallet From Seed 102 5.5.1 Initiation 103 5.5.2 Steps for Creating an HD Wallet From a 24-Word Seed Phrase Through Particl-qt Tool 104 5.5.3 Steps for Encrypting the HD Wallet 106 5.5.4 Utilization 108 5.5.5 Steps for Generating Address to Access Transactions on the HD Wallet 108 5.6 Navigating HD Wallet 109 5.7 Conclusion 110 References 110 6 Blockchain and Governance: Theory, Applications and Challenges 113Bhavya Ahuja Grover, Bhawna Chaudhary, Nikhil Kumar Rajput and Om Dukiya 6.1 Introduction 114 6.2 Governance: Centralized vs Decentralized 115 6.3 Blockchain’s Features Supportive of Decentralization 117 6.4 Noteworthy Application Areas for Blockchain-Based Governance 119 6.4.1 Public Service Governance 119 6.4.2 Knowledge and Shared Governance 121 6.4.3 Governance in Supply Chain 123 6.4.4 Governance of Foreign Aid 124 6.4.5 Environmental Governance 125 6.4.6 Corporate Governance 126 6.4.7 Economic Governance 128 6.5 Scopes and Challenges 128 6.6 Conclusion 136 References 137 7 Blockchain-Based Identity Management 141Abhishek Bhattacharya 7.1 Introduction 141 7.2 Existing Identity Management Systems and Their Challenges 142 7.3 Concept of Decentralized Identifiers 144 7.4 The Workflow of Blockchain Identity Management Systems 145 7.5 How Does it Contribute to Data Security? 148 7.6 Trending Blockchain Identity Management Projects 150 7.7 Why and How of Revocation 152 7.8 Points to Ponder 154 7.8.1 Comparison Between Traditional and Blockchain-Based Identity Management Systems 156 7.9 Conclusion 157 References 158 8 Blockchain & IoT: A Paradigm Shift for Supply Chain Management 159Abhishek Bhattacharya 8.1 Introduction 159 8.2 Supply Chain Management 160 8.2.1 The Aspects of a Supply Chain 161 8.2.2 Supply Chain Performance Dimensions 162 8.2.3 Supply Chain Migration Towards Digitalization 163 8.3 Blockchain and IoT 164 8.3.1 What Makes Blockchain Suitable for SCM? 166 8.3.1.1 Shared Ledger 167 8.3.1.2 Permissions 168 8.3.1.3 Consensus 168 8.3.1.4 Smart Contracts 169 8.3.2 The Role of Blockchain in Achieving the SCM Performance Dimensions 170 8.3.3 The Role of IoT in the Implementation of Blockchain Technology 171 8.4 Blockchain Technology and IoT Use Cases in Supply Chain Management 172 8.5 Benefits and Challenges in Blockchain-Based Supply Chain Management 173 8.6 Conclusion 176 References 176 9 Blockchain-Enabled Supply Chain Management System 179Sonal Pathak 9.1 Introduction 180 9.1.1 Supply Chain Management 180 9.2 Blockchain Technology 184 9.3 Blockchain Technology in Supply Chain Management 186 9.4 Elements of Blockchain That Affects Supply Chain 190 9.4.1 Bitcoin 195 9.5 Challenges in Implementation of Blockchain-Enabled Supply Chain 197 9.6 Conclusion 197 References 199 10 Security Concerns of Blockchain 201Neha Jain and Kamiya Chugh 10.1 Introduction: Security Concerns of Blockchain 201 10.2 Cryptocurrencies Scenarios 202 10.3 Privacy Challenges of Blockchain 203 10.3.1 Protection Problems in Blockchain 203 10.3.2 Privacy-Preserving Mechanisms Analysis 207 10.3.3 Data Anonymization-Mixing 207 10.4 Decentralization in Blockchain 208 10.4.1 Role of Decentralization in Blockchain 209 10.4.2 Analysis of PoS and DPoS 210 10.4.3 Problems With Decentralization 210 10.4.4 Decentralization Recovery Methods 212 10.5 Legal and Regulatory Issues in Blockchain 213 10.5.1 Legal Value of Blockchain and its Problems 214 10.6 Smart Contracts 218 10.7 Scandals of Blockchain 220 10.7.1 Blockchain Technologies as Stumbling Blocks to Financial Legitimacy 223 10.8 Is Blockchain the Rise of Trustless Trust? 223 10.8.1 Why Do We Need a System of Trust? 226 10.9 Blockchain Model Challenges 227 References 229 11 Acceptance and Adoption of Blockchain Technology: An Examination of the Security & Privacy Challenges 231Amandeep Dhaliwal and Sahil Malik 11.1 Introduction 231 11.1.1 Research Methodology 233 11.1.2 Analysis 233 11.2 Security Issues of Blockchain 233 11.2.1 The Majority Attack (51% Attacks) 233 11.2.2 The Fork Problems 234 11.2.2.1 Hard Fork 234 11.2.2.2 Soft Fork 235 11.2.3 Scale of Blockchain 235 11.2.4 Time Confirmation of Blockchain Data— Double-Spend Attack/Race Attack 235 11.2.5 Current Regulations Problems 236 11.2.6 Scalability and Storage Capacity 236 11.2.7 DOS Attack/Sybil Attack/Eclipse Attack/Bugs 237 11.2.8 Legal Issues 237 11.2.9 Security of Wallets 238 11.2.10 The Increased Computing Power 238 11.3 Privacy Challenges of Bitcoin 238 11.3.1 De-Anonymization 239 11.3.1.1 Network Analysis 239 11.3.1.2 Address Clustering 239 11.3.1.3 Transaction Finger Printing 240 11.3.2 Transaction Pattern Exposure 240 11.3.2.1 Transaction Graph Analysis 240 11.3.2.2 Autonomous System-Level Deployment Analysis 241 11.4 Blockchain Application-Based Solutions 241 11.4.1 Bitcoins 241 11.4.2 IoT 242 11.4.2.1 MyBit 242 11.4.3 Aero Token 242 11.4.4 The Chain of Things 243 11.4.5 The Modum 243 11.4.6 Twin of Things 243 11.4.7 The Blockchain of Things 244 11.4.8 Blockchain Solutions: Cloud Computing 244 11.5 Conclusion and Future Work 245 References 245 12 Deficiencies in Blockchain Technology and Potential Augmentation in Cyber Security 251Eshan Bajal, Madhulika Bhatia, Lata Nautiyal and Madhurima Hooda 12.1 Introduction 252 12.2 Security Issues in Blockchain Technology 252 12.3 Privacy Challenges 253 12.3.1 BGP Hijacking Attack 255 12.3.2 BDoS (Blockchain Denial of Service) 255 12.3.3 Forcing Other Miners to Stop Mining 256 12.4 Decentralization Challenges 256 12.5 Regulatory Challenges 260 12.5.1 Principles to Follow While Regulating 262 12.5.1.1 Flexible to Legal Innovation 262 12.5.1.2 Experimentation Should be Encouraged 263 12.5.1.3 Focus on the Immediate Implications 264 12.5.1.4 Regulators Should Engage in a Transnational Conversation 264 12.5.2 Regulatory Strategies 265 12.5.2.1 Wait-and-See 265 12.5.2.2 Imposing Narrowing and Broadening Guidance 266 12.5.2.3 Sandboxing 266 12.5.2.4 Issue a New Legislation 267 12.5.2.5 Use Blockchain in Regulation 268 12.6 Business Model Challenges 269 12.7 Scandals and Public Perception 271 12.8 Why Blockchain is Trustless 277 12.8.1 Trust Mechanism 278 12.8.2 Anonymity 279 12.8.3 Use in Digital Wallets 279 12.8.4 Forgery Resistance 279 12.9 Use of Blockchain in Cybersecurity 280 12.9.1 Blockchain Database 281 12.9.2 DNS Security 283 12.9.3 IoT Security 283 12.9.4 DDoS Prevention 286 12.9.5 CDN (Content Delivery Network) 286 12.9.6 SMS Authentication 287 References 288 13 Internet of Things and Blockchain 295Priyanka Sharma 13.1 History of ‘Internet of Things’ 296 13.2 IoT Devices 298 13.3 Sensors and Actuators 302 13.4 Cloud and Haze-Based Engineering 307 13.5 Blockchain and IoT 315 13.6 Edge Computing 321 13.7 Contextual Analyses 324 13.8 Fate of Blockchain and IoT 332 References 332 14 Blockchain Applications 337Boby Singh, Rohit Pahwa, Hari Om Tanwar and Nikita Gupta 14.1 Introduction to Blockchain 337 14.1.1 Uses of Blockchain in Administration 339 14.2 Blockchain in Big Data Predictive Task Automation 340 14.2.1 How Can Blockchain Help Big Data? 341 14.2.2 Blockchain Use Cases in Big Data 341 14.3 Digital Identity Verification 342 14.3.1 Why Digital Identity Matters? 343 14.3.2 Blockchain (Definition and its Features) 343 14.3.3 Why do we Need Blockchain in Digital Identity? 344 14.3.4 How Does a Blockchain Works? 345 14.3.5 Why is a Blockchain Secure? 345 14.3.6 What’s Blockchain Identification Management? 346 14.3.7 Advantages 347 14.4 Blockchain Government 348 14.4.1 Decentralized Government Services 349 14.4.2 Liquid Democracy and Random Sample Election 350 14.5 Blockchain Science 351 14.5.1 FoldingCoin 351 14.5.2 GridCoin (GRC) 352 14.5.3 Global Public Health 353 14.5.4 Bitcoin Genomics 354 14.6 Blockchain Health 355 14.6.1 Health Coin 355 14.6.2 EMR on Blockchain 355 14.6.3 Bit Coin Health Notary 356 14.7 Blockchain Learning 357 14.7.1 Bitcoin MOOCs 357 14.7.2 Smart Contract Literacy 357 14.7.3 LearnCoin 359 References 359 15 Advance Concepts of Blockchain 361Raj Kumar 15.1 Community Supercomputing 361 15.2 Blockchain Genomics 364 15.3 Blockchain Learning 365 15.4 Community Coin 366 15.4.1 Monetary and Non-Monetary Currencies 367 15.4.2 Difference Between Monetary and Non-Monetary Assets 369 15.4.3 Currency Multiplicity 369 15.4.4 List of Some Prominent Alternate Coins is Given Below 370 15.5 Demurrage Currencies 371 Reading List 371 Index 373

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Book SynopsisTable of ContentsPreface xv 1 Rumor Detection and Tracing its Source to Prevent Cyber-Crimes on Social Media 1Ravi Kishore Devarapalli and Anupam Biswas 1.1 Introduction 2 1.2 Social Networks 4 1.2.1 Types of Social Networks 4 1.3 What is Cyber-Crime? 7 1.3.1 Definition 7 1.3.2 Types of Cyber-Crimes 7 1.3.2.1 Hacking 7 1.3.2.2 Cyber Bullying 7 1.3.2.3 Buying Illegal Things 8 1.3.2.4 Posting Videos of Criminal Activity 8 1.3.3 Cyber-Crimes on Social Networks 8 1.4 Rumor Detection 9 1.4.1 Models 9 1.4.1.1 Naïve Bayes Classifier 10 1.4.1.2 Support Vector Machine 13 1.4.2 Combating Misinformation on Instagram 14 1.5 Factors to Detect Rumor Source 15 1.5.1 Network Structure 15 1.5.1.1 Network Topology 16 1.5.1.2 Network Observation 16 1.5.2 Diffusion Models 18 1.5.2.1 SI Model 18 1.5.2.2 SIS Model 19 1.5.2.3 SIR Model 19 1.5.2.4 SIRS Model 20 1.5.3 Centrality Measures 21 1.5.3.1 Degree Centrality 21 1.5.3.2 Closeness Centrality 21 1.5.3.3 Betweenness Centrality 22 1.6 Source Detection in Network 22 1.6.1 Single Source Detection 23 1.6.1.1 Network Observation 23 1.6.1.2 Query-Based Approach 25 1.6.1.3 Anti-Rumor-Based Approach 26 1.6.2 Multiple Source Detection 26 1.7 Conclusion 27 References 28 2 Internet of Things (IoT) and Machine to Machine (M2M) Communication Techniques for Cyber Crime Prediction 31Jaiprakash Narain Dwivedi 2.1 Introduction 32 2.2 Advancement of Internet 33 2.3 Internet of Things (IoT) and Machine to Machine (M2M) Communication 34 2.4 A Definition of Security Frameworks 38 2.5 M2M Devices and Smartphone Technology 39 2.6 Explicit Hazards to M2M Devices Declared by Smartphone Challenges 41 2.7 Security and Privacy Issues in IoT 43 2.7.1 Dynamicity and Heterogeneity 43 2.7.2 Security for Integrated Operational World with Digital World 44 2.7.3 Information Safety with Equipment Security 44 2.7.4 Data Source Information 44 2.7.5 Information Confidentiality 44 2.7.6 Trust Arrangement 44 2.8 Protection in Machine to Machine Communication 48 2.9 Use Cases for M2M Portability 52 2.10 Conclusion 53 References 54 3 Crime Predictive Model Using Big Data Analytics 57Hemanta Kumar Bhuyan and Subhendu Kumar Pani 3.1 Introduction 58 3.1.1 Geographic Information System (GIS) 59 3.2 Crime Data Mining 60 3.2.1 Different Methods for Crime Data Analysis 62 3.3 Visual Data Analysis 63 3.4 Technological Analysis 65 3.4.1 Hadoop and MapReduce 65 3.4.1.1 Hadoop Distributed File System (HDFS) 65 3.4.1.2 MapReduce 65 3.4.2 Hive 67 3.4.2.1 Analysis of Crime Data using Hive 67 3.4.2.2 Data Analytic Module With Hive 68 3.4.3 Sqoop 68 3.4.3.1 Pre-Processing and Sqoop 68 3.4.3.2 Data Migration Module With Sqoop 68 3.4.3.3 Partitioning 68 3.4.3.4 Bucketing 68 3.4.3.5 R-Tool Analyse Crime Data 69 3.4.3.6 Correlation Matrix 69 3.5 Big Data Framework 69 3.6 Architecture for Crime Technical Model 72 3.7 Challenges 73 3.8 Conclusions 74 References 75 4 The Role of Remote Sensing and GIS in Military Strategy to Prevent Terror Attacks 79Sushobhan Majumdar 4.1 Introduction 80 4.2 Database and Methods 81 4.3 Discussion and Analysis 82 4.4 Role of Remote Sensing and GIS 83 4.5 Cartographic Model 83 4.5.1 Spatial Data Management 85 4.5.2 Battlefield Management 85 4.5.3 Terrain Analysis 86 4.6 Mapping Techniques Used for Defense Purposes 87 4.7 Naval Operations 88 4.7.1 Air Operations 89 4.7.2 GIS Potential in Military 89 4.8 Future Sphere of GIS in Military Science 89 4.8.1 Defense Site Management 90 4.8.2 Spatial Data Management 90 4.8.3 Intelligence Capability Approach 90 4.8.4 Data Converts Into Information 90 4.8.5 Defense Estate Management 91 4.9 Terrain Evolution 91 4.9.1 Problems Regarding the Uses of Remote Sensing and GIS 91 4.9.2 Recommendations 92 4.10 Conclusion 92 References 93 5 Text Mining for Secure Cyber Space 95Supriya Raheja and Geetika Munjal 5.1 Introduction 95 5.2 Literature Review 97 5.2.1 Text Mining With Latent Semantic Analysis 100 5.3 Latent Semantic Analysis 101 5.4 Proposed Work 102 5.5 Detailed Work Flow of Proposed Approach 104 5.5.1 Defining the Stop Words 106 5.5.2 Stemming 107 5.5.3 Proposed Algorithm: A Hybrid Approach 109 5.6 Results and Discussion 111 5.6.1 Analysis Using Hybrid Approach 111 5.7 Conclusion 115 References 115 6 Analyses on Artificial Intelligence Framework to Detect Crime Pattern 119R. Arshath Raja, N. Yuvaraj and N.V. Kousik 6.1 Introduction 120 6.2 Related Works 121 6.3 Proposed Clustering for Detecting Crimes 122 6.3.1 Data Pre-Processing 123 6.3.2 Object-Oriented Model 124 6.3.3 MCML Classification 124 6.3.4 GAA 124 6.3.5 Consensus Clustering 124 6.4 Performance Evaluation 124 6.4.1 Precision 125 6.4.2 Sensitivity 125 6.4.3 Specificity 131 6.4.4 Accuracy 131 6.5 Conclusions 131 References 132 7 A Biometric Technology-Based Framework for Tackling and Preventing Crimes 133Ebrahim A.M. Alrahawe, Vikas T. Humbe and G.N. Shinde 7.1 Introduction 134 7.2 Biometrics 135 7.2.1 Biometric Systems Technologies 137 7.2.2 Biometric Recognition Framework 141 7.2.3 Biometric Applications/Usages 142 7.3 Surveillance Systems (CCTV) 144 7.3.1 CCTV Goals 146 7.3.2 CCTV Processes 146 7.3.3 Fusion of Data From Multiple Cameras 149 7.3.4 Expanding the Use of CCTV 149 7.3.5 CCTV Effectiveness 150 7.3.6 CCTV Limitations 150 7.3.7 Privacy and CCTV 150 7.4 Legality to Surveillance and Biometrics vs. Privacy and Human Rights 151 7.5 Proposed Work (Biometric-Based CCTV System) 153 7.5.1 Biometric Surveillance System 154 7.5.1.1 System Component and Flow Diagram 154 7.5.2 Framework 156 7.6 Conclusion 158 References 159 8 Rule-Based Approach for Botnet Behavior Analysis 161Supriya Raheja, Geetika Munjal, Jyoti Jangra and Rakesh Garg 8.1 Introduction 161 8.2 State-of-the-Art 163 8.3 Bots and Botnets 166 8.3.1 Botnet Life Cycle 166 8.3.2 Botnet Detection Techniques 167 8.3.3 Communication Architecture 168 8.4 Methodology 171 8.5 Results and Analysis 175 8.6 Conclusion and Future Scope 177 References 177 9 Securing Biometric Framework with Cryptanalysis 181Abhishek Goel, Siddharth Gautam, Nitin Tyagi, Nikhil Sharma and Martin Sagayam 9.1 Introduction 182 9.2 Basics of Biometric Systems 184 9.2.1 Face 185 9.2.2 Hand Geometry 186 9.2.3 Fingerprint 187 9.2.4 Voice Detection 187 9.2.5 Iris 188 9.2.6 Signature 189 9.2.7 Keystrokes 189 9.3 Biometric Variance 192 9.3.1 Inconsistent Presentation 192 9.3.2 Unreproducible Presentation 192 9.3.3 Fault Signal/Representational Accession 193 9.4 Performance of Biometric System 193 9.5 Justification of Biometric System 195 9.5.1 Authentication (“Is this individual really the authenticate user or not?”) 195 9.5.2 Recognition (“Is this individual in the database?”) 196 9.5.3 Concealing (“Is this a needed person?”) 196 9.6 Assaults on a Biometric System 196 9.6.1 Zero Effort Attacks 197 9.6.2 Adversary Attacks 198 9.6.2.1 Circumvention 198 9.6.2.2 Coercion 198 9.6.2.3 Repudiation 198 9.6.2.4 DoB (Denial of Benefit) 199 9.6.2.5 Collusion 199 9.7 Biometric Cryptanalysis: The Fuzzy Vault Scheme 199 9.8 Conclusion & Future Work 203 References 205 10 The Role of Big Data Analysis in Increasing the Crime Prediction and Prevention Rates 209Galal A. AL-Rummana, Abdulrazzaq H. A. Al-Ahdal and G.N. Shinde 10.1 Introduction: An Overview of Big Data and Cyber Crime 210 10.2 Techniques for the Analysis of BigData 211 10.3 Important Big Data Security Techniques 216 10.4 Conclusion 219 References 219 11 Crime Pattern Detection Using Data Mining 221Dipalika Das and Maya Nayak 11.1 Introduction 221 11.2 Related Work 222 11.3 Methods and Procedures 224 11.4 System Analysis 227 11.5 Analysis Model and Architectural Design 230 11.6 Several Criminal Analysis Methods in Use 233 11.7 Conclusion and Future Work 235 References 235 12 Attacks and Security Measures in Wireless Sensor Network 237Nikhil Sharma, Ila Kaushik, Vikash Kumar Agarwal, Bharat Bhushan and Aditya Khamparia 12.1 Introduction 238 12.2 Layered Architecture of WSN 239 12.2.1 Physical Layer 239 12.2.2 Data Link Layer 239 12.2.3 Network Layer 240 12.2.4 Transport Layer 240 12.2.5 Application Layer 241 12.3 Security Threats on Different Layers in WSN 241 12.3.1 Threats on Physical Layer 241 12.3.1.1 Eavesdropping Attack 241 12.3.1.2 Jamming Attack 242 12.3.1.3 Imperil or Compromised Node Attack 242 12.3.1.4 Replication Node Attack 242 12.3.2 Threats on Data Link Layer 242 12.3.2.1 Collision Attack 243 12.3.2.2 Denial of Service (DoS) Attack 243 12.3.2.3 Intelligent Jamming Attack 243 12.3.3 Threats on Network Layer 243 12.3.3.1 Sybil Attack 243 12.3.3.2 Gray Hole Attack 243 12.3.3.3 Sink Hole Attack 244 12.3.3.4 Hello Flooding Attack 244 12.3.3.5 Spoofing Attack 244 12.3.3.6 Replay Attack 244 12.3.3.7 Black Hole Attack 244 12.3.3.8 Worm Hole Attack 245 12.3.4 Threats on Transport Layer 245 12.3.4.1 De-Synchronization Attack 245 12.3.4.2 Flooding Attack 245 12.3.5 Threats on Application Layer 245 12.3.5.1 Malicious Code Attack 245 12.3.5.2 Attack on Reliability 246 12.3.6 Threats on Multiple Layer 246 12.3.6.1 Man-in-the-Middle Attack 246 12.3.6.2 Jamming Attack 246 12.3.6.3 Dos Attack 246 12.4 Threats Detection at Various Layers in WSN 246 12.4.1 Threat Detection on Physical Layer 247 12.4.1.1 Compromised Node Attack 247 12.4.1.2 Replication Node Attack 247 12.4.2 Threat Detection on Data Link Layer 247 12.4.2.1 Denial of Service Attack 247 12.4.3 Threat Detection on Network Layer 248 12.4.3.1 Black Hole Attack 248 12.4.3.2 Worm Hole Attack 248 12.4.3.3 Hello Flooding Attack 249 12.4.3.4 Sybil Attack 249 12.4.3.5 Gray Hole Attack 250 12.4.3.6 Sink Hole Attack 250 12.4.4 Threat Detection on the Transport Layer 251 12.4.4.1 Flooding Attack 251 12.4.5 Threat Detection on Multiple Layers 251 12.4.5.1 Jamming Attack 251 12.5 Various Parameters for Security Data Collection in WSN 252 12.5.1 Parameters for Security of Information Collection 252 12.5.1.1 Information Grade 252 12.5.1.2 Efficacy and Proficiency 253 12.5.1.3 Reliability Properties 253 12.5.1.4 Information Fidelity 253 12.5.1.5 Information Isolation 254 12.5.2 Attack Detection Standards in WSN 254 12.5.2.1 Precision 254 12.5.2.2 Germane 255 12.5.2.3 Extensibility 255 12.5.2.4 Identifiability 255 12.5.2.5 Fault Forbearance 255 12.6 Different Security Schemes in WSN 256 12.6.1 Clustering-Based Scheme 256 12.6.2 Cryptography-Based Scheme 256 12.6.3 Cross-Checking-Based Scheme 256 12.6.4 Overhearing-Based Scheme 257 12.6.5 Acknowledgement-Based Scheme 257 12.6.6 Trust-Based Scheme 257 12.6.7 Sequence Number Threshold-Based Scheme 258 12.6.8 Intrusion Detection System-Based Scheme 258 12.6.9 Cross-Layer Collaboration-Based Scheme 258 12.7 Conclusion 264 References 264 13 Large Sensing Data Flows Using Cryptic Techniques 269Hemanta Kumar Bhuyan 13.1 Introduction 270 13.2 Data Flow Management 271 13.2.1 Data Flow Processing 271 13.2.2 Stream Security 272 13.2.3 Data Privacy and Data Reliability 272 13.2.3.1 Security Protocol 272 13.3 Design of Big Data Stream 273 13.3.1 Data Stream System Architecture 273 13.3.1.1 Intrusion Detection Systems (IDS) 274 13.3.2 Malicious Model 275 13.3.3 Threat Approaches for Attack Models 276 13.4 Utilization of Security Methods 277 13.4.1 System Setup 278 13.4.2 Re-Keying 279 13.4.3 New Node Authentication 279 13.4.4 Cryptic Techniques 280 13.5 Analysis of Security on Attack 280 13.6 Artificial Intelligence Techniques for Cyber Crimes 281 13.6.1 Cyber Crime Activities 282 13.6.2 Artificial Intelligence for Intrusion Detection 282 13.6.3 Features of an IDPS 284 13.7 Conclusions 284 References 285 14 Cyber-Crime Prevention Methodology 291Chandra Sekhar Biswal and Subhendu Kumar Pani 14.1 Introduction 292 14.1.1 Evolution of Cyber Crime 294 14.1.2 Cybercrime can be Broadly Defined as Two Types 296 14.1.3 Potential Vulnerable Sectors of Cybercrime 296 14.2 Credit Card Frauds and Skimming 297 14.2.1 Matrimony Fraud 297 14.2.2 Juice Jacking 298 14.2.3 Technicality Behind Juice Jacking 299 14.3 Hacking Over Public WiFi or the MITM Attacks 299 14.3.1 Phishing 300 14.3.2 Vishing/Smishing 302 14.3.3 Session Hijacking 303 14.3.4 Weak Session Token Generation/Predictable Session Token Generation 304 14.3.5 IP Spoofing 304 14.3.6 Cross-Site Scripting (XSS) Attack 305 14.4 SQLi Injection 306 14.5 Denial of Service Attack 307 14.6 Dark Web and Deep Web Technologies 309 14.6.1 The Deep Web 309 14.6.2 The Dark Web 310 14.7 Conclusion 311 References 312 Index 313

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Book SynopsisINTEGRATION OF RENEWABLE ENERGY SOURCES WITH SMART GRID Provides comprehensive coverage of renewable energy and its integration with smart grid technologies. This book starts with an overview of renewable energy technologies, smart grid technologies, and energy storage systems and covers the details of renewable energy integration with smart grid and the corresponding controls. It also provides an enhanced perspective on the power scenario in developing countries. The requirement of the integration of smart grid along with the energy storage systems is deeply discussed to acknowledge the importance of sustainable development of a smart city. The methodologies are made quite possible with highly efficient power convertor topologies and intelligent control schemes. These control schemes are capable of providing better control with the help of machine intelligence techniques and artificial intelligence. The book also addresses modern power convertor topologies and theTable of ContentsPreface xv 1 Renewable Energy Technologies 1V. Chamundeswari, R. Niraimathi, M. Shanthi and A. Mahaboob Subahani 1. Introduction 1 1.1 Types of Renewable Energy 2 1.1.1 Solar Energy 3 1.1.2 Wind Energy 7 1.1.3 Fuel Cell 8 1.1.4 Biomass Energy 11 1.1.5 Hydro-Electric Energy 13 1.1.6 Geothermal Energy 14 References 17 2 Present Power Scenario in India 19Niraimathi R., Pradeep V., Shanthi M. and Kathiresh M. 2.1 Introduction 20 2.2 Thermal Power Plant 20 2.2.1 Components of Thermal Power Plant 21 2.2.2 Major Thermal Power Plants in India 23 2.3 Gas-Based Power Generation 24 2.3.1 Basics of Gas-Based Power Generation 24 2.3.2 Major Gas-Based Power Plants in India 25 2.4 Nuclear Power Plants 26 2.4.1 India’s Hold in Nuclear Power 27 2.4.2 Major Nuclear Power Plants 27 2.4.3 Currently Operational Nuclear Power Plants 28 2.4.4 Challenges of Nuclear Power Plants 28 2.5 Hydropower Generation 29 2.5.1 Pumped Storage Plants 29 2.6 Solar Power 30 2.6.1 Photovoltaic 30 2.6.2 Photovoltaic Solar Power System 30 2.6.3 Concentrated Solar Power System 31 2.6.4 Major Solar Parks in India 32 2.7 Wind Energy 32 2.8 The Inherited Structure 34 References 34 3 Introduction to Smart Grid 37G. R. Hemanth, S. Charles Raja and P. Venkatesh 3.1 Need for Smart Grid in India 38 3.2 Present Power Scenario in India 38 3.2.1 Performance of Generation From Conventional Sources 40 3.2.2 Status of Renewable Energy Sources 40 3.3 Electric Grid 43 3.3.1 Evolving Scenario of the Electric Grid 45 3.3.1.1 Integrated Grid 46 3.3.1.2 Prosumers 46 3.3.1.3 Transmission v/s Energy Storage 47 3.3.1.4 Changing Nature of Loads 47 3.3.1.5 Electric Vehicles 48 3.3.1.6 Microgrids 48 3.4 Overview of Smart Grids 49 3.4.1 Purpose of Smart Grid 49 3.5 Smart Grid Components for Transmission System 50 3.5.1 Supervisory Control and Data Acquisition System 50 3.5.1.1 SCADA Overview 51 3.5.1.2 Components of SCADA 51 3.5.2 Energy Management System 52 3.5.3 Wide-Area Monitoring System 52 3.6 Smart Grid Functions Used in Distribution System 53 3.6.1 Supervisory Control and Data Acquisition System 53 3.6.2 Distribution Management System 54 3.6.3 Distribution Automation 54 3.6.4 Substation Automation 55 3.6.5 Advanced Metering Infrastructure 55 3.6.6 Geographical Information System 57 3.6.7 Peak Load Management 58 3.6.8 Demand Response 58 3.6.9 Power Quality Management 59 3.6.10 Outage Management System 59 3.6.11 Distribution Transformer Monitoring System 59 3.6.12 Enterprise Application Integration 59 3.6.13 Smart Street Lights 60 3.6.14 Energy Storage 60 3.6.15 Cyber Security 60 3.6.16 Analytics 60 3.7 Case Study: Techno-Economic Analysis 61 3.7.1 Peak Load Shaving and Metering Efficiency 61 3.7.2 Outage Management System 63 3.7.3 Loss Detection 64 3.7.4 Tamper Analysis 66 3.8 Case Study: Solar PV Awareness of Puducherry SG Pilot Project 69 3.9 Recent Trends in Smart Grids 70 3.9.1 Smart GRIP Architecture 70 3.9.2 Implementation of Smart Meter With Prepaid Facility 74 References 74 4 Internet of Things–Based Advanced Metering Infrastructure (AMI) for Smart Grids 77V. Gomathy, V. Kavitha, C. Nayantara, J. Mohammed Feros Khan, Vimalarani G. and S. Sheeba Rani 4.1 Introduction 78 4.1.1 Smart Grids 78 4.1.2 Smart Meters 80 4.2 Advanced Metering Infrastructure 81 4.2.1 Smart Devices 82 4.2.2 Communication 83 4.2.3 Data Management System 85 4.2.4 Mathematical Modeling 87 4.2.5 Energy Theft Detection Techniques 89 4.3 IoT-Based Advanced Metering Infrastructure 89 4.3.1 Intrusion Detection System 90 4.4 Results 93 4.5 Discussion 94 4.6 Conclusion and Future Scope 97 References 97 5 Requirements for Integrating Renewables With Smart Grid 101Indrajit Sarkar 5.1 Introduction 102 5.1.1 Smart Grid 102 5.1.2 Renewable Energy Resources 105 5.1.3 How Smart Grids Enable Renewables 111 5.1.4 Smart Grid and Distributed Generation 111 5.1.5 Grid Integration Terminologies 112 5.2 Challenges in Integrating Renewables Into Smart Grid 112 5.2.1 The Power Flow Control of Distributed Energy Resources 113 5.2.2 Investments on New Renewable Energy Generations 113 5.2.3 Transmission Expansion 114 5.2.4 Improved Flexibility 114 5.2.5 High Penetration of Renewables in Future 115 5.2.6 Standardizing Control of ESS 115 5.2.7 Regulations 116 5.2.8 Standards 116 5.3 Conclusion 116 References 117 6 Grid Energy Storage Technologies 119Chandra Sekhar Nalamati 6.1 Introduction 120 6.1.1 Need of Energy Storage System 121 6.1.2 Services Provided by Energy Storage System 122 6.2 Grid Energy Storage Technologies: Classification 123 6.2.1 Pumped Hydro Storage System 123 6.2.2 Compressed Air Storage System 124 6.2.3 Flywheel Energy Storage System 125 6.2.4 Superconducting Magnet Storage System 125 6.2.5 Battery Storage System 127 6.2.6 Capacitors and Super Capacitor Storage System 129 6.2.7 Fuel Cell Energy Storage System 130 6.2.8 Thermal Storage System 131 6.3 Grid Energy Storage Technologies: Analogy 132 6.4 Applications of Energy Storage System 135 6.5 Power Conditioning of Energy Storage System 136 6.6 Conclusions 136 References 137 7 Multi-Mode Power Converter Topology for Renewable Energy Integration With Smart Grid 141M. Sathiyanathan, S. Jaganathan and R. L. Josephine 7.1 Introduction 142 7.2 Literature Survey 144 7.3 System Architecture 145 7.3.1 Solar PV Array 146 7.3.2 Wind Energy Generator 147 7.4 Modes of Operation of Multi-Mode Power Converter 149 7.4.1 Buck Mode 150 7.4.2 Boost Mode 152 7.4.3 Bi-Directional Mode 155 7.5 Control Scheme 158 7.5.1 Mode Selection 159 7.5.2 Maximum Power Point Tracking 159 7.5.3 Reconfigurable SPWM Generation 161 7.6 Results and Discussion 163 7.7 Conclusion 167 References 168 8 Decoupled Control With Constant DC Link Voltage for PV-Fed Single-Phase Grid Connected Systems 171C. Maria Jenisha 8.1 Introduction 171 8.2 Schematic of the Grid-Tied Solar PV System 173 8.2.1 DC Link Voltage Controller 175 8.2.2 MPPT Controller 176 8.2.3 SPWM-Based dq Controller 176 8.3 Simulation and Experimental Results of the Grid Tied Solar PV System 178 8.4 Conclusion 183 References 184 9 Wind Energy Conversion System Feeding Remote Microgrid 187K. Arthishri and N. Kumaresan 9.1 Introduction 188 9.2 Literature Review 189 9.3 Direct Grid Connected Configurations of Three-Phase WDIG Feeding Single-Phase Grid 191 9.4 Three-Phase WDIG Feeding Single-Phase Grid With Power Converters 191 9.5 Performance of the Three-Phase Wind Generator System Feeding Power to Single-Phase Grid 193 9.5.1 Wind Turbine Characteristics 193 9.5.2 Generator Analysis 194 9.6 Power Converter Configurations 198 9.6.1 Configuration 1: WDIG With Uncontrolled Rectifier–Line Commutated Inverter 198 9.6.2 Configuration 2: WDIG With Uncontrolled Rectifier–(DC-DC)–Line Commutated Inverter 200 9.6.2.1 Closed-Loop Operation of UR-DC/DC-LCI Configuration 200 9.6.3 Configuration 3: WDIG With Uncontrolled Rectifier–Voltage Source Inverter 201 9.6.3.1 Closed-Loop Operation of UR-VSI Configuration 202 9.7 Conclusion 204 References 204 10 Microgrid Protection 209Suman M., Srividhya S. and Padmagirisan P. 10.1 Introduction 209 10.2 Necessity of Distributed Energy Resources 210 10.3 Concept of Microgrid 210 10.4 Why the Protection With Microgrid is Different From the Conventional Distribution System Protection 211 10.4.1 Role of the Type of DER on Protection 212 10.5 Foremost Challenges in Microgrid Protection 212 10.5.1 Relay Blinding 212 10.5.2 Variations in Fault Current Level 213 10.5.3 Selectivity 214 10.5.4 False/Unnecessary Tripping 214 10.5.5 Loss of Mains (Islanding Condition) 214 10.6 Microgrid Protection 215 10.6.1 Overcurrent Protection 215 10.6.2 Distance Protection 216 10.6.2.1 Effect of Distributed Generator Inclusion in the Distribution System on Distance Relay 218 10.6.3 Differential Protection 219 10.6.3.1 Drawbacks in Differential Protection 220 10.6.4 Hybrid Tripping Relay Characteristic 220 10.6.5 Voltage-Based Methods 221 10.6.6 Adaptive Protection Methods 222 10.7 Literature Survey 223 10.8 Comparison of Various Existing Protection Schemes for Microgrids 225 10.9 Loss of Mains (Islanding) 225 10.10 Necessity to Detect the Unplanned Islanding 227 10.10.1 Health Hazards to Maintenance Personnel 227 10.10.2 Unsynchronized Reclosing 228 10.10.3 Ineffective Grounding 228 10.10.4 Inept Protection 229 10.10.5 Loss of Voltage and Frequency Control 229 10.11 Unplanned Islanding Identification Methods 229 10.11.1 Communication-Based Methods (Remote Method) 230 10.11.2 Non-Communication–Based Methods (Local Method) 230 10.11.2.1 Passive Method 230 10.11.2.2 Active Method 231 10.11.2.3 Hybrid Method 232 10.12 Comparison of Unplanned Islanding Identification Methods 234 10.13 Discussion 234 10.14 Conclusion 235 References 235 11 Microgrid Optimization and Integration of Renewable Energy Resources: Innovation, Challenges and Prospects 239Blesslin Sheeba T., G. Jims John Wessley, Kanagaraj V., Kamatchi S., A. Radhika and Janeera D.A. 11.1 Introduction 240 11.2 Microgrids 242 11.3 Renewable Energy Sources 245 11.3.1 Renewable Energy Technologies (RETs) 246 11.3.2 Distributed Storage Technologies 247 11.3.3 Combined Heat and Power 248 11.4 Integration of RES in Microgrid 248 11.5 Microgrid Optimization Schemes 250 11.5.1 Load Forecasting Schemes 251 11.5.2 Generation Unit Control 252 11.5.3 Storage Unit Control 252 11.5.4 Data Monitoring and Transmission 253 11.5.4.1 Communication Systems 254 11.5.5 Energy Management and Power Flow 256 11.6 Challenges in Implementation of Microgrids 257 11.7 Future Prospects of Microgrids 259 11.8 Conclusion 259 References 260 12 Challenges in Planning and Operation of Large-Scale Renewable Energy Resources Such as Solar and Wind 263J. Vishnupriyan and A. Dhanasekaran 12.1 Introduction 264 12.2 Solar Grid Integration 265 12.3 Wind Energy Grid Integration 267 12.4 Challenges in the Integration of Renewable Energy Systems with Grid 267 12.4.1 Disturbances in the Grid Side 269 12.4.2 Virtual Synchronous Machine Method 271 12.4.3 Frequency Control 272 12.4.4 Solar Photovoltaic Array in Frequency Regulation 275 12.4.5 Harmonics 275 12.5 Electrical Energy Storage (EES) 276 12.6 Conclusion 277 References 278 13 Mitigating Measures to Address Challenges of Renewable Integration—Forecasting, Scheduling, Dispatch, Balancing, Monitoring, and Control 281K. Latha Maheswari, B. Sathya and A. Maideen Abdhulkader Jeylani 13.1 Introduction 282 13.2 Microgrid 283 13.2.1 Types of Microgrid 284 13.2.1.1 DC Microgrid 284 13.2.1.2 AC Microgrid 285 13.2.1.3 Hybrid AC-DC Microgrid 286 13.3 Large-Scale Integration of Renewables: Issues and Challenges 287 13.4 A Review on Short-Term Load Forecasting Methods 288 13.4.1 Short-Term Load Forecasting Methods 290 13.4.1.1 Statistical Technique 290 13.5 Overview on Control of Microgrid 291 13.5.1 Need for Microgrid Control 291 13.5.2 Fully Centralized Control 292 13.5.3 Decentralized Control 292 13.5.4 Hierarchical Control 293 13.5.4.1 Primary Control 293 13.5.4.2 Secondary Control 295 13.5.4.3 Tertiary Control 295 13.6 Measures to Support Large-Scale Renewable Integration 296 13.6.1 Basic Idea of Preventive Control 297 13.6.1.1 Maximum Output Control Mode 297 13.6.1.2 Output Following Mode 298 References 298 14 Mitigation Measures for Power Quality Issues in Renewable Energy Integration and Impact of IoT in Grid Control 305Hepsiba D., L.D. Vijay Anand, Granty Regina Elwin J., J.B. Shajilin and D. Ruth Anita Shirley 14.1 Introduction 306 14.2 Impact of Power Quality Issues 308 14.2.1 Power Quality in Renewable Energy 314 14.2.2 Power Quality Issues in Wind and Solar Renewable Energy 316 14.2.2.1 Wind Renewable Energy 316 14.2.2.2 Solar Renewable Energy 317 14.3 Mitigation of Power Quality Issues 317 14.3.1 UPQC 317 14.3.2 DVR 318 14.3.3 D-STATCOM 319 14.3.4 UPS 319 14.3.5 TVSS 320 14.3.6 Internet of Things in Distributed Generations Systems 320 14.4 Discussions 321 14.5 Conclusion and Future Scope 322 References 323 15 Smart Grid Implementations and Feasibilities 327Suresh N. S., Padmavathy N. S., S. Arul Daniel and Ramakrishna Kappagantu 15.1 Introduction 328 15.1.1 Smart Grid Technologies—Literature Review 328 15.2 Need for Smart Grid 329 15.2.1 Smart Grid Description 330 15.3 Smart Grid Sensing, Measurement, Control, and Automation Technologies 331 15.3.1 Advanced Metering Infrastructure 332 15.3.2 Key Components of AMI 332 15.3.3 Smart Meter 332 15.3.4 Communication Infrastructure and Protocols for AMI 333 15.3.4.1 Data Concentrator Unit 334 15.3.5 Benefits of AMI 335 15.3.6 Peak Load Management 336 15.3.7 Distribution Management System 336 15.3.8 Distribution Automation System 337 15.4 Implementation of Smart Grid Project 339 15.4.1 Challenges and Issues of SG Implementation 339 15.4.2 Smart Grid Implementation in India: Puducherry Pilot Project 341 15.4.3 Power Quality of the Smart Grid 341 15.5 Solar PV System Implementation Barriers 342 15.6 Smart Grid and Microgrid in Other Areas 343 15.6.1 Maritime Power System 343 15.6.2 Space Electrical Grids 343 15.7 Conclusion 344 References 345 Index 347

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Book SynopsisMACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT's potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning anTable of ContentsPreface xiii 1 Machine Learning Concept–Based IoT Platforms for Smart Cities’ Implementation and Requirements 1M. Saravanan, J. Ajayan, R. Maheswar, Eswaran Parthasarathy and K. Sumathi 1.1 Introduction 2 1.2 Smart City Structure in India 3 1.2.1 Bhubaneswar City 3 1.2.1.1 Specifications 3 1.2.1.2 Healthcare and Mobility Services 3 1.2.1.3 Productivity 4 1.2.2 Smart City in Pune 4 1.2.2.1 Specifications 5 1.2.2.2 Transport and Mobility 5 1.2.2.3 Water and Sewage Management 5 1.3 Status of Smart Cities in India 5 1.3.1 Funding Process by Government 6 1.4 Analysis of Smart City Setup 7 1.4.1 Physical Infrastructure-Based 7 1.4.2 Social Infrastructure-Based 7 1.4.3 Urban Mobility 8 1.4.4 Solid Waste Management System 8 1.4.5 Economical-Based Infrastructure 9 1.4.6 Infrastructure-Based Development 9 1.4.7 Water Supply System 10 1.4.8 Sewage Networking 10 1.5 Ideal Planning for the Sewage Networking Systems 10 1.5.1 Availability and Ideal Consumption of Resources 10 1.5.2 Anticipating Future Demand 11 1.5.3 Transporting Networks to Facilitate 11 1.5.4 Control Centers for Governing the City 12 1.5.5 Integrated Command and Control Center 12 1.6 Heritage of Culture Based on Modern Advancement 13 1.7 Funding and Business Models to Leverage 14 1.7.1 Fundings 15 1.8 Community-Based Development 16 1.8.1 Smart Medical Care 16 1.8.2 Smart Safety for The IT 16 1.8.3 IoT Communication Interface With ML 17 1.8.4 Machine Learning Algorithms 17 1.8.5 Smart Community 18 1.9 Revolutionary Impact With Other Locations 18 1.10 Finding Balanced City Development 20 1.11 E-Industry With Enhanced Resources 20 1.12 Strategy for Development of Smart Cities 21 1.12.1 Stakeholder Benefits 21 1.12.2 Urban Integration 22 1.12.3 Future Scope of City Innovations 22 1.12.4 Conclusion 23 References 24 2 An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan 27W. H. Rankothge 2.1 Introduction 28 2.2 Background 29 2.2.1 Prediction of Future Paddy Harvest and Rice Consumption Demand 29 2.2.2 Rice Distribution 31 2.3 Methodology 31 2.3.1 Requirements of the Proposed Platform 32 2.3.2 Data to Evaluate the ‘isRice” Platform 34 2.3.3 Implementation of Prediction Modules 34 2.3.3.1 Recurrent Neural Network 35 2.3.3.2 Long Short-Term Memory 36 2.3.3.3 Paddy Harvest Prediction Function 37 2.3.3.4 Rice Demand Prediction Function 39 2.3.4 Implementation of Rice Distribution Planning Module 40 2.3.4.1 Genetic Algorithm–Based Rice Distribution Planning 41 2.3.5 Front-End Implementation 44 2.4 Results and Discussion 45 2.4.1 Paddy Harvest Prediction Function 45 2.4.2 Rice Demand Prediction Function 46 2.4.3 Rice Distribution Planning Module 46 2.5 Conclusion 49 References 49 3 A Collaborative Data Publishing Model with Privacy Preservation Using Group-Based Classification and Anonymity 53Carmel Mary Belinda M. J., K. Antonykumar, S. Ravikumar and Yogesh R. Kulkarni 3.1 Introduction 54 3.2 Literature Survey 56 3.3 Proposed Model 58 3.4 Results 61 3.5 Conclusion 64 References 64 4 Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC) 67Dineshbabu V., Arul Kumar V. P. and Gowtham M. S. 4.1 Introduction 68 4.2 Related Works 69 4.3 Industry 4.0 Production and Dashboard Design 69 4.4 Results and Discussion 70 4.5 Conclusion 73 References 73 5 Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation 75S. Pradeep Kumar and G. Kalpana 5.1 Introduction 75 5.2 Types of CAPTCHAs 78 5.2.1 Text-Based CAPTCHA 78 5.2.2 Image-Based CAPTCHA 80 5.2.3 Audio-Based CAPTCHA 80 5.2.4 Video-Based CAPTCHA 81 5.2.5 Puzzle-Based CAPTCHA 82 5.3 Related Work 82 5.4 Proposed Technique 82 5.5 Text-Based CAPTCHA Scheme 83 5.6 Breaking Text-Based CAPTCHA’s Scheme 85 5.6.1 Individual Character-Based Segmentation Method 85 5.6.2 Character Width-Based Segmentation Method 86 5.7 Implementation of Text-Based CAPTCHA Using Graphical Operation 87 5.7.1 Graphical Operation 87 5.7.2 Two-Dimensional Composite Transformation Calculation 89 5.8 Graphical Text-Based CAPTCHA in Online Application 91 5.9 Conclusion and Future Enhancement 93 References 94 6 Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning 97Pradeep Kumar S., Jayanthi K. and Selvakumari S. 6.1 Introduction 98 6.1.1 Internet of Things 98 6.1.2 Deep Learning 98 6.1.3 Detecting the Traffic Sign With the Mask R-CNN 99 6.1.3.1 Mask R-Convolutional Neural Network 99 6.1.3.2 Color Space Conversion 100 6.2 Experimental Evaluation 101 6.2.1 Implementation Details 101 6.2.2 Traffic Sign Classification 101 6.2.3 Traffic Sign Detection 102 6.2.4 Sample Outputs 103 6.2.5 Raspberry Pi 4 Controls Vehicle Using OpenCV 103 6.2.5.1 Smart IoT-Enabled Traffic Signs Recognizing With High Accuracy Using Deep Learning 103 6.2.6 Python Code 108 6.3 Conclusion 109 References 110 7 Offline and Online Performance Evaluation Metrics of Recommender System: A Bird’s Eye View 113R. Bhuvanya and M. Kavitha 7.1 Introduction 114 7.1.1 Modules of Recommender System 114 7.1.2 Evaluation Structure 115 7.1.3 Contribution of the Paper 115 7.1.4 Organization of the Paper 116 7.2 Evaluation Metrics 116 7.2.1 Offline Analytics 116 7.2.1.1 Prediction Accuracy Metrics 116 7.2.1.2 Decision Support Metrics 118 7.2.1.3 Rank Aware Top-N Metrics 120 7.2.2 Item and List-Based Metrics 122 7.2.2.1 Coverage 122 7.2.2.2 Popularity 123 7.2.2.3 Personalization 123 7.2.2.4 Serendipity 123 7.2.2.5 Diversity 123 7.2.2.6 Churn 124 7.2.2.7 Responsiveness 124 7.2.3 User Studies and Online Evaluation 125 7.2.3.1 Usage Log 125 7.2.3.2 Polls 126 7.2.3.3 Lab Experiments 126 7.2.3.4 Online A/B Test 126 7.3 Related Works 127 7.3.1 Categories of Recommendation 129 7.3.2 Data Mining Methods of Recommender System 129 7.3.2.1 Data Pre-Processing 129 7.3.2.2 Data Analysis 131 7.4 Experimental Setup 135 7.5 Summary and Conclusions 142 References 143 8 Deep Learning–Enabled Smart Safety Precautions and Measures in Public Gathering Places for COVID-19 Using IoT 147Pradeep Kumar S., Pushpakumar R. and Selvakumari S. 8.1 Introduction 148 8.2 Prelims 148 8.2.1 Digital Image Processing 148 8.2.2 Deep Learning 149 8.2.3 WSN 149 8.2.4 Raspberry Pi 152 8.2.5 Thermal Sensor 152 8.2.6 Relay 152 8.2.7 TensorFlow 153 8.2.8 Convolution Neural Network (CNN) 153 8.3 Proposed System 154 8.4 Math Model 156 8.5 Results 158 8.6 Conclusion 161 References 161 9 Route Optimization for Perishable Goods Transportation System 167Kowsalyadevi A. K., Megala M. and Manivannan C. 9.1 Introduction 167 9.2 Related Works 168 9.2.1 Need for Route Optimization 170 9.3 Proposed Methodology 171 9.4 Proposed Work Implementation 174 9.5 Conclusion 178 References 178 10 Fake News Detection Using Machine Learning Algorithms 181M. Kavitha, R. Srinivasan and R. Bhuvanya 10.1 Introduction 181 10.2 Literature Survey 183 10.3 Methodology 193 10.3.1 Data Retrieval 195 10.3.2 Data Pre-Processing 195 10.3.3 Data Visualization 196 10.3.4 Tokenization 196 10.3.5 Feature Extraction 196 10.3.6 Machine Learning Algorithms 197 10.3.6.1 Logistic Regression 197 10.3.6.2 Naïve Bayes 198 10.3.6.3 Random Forest 200 10.3.6.4 XGBoost 200 10.4 Experimental Results 202 10.5 Conclusion 203 References 203 11 Opportunities and Challenges in Machine Learning With IoT 209Sarvesh Tanwar, Jatin Garg, Medini Gupta and Ajay Rana 11.1 Introduction 209 11.2 Literature Review 210 11.2.1 A Designed Architecture of ML on Big Data 210 11.2.2 Machine Learning 211 11.2.3 Types of Machine Learning 212 11.2.3.1 Supervised Learning 212 11.2.3.2 Unsupervised Learning 215 11.3 Why Should We Care About Learning Representations? 217 11.4 Big Data 218 11.5 Data Processing Opportunities and Challenges 219 11.5.1 Data Redundancy 219 11.5.2 Data Noise 220 11.5.3 Heterogeneity of Data 220 11.5.4 Discretization of Data 220 11.5.5 Data Labeling 221 11.5.6 Imbalanced Data 221 11.6 Learning Opportunities and Challenges 221 11.7 Enabling Machine Learning With IoT 223 11.8 Conclusion 224 References 225 12 Machine Learning Effects on Underwater Applications and IoUT 229Mamta Nain, Nitin Goyal and Manni Kumar 12.1 Introduction 229 12.2 Characteristics of IoUT 231 12.3 Architecture of IoUT 232 12.3.1 Perceptron Layer 233 12.3.2 Network Layer 234 12.3.3 Application Layer 234 12.4 Challenges in IoUT 234 12.5 Applications of IoUT 235 12.6 Machine Learning 240 12.7 Simulation and Analysis 241 12.8 Conclusion 242 References 242 13 Internet of Underwater Things: Challenges, Routing Protocols, and ML Algorithms 247Monika Chaudhary, Nitin Goyal and Aadil Mushtaq 13.1 Introduction 248 13.2 Internet of Underwater Things 248 13.2.1 Challenges in IoUT 249 13.3 Routing Protocols of IoUT 250 13.4 Machine Learning in IoUT 255 13.4.1 Types of Machine Learning Algorithms 258 13.5 Performance Evaluation 259 13.6 Conclusion 260 References 260 14 Chest X-Ray for Pneumonia Detection 265Sarang Sharma, Sheifali Gupta and Deepali Gupta 14.1 Introduction 266 14.2 Background 267 14.3 Research Methodology 268 14.4 Results and Discussion 271 14.4.1 Results 271 14.4.2 Discussion 271 14.5 Conclusion 273 Acknowledgment 273 References 274 Index 275

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Book SynopsisDEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions 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. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these arTable of ContentsForeword xv Preface xvii 1 Biometric Identification Using Deep Learning for Advance Cloud Security 1Navani Siroya and Manju Mandot 1.1 Introduction 2 1.2 Techniques of Biometric Identification 3 1.2.1 Fingerprint Identification 3 1.2.2 Iris Recognition 4 1.2.3 Facial Recognition 4 1.2.4 Voice Recognition 5 1.3 Approaches 6 1.3.1 Feature Selection 6 1.3.2 Feature Extraction 6 1.3.3 Face Marking 7 1.3.4 Nearest Neighbor Approach 8 1.4 Related Work, A Review 9 1.5 Proposed Work 10 1.6 Future Scope 12 1.7 Conclusion 12 References 12 2 Privacy in Multi-Tenancy Cloud Using Deep Learning 15Shweta Solanki and Prafull Narooka 2.1 Introduction 15 2.2 Basic Structure 16 2.2.1 Basic Structure of Cloud Computing 17 2.2.2 Concept of Multi-Tenancy 18 2.2.3 Concept of Multi-Tenancy with Cloud Computing 19 2.3 Privacy in Cloud Environment Using Deep Learning 21 2.4 Privacy in Multi-Tenancy with Deep Learning Concept 22 2.5 Related Work 23 2.6 Conclusion 24 References 25 3 Emotional Classification Using EEG Signals and Facial Expression: A Survey 27S J Savitha, Dr. M Paulraj and K Saranya 3.1 Introduction 27 3.2 Related Works 29 3.3 Methods 32 3.3.1 EEG Signal Pre-Processing 32 3.3.1.1 Discrete Fourier Transform (DFT) 32 3.3.1.2 Least Mean Square (LMS) Algorithm 32 3.3.1.3 Discrete Cosine Transform (DCT) 33 3.3.2 Feature Extraction Techniques 33 3.3.3 Classification Techniques 33 3.4 BCI Applications 34 3.4.1 Possible BCI Uses 36 3.4.2 Communication 36 3.4.3 Movement Control 36 3.4.4 Environment Control 37 3.4.5 Locomotion 38 3.5 Cloud-Based EEG Overview 38 3.5.1 Data Backup and Restoration 39 3.6 Conclusion 40 References 40 4 Effective and Efficient Wind Power Generation Using Bifarious Solar PV System 43R. Amirtha Katesa Sai Raj, M. Arun Kumar, S. Dinesh, U. Harisudhan and Dr. R. Uthirasamy 4.1 Introduction 44 4.2 Study of Bi-Facial Solar Panel 45 4.3 Proposed System 46 4.3.1 Block Diagram 46 4.3.2 DC Motor Mechanism 47 4.3.3 Battery Bank 48 4.3.4 System Management Using IoT 48 4.3.5 Structure of Proposed System 50 4.3.6 Spoiler Design 51 4.3.7 Working Principle of Proposed System 52 4.3.8 Design and Analysis 53 4.4 Applications of IoT in Renewable Energy Resources 53 4.4.1 Wind Turbine Reliability Using IoT 54 4.4.2 Siting of Wind Resource Using IoT 55 4.4.3 Application of Renewable Energy in Medical Industries 56 4.4.4 Data Analysis Using Deep Learning 57 4.5 Conclusion 59 References 59 5 Background Mosaicing Model for Wide Area Surveillance System 63Dr. E. Komagal 5.1 Introduction 64 5.2 Related Work 64 5.3 Methodology 65 5.3.1 Feature Extraction 66 5.3.2 Background Deep Learning Model Based on Mosaic 67 5.3.3 Foreground Segmentation 70 5.4 Results and Discussion 70 5.5 Conclusion 72 References 72 6 Prediction of CKD Stage 1 Using Three Different Classifiers 75Thamizharasan, K., Yamini, P., Shimola, A. and Sudha, S. 6.1 Introduction 75 6.2 Materials and Methods 78 6.3 Results and Discussion 84 6.4 Conclusions and Future Scope 89 References 89 7 Classification of MRI Images to Aid in Diagnosis of Neurological Disorder Using SVM 93Phavithra Selvaraj, Sruthi, M.S., Sridaran, M. and Dr. Jobin Christ M.C. 7.1 Introduction 93 7.2 Methodology 95 7.2.1 Data Acquisition 95 7.2.2 Image Preprocessing 96 7.2.3 Segmentation 97 7.2.4 Feature Extraction 98 7.2.5 Classification 99 7.3 Results and Discussions 100 7.3.1 Preprocessing 100 7.3.2 Classification 103 7.3.3 Validation 104 7.4 Conclusion 106 References 106 8 Convolutional Networks 109Simran Kaur and Rashmi Agrawal 8.1 Introduction 110 8.2 Convolution Operation 110 8.3 CNN 110 8.4 Practical Applications 112 8.4.1 Audio Data 112 8.4.2 Image Data 112 8.4.3 Text Data 113 8.5 Challenges of Profound Models 113 8.6 Deep Learning In Object Detection 114 8.7 CNN Architectures 114 8.8 Challenges of Item Location 118 8.8.1 Scale Variation Problem 118 8.8.2 Occlusion Problem 119 8.8.3 Deformation Problem 120 References 121 9 Categorization of Cloud Computing & Deep Learning 123Disha Shrmali 9.1 Introduction to Cloud Computing 123 9.1.1 Cloud Computing 123 9.1.2 Cloud Computing: History and Evolution 124 9.1.3 Working of Cloud 125 9.1.4 Characteristics of Cloud Computing 127 9.1.5 Different Types of Cloud Computing Service Models 128 9.1.5.1 Infrastructure as A Service (IAAS) 128 9.1.5.2 Platform as a Service (PAAS) 129 9.1.5.3 Software as a Service (SAAS) 129 9.1.6 Cloud Computing Advantages and Disadvantages 130 9.1.6.1 Advantages of Cloud Computing 130 9.1.6.2 Disadvantages of Cloud Computing 132 9.2 Introduction to Deep Learning 133 9.2.1 History and Revolution of Deep Learning 134 9.2.1.1 Development of Deep Learning Algorithms 134 9.2.1.2 The FORTRAN Code for Back Propagation 135 9.2.1.3 Deep Learning from the 2000s and Beyond 135 9.2.1.4 The Cat Experiment 136 9.2.2 Neural Networks 137 9.2.2.1 Artificial Neural Networks 137 9.2.2.2 Deep Neural Networks 138 9.2.3 Applications of Deep Learning 138 9.2.3.1 Automatic Speech Recognition 138 9.2.3.2 Electromyography (EMG) Recognition 139 9.2.3.3 Image Recognition 139 9.2.3.4 Visual Art Processing 140 9.2.3.5 Natural Language Processing 140 9.2.3.6 Drug Discovery and Toxicology 140 9.2.3.7 Customer Relationship Management 141 9.2.3.8 Recommendation Systems 141 9.2.3.9 Bioinformatics 141 9.2.3.10 Medical Image Analysis 141 9.2.3.11 Mobile Advertising 141 9.2.3.12 Image Restoration 142 9.2.3.13 Financial Fraud Detection 142 9.2.3.14 Military 142 9.3 Conclusion 142 References 143 10 Smart Load Balancing in Cloud Using Deep Learning 145Astha Parihar and Shweta Sharma 10.1 Introduction 146 10.2 Load Balancing 147 10.2.1 Static Algorithm 148 10.2.2 Dynamic (Run-Time) Algorithms 148 10.3 Load Adjusting in Distributing Computing 149 10.3.1 Working of Load Balancing 151 10.4 Cloud Load Balancing Criteria (Measures) 152 10.5 Load Balancing Proposed for Cloud Computing 153 10.5.1 Calculation of Load Balancing in the Whole System 154 10.6 Load Balancing in Next Generation Cloud Computing 155 10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations 157 10.7.1 Quantum Isochronous Parallel 158 10.7.2 Phase Isochronous Parallel 159 10.7.3 Dynamic Isochronous Coordinate Strategy 161 10.8 Adaptive-Dynamic Synchronous Coordinate Strategy 161 10.8.1 Adaptive Quick Reassignment (AdaptQR) 162 10.8.2 A-DIC (Adaptive-Dynamic Synchronous Parallel) 163 10.9 Conclusion 164 References 165 11 Biometric Identification for Advanced Cloud Security 167Yojna khandelwal and Kapil Chauhan 11.1 Introduction 168 11.1.1 Biometric Identification 168 11.1.2 Biometric Characteristic 169 11.1.3 Types of Biometric Data 169 11.1.3.1 Face Recognition 169 11.1.3.2 Hand Vein 170 11.1.3.3 Signature Verification 170 11.1.3.4 Iris Recognition 170 11.1.3.5 Voice Recognition 170 11.1.3.6 Fingerprints 171 11.2 Literature Survey 172 11.3 Biometric Identification in Cloud Computing 174 11.3.1 How Biometric Authentication is Being Used on the Cloud Platform 176 11.4 Models and Design Goals 177 11.4.1 Models 177 11.4.1.1 System Model 177 11.4.1.2 Threat Model 177 11.4.2 Design Goals 178 11.5 Face Recognition Method as a Biometric Authentication 179 11.6 Deep Learning Techniques for Big Data in Biometrics 180 11.6.1 Issues and Challenges 181 11.6.2 Deep Learning Strategies For Biometric Identification 182 11.7 Conclusion 185 References 185 12 Application of Deep Learning in Cloud Security 189Jaya Jain 12.1 Introduction 190 12.2 Literature Review 191 12.3 Deep Learning 192 12.4 The Uses of Fields in Deep Learning 195 12.5 Conclusion 202 References 203 13 Real Time Cloud Based Intrusion Detection 207Ekta Bafna 13.1 Introduction 207 13.2 Literature Review 209 13.3 Incursion In Cloud 211 13.3.1 Denial of Service (DoS) Attack 212 13.3.2 Insider Attack 212 13.3.3 User To Root (U2R) Attack 213 13.3.4 Port Scanning 213 13.4 Intrusion Detection System 213 13.4.1 Signature-Based Intrusion Detection System (SIDS) 213 13.4.2 Anomaly-Based Intrusion Detection System (AIDS) 214 13.4.3 Intrusion Detection System Using Deep Learning 215 13.5 Types of IDS in Cloud 216 13.5.1 Host Intrusion Detection System 216 13.5.2 Network Based Intrusion Detection System 217 13.5.3 Distributed Based Intrusion Detection System 217 13.6 Model of Deep Learning 218 13.6.1 ConvNet Model 218 13.6.2 Recurrent Neural Network 219 13.6.3 Multi-Layer Perception Model 219 13.7 KDD Dataset 221 13.8 Evaluation 221 13.9 Conclusion 223 References 223 14 Applications of Deep Learning in Cloud Security 225Disha Shrmali and Shweta Sharma 14.1 Introduction 226 14.1.1 Data Breaches 226 14.1.2 Accounts Hijacking 227 14.1.3 Insider Threat 227 14.1.3.1 Malware Injection 227 14.1.3.2 Abuse of Cloud Services 228 14.1.3.3 Insecure APIs 228 14.1.3.4 Denial of Service Attacks 228 14.1.3.5 Insufficient Due Diligence 229 14.1.3.6 Shared Vulnerabilities 229 14.1.3.7 Data Loss 229 14.2 Deep Learning Methods for Cloud Cyber Security 230 14.2.1 Deep Belief Networks 230 14.2.1.1 Deep Autoencoders 230 14.2.1.2 Restricted Boltzmann Machines 232 14.2.1.3 DBNs, RBMs, or Deep Autoencoders Coupled with Classification Layers 233 14.2.1.4 Recurrent Neural Networks 233 14.2.1.5 Convolutional Neural Networks 234 14.2.1.6 Generative Adversarial Networks 235 14.2.1.7 Recursive Neural Networks 236 14.2.2 Applications of Deep Learning in Cyber Security 237 14.2.2.1 Intrusion Detection and Prevention Systems (IDS/IPS) 237 14.2.2.2 Dealing with Malware 237 14.2.2.3 Spam and Social Engineering Detection 238 14.2.2.4 Network Traffic Analysis 238 14.2.2.5 User Behaviour Analytics 238 14.2.2.6 Insider Threat Detection 239 14.2.2.7 Border Gateway Protocol Anomaly Detection 239 14.2.2.8 Verification if Keystrokes were Typed by a Human 240 14.3 Framework to Improve Security in Cloud Computing 240 14.3.1 Introduction to Firewalls 241 14.3.2 Importance of Firewalls 242 14.3.2.1 Prevents the Passage of Unwanted Content 242 14.3.2.2 Prevents Unauthorized Remote Access 243 14.3.2.3 Restrict Indecent Content 243 14.3.2.4 Guarantees Security Based on Protocol and IP Address 244 14.3.2.5 Protects Seamless Operations in Enterprises 244 14.3.2.6 Protects Conversations and Coordination Contents 244 14.3.2.7 Restricts Online Videos and Games from Displaying Destructive Content 245 14.3.3 Types of Firewalls 245 14.3.3.1 Proxy-Based Firewalls 245 14.3.3.2 Stateful Firewalls 246 14.3.3.3 Next-Generation Firewalls (NGF) 247 14.3.3.4 Web Application Firewalls (WAF) 247 14.3.3.5 Working of WAF 248 14.3.3.6 How Web Application Firewalls (WAF) Work 248 14.3.3.7 Attacks that Web Application Firewalls Prevent 250 14.3.3.8 Cloud WAF 251 14.4 WAF Deployment 251 14.4.1 Web Application Firewall (WAF) Security Models 252 14.4.2 Firewall-as-a-Service (FWaaS) 252 14.4.3 Basic Difference Between a Cloud Firewall and a Next-Generation Firewall (NGFW) 253 14.4.4 Introduction and Effects of Firewall Network Parameters on Cloud Computing 253 14.5 Conclusion 254 References 254 About the Editors 257 Index 263

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Book SynopsisSMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges. The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthc

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Book SynopsisINDUSTRIAL INTERNET OF THINGS (IIOT) This book discusses how the industrial internet will be augmented through increased network agility, integrated artificial intelligence (AI) and the capacity to deploy, automate, orchestrate, and secure diverse user cases at hyperscale. Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach $73.5 billion in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control. The 15 chapters in the book showcase industrial automation through the IoT by including case stuTable of ContentsPreface xvii 1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field 1Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur, Yuzo Iano, Andrea Coimbra Segatti, Giulliano Paes Carnielli, Julio Cesar Pereira, Henri Alves de Godoy and Elder Carlos Fernandes 1.1 Introduction 2 1.2 Relationship Between Artificial Intelligence and IoT 5 1.2.1 AI Concept 6 1.2.2 IoT Concept 10 1.3 IoT Ecosystem 15 1.3.1 Industry 4.0 Concept 18 1.3.2 Industrial Internet of Things 19 1.4 Discussion 21 1.5 Trends 23 1.6 Conclusions 24 References 26 2 Analysis on Security in IoT Devices—An Overview 31T. Nalini and T. Murali Krishna 2.1 Introduction 32 2.2 Security Properties 33 2.3 Security Challenges of IoT 34 2.3.1 Classification of Security Levels 35 2.3.1.1 At Information Level 36 2.3.1.2 At Access Level 36 2.3.1.3 At Functional Level 36 2.3.2 Classification of IoT Layered Architecture 37 2.3.2.1 Edge Layer 37 2.3.2.2 Access Layer 37 2.3.2.3 Application Layer 37 2.4 IoT Security Threats 38 2.4.1 Physical Device Threats 39 2.4.1.1 Device-Threats 39 2.4.1.2 Resource Led Constraints 39 2.4.2 Network-Oriented Communication Assaults 39 2.4.2.1 Structure 40 2.4.2.2 Protocol 40 2.4.3 Data-Based Threats 41 2.4.3.1 Confidentiality 41 2.4.3.2 Availability 41 2.4.3.3 Integrity 42 2.5 Assaults in IoT Devices 43 2.5.1 Devices of IoT 43 2.5.2 Gateways and Networking Devices 44 2.5.3 Cloud Servers and Control Devices 45 2.6 Security Analysis of IoT Platforms 46 2.6.1 ARTIK 46 2.6.2 GiGA IoT Makers 47 2.6.3 AWS IoT 47 2.6.4 Azure IoT 47 2.6.5 Google Cloud IoT (GC IoT) 48 2.7 Future Research Approaches 49 2.7.1 Blockchain Technology 51 2.7.2 5G Technology 52 2.7.3 Fog Computing (FC) and Edge Computing (EC) 52 References 54 3 Smart Automation, Smart Energy, and Grid Management Challenges 59J. Gayathri Monicka and C. Amuthadevi 3.1 Introduction 60 3.2 Internet of Things and Smart Grids 62 3.2.1 Smart Grid in IoT 63 3.2.2 IoT Application 64 3.2.3 Trials and Imminent Investigation Guidelines 66 3.3 Conceptual Model of Smart Grid 67 3.4 Building Computerization 71 3.4.1 Smart Lighting 73 3.4.2 Smart Parking 73 3.4.3 Smart Buildings 74 3.4.4 Smart Grid 75 3.4.5 Integration IoT in SG 77 3.5 Challenges and Solutions 81 3.6 Conclusions 83 References 83 4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management 89C. Amuthadevi and J. Gayathri Monicka 4.1 Introduction 89 4.1.1 Fundamental Terms in IIoT 91 4.1.1.1 Cloud Computing 92 4.1.1.2 Big Data Analytics 92 4.1.1.3 Fog/Edge Computing 92 4.1.1.4 Internet of Things 93 4.1.1.5 Cyber-Physical-System 94 4.1.1.6 Artificial Intelligence 95 4.1.1.7 Machine Learning 95 4.1.1.8 Machine-to-Machine Communication 99 4.1.2 Intelligent Analytics 99 4.1.3 Predictive Maintenance 100 4.1.4 Disaster Predication and Safety Management 101 4.1.4.1 Natural Disasters 101 4.1.4.2 Disaster Lifecycle 102 4.1.4.3 Disaster Predication 103 4.1.4.4 Safety Management 104 4.1.5 Optimization 105 4.2 Existing Technology and Its Review 106 4.2.1 Survey on Predictive Analysis in Natural Disasters 106 4.2.2 Survey on Safety Management and Recovery 108 4.2.3 Survey on Optimizing Solutions in Natural Disasters 109 4.3 Research Limitation 110 4.3.1 Forward-Looking Strategic Vision (FVS) 110 4.3.2 Availability of Data 111 4.3.3 Load Balancing 111 4.3.4 Energy Saving and Optimization 111 4.3.5 Cost Benefit Analysis 112 4.3.6 Misguidance of Analysis 112 4.4 Finding 113 4.4.1 Data Driven Reasoning 113 4.4.2 Cognitive Ability 113 4.4.3 Edge Intelligence 113 4.4.4 Effect of ML Algorithms and Optimization 114 4.4.5 Security 114 4.5 Conclusion and Future Research 114 4.5.1 Conclusion 114 4.5.2 Future Research 114 References 115 5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques 119Kuntal Bhattacharjee, Akhilesh Arvind Nimje, Shanker D. Godwal and Sudeep Tanwar 5.1 Introduction 120 5.2 Fuzzy Logic 121 5.2.1 Fuzzy Sets 121 5.2.2 Fuzzy Logic Basics 122 5.2.3 Fuzzy Logic and Power System 122 5.2.4 Fuzzy Logic—Automatic Generation Control 123 5.2.5 Fuzzy Microgrid Wind 123 5.3 Genetic Algorithm 123 5.3.1 Important Aspects of Genetic Algorithm 124 5.3.2 Standard Genetic Algorithm 126 5.3.3 Genetic Algorithm and Its Application 127 5.3.4 Power System and Genetic Algorithm 127 5.3.5 Economic Dispatch Using Genetic Algorithm 128 5.4 Artificial Neural Network 128 5.4.1 The Biological Neuron 129 5.4.2 A Formal Definition of Neural Network 130 5.4.3 Neural Network Models 131 5.4.4 Rosenblatt’s Perceptron 131 5.4.5 Feedforward and Recurrent Networks 132 5.4.6 Back Propagation Algorithm 133 5.4.7 Forward Propagation 133 5.4.8 Algorithm 134 5.4.9 Recurrent Network 135 5.4.10 Examples of Neural Networks 136 5.4.10.1 AND Operation 136 5.4.10.2 OR Operation 137 5.4.10.3 XOR Operation 137 5.4.11 Key Components of an Artificial Neuron Network 138 5.4.12 Neural Network Training 141 5.4.13 Training Types 142 5.4.13.1 Supervised Training 142 5.4.13.2 Unsupervised Training 142 5.4.14 Learning Rates 142 5.4.15 Learning Laws 143 5.4.16 Restructured Power System 144 5.4.17 Advantages of Precise Forecasting of the Price 145 5.5 Conclusion 145 References 146 6 Recent Advances in Wearable Antennas: A Survey 149Harvinder Kaur and Paras Chawla 6.1 Introduction 150 6.2 Types of Antennas 153 6.2.1 Description of Wearable Antennas 153 6.2.1.1 Microstrip Patch Antenna 153 6.2.1.2 Substrate Integrated Waveguide Antenna 153 6.2.1.3 Planar Inverted-F Antenna 153 6.2.1.4 Monopole Antenna 153 6.2.1.5 Metasurface Loaded Antenna 154 6.3 Design of Wearable Antennas 154 6.3.1 Effect of Substrate and Ground Geometries on Antenna Design 154 6.3.1.1 Conducting Coating on Substrate 154 6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure 157 6.3.1.3 Partial Ground Plane 158 6.3.2 Logo Antennas 159 6.3.3 Embroidered Antenna 159 6.3.4 Wearable Antenna Based on Electromagnetic Band Gap 160 6.3.5 Wearable Reconfigurable Antenna 161 6.4 Textile Antennas 162 6.5 Comparison of Wearable Antenna Designs 168 6.6 Fractal Antennas 168 6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas 171 6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane 172 6.6.3 Double-Fractal Layer Wearable Antenna 172 6.6.4 Development of Embroidered Sierpinski Carpet Antenna 172 6.7 Future Challenges of Wearable Antenna Designs 174 6.8 Conclusion 174 References 175 7 An Overview of IoT and Its Application With Machine Learning in Data Center 181Manikandan Ramanathan and Kumar Narayanan 7.1 Introduction 181 7.1.1 6LoWPAN 183 7.1.2 Data Protocols 185 7.1.2.1 CoAP 185 7.1.2.2 MQTT 187 7.1.2.3 Rest APIs 187 7.1.3 IoT Components 189 7.1.3.1 Hardware 190 7.1.3.2 Middleware 190 7.1.3.3 Visualization 191 7.2 Data Center and Internet of Things 191 7.2.1 Modern Data Centers 191 7.2.2 Data Storage 191 7.2.3 Computing Process 192 7.2.3.1 Fog Computing 192 7.2.3.2 Edge Computing 194 7.2.3.3 Cloud Computing 194 7.2.3.4 Distributed Computing 195 7.2.3.5 Comparison of Cloud Computing and Fog Computing 196 7.3 Machine Learning Models and IoT 196 7.3.1 Classifications of Machine Learning Supported in IoT 197 7.3.1.1 Supervised Learning 197 7.3.1.2 Unsupervised Learning 198 7.3.1.3 Reinforcement Learning 198 7.3.1.4 Ensemble Learning 199 7.3.1.5 Neural Network 199 7.4 Challenges in Data Center and IoT 199 7.4.1 Major Challenges 199 7.5 Conclusion 201 References 201 8 Impact of IoT to Meet Challenges in Drone Delivery System 203J. Ranjani, P. Kalaichelvi, V.K.G Kalaiselvi, D. Deepika Sree and K. Swathi 8.1 Introduction 204 8.1.1 IoT Components 204 8.1.2 Main Division to Apply IoT in Aviation 205 8.1.3 Required Field of IoT in Aviation 206 8.1.3.1 Airports as Smart Cities or Airports as Platforms 207 8.1.3.2 Architecture of Multidrone 208 8.1.3.3 The Multidrone Design has the Accompanying Prerequisites 208 8.2 Literature Survey 209 8.3 Smart Airport Architecture 211 8.4 Barriers to IoT Implementation 215 8.4.1 How is the Internet of Things Converting the Aviation Enterprise? 216 8.5 Current Technologies in Aviation Industry 216 8.5.1 Methodology or Research Design 217 8.6 IoT Adoption Challenges 218 8.6.1 Deployment of IoT Applications on Broad Scale Includes the Underlying Challenges 218 8.7 Transforming Airline Industry With Internet of Things 219 8.7.1 How the IoT Is Improving the Aviation Industry 219 8.7.1.1 IoT: Game Changer for Aviation Industry 220 8.7.2 Applications of AI in the Aviation Industry 220 8.7.2.1 Ticketing Systems 220 8.7.2.2 Flight Maintenance 221 8.7.2.3 Fuel Efficiency 221 8.7.2.4 Crew Management 221 8.7.2.5 Flight Health Checks and Maintenance 221 8.7.2.6 In-Flight Experience Management 222 8.7.2.7 Luggage Tracking 222 8.7.2.8 Airport Management 222 8.7.2.9 Just the Beginning 222 8.8 Revolution of Change (Paradigm Shift) 222 8.9 The Following Diagram Shows the Design of the Application 223 8.10 Discussion, Limitations, Future Research, and Conclusion 224 8.10.1 Growth of Aviation IoT Industry 224 8.10.2 IoT Applications—Benefits 225 8.10.3 Operational Efficiency 225 8.10.4 Strategic Differentiation 225 8.10.5 New Revenue 226 8.11 Present and Future Scopes 226 8.11.1 Improving Passenger Experience 226 8.11.2 Safety 227 8.11.3 Management of Goods and Luggage 227 8.11.4 Saving 227 8.12 Conclusion 227 References 227 9 IoT-Based Water Management System for a Healthy Life 229N. Meenakshi, V. Pandimurugan and S. Rajasoundaran 9.1 Introduction 230 9.1.1 Human Activities as a Source of Pollutants 230 9.2 Water Management Using IoT 231 9.2.1 Water Quality Management Based on IoT Framework 232 9.3 IoT Characteristics and Measurement Parameters 233 9.4 Platforms and Configurations 235 9.5 Water Quality Measuring Sensors and Data Analysis 239 9.6 Wastewater and Storm Water Monitoring Using IoT 241 9.6.1 System Initialization 241 9.6.2 Capture and Storage of Information 241 9.6.3 Information Modeling 241 9.6.4 Visualization and Management of the Information 243 9.7 Sensing and Sampling of Water Treatment Using IoT 244 References 246 10 Fuel Cost Optimization Using IoT in Air Travel 249P. Kalaichelvi, V. Akila, J. Ranjani, S. Sowmiya and C. Divya 10.1 Introduction 250 10.1.1 Introduction to IoT 250 10.1.2 Processing IoT Data 250 10.1.3 Advantages of IoT 251 10.1.4 Disadvantages of IoT 251 10.1.5 IoT Standards 251 10.1.6 Lite Operating System (Lite OS) 251 10.1.7 Low Range Wide Area Network (LoRaWAN) 252 10.2 Emerging Frameworks in IoT 252 10.2.1 Amazon Web Service (AWS) 252 10.2.2 Azure 252 10.2.3 Brillo/Weave Statement 252 10.2.4 Calvin 252 10.3 Applications of IoT 253 10.3.1 Healthcare in IoT 253 10.3.2 Smart Construction and Smart Vehicles 254 10.3.3 IoT in Agriculture 254 10.3.4 IoT in Baggage Tracking 254 10.3.5 Luggage Logbook 254 10.3.6 Electrical Airline Logbook 254 10.4 IoT for Smart Airports 255 10.4.1 IoT in Smart Operation in Airline Industries 257 10.4.2 Fuel Emissions on Fly 258 10.4.3 Important Things in Findings 258 10.5 Related Work 258 10.6 Existing System and Analysis 264 10.6.1 Technology Used in the System 265 10.7 Proposed System 268 10.8 Components in Fuel Reduction 276 10.9 Conclusion 276 10.10 Future Enhancements 277 References 277 11 Object Detection in IoT-Based Smart Refrigerators Using CNN 281Ashwathan R., Asnath Victy Phamila Y., Geetha S. and Kalaivani K. 11.1 Introduction 282 11.2 Literature Survey 283 11.3 Materials and Methods 287 11.3.1 Image Processing 292 11.3.2 Product Sensing 292 11.3.3 Quality Detection 293 11.3.4 Android Application 293 11.4 Results and Discussion 294 11.5 Conclusion 299 References 299 12 Effective Methodologies in Pharmacovigilance for Identifying Adverse Drug Reactions Using IoT 301Latha Parthiban, Maithili Devi Reddy and A. Kumaravel 12.1 Introduction 302 12.2 Literature Review 302 12.3 Data Mining Tasks 304 12.3.1 Classification 305 12.3.2 Regression 306 12.3.3 Clustering 306 12.3.4 Summarization 306 12.3.5 Dependency Modeling 306 12.3.6 Association Rule Discovery 307 12.3.7 Outlier Detection 307 12.3.8 Prediction 307 12.4 Feature Selection Techniques in Data Mining 308 12.4.1 GAs for Feature Selection 308 12.4.2 GP for Feature Selection 309 12.4.3 PSO for Feature Selection 310 12.4.4 ACO for Feature Selection 311 12.5 Classification With Neural Predictive Classifier 312 12.5.1 Neural Predictive Classifier 313 12.5.2 MapReduce Function on Neural Class 317 12.6 Conclusions 319 References 319 13 Impact of COVID-19 on IIoT 321K. Priyadarsini, S. Karthik, K. Malathi and M.V.V Rama Rao 13.1 Introduction 321 13.1.1 The Use of IoT During COVID-19 321 13.1.2 Consumer IoT 322 13.1.3 Commercial IoT 322 13.1.4 Industrial Internet of Things (IIoT) 322 13.1.5 Infrastructure IoT 322 13.1.6 Role of IoT in COVID-19 Response 323 13.1.7 Telehealth Consultations 323 13.1.8 Digital Diagnostics 323 13.1.9 Remote Monitoring 323 13.1.10 Robot Assistance 323 13.2 The Benefits of Industrial IoT 326 13.2.1 How IIoT is Being Used 327 13.2.2 Remote Monitoring 327 13.2.3 Predictive Maintenance 328 13.3 The Challenges of Wide-Spread IIoT Implementation 329 13.3.1 Health and Safety Monitoring Will Accelerate Automation and Remote Monitoring 330 13.3.2 Integrating Sensor and Camera Data Improves Safety and Efficiency 330 13.3.3 IIoT-Supported Safety for Customers Reduces Liability for Businesses 331 13.3.4 Predictive Maintenance Will Deliver for Organizations That Do the Work 332 13.3.5 Building on the Lessons of 2020 332 13.4 Effects of COVID-19 on Industrial Manufacturing 332 13.4.1 New Challenges for Industrial Manufacturing 333 13.4.2 Smarter Manufacturing for Actionable Insights 333 13.4.3 A Promising Future for IIoT Adoption 334 13.5 Winners and Losers—The Impact on IoT/Connected Applications and Digital Transformation due to COVID-19 Impact 335 13.6 The Impact of COVID-19 on IoT Applications 337 13.6.1 Decreased Interest in Consumer IoT Devices 338 13.6.2 Remote Asset Access Becomes Important 338 13.6.3 Digital Twins Help With Scenario Planning 339 13.6.4 New Uses for Drones 339 13.6.5 Specific IoT Health Applications Surge 340 13.6.6 Track and Trace Solutions Get Used More Extensively 340 13.6.7 Smart City Data Platforms Become Key 340 13.7 The Impact of COVID-19 on Technology in General 341 13.7.1 Ongoing Projects Are Paused 341 13.7.2 Some Enterprise Technologies Take Off 341 13.7.3 Declining Demand for New Projects/Devices/ Services 342 13.7.4 Many Digitalization Initiatives Get Accelerated or Intensified 342 13.7.5 The Digital Divide Widens 343 13.8 The Impact of COVID-19 on Specific IoT Technologies 343 13.8.1 IoT Networks Largely Unaffected 343 13.8.2 Technology Roadmaps Get Delayed 344 13.9 Coronavirus With IoT, Can Coronavirus Be Restrained? 344 13.10 The Potential of IoT in Coronavirus Like Disease Control 345 13.11 Conclusion 346 References 346 14 A Comprehensive Composite of Smart Ambulance Booking and Tracking Systems Using IoT for Digital Services 349Sumanta Chatterjee, Pabitra Kumar Bhunia, Poulami Mondal, Aishwarya Sadhu and Anusua Biswas 14.1 Introduction 350 14.2 Literature Review 353 14.3 Design of Smart Ambulance Booking System Through App 356 14.4 Smart Ambulance Booking 359 14.4.1 Welcome Page 360 14.4.2 Sign Up 360 14.4.3 Home Page 361 14.4.4 Ambulance Section 361 14.4.5 Ambulance Selection Page 362 14.4.6 Confirmation of Booking and Tracking 363 14.5 Result and Discussion 363 14.5.1 How It Works? 365 14.6 Conclusion 365 14.7 Future Scope 366 References 366 15 An Efficient Elderly Disease Prediction and Privacy Preservation Using Internet of Things 369Resmi G. Nair and N. Kumar 15.1 Introduction 370 15.2 Literature Survey 371 15.3 Problem Statement 372 15.4 Proposed Methodology 373 15.4.1 Design a Smart Wearable Device 373 15.4.2 Normalization 374 15.4.3 Feature Extraction 377 15.4.4 Classification 378 15.4.5 Polynomial HMAC Algorithm 379 15.5 Result and Discussion 382 15.5.1 Accuracy 382 15.5.2 Positive Predictive Value 382 15.5.3 Sensitivity 383 15.5.4 Specificity 383 15.5.5 False Out 383 15.5.6 False Discovery Rate 383 15.5.7 Miss Rate 383 15.5.8 F-Score 383 15.6 Conclusion 390 References 390 Index 393

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Book SynopsisTable of ContentsPreface xvii 1 Unmanned Aerial Vehicle (UAV): A Comprehensive Survey 1Rohit Chaurasia and Vandana Mohindru 1.1 Introduction 2 1.2 Related Work 2 1.3 UAV Technology 3 1.3.1 UAV Platforms 3 1.3.1.1 Fixed-Wing Drones 3 1.3.1.2 Multi-Rotor Drones 4 1.3.1.3 Single-Rotor Drones 5 1.3.1.4 Fixed-Wing Hybrid VTOL 6 1.3.2 Categories of the Military Drones 6 1.3.3 How Drones Work 8 1.3.3.1 Firmware—Platform Construction and Design 9 1.3.4 Comparison of Various Technologies 10 1.3.4.1 Drone Types & Sizes 10 1.3.4.2 Radar Positioning and Return to Home 10 1.3.4.3 GNSS on Ground Control Station 11 1.3.4.4 Collision Avoidance Technology and Obstacle Detection 11 1.3.4.5 Gyroscopic Stabilization, Flight Controllers and IMU 12 1.3.4.6 UAV Drone Propulsion System 12 1.3.4.7 Flight Parameters Through Telemetry 13 1.3.4.8 Drone Security & Hacking 13 1.3.4.9 3D Maps and Models With Drone Sensors 13 1.3.5 UAV Communication Network 15 1.3.5.1 Classification on the Basis of Spectrum Perspective 15 1.3.5.2 Various Types of Radio communication Links 16 1.3.5.3 VLOS (Visual Line-of-Sight) and BLOS (Beyond Line-of-Sight) Communication in Unmanned Aircraft System 18 1.3.5.4 Frequency Bands for the Operation of UAS 19 1.3.5.5 Cellular Technology for UAS Operation 19 1.4 Application of UAV 21 1.4.1 In Military 21 1.4.2 In Geomorphological Mapping and Other Similar Sectors 22 1.4.3 In Agriculture 22 1.5 UAV Challenges 23 1.6 Conclusion and Future Scope 24 References 24 2 Unmanned Aerial Vehicles: State-of-the-Art, Challenges and Future Scope 29Jolly Parikh and Anuradha Basu 2.1 Introduction 30 2.2 Technical Challenges 30 2.2.1 Variations in Channel Characteristics 32 2.2.2 UAV-Assisted Cellular Network Planning and Provisioning 33 2.2.3 Millimeter Wave Cellular Connected UAVs 34 2.2.4 Deployment of UAV 35 2.2.5 Trajectory Optimization 36 2.2.6 On-Board Energy 37 2.3 Conclusion 37 References 37 3 Battery and Energy Management in UAV-Based Networks 43Santosh Kumar, Amol Vasudeva and Manu Sood 3.1 Introduction 43 3.2 The Need for Energy Management in UAV-Based Communication Networks 45 3.2.1 Unpredictable Trajectories of UAVs in Cellular UAV Networks 46 3.2.2 Non-Homogeneous Power Consumption 47 3.2.3 High Bandwidth Requirement/Low Spectrum Availability/Spectrum Scarcity 47 3.2.4 Short-Range Line-of-Sight Communication 48 3.2.5 Time Constraint (Time-Limited Spectrum Access) 48 3.2.6 Energy Constraint 49 3.2.7 The Joint Design for the Sensor Nodes’ Wake-Up Schedule and the UAV’s Trajectory (Data Collection) 49 3.3 Efficient Battery and Energy Management Proposed Techniques in Literature 50 3.3.1 Cognitive Radio (CR)-Based UAV Communication to Solve Spectrum Congestion 51 3.3.2 Compressed Sensing 52 3.3.3 Power Allocation and Position Optimization 53 3.3.4 Non-Orthogonal Multiple Access (NOMA) 53 3.3.5 Wireless Charging/Power Transfer (WPT) 54 3.3.6 UAV Trajectory Design Using a Reinforcement Learning Framework in a Decentralized Manner 55 3.3.7 Efficient Deployment and Movement of UAVs 55 3.3.8 3D Position Optimization Mixed With Resource Allocation to Overcome Spectrum Scarcity and Limited Energy Constraint 56 3.3.9 UAV-Enabled WSN: Energy-Efficient Data Collection 57 3.3.10 Trust Management 57 3.3.11 Self-Organization-Based Clustering 58 3.3.12 Bandwidth/Spectrum-Sharing Between UAVs 59 3.3.13 Using Millimeter Wave With SWIPT 59 3.3.14 Energy Harvesting 60 3.4 Conclusion 61 References 67 4 Energy Efficient Communication Methods for Unmanned Ariel Vehicles (UAVs): Last Five Years’ Study 73Nagesh Kumar 4.1 Introduction 73 4.1.1 Introduction to UAV 74 4.1.2 Communication in UAV 75 4.2 Literature Survey Process 77 4.2.1 Research Questions 77 4.2.2 Information Source 77 4.3 Routing in UAV 78 4.3.1 Communication Methods in UAV 78 4.3.1.1 Single-Hop Communication 79 4.3.1.2 Multi-Hop Communication 80 4.4 Challenges and Issues 82 4.4.1 Energy Consumption 82 4.4.2 Mobility of Devices 82 4.4.3 Density of UAVs 82 4.4.4 Changes in Topology 85 4.4.5 Propagation Models 85 4.4.6 Security in Routing 85 4.5 Conclusion 85 References 86 5 A Review on Challenges and Threats to Unmanned Aerial Vehicles (UAVs) 89Shaik Johny Basha and Jagan Mohan Reddy Danda 5.1 Introduction 89 5.2 Applications of UAVs and Their Market Opportunity 90 5.2.1 Applications 90 5.2.2 Market Opportunity 92 5.3 Attacks and Solutions to Unmanned Aerial Vehicles (UAVs) 92 5.3.1 Confidentiality Attacks 93 5.3.2 Integrity Attacks 95 5.3.3 Availability Attacks 96 5.3.4 Authenticity Attacks 97 5.4 Research Challenges 99 5.4.1 Security Concerns 99 5.4.2 Safety Concerns 99 5.4.3 Privacy Concerns 100 5.4.4 Scalability Issues 100 5.4.5 Limited Resources 100 5.5 Conclusion 101 References 101 6 Internet of Things and UAV: An Interoperability Perspective 105Bharti Rana and Yashwant Singh 6.1 Introduction 106 6.2 Background 108 6.2.1 Issues, Controversies, and Problems 109 6.3 Internet of Things (IoT) and UAV 110 6.4 Applications of UAV-Enabled IoT 113 6.5 Research Issues in UAV-Enabled IoT 114 6.6 High-Level UAV-Based IoT Architecture 117 6.6.1 UAV Overview 117 6.6.2 Enabling IoT Scalability 119 6.6.3 Enabling IoT Intelligence 120 6.6.4 Enabling Diverse IoT Applications 121 6.7 Interoperability Issues in UAV-Based IoT 121 6.8 Conclusion 123 References 124 7 Practices of Unmanned Aerial Vehicle (UAV) for Security Intelligence 129Swarnjeet Kaur, Kulwant Singh and Amanpreet Singh 7.1 Introduction 130 7.2 Military 132 7.3 Attack 133 7.4 Journalism 134 7.5 Search and Rescue 136 7.6 Disaster Relief 138 7.7 Conclusion 139 References 139 8 Blockchain-Based Solutions for Various Security Issues in UAV-Enabled IoT 143Madhuri S. Wakode and Rajesh B. Ingle 8.1 Introduction 144 8.1.1 Organization of the Work 145 8.2 Introduction to UAV and IoT 145 8.2.1 UAV 145 8.2.2 IoT 146 8.2.3 UAV-Enabled IoT 147 8.2.4 Blockchain 150 8.3 Security and Privacy Issues in UAV-Enabled IoT 151 8.4 Blockchain-Based Solutions to Various Security Issues 153 8.5 Research Directions 154 8.6 Conclusion 154 8.7 Future Work 155 References 155 9 Efficient Energy Management Systems in UAV-Based IoT Networks 159V. Mounika Reddy, Neelima K. and G. Naresh 9.1 Introduction 160 9.2 Energy Harvesting Methods 161 9.2.1 Basic Energy Harvesting Mechanisms 162 9.2.2 Markov Decision Process-Based Energy Harvesting Mechanisms 163 9.2.3 mm Wave Energy Harvesting Mechanism 164 9.2.4 Full Duplex Wireless Energy Harvesting Mechanism 165 9.3 Energy Recharge Methods 165 9.4 Efficient Energy Utilization Methods 166 9.4.1 GLRM Method 166 9.4.2 DRL Mechanism 167 9.4.3 Onboard Double Q-Learning Mechanism 168 9.4.4 Collision-Free Scheduling Mechanism 168 9.5 Conclusion 170 References 170 10 A Survey on IoE-Enabled Unmanned Aerial Vehicles 173K. Siddharthraju, R. Dhivyadevi, M. Supriya, B. Jaishankar and Shanmugaraja T. 10.1 Introduction 174 10.2 Overview of Internet of Everything 176 10.2.1 Emergence of IoE 176 10.2.2 Expectation of IoE 177 10.2.2.1 Scalability 177 10.2.2.2 Intelligence 178 10.2.2.3 Diversity 178 10.2.3 Possible Technologies 179 10.2.3.1 Enabling Scalability 179 10.2.3.2 Enabling Intelligence 180 10.2.3.3 Enabling Diversity 180 10.2.4 Challenges of IoE 181 10.2.4.1 Coverage Constraint 181 10.2.4.2 Battery Constraint 181 10.2.4.3 Computing Constraint 181 10.2.4.4 Security Constraint 182 10.3 Overview of Unmanned Aerial Vehicle (UAV) 182 10.3.1 Unmanned Aircraft System (UAS) 183 10.3.2 UAV Communication Networks 183 10.3.2.1 Ad Hoc Multi-UAV Networks 183 10.3.2.2 UAV-Aided Communication Networks 184 10.4 UAV and IoE Integration 184 10.4.1 Possibilities to Carry UAVs 184 10.4.1.1 Widespread Connectivity 185 10.4.1.2 Environmentally Aware 185 10.4.1.3 Peer-Maintenance of Communications 185 10.4.1.4 Detector Control and Reusing 185 10.4.2 UAV-Enabled IoE 186 10.4.3 Vehicle Detection Enabled IoE Optimization 186 10.4.3.1 Weak-Connected Locations 186 10.4.3.2 Regions with Low Network Support 186 10.5 Open Research Issues 187 10.6 Discussion 187 10.6.1 Resource Allocation 187 10.6.2 Universal Standard Design 188 10.6.3 Security Mechanism 188 10.7 Conclusion 189 References 189 11 Role of AI and Big Data Analytics in UAV-Enabled IoT Applications for Smart Cities 193Madhuri S. Wakode 11.1 Introduction 194 11.1.1 Related Work 195 11.1.2 Contributions 195 11.1.3 Organization of the Work 195 11.2 Overview of UAV-Enabled IoT Systems 196 11.2.1 UAV-Enabled IoT Systems for Smart Cities 197 11.3 Overview of Big Data Analytics 197 11.4 Big Data Analytics Requirements in UAV-Enabled IoT Systems 198 11.4.1 Big Data Analytics in UAV-Enabled IoT Applications 199 11.4.2 Big Data Analytics for Governance of UAV-Enabled IoT Systems 201 11.5 Challenges 202 11.6 Conclusion 202 11.7 Future Work 203 References 203 12 Design and Development of Modular and Multifunctional UAV with Amphibious Landing, Processing and Surround Sense Module 207Lakshit Kohli, Manglesh Saurabh, Ishaan Bhatia, Nidhi Sindhwani and Manjula Vijh 12.1 Introduction 208 12.2 Existing System 208 12.3 Proposed System 210 12.4 IoT Sensors and Architecture 212 12.4.1 Sensors and Theory 212 12.4.2 Architectures Available 213 12.4.2.1 3-Layer IoT Architecture 213 12.4.2.2 5-Layer IoT Architecture 214 12.4.2.3 Architecture & Supporting Modules 215 12.4.2.4 Integration Approach 215 12.4.2.5 System of Modules 216 12.5 Advantages of the Proposed System 217 12.6 Design 218 12.6.1 System Design 219 12.6.2 Auto-Leveling 219 12.6.3 Amphibious Landing Module 221 12.6.4 Processing Module 223 12.6.5 Surround Sense Module 223 12.7 Results 224 12.8 Conclusion 227 12.9 Future Scope 228 References 228 13 Mind Controlled Unmanned Aerial Vehicle (UAV) Using Brain–Computer Interface (BCI) 231Prasath M.S., Naveen R. and Sivaraj G. 13.1 Introduction 232 13.1.1 Classification of UAVs 232 13.1.2 Drone Controlling 232 13.2 Mind-Controlled UAV With BCI Technology 233 13.3 Layout and Architecture of BCI Technology 234 13.4 Hardware Components 235 13.4.1 Neurosky Mindwave Headset 235 13.4.2 Microcontroller Board—Arduino 236 13.4.3 A Computer 237 13.4.4 Drone for Quadcopter 238 13.5 Software Components 239 13.5.1 Processing P3 Software 239 13.5.2 Arduino IDE Software 240 13.5.3 ThinkGear Connector 240 13.6 Hardware and Software Integration 241 13.7 Conclusion 243 References 244 14 Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0 247Dhirendra Siddharth, Dilip Kumar Saini and Ajay Kumar 14.1 Introduction 247 14.2 Drone Technology as an Instrument for Increasing Farm Productivity 248 14.3 Mapping and Tracking of Rice Farm Areas With Information and Communication Technology (ICT) and Remote Sensing Technology 249 14.3.1 Methodology and Development of ICT 250 14.4 Strong Intelligence From UAV to the Agricultural Sector 252 14.4.1 Latest Agricultural Drone History 252 14.4.2 The Challenges 254 14.4.3 SAP’s Next Wave of Drone Technologies 254 14.4.4 SAP Connected Agriculture 256 14.4.5 Cases of Real-World Use 257 14.4.5.1 Crop Surveying 257 14.4.5.2 Capture the Plantation 258 14.4.5.3 Image Processing 258 14.4.5.4 Working to Create GeoTiles and an Image Pyramid 259 14.5 Drones-Based Sensor Platforms 260 14.5.1 Context and Challenges 260 14.5.2 Stakeholder and End Consumer Benefits 261 14.5.3 The Technology 262 14.5.3.1 Provisions of the Unmanned Aerial Vehicles 262 14.6 Jobs of Space Technology in Crop Insurance 263 14.7 The Institutionalization of Drone Imaging Technologies in Agriculture for Disaster Managing Risk 267 14.7.1 A Modern Working 267 14.7.2 Discovering Drone Mapping Technology 268 14.7.3 From Lowland to Uplands, Drone Mapping Technology 269 14.7.4 Institutionalization of Drone Monitoring Systems and Farming Capability 269 14.8 Usage of Internet of Things in Agriculture and Use of Unmanned Aerial Vehicles 270 14.8.1 System and Application Based on UAV-WSN 270 14.8.2 Using a Complex Comprehensive System 271 14.8.3 Benefits Assessment of Conventional System and the UAV-Based System 271 14.8.3.1 Merit 272 14.8.3.2 Saving Expenses 272 14.8.3.3 Traditional Agriculture 273 14.8.3.4 UAV-WSN System-Based Agriculture 273 14.9 Conclusion 273 References 273 15 IoT-Based UAV Platform Revolutionized in Smart Healthcare 277Umesh Kumar Gera, Dilip Kumar Saini, Preeti Singh and Dhirendra Siddharth 15.1 Introduction 278 15.2 IoT-Based UAV Platform for Emergency Services 279 15.3 Healthcare Internet of Things: Technologies, Advantages 281 15.3.1 Advantage 281 15.3.1.1 Concurrent Surveillance and Tracking 281 15.3.1.2 From End-To-End Networking and Availability 282 15.3.1.3 Information and Review Assortment 282 15.3.1.4 Warnings and Recording 282 15.3.1.5 Wellbeing Remote Assistance 283 15.3.1.6 Research 283 15.3.2 Complications 283 15.3.2.1 Privacy and Data Security 283 15.3.2.2 Integration: Various Protocols and Services 284 15.3.2.3 Overload and Accuracy of Data 284 15.3.2.4 Expenditure 284 15.4 Healthcare’s IoT Applications: Surgical and Medical Applications of Drones 285 15.4.1 Hearables 285 15.4.2 Ingestible Sensors 285 15.4.3 Moodables 285 15.4.4 Technology of Computer Vision 286 15.4.5 Charting for Healthcare 286 15.5 Drones That Will Revolutionize Healthcare 286 15.5.1 Integrated Enhancement in Efficiency 286 15.5.2 Offering Personalized Healthcare 287 15.5.3 The Big Data Manipulation 287 15.5.4 Safety and Privacy Optimization 287 15.5.5 Enabling M2M Communication 288 15.6 Healthcare Revolutionizing Drones 288 15.6.1 Google Drones 288 15.6.2 Healthcare Integrated Rescue Operations (HiRO) 289 15.6.3 EHang 289 15.6.4 TU Delft 289 15.6.5 Project Wing 289 15.6.6 Flirtey 289 15.6.7 Seattle’s VillageReach 290 15.6.8 ZipLine 290 15.7 Conclusion 290 References 290 Index 295

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Book SynopsisTable of ContentsPreface xiii 1 A Study on Various Machine Learning Algorithms and Their Role in Agriculture 1Kalpana Rangra and Amitava Choudhury 1.1 Introduction 1 1.2 Conclusions 9 2 Smart Farming Using Machine Learning and IoT 13Alo Sen, Rahul Roy and Satya Ranjan Dash 2.1 Introduction 14 2.2 Related Work 15 2.3 Problem Identification 22 2.4 Objective Behind the Integrated Agro-IoT System 23 2.5 Proposed Prototype of the Integrated Agro-IoT System 23 2.6 Hardware Component Requirement for the Integrated Agro-IoT System 26 2.7 Comparative Study Between Raspberry Pi vs Beaglebone Black 30 2.8 Conclusions 31 2.9 Future Work 32 3 Agricultural Informatics vis-à-vis Internet of Things (IoT): The Scenario, Applications and Academic Aspects--International Trend & Indian Possibilities 35P.K. Paul 3.1 Introduction 36 3.2 Objectives 36 3.3 Methods 37 3.4 Agricultural Informatics: An Account 37 3.5 Agricultural Informatics & Technological Components: Basics & Emergence 40 3.6 IoT: Basics and Characteristics 41 3.7 IoT: The Applications & Agriculture Areas 43 3.8 Agricultural Informatics & IoT: The Scenario 45 3.9 IoT in Agriculture: Requirement, Issues & Challenges 49 3.10 Development, Economy and Growth: Agricultural Informatics Context 50 3.11 Academic Availability and Potentiality of IoT in Agricultural Informatics: International Scenario & Indian Possibilities 51 3.12 Suggestions 60 3.13 Conclusion 60 4 Application of Agricultural Drones and IoT to Understand Food Supply Chain During Post COVID-19 67Pushan Kumar Dutta and Susanta Mitra 4.1 Introduction 68 4.2 Related Work 69 4.3 Smart Production With the Introduction of Drones and IoT 72 4.4 Agricultural Drones 75 4.5 IoT Acts as a Backbone in Addressing COVID-19 Problems in Agriculture 77 4.6 Conclusion 81 5 IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review 89Parijata Majumdar and Sanjoy Mitra 5.1 Introduction 90 5.2 Machine Learning (ML)-Based IoT Solution 90 5.3 Motivation of the Work 91 5.4 Literature Review of IoT-Based Weather and Irrigation Monitoring for Precision Agriculture 91 5.5 Literature Review of Machine Learning-Based Weather and Irrigation Monitoring for Precision Agriculture 92 5.6 Challenges 112 5.7 Conclusion and Future Work 113 6 Deep Neural Network-Based Multi-Class Image Classification for Plant Diseases 117Alok Negi, Krishan Kumar and Prachi Chauhan 6.1 Introduction 117 6.2 Related Work 119 6.3 Proposed Work 121 6.4 Results and Evaluation 124 6.5 Conclusion 127 7 Deep Residual Neural Network for Plant Seedling Image Classification 131Prachi Chauhan, Hardwari Lal Mandoria and Alok Negi 7.1 Introduction 131 7.2 Related Work 136 7.3 Proposed Work 139 7.4 Result and Evaluation 142 7.5 Conclusion 144 8 Development of IoT-Based Smart Security and Monitoring Devices for Agriculture 147Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar 8.1 Introduction 148 8.2 Background & Related Works 150 8.3 Proposed Model 155 8.4 Methodology 160 8.5 Performance Analysis 165 8.6 Future Research Direction 166 8.7 Conclusion 167 9 An Integrated Application of IoT-Based WSN in the Field of Indian Agriculture System Using Hybrid Optimization Technique and Machine Learning 171Avishek Banerjee, Arnab Mitra and Arindam Biswas 9.1 Introduction 172 9.2 Literature Review 175 9.3 Proposed Hybrid Algorithms (GA-MWPSO) 177 9.4 Reliability Optimization and Coverage Optimization Model 179 9.5 Problem Description 181 9.6 Numerical Examples, Results and Discussion 182 9.7 Conclusion 183 10 Decryption and Design of a Multicopter Unmanned Aerial Vehicle (UAV) for Heavy Lift Agricultural Operations 189Raghuvirsinh Pravinsinh Parmar 10.1 Introduction 190 10.2 History of Multicopter UAVs 192 10.3 Basic Components of Multicopter UAV 193 10.4 Working and Control Mechanism of Multicopter UAV 207 10.5 Design Calculations and Selection of Components 210 10.6 Conclusion 218 11 IoT-Enabled Agricultural System Application, Challenges and Security Issues 223Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar 11.1 Introduction 224 11.2 Background & Related Works 226 11.3 Challenges to Implement IoT-Enabled Systems 232 11.4 Security Issues and Measures 240 11.5 Future Research Direction 243 11.6 Conclusion 244 12 Plane Region Step Farming, Animal and Pest Attack Control Using Internet of Things 249Sahadev Roy, Kaushal Mukherjee and Arindam Biswas 12.1 Introduction 250 12.2 Proposed Work 254 12.3 Irrigation Methodology 257 12.4 Sensor Connection Using Internet of Things 259 12.5 Placement of Sensor in the Field 263 12.6 Conclusion 267 References 268 Index 271

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Book SynopsisADVANCED HEALTHCARE SYSTEMS This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists. The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployedTable of ContentsPreface xvii 1 Internet of Medical Things—State-of-the-Art 1Kishor Joshi and Ruchi Mehrotra 1.1 Introduction 2 1.2 Historical Evolution of IoT to IoMT 2 1.2.1 IoT and IoMT—Market Size 4 1.3 Smart Wearable Technology 4 1.3.1 Consumer Fitness Smart Wearables 4 1.3.2 Clinical-Grade Wearables 5 1.4 Smart Pills 7 1.5 Reduction of Hospital-Acquired Infections 8 1.5.1 Navigation Apps for Hospitals 8 1.6 In-Home Segment 8 1.7 Community Segment 9 1.8 Telehealth and Remote Patient Monitoring 9 1.9 IoMT in Healthcare Logistics and Asset Management 12 1.10 IoMT Use in Monitoring During COVID-19 13 1.11 Conclusion 14 References 15 2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 21Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma 2.1 Introduction 22 2.2 Related Works 23 2.3 Architecture 25 2.3.1 Device Layer 25 2.3.2 Fog Layer 26 2.3.3 Cloud Layer 26 2.4 Issues and Challenges 26 2.5 Conclusion 29 References 30 3 Study of Thyroid Disease Using Machine Learning 33Shanu Verma, Rashmi Popli and Harish Kumar 3.1 Introduction 34 3.2 Related Works 34 3.3 Thyroid Functioning 35 3.4 Category of Thyroid Cancer 36 3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 37 3.5.1 Decision Tree Algorithm 38 3.5.2 Support Vector Machines 39 3.5.3 Random Forest 39 3.5.4 Logistic Regression 39 3.5.5 Naïve Bayes 40 3.6 Conclusion 41 References 41 4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 43Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi 4.1 Introduction 44 4.1.1 Introduction to IoT 44 4.1.2 Introduction to Vulnerability, Attack, and Threat 45 4.2 IoT in Healthcare 46 4.2.1 Confidentiality 46 4.2.2 Integrity 46 4.2.3 Authorization 46 4.2.4 Availability 47 4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 48 4.4 Conclusion 54 References 54 5 Methods of Lung Segmentation Based on CT Images 59Amit Verma and Thipendra P. Singh 5.1 Introduction 59 5.2 Semi-Automated Algorithm for Lung Segmentation 60 5.2.1 Algorithm for Tracking to Lung Edge 60 5.2.2 Outlining the Region of Interest in CT Images 62 5.2.2.1 Locating the Region of Interest 62 5.2.2.2 Seed Pixels and Searching Outline 62 5.3 Automated Method for Lung Segmentation 63 5.3.1 Knowledge-Based Automatic Model for Segmentation 63 5.3.2 Automatic Method for Segmenting the Lung CT Image 64 5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 64 5.5 Conclusion 65 References 65 6 Handling Unbalanced Data in Clinical Images 69Amit Verma 6.1 Introduction 70 6.2 Handling Imbalance Data 71 6.2.1 Cluster-Based Under-Sampling Technique 72 6.2.2 Bootstrap Aggregation (Bagging) 75 6.3 Conclusion 76 References 76 7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer 81Ishita Banerjee and Madhumathy P. 7.1 Introduction 82 7.2 Literature Survey 84 7.3 Procedure 86 7.4 Results 93 7.5 Conclusion 97 References 97 8 Smart IoT Devices for the Elderly and People with Disabilities 101K. N. D. Saile and Kolisetti Navatha 8.1 Introduction 101 8.2 Need for IoT Devices 102 8.3 Where Are the IoT Devices Used? 103 8.3.1 Home Automation 103 8.3.2 Smart Appliances 104 8.3.3 Healthcare 104 8.4 Devices in Home Automation 104 8.4.1 Automatic Lights Control 104 8.4.2 Automated Home Safety and Security 104 8.5 Smart Appliances 105 8.5.1 Smart Oven 105 8.5.2 Smart Assistant 105 8.5.3 Smart Washers and Dryers 106 8.5.4 Smart Coffee Machines 106 8.5.5 Smart Refrigerator 106 8.6 Healthcare 106 8.6.1 Smart Watches 107 8.6.2 Smart Thermometer 107 8.6.3 Smart Blood Pressure Monitor 107 8.6.4 Smart Glucose Monitors 107 8.6.5 Smart Insulin Pump 108 8.6.6 Smart Wearable Asthma Monitor 108 8.6.7 Assisted Vision Smart Glasses 109 8.6.8 Finger Reader 109 8.6.9 Braille Smart Watch 109 8.6.10 Smart Wand 109 8.6.11 Taptilo Braille Device 110 8.6.12 Smart Hearing Aid 110 8.6.13 E-Alarm 110 8.6.14 Spoon Feeding Robot 110 8.6.15 Automated Wheel Chair 110 8.7 Conclusion 112 References 112 9 IoT-Based Health Monitoring and Tracking System for Soldiers 115Kavitha N. and Madhumathy P. 9.1 Introduction 116 9.2 Literature Survey 117 9.3 System Requirements 118 9.3.1 Software Requirement Specification 119 9.3.2 Functional Requirements 119 9.4 System Design 119 9.4.1 Features 121 9.4.1.1 On-Chip Flash Memory 122 9.4.1.2 On-Chip Static RAM 122 9.4.2 Pin Control Block 122 9.4.3 UARTs 123 9.4.3.1 Features 123 9.4.4 System Control 123 9.4.4.1 Crystal Oscillator 123 9.4.4.2 Phase-Locked Loop 124 9.4.4.3 Reset and Wake-Up Timer 124 9.4.4.4 Brown Out Detector 125 9.4.4.5 Code Security 125 9.4.4.6 External Interrupt Inputs 125 9.4.4.7 Memory Mapping Control 125 9.4.4.8 Power Control 126 9.4.5 Real Monitor 126 9.4.5.1 GPS Module 126 9.4.6 Temperature Sensor 127 9.4.7 Power Supply 128 9.4.8 Regulator 128 9.4.9 LCD 128 9.4.10 Heart Rate Sensor 129 9.5 Implementation 129 9.5.1 Algorithm 130 9.5.2 Hardware Implementation 130 9.5.3 Software Implementation 131 9.6 Results and Discussions 133 9.6.1 Heart Rate 133 9.6.2 Temperature Sensor 135 9.6.3 Panic Button 135 9.6.4 GPS Receiver 135 9.7 Conclusion 136 References 136 10 Cloud-IoT Secured Prediction System for Processing and Analysis of Healthcare Data Using Machine Learning Techniques 137G. K. Kamalam and S. Anitha 10.1 Introduction 138 10.2 Literature Survey 139 10.3 Medical Data Classification 141 10.3.1 Structured Data 142 10.3.2 Semi-Structured Data 142 10.4 Data Analysis 142 10.4.1 Descriptive Analysis 142 10.4.2 Diagnostic Analysis 143 10.4.3 Predictive Analysis 143 10.4.4 Prescriptive Analysis 143 10.5 ML Methods Used in Healthcare 144 10.5.1 Supervised Learning Technique 144 10.5.2 Unsupervised Learning 145 10.5.3 Semi-Supervised Learning 145 10.5.4 Reinforcement Learning 145 10.6 Probability Distributions 145 10.6.1 Discrete Probability Distributions 146 10.6.1.1 Bernoulli Distribution 146 10.6.1.2 Uniform Distribution 147 10.6.1.3 Binomial Distribution 147 10.6.1.4 Normal Distribution 148 10.6.1.5 Poisson Distribution 148 10.6.1.6 Exponential Distribution 149 10.7 Evaluation Metrics 150 10.7.1 Classification Accuracy 150 10.7.2 Confusion Matrix 150 10.7.3 Logarithmic Loss 151 10.7.4 Receiver Operating Characteristic Curve, or ROC Curve 152 10.7.5 Area Under Curve (AUC) 152 10.7.6 Precision 153 10.7.7 Recall 153 10.7.8 F1 Score 153 10.7.9 Mean Absolute Error 154 10.7.10 Mean Squared Error 154 10.7.11 Root Mean Squared Error 155 10.7.12 Root Mean Squared Logarithmic Error 155 10.7.13 R-Squared/Adjusted R-Squared 156 10.7.14 Adjusted R-Squared 156 10.8 Proposed Methodology 156 10.8.1 Neural Network 158 10.8.2 Triangular Membership Function 158 10.8.3 Data Collection 159 10.8.4 Secured Data Storage 159 10.8.5 Data Retrieval and Merging 161 10.8.6 Data Aggregation 162 10.8.7 Data Partition 162 10.8.8 Fuzzy Rules for Prediction of Heart Disease 163 10.8.9 Fuzzy Rules for Prediction of Diabetes 164 10.8.10 Disease Prediction With Severity and Diagnosis 165 10.9 Experimental Results 166 10.10 Conclusion 169 References 169 11 CloudIoT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues 173Junaid Latief Shah, Heena Farooq Bhat and Asif Iqbal Khan 11.1 Introduction 174 11.2 Background Elements 180 11.2.1 Security Comparison Between Traditional and IoT Networks 185 11.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 187 11.3.1 Security Protocols 187 11.3.2 Enabling Technologies 188 11.4 CloudIoT Health System Framework 191 11.4.1 Data Perception/Acquisition 192 11.4.2 Data Transmission/Communication 193 11.4.3 Cloud Storage and Warehouse 194 11.4.4 Data Flow in Healthcare Architecture - A Conceptual Framework 194 11.4.5 Design Considerations 197 11.5 Security Challenges and Vulnerabilities 199 11.5.1 Security Characteristics and Objectives 200 11.5.1.1 Confidentiality 202 11.5.1.2 Integrity 202 11.5.1.3 Availability 202 11.5.1.4 Identification and Authentication 202 11.5.1.5 Privacy 203 11.5.1.6 Light Weight Solutions 203 11.5.1.7 Heterogeneity 203 11.5.1.8 Policies 203 11.5.2 Security Vulnerabilities 203 11.5.2.1 IoT Threats and Vulnerabilities 205 11.5.2.2 Cloud-Based Threats 208 11.6 Security Countermeasures and Considerations 214 11.6.1 Security Countermeasures 214 11.6.1.1 Security Awareness and Survey 214 11.6.1.2 Security Architecture and Framework 215 11.6.1.3 Key Management 216 11.6.1.4 Authentication 217 11.6.1.5 Trust 218 11.6.1.6 Cryptography 219 11.6.1.7 Device Security 219 11.6.1.8 Identity Management 220 11.6.1.9 Risk-Based Security/Risk Assessment 220 11.6.1.10 Block Chain–Based Security 220 11.6.1.11 Automata-Based Security 220 11.6.2 Security Considerations 234 11.7 Open Research Issues and Security Challenges 237 11.7.1 Security Architecture 237 11.7.2 Resource Constraints 238 11.7.3 Heterogeneous Data and Devices 238 11.7.4 Protocol Interoperability 238 11.7.5 Trust Management and Governance 239 11.7.6 Fault Tolerance 239 11.7.7 Next-Generation 5G Protocol 240 11.8 Discussion and Analysis 240 11.9 Conclusion 241 References 242 12 A Novel Usage of Artificial Intelligence and Internet of Things in Remote-Based Healthcare Applications 255V. Arulkumar, D. Mansoor Hussain, S. Sridhar and P. Vivekanandan 12.1 Introduction Machine Learning 256 12.2 Importance of Machine Learning 256 12.2.1 ML vs. Classical Algorithms 258 12.2.2 Learning Supervised 259 12.2.3 Unsupervised Learning 261 12.2.4 Network for Neuralism 263 12.2.4.1 Definition of the Neural Network 263 12.2.4.2 Neural Network Elements 263 12.3 Procedure 265 12.3.1 Dataset and Seizure Identification 265 12.3.2 System 265 12.4 Feature Extraction 266 12.5 Experimental Methods 266 12.5.1 Stepwise Feature Optimization 266 12.5.2 Post-Classification Validation 268 12.5.3 Fusion of Classification Methods 268 12.6 Experiments 269 12.7 Framework for EEG Signal Classification 269 12.8 Detection of the Preictal State 270 12.9 Determination of the Seizure Prediction Horizon 271 12.10 Dynamic Classification Over Time 272 12.11 Conclusion 273 References 273 13 Use of Machine Learning in Healthcare 275V. Lakshman Narayana, R. S. M. Lakshmi Patibandla, B. Tarakeswara Rao and Arepalli Peda Gopi 13.1 Introduction 276 13.2 Uses of Machine Learning in Pharma and Medicine 276 13.2.1 Distinguish Illnesses and Examination 277 13.2.2 Drug Discovery and Manufacturing 277 13.2.3 Scientific Imaging Analysis 278 13.2.4 Twisted Therapy 278 13.2.5 AI to Know-Based Social Change 278 13.2.6 Perception Wellness Realisms 279 13.2.7 Logical Preliminary and Exploration 279 13.2.8 Publicly Supported Perceptions Collection 279 13.2.9 Better Radiotherapy 280 13.2.10 Incidence Forecast 280 13.3 The Ongoing Preferences of ML in Human Services 281 13.4 The Morals of the Use of Calculations in Medicinal Services 284 13.5 Opportunities in Healthcare Quality Improvement 288 13.5.1 Variation in Care 288 13.5.2 Inappropriate Care 289 13.5.3 Prevents Care–Associated Injurious and Death for Carefrontation 289 13.5.4 The Fact That People Are Unable to do What They Know Works 289 13.5.5 A Waste 290 13.6 A Team-Based Care Approach Reduces Waste 290 13.7 Conclusion 291 References 292 14 Methods of MRI Brain Tumor Segmentation 295Amit Verma 14.1 Introduction 295 14.2 Generative and Descriptive Models 296 14.2.1 Region-Based Segmentation 300 14.2.2 Generative Model With Weighted Aggregation 300 14.3 Conclusion 302 References 303 15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network–Based Model 305Varun Sapra and Luxmi Sapra 15.1 Introduction 306 15.2 Data Set 307 15.2.1 Data Insights 308 15.3 Feature Engineering 310 15.4 Framework for Early Detection of Disease 312 15.4.1 Deep Neural Network 313 15.5 Result 314 15.6 Conclusion 315 References 315 16 A Comprehensive Analysis on Masked Face Detection Algorithms 319Pranjali Singh, Amitesh Garg and Amritpal Singh 16.1 Introduction 320 16.2 Literature Review 321 16.3 Implementation Approach 325 16.3.1 Feature Extraction 325 16.3.2 Image Processing 325 16.3.3 Image Acquisition 325 16.3.4 Classification 325 16.3.5 MobileNetV2 326 16.3.6 Deep Learning Architecture 326 16.3.7 LeNet-5, AlexNet, and ResNet-50 326 16.3.8 Data Collection 326 16.3.9 Development of Model 327 16.3.10 Training of Model 328 16.3.11 Model Testing 328 16.4 Observation and Analysis 328 16.4.1 CNN Algorithm 328 16.4.2 SSDNETV2 Algorithm 330 16.4.3 SVM 331 16.5 Conclusion 332 References 333 17 IoT-Based Automated Healthcare System 335Darpan Anand and Aashish Kumar 17.1 Introduction 335 17.1.1 Software-Defined Network 336 17.1.2 Network Function Virtualization 337 17.1.3 Sensor Used in IoT Devices 338 17.2 SDN-Based IoT Framework 341 17.3 Literature Survey 343 17.4 Architecture of SDN-IoT for Healthcare System 344 17.5 Challenges 345 17.6 Conclusion 347 References 347 Index 351

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Book SynopsisCOGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention. As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. This book tackles all these issues and provides insight into these diversifieTable of ContentsPreface xv 1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta 1.1 Introduction 2 1.2 Cognitive Science 3 1.3 Gap Between Classical Theory of Cognition 4 1.4 Cognitive Computing’s Evolution 6 1.5 The Coming Era of Cognitive Computing 7 1.6 Cognitive Computing Architecture 9 1.6.1 The Internet-of-Things and Cognitive Computing 10 1.6.2 Big Data and Cognitive Computing 11 1.6.3 Cognitive Computing and Cloud Computing 13 1.7 Enabling Technologies in Cognitive Computing 13 1.7.1 Reinforcement Learning and Cognitive Computing 13 1.7.2 Cognitive Computing with Deep Learning 15 1.7.2.1 Relational Technique and Perceptual Technique 15 1.7.2.2 Cognitive Computing and Image Understanding 16 1.8 Intelligent Systems in Healthcare 17 1.8.1 Intelligent Cognitive System in Healthcare (Why and How) 20 1.9 The Cognitive Challenge 32 1.9.1 Case Study: Patient Evacuation 32 1.9.2 Case Study: Anesthesiology 32 1.10 Conclusion 34 References 35 2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano 2.1 Introduction 42 2.2 Literature Concept 44 2.2.1 Cognitive Computing Concept 44 2.2.2 Neural Networks Concepts 47 2.2.3 Convolutional Neural Network 49 2.2.4 Deep Learning 52 2.3 Materials and Methods (Metaheuristic Algorithm Proposal) 55 2.4 Case Study and Discussion 57 2.5 Conclusions with Future Research Scopes 60 References 61 3 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran 3.1 Introduction 68 3.2 Literature Review 70 3.2.1 Role of Cognitive Computing in Healthcare Applications 70 3.2.2 Research Problem Study by IBM 73 3.2.3 Purpose of Big Data in Healthcare 74 3.2.4 Convergence of Big Data with Cognitive Computing 74 3.2.4.1 Smart Healthcare 74 3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 75 3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 76 3.3.1 EEG Pathology Diagnoses 76 3.3.2 Cognitive–Big Data-Based Smart Healthcare 77 3.3.3 System Architecture 79 3.3.4 Detection and Classification of Pathology 80 3.3.4.1 EEG Preprocessing and Illustration 80 3.3.4.2 CNN Model 80 3.3.5 Case Study 81 3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 83 3.4.1 Cloud Computing with Big Data in Healthcare 86 3.4.2 Heart Diseases 87 3.4.3 Healthcare Big Data Techniques 88 3.4.3.1 Rule Set Classifiers 88 3.4.3.2 Neuro Fuzzy Classifiers 89 3.4.3.3 Experimental Results 91 3.5 Conclusion 92 References 93 4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh 4.1 Introduction 98 4.2 The Role of Technology in an Aging Society 99 4.3 Literature Survey 100 4.4 Health Monitoring 101 4.5 Nutrition Monitoring 105 4.6 Stress-Log: An IoT-Based Smart Monitoring System 106 4.7 Active Aging 108 4.8 Localization 108 4.9 Navigation Care 111 4.10 Fall Monitoring 113 4.10.1 Fall Detection System Architecture 114 4.10.2 Wearable Device 114 4.10.3 Wireless Communication Network 114 4.10.4 Smart IoT Gateway 115 4.10.5 Interoperability 115 4.10.6 Transformation of Data 115 4.10.7 Analyzer for Big Data 115 4.11 Conclusion 115 References 116 5 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar 5.1 Introduction 122 5.2 Bond Between Big Data and Cognitive Computing 124 5.3 Need for Cognitive Computing in Healthcare 126 5.4 Conceptual Model Linking Big Data and Cognitive Computing 128 5.4.1 Significance of Big Data 128 5.4.2 The Need for Cognitive Computing 129 5.4.3 The Association Between the Big Data and Cognitive Computing 130 5.4.4 The Advent of Cognition in Healthcare 132 5.5 IBM’s Watson and Cognitive Computing 133 5.5.1 Industrial Revolution with Watson 134 5.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 135 5.6 Future Directions 137 5.6.1 Retail 138 5.6.2 Research 139 5.6.3 Travel 139 5.6.4 Security and Threat Detection 139 5.6.5 Cognitive Training Tools 140 5.7 Conclusion 141 References 141 6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano 6.1 Introduction 146 6.2 Literature Concept 148 6.2.1 Cognitive Computing Concept 148 6.2.1.1 Application Potential 151 6.2.2 Cognitive Computing in Healthcare 153 6.2.3 Deep Learning in Healthcare 157 6.2.4 Natural Language Processing in Healthcare 160 6.3 Discussion 162 6.4 Trends 163 6.5 Conclusions 164 References 165 7 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa 7.1 Introduction 170 7.2 Literature Survey 175 7.3 Two-Factor Authentication 177 7.3.1 Novel Features of Two-Factor Authentication 178 7.3.2 Two-Factor Authentication Sorgen 178 7.3.3 Two-Factor Security Libraries 179 7.3.4 Challenges for Fitness Concern 180 7.4 Proposed Methodology 181 7.5 Medical Treatment and the Preservation of Records 186 7.5.1 Remote Method of Control 187 7.5.2 Enabling Healthcare System Technology 187 7.6 Conclusion 189 References 190 8 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi 8.1 An Overview of Healthcare Systems 198 8.2 Need of Healthcare Systems 198 8.3 Basic Principle of Healthcare Systems 199 8.4 Design and Recommended Structure of Healthcare Systems 199 8.4.1 Healthcare System Designs on the Basis of these Parameters 200 8.4.2 Details of Healthcare Organizational Structure 201 8.5 Various Challenges in Conventional Existing Healthcare System 202 8.6 Health Informatics 202 8.7 Information Technology Use in Healthcare Systems 203 8.8 Details of Various Information Technology Application Use in Healthcare Systems 203 8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 204 8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 205 8.11 Healthcare Data Analytics 206 8.12 Healthcare as a Concept 206 8.13 Healthcare’s Key Technologies 207 8.14 The Present State of Smart Healthcare Application 207 8.15 Data Analytics with Machine Learning Use in Healthcare Systems 208 8.16 Benefit of Data Analytics in Healthcare System 210 8.17 Data Analysis and Visualization: COVID-19 Case Study in India 210 8.18 Bioinformatics Data Analytics 222 8.18.1 Notion of Bioinformatics 222 8.18.2 Bioinformatics Data Challenges 222 8.18.3 Sequence Analysis 222 8.18.4 Applications 223 8.18.5 COVID-19: A Bioinformatics Approach 224 8.19 Conclusion 224 References 225 9 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha 9.1 Introduction 230 9.1.1 Parkinson’s Disease 230 9.1.2 Spect Scan 231 9.2 Literature Survey 232 9.3 Methods and Materials 233 9.3.1 Database Details 233 9.3.2 Procedure 234 9.3.3 Pre-Processing Done by PPMI 235 9.3.4 Image Analysis and Features Extraction 235 9.3.4.1 Image Slicing 235 9.3.4.2 Intensity Normalization 237 9.3.4.3 Image Segmentation 239 9.3.4.4 Shape Features Extraction 240 9.3.4.5 SBR Features 241 9.3.4.6 Feature Set Analysis 242 9.3.4.7 Surface Fitting 242 9.3.5 Classification Modeling 243 9.3.6 Feature Importance Estimation 246 9.3.6.1 Need for Analysis of Important Features 246 9.3.6.2 Random Forest 247 9.4 Results and Discussion 248 9.4.1 Segmentation 248 9.4.2 Shape Analysis 249 9.4.3 Classification 249 9.5 Conclusion 252 References 253 10 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta 10.1 Introduction 258 10.2 Need for Big Data Analytics 260 10.3 Characteristics of Big Data 264 10.3.1 Volume 264 10.3.2 Velocity 265 10.3.3 Variety 265 10.3.4 Veracity 265 10.3.5 Value 265 10.3.6 Validity 265 10.3.7 Variability 266 10.3.8 Viscosity 266 10.3.9 Virality 266 10.3.10 Visualization 266 10.4 Big Data Analysis in Disease Treatment and Management 267 10.4.1 For Diabetes 267 10.4.2 For Heart Disease 268 10.4.3 For Chronic Disease 270 10.4.4 For Neurological Disease 271 10.4.5 For Personalized Medicine 271 10.5 Big Data: Databases and Platforms in Healthcare 279 10.6 Importance of Big Data in Healthcare 285 10.6.1 Evidence-Based Care 285 10.6.2 Reduced Cost of Healthcare 285 10.6.3 Increases the Participation of Patients in the Care Process 285 10.6.4 The Implication in Health Surveillance 285 10.6.5 Reduces Mortality Rate 285 10.6.6 Increase of Communication Between Patients and Healthcare Providers 286 10.6.7 Early Detection of Fraud and Security Threats in Health Management 286 10.6.8 Improvement in the Care Quality 286 10.7 Application of Big Data Analytics 286 10.7.1 Image Processing 286 10.7.2 Signal Processing 287 10.7.3 Genomics 288 10.7.4 Bioinformatics Applications 289 10.7.5 Clinical Informatics Application 291 10.8 Conclusion 293 References 294 11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi 11.1 Introduction 304 11.1.1 Glaucoma 304 11.2 Literature Survey 306 11.3 Methodology 309 11.3.1 Sclera Segmentation 310 11.3.1.1 Fully Convolutional Network 311 11.3.2 Pupil/Iris Ratio 313 11.3.2.1 Canny Edge Detection 314 11.3.2.2 Mean Redness Level (MRL) 315 11.3.2.3 Red Area Percentage (RAP) 316 11.4 Results and Discussion 317 11.4.1 Feature Extraction from Frontal Eye Images 318 11.4.1.1 Level of Mean Redness (MRL) 318 11.4.1.2 Percentage of Red Area (RAP) 318 11.4.2 Images of the Frontal Eye Pupil/Iris Ratio 318 11.4.2.1 Histogram Equalization 319 11.4.2.2 Morphological Reconstruction 319 11.4.2.3 Canny Edge Detection 319 11.4.2.4 Adaptive Thresholding 320 11.4.2.5 Circular Hough Transform 321 11.4.2.6 Classification 322 11.5 Conclusion and Future Work 324 References 325 12 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh 12.1 Introduction 328 12.2 Introduction to Big Data and Data Mining 328 12.3 Role of Sentimental Analysis in the Healthcare Sector 330 12.4 Case Study: Analyzing Mental Health 332 12.4.1 Problem Statement 332 12.4.2 Research Objectives 333 12.4.3 Methodology and Framework 333 12.4.3.1 Big 5 Personality Model 333 12.4.3.2 Openness to Explore 334 12.4.3.3 Methodology 335 12.4.3.4 Detailed Design Methodologies 340 12.4.3.5 Work Done Details as Required 341 12.5 Results and Discussion 343 12.6 Conclusion and Future 345 References 346 13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav 13.1 Introduction 350 13.2 Artificial Intelligence and Management of Chronic Diseases 351 13.3 Blockchain and Healthcare 354 13.3.1 Blockchain and Healthcare Management of Chronic Disease 355 13.4 Internet-of-Things and Healthcare Management of Chronic Disease 358 13.5 Conclusions 360 References 360 14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri 14.1 Introduction 367 14.2 Cognitive Computing Framework in Healthcare 371 14.3 Benefits of Using Cognitive Computing for Healthcare 372 14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 374 14.4.1 Using Cognitive Services for a Patient’s Healthcare Management 375 14.4.2 Using Cognitive Services for Healthcare Providers 376 14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 377 14.6 Future Directions for Extending Heathcare Services Using CATs 380 14.7 Addressing CAT Challenges in Healthcare as a General Framework 384 14.8 Conclusion 384 References 385 Index 391

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Book SynopsisARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY AND CLIMATE CHANGE Written and edited by a global team of experts in the field, this groundbreaking new volume presents the concepts and fundamentals of using artificial intelligence in renewable energy and climate change, while also covering the practical applications that can be utilized across multiple disciplines and industries, for the engineer, the student, and other professionals and scientists. Renewable energy and climate change are two of the most important and difficult issues facing the world today. The state of the art in these areas is changing rapidly, with new techniques and theories coming online seemingly every day. It is important for scientists, engineers, and other professionals working in these areas to stay abreast of developments, advances, and practical applications, and this volume is an outstanding reference and tool for this purpose. The paradigm in renewable energy and climatTable of ContentsPreface xv Section I: Renewable Energy 1 1 Artificial Intelligence for Sustainability: Opportunities and Challenges 3Amany Alshawi 1.1 Introduction 3 1.2 History of AI for Sustainability and Smart Energy Practices 4 1.3 Energy and Resources Scenarios on the Global Scale 5 1.4 Statistical Basis of AI in Sustainability Practices 6 1.4.1 General Statistics 6 1.4.2 Environmental Stress–Based Statistics 8 1.4.2.1 Climate Change 9 1.4.2.2 Biodiversity 10 1.4.2.3 Deforestation 10 1.4.2.4 Changes in Chemistry of Oceans 10 1.4.2.5 Nitrogen Cycle 10 1.4.2.6 Water Crisis 11 1.4.2.7 Air Pollution 11 1.5 Major Challenges Faced by AI in Sustainability 11 1.5.1 Concentration of Wealth 11 1.5.2 Talent-Related and Business-Related Challenges of AI 12 1.5.3 Dependence on Machine Learning 14 1.5.4 Cybersecurity Risks 15 1.5.5 Carbon Footprint of AI 16 1.5.6 Issues in Performance Measurement 16 1.6 Major Opportunities of AI in Sustainability 17 1.6.1 AI and Water-Related Hazards Management 17 1.6.2 AI and Smart Cities 18 1.6.3 AI and Climate Change 21 1.6.4 AI and Environmental Sustainability 23 1.6.5 Impacts of AI in Transportation 24 1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 25 1.6.7 Opportunities in the Energy Sector 26 1.7 Conclusion and Future Direction 26 References 27 2 Recent Applications of Machine Learning in Solar Energy Prediction 33N. Kapilan, R.P. Reddy and Vidhya P. 2.1 Introduction 34 2.2 Solar Energy 34 2.3 AI, ML and DL 36 2.4 Data Preprocessing Techniques 38 2.5 Solar Radiation Estimation 38 2.6 Solar Power Prediction 43 2.7 Challenges and Opportunities 45 2.8 Future Research Directions 46 2.9 Conclusion 46 Acknowledgement 47 References 47 3 Mathematical Analysis on Power Generation – Part I 53G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy 3.1 Introduction 54 3.2 Methodology for Derivations 55 3.3 Energy Discussions 59 3.4 Data Analysis 63 Acknowledgement 67 References 67 Supplementary 69 4 Mathematical Analysis on Power Generation – Part II 87G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy 4.1 Energy Analysis 88 4.2 Power Efficiency Method 89 4.3 Data Analysis 91 Acknowledgement 96 References 97 Supplementary - II 100 5 Sustainable Energy Materials 117G. Udhaya Sankar 5.1 Introduction 117 5.2 Different Methods 119 5.2.1 Co-Precipitation Method 119 5.2.2 Microwave-Assisted Solvothermal Method 120 5.2.3 Sol-Gel Method 120 5.3 X-R ay Diffraction Analysis 120 5.4 FTIR Analysis 122 5.5 Raman Analysis 124 5.6 UV Analysis 125 5.7 SEM Analysis 127 5.8 Energy Dispersive X-Ray Analysis 127 5.9 Thermoelectric Application 129 5.9.1 Thermal Conductivity 129 5.9.2 Electrical Conductivity 131 5.9.3 Seebeck Coefficient 131 5.9.4 Power Factor 132 5.9.5 Figure of Merit 133 5.10 Limitations and Future Direction 133 5.11 Conclusion 133 Acknowledgement 134 References 134 6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal 6.1 Introduction 137 6.1.1 Conventional MPPT Control Techniques 138 6.2 Other MPPT Control Methods 142 6.2.1 Proportional Integral Derivative Controllers 142 6.2.2 Fuzzy Logic Controller 144 6.2.2.1 Fuzzy Inference System 150 6.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 151 6.2.3 Artificial Neural Network 151 6.2.3.1 Biological Neural Networks 152 6.2.3.2 Architectures of Artificial Neural Networks 155 6.2.3.3 Training of Artificial Neural Networks 157 6.2.3.4 Radial Basis Function 158 6.2.4 Neuro-Fuzzy Inference Approach 158 6.2.4.1 Adaptive Neuro-Fuzzy Approach 161 6.2.4.2 Hybrid Training Algorithm 161 6.3 Conclusion 167 References 167 Section II: Climate Change 171 7 The Contribution of AI-Based Approaches in the Determination of CO2 Emission Gas Amounts of Vehicles, Determination of CO2 Emission Rates Yearly of Countries, Air Quality Measurement and Determination of Smart Electric Grids’ Stability 173Mesut Toğaçar 7.1 Introduction 174 7.2 Materials 177 7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 177 7.2.2 CO2 Emission of Vehicles 178 7.2.3 Countries’ CO2 Emission Amount 179 7.2.4 Stability Level in Electric Grids 179 7.3 Artificial Intelligence Approaches 181 7.3.1 Machine Learning Methods 182 7.3.1.1 Support Vector Machine 183 7.3.1.2 eXtreme Gradient Boosting (XG Boost) 184 7.3.1.3 Gradient Boost 185 7.3.1.4 Decision Tree 186 7.3.1.5 Random Forest 186 7.3.2 Deep Learning Methods 188 7.3.2.1 Convolutional Neural Networks 189 7.3.2.2 Long Short-Term Memory 191 7.3.2.3 Bi-Directional LSTM and CNN 192 7.3.2.4 Recurrent Neural Network 193 7.3.3 Activation Functions 195 7.3.3.1 Rectified Linear Unit 195 7.3.3.2 Softmax Function 196 7.4 Experimental Analysis 196 7.5 Discussion 210 7.6 Conclusion 211 Funding 212 Ethical Approval 212 Conflicts of Interest 212 References 212 8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217Sumit Sharma, J. Joshua Thomas and Pandian Vasant 8.1 Introduction 218 8.1.1 Indian Scenario of Renewable Energy 218 8.1.2 Solar Radiation at Earth 220 8.1.3 Solar Photovoltaic Technologies 220 8.1.3.1 Types of SPV Systems 221 8.1.3.2 Types of Solar Photovoltaic Cells 222 8.1.3.3 Effects of Temperature 223 8.1.3.4 Conversion Efficiency 223 8.1.4 Losses in PV Systems 224 8.1.5 Performance of Solar Power Plants 224 8.2 Literature Review 225 8.3 Experimental Setup 228 8.3.1 Selection of Site and Development of Experimental Facilities 229 8.3.2 Methodology 229 8.3.3 Experimental Instrumentation 230 8.3.3.1 Solar Photovoltaic Modules 230 8.3.3.2 PV Grid-Connected Inverter 232 8.3.3.3 Pyranometer 232 8.3.3.4 Digital Thermometer 234 8.3.3.5 Lightning Arrester 235 8.3.3.6 Data Acquisition System 236 8.3.4 Formula Used and Sample Calculations 236 8.3.5 Assumptions and Limitations 237 8.4 Results Discussion 238 8.4.1 Phases of Data Collection 238 8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 238 8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 238 8.4.2.2 Capacity Utilization Factor and Performance Ratio 241 8.4.2.3 Evaluation of MLR Model 242 8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 246 8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 246 8.4.3.2 Capacity Utilization Factor and Performance Ratio 246 8.4.3.3 Evaluation of MLR Model 246 8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 252 8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 252 8.4.4.2 Capacity Utilization Factor and Performance Ratio 255 8.4.4.3 Evaluation of MLR Model 256 8.4.5 Regression Analysis for the Whole Period 258 8.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 267 8.4.7 Regression Outputs Summary 268 8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 268 8.4.9 Losses Due to Dust Accumulation 270 8.4.10 Economic Analysis 270 8.5 Future Research Directions 271 8.6 Conclusion 271 References 272 9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher 9.1 Introduction 278 9.1.1 Benefits of the Use of Biogas as a Fuel in India 278 9.1.2 Biogas Generators in India 279 9.1.3 Biogas 279 9.1.3.1 Process of Biogas Production 280 9.2 Literature Review 281 9.2.1 Wastes and Environment 281 9.2.2 Economic and Environmental Considerations 283 9.2.3 Factor Affecting Yield and Production of Biogas 285 9.2.3.1 The Temperature 285 9.2.3.2 PH and Buffering Systems 287 9.2.3.3 C/N Ratio 287 9.2.3.4 Substrate Type 289 9.2.3.5 Retention Time 289 9.2.3.6 Total Solids 289 9.2.4 Advantages of Anaerobic Digestion to Society 290 9.2.4.1 Electricity Generation 290 9.2.4.2 Fertilizer Production 290 9.2.4.3 Pathogen Reduction 290 9.3 Methodology 290 9.3.1 Set Up of Compact Biogas Plant and Equipments 290 9.3.2 Assembling and Fabrication of Biogas Plant 292 9.3.3 Design and Technology of Compact Biogas Plant 294 9.3.4 Gas Quantity and Quality 295 9.3.5 Calculation of Gas Quantity in Gas Holder 295 9.4 Analysis of Compact Biogas Plant 299 9.4.1 Experiment Result 299 9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 299 9.4.1.2 Testing on Kitchen Waste 300 9.4.1.3 Testing on Fruits Waste 302 9.4.2 Comparison of Biogas by Different Substrate 304 9.4.3 Production of Biogas Per Day at Different Waste 304 9.4.4 Variation of PH Value 307 9.4.5 Variation of Average pH Value 307 9.4.6 Variation of Temperature 308 9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 309 9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 311 9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 313 9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 313 9.5.2 Calculation 316 9.5.3 Heat Balance Sheet 322 9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 326 9.5.5 Calculation 330 9.5.6 Heat Balance Sheet 335 9.6 General Comments 336 9.7 Conclusion 339 9.8 Future Scope 340 References 340 10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar Singh Abbreviations 346 10.1 Introduction 346 10.1.1 Global Scenario of Energy and Emissions 347 10.1.2 Diesel Engine Emissions 348 10.1.3 Mitigation of NOx and Particulate Matter 350 10.1.4 Low-Temperature Combustion Engine Fuels 350 10.2 Scope of the Current Article 351 10.3 HCCI Technology 352 10.3.1 Principle of HCCI 353 10.3.2 Performance and Emissions with HCCI 354 10.4 Partially Premixed Compression Ignition (PPCI) 354 10.5 Exhaust Gas Recirculation (EGR) 355 10.6 Reactivity Controlled Compression Ignition (RCCI) 356 10.7 LTC Through Fuel Additives 357 10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 358 10.8.1 Brake Thermal Efficiency (BTE) 359 10.8.2 Nitrogen Oxide (NOx) 359 10.8.3 Soot and Particulate Matter (PM) 360 10.9 Conclusion and Future Scope 361 Acknowledgement 361 References 361 11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich 11.1 Introduction 372 11.2 Materials and Methods 374 11.3 Results 379 11.4 Discussion 382 11.5 Conclusions 385 References 386 12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin 12.1 Introduction 390 12.2 Background 392 12.3 Main Focus of the Chapter 402 12.4 Solutions and Recommendations 417 Acknowledgements 417 References 418 13 Monitoring System Based Micro-Controller for Biogas Digester 423Ahmed Abdelouareth and Mohamed Tamali 13.1 Introduction 423 13.2 Related Work 424 13.3 Methods and Material 425 13.3.1 Identification of Needs 425 13.3.2 ADOLMS Software Setup 425 13.3.3 ADOLMS Sensors 426 13.3.4 ADOLMS Hardware Architecture 428 13.4 Results 430 13.5 Conclusion 432 Acknowledgements 433 References 433 14 Greenhouse Gas Statistics and Methods of Combating Climate Change 435Tatyana G. Krotova Introduction 435 Methodology 436 Findings 436 Conclusion 454 References 455 About the Editors 457 Index 459

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Book SynopsisINTELLIGENT SECURITY SYSTEMS Dramatically improve your cybersecurity using AI and machine learning In Intelligent Security Systems, distinguished professor and computer scientist Dr. Leon Reznik delivers an expert synthesis of artificial intelligence, machine learning and data science techniques, applied to computer security to assist readers in hardening their computer systems against threats. Emphasizing practical and actionable strategies that can be immediately implemented by industry professionals and computer device's owners, the author explains how to install and harden firewalls, intrusion detection systems, attack recognition tools, and malware protection systems. He also explains how to recognize and counter common hacking activities. This book bridges the gap between cybersecurity education and new data science programs, discussing how cutting-edge artificial intelligence and machine learning techniques can work for and against cybersecurity effTable of ContentsAcknowledgments ix Introduction xi 1 Computer Security with Artificial Intelligence, Machine Learning, and Data Science Combination: What? How? Why? And Why Now and Together? 1 1.1 The Current Security Landscape 1 1.2 Computer Security Basic Concepts 7 1.3 Sources of Security Threats 9 1.4 Attacks Against IoT and Wireless Sensor Networks 13 1.5 Introduction into Artificial Intelligence, Machine Learning, and Data Science 18 1.6 Fuzzy Logic and Systems 31 1.7 Machine Learning 35 1.8 Artificial Neural Networks (ANN) 43 1.9 Genetic Algorithms (GA) 50 1.10 Hybrid Intelligent Systems 51 Review Questions 52 Exercises 53 References 54 2 Firewall Design and Implementation: How to Configure Knowledge for the First Line of Defense? 57 2.1 Firewall Definition, History, and Functions: What Is It? And Where Does It Come From? 57 2.2 Firewall Operational Models or How Do They Work? 65 2.3 Basic Firewall Architectures or How Are They Built Up? 70 2.4 Process of Firewall Design, Implementation, and Maintenance or What Is the Right Way to Put All Things Together? 75 2.5 Firewall Policy Formalization with Rules or How Is the Knowledge Presented? 82 2.6 Firewalls Evaluation and Current Developments or How Are They Getting More and More Intelligent? 96 Review Questions 104 Exercises 106 References 107 3 Intrusion Detection Systems: What Do They Do Beyond the First Line of Defense? 109 3.1 Definition, Goals, and Primary Functions 109 3.2 IDS from a Historical Perspective 113 3.3 Typical IDS Architecture Topologies, Components, and Operational Ranges 116 3.4 IDS Types: Classification Approaches 121 3.5 IDS Performance Evaluation 131 3.6 Artificial Intelligence and Machine Learning Techniques in IDS Design 136 3.7 Intrusion Detection Challenges and Their Mitigation in IDS Design and Deployment 159 3.8 Intrusion Detection Tools 163 Review Questions 172 Exercises 174 References 175 4 Malware and Vulnerabilities Detection and Protection: What Are We Looking for and How? 177 4.1 Malware Definition, History, and Trends in Development 177 4.2 Malware Classification 182 4.3 Spam 214 4.4 Software Vulnerabilities 216 4.5 Principles of Malware Detection and Anti-malware Protection 219 4.6 Malware Detection Algorithms 229 4.7 Anti-malware Tools 237 Review Questions 240 Exercises 242 References 243 5 Hackers versus Normal Users: Who Is Our Enemy and How to Differentiate Them from Us? 247 5.1 Hacker’s Activities and Protection Against 247 5.2 Data Science Investigation of Ordinary Users’ Practice 273 5.3 User’s Authentication 288 5.4 User’s Anonymity, Attacks Against It, and Protection 301 Review Questions 309 Exercises 310 References 311 6 Adversarial Machine Learning: Who Is Machine Learning Working For? 315 6.1 Adversarial Machine Learning Definition 315 6.2 Adversarial Attack Taxonomy 316 6.3 Defense Strategies 320 6.4 Investigation of the Adversarial Attacks Influence on the Classifier Performance Use Case 322 6.5 Generative Adversarial Networks 327 Review Questions 333 Exercises 334 References 335 Index 337

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Book SynopsisCYBERSECURITY IN INTELLIGENT NETWORKING SYSTEMS Help protect your network system with this important reference work on cybersecurity Cybersecurity and privacy are critical to modern network systems. As various malicious threats have been launched that target critical online servicessuch as e-commerce, e-health, social networks, and other major cyber applicationsit has become more critical to protect important information from being accessed. Data-driven network intelligence is a crucial development in protecting the security of modern network systems and ensuring information privacy. Cybersecurity in Intelligent Networking Systems provides a background introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It offers a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing on encryption-based security protocol, this book also highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology. Cybersecurity in Intelligent Networking Systems readers will also find: Fundamentals in AI for cybersecurity, including artificial intelligence, machine learning, and security threats Latest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryption Key areas in adversarial machine learning, from both offense and defense perspectives Descriptions of network anomalies and cyber threats Background information on data-driven network intelligence for cybersecurity Robust and secure edge intelligence for network anomaly detection against cyber intrusions Detailed descriptions of the design of privacy-preserving security protocols Cybersecurity in Intelligent Networking Systems is an essential reference for all professional computer engineers and researchers in cybersecurity and artificial intelligence, as well as graduate students in these fields.Table of ContentsContents Preface xiii Acknowledgments xvii Acronyms xix 1 Cybersecurity in the Era of Artificial Intelligence 1 1.1 Artificial Intelligence for Cybersecurity . 2 1.1.1 Artificial Intelligence 2 1.1.2 Machine Learning 4 1.1.3 Data-Driven Workflow for Cybersecurity . 6 1.2 Key Areas and Challenges 7 1.2.1 Anomaly Detection . 8 1.2.2 Trustworthy Artificial Intelligence . 10 1.2.3 Privacy Preservation . 10 1.3 Toolbox to Build Secure and Intelligent Systems . 11 1.3.1 Machine Learning and Deep Learning . 12 1.3.2 Privacy-Preserving Machine Learning . 14 1.3.3 Adversarial Machine Learning . 15 1.4 Data Repositories for Cybersecurity Research . 16 1.4.1 NSL-KDD . 17 1.4.2 UNSW-NB15 . 17 v 1.4.3 EMBER 18 1.5 Summary 18 2 Cyber Threats and Gateway Defense 19 2.1 Cyber Threats . 19 2.1.1 Cyber Intrusions . 20 2.1.2 Distributed Denial of Services Attack . 22 2.1.3 Malware and Shellcode . 23 2.2 Gateway Defense Approaches 23 2.2.1 Network Access Control 24 2.2.2 Anomaly Isolation 24 2.2.3 Collaborative Learning . 24 2.2.4 Secure Local Data Learning 25 2.3 Emerging Data-Driven Methods for Gateway Defense 26 2.3.1 Semi-Supervised Learning for Intrusion Detection 26 2.3.2 Transfer Learning for Intrusion Detection 27 2.3.3 Federated Learning for Privacy Preservation . 28 2.3.4 Reinforcement Learning for Penetration Test 29 2.4 Case Study: Reinforcement Learning for Automated Post-Breach Penetration Test . 30 2.4.1 Literature Review 30 2.4.2 Research Idea 31 2.4.3 Training Agent using Deep Q-Learning 32 2.5 Summary 34 vi 3 Edge Computing and Secure Edge Intelligence 35 3.1 Edge Computing . 35 3.2 Key Advances in Edge Computing . 38 3.2.1 Security 38 3.2.2 Reliability . 41 3.2.3 Survivability . 42 3.3 Secure Edge Intelligence . 43 3.3.1 Background and Motivation 44 3.3.2 Design of Detection Module 45 3.3.3 Challenges against Poisoning Attacks . 48 3.4 Summary 49 4 Edge Intelligence for Intrusion Detection 51 4.1 Edge Cyberinfrastructure . 51 4.2 Edge AI Engine 53 4.2.1 Feature Engineering . 53 4.2.2 Model Learning . 54 4.2.3 Model Update 56 4.2.4 Predictive Analytics . 56 4.3 Threat Intelligence 57 4.4 Preliminary Study . 57 4.4.1 Dataset 57 4.4.2 Environment Setup . 59 4.4.3 Performance Evaluation . 59 vii 4.5 Summary 63 5 Robust Intrusion Detection 65 5.1 Preliminaries 65 5.1.1 Median Absolute Deviation . 65 5.1.2 Mahalanobis Distance 66 5.2 Robust Intrusion Detection . 67 5.2.1 Problem Formulation 67 5.2.2 Step 1: Robust Data Preprocessing 68 5.2.3 Step 2: Bagging for Labeled Anomalies 69 5.2.4 Step 3: One-Class SVM for Unlabeled Samples . 70 5.2.5 Step 4: Final Classifier . 74 5.3 Experiment and Evaluation . 76 5.3.1 Experiment Setup 76 5.3.2 Performance Evaluation . 81 5.4 Summary 92 6 Efficient Preprocessing Scheme for Anomaly Detection 93 6.1 Efficient Anomaly Detection . 93 6.1.1 Related Work . 95 6.1.2 Principal Component Analysis . 97 6.2 Efficient Preprocessing Scheme for Anomaly Detection . 98 6.2.1 Robust Preprocessing Scheme . 99 6.2.2 Real-Time Processing 103 viii 6.2.3 Discussions 103 6.3 Case Study . 104 6.3.1 Description of the Raw Data 105 6.3.2 Experiment 106 6.3.3 Results 108 6.4 Summary 109 7 Privacy Preservation in the Era of Big Data 111 7.1 Privacy Preservation Approaches 111 7.1.1 Anonymization 111 7.1.2 Differential Privacy . 112 7.1.3 Federated Learning . 114 7.1.4 Homomorphic Encryption 116 7.1.5 Secure Multi-Party Computation . 117 7.1.6 Discussions 118 7.2 Privacy-Preserving Anomaly Detection . 120 7.2.1 Literature Review 121 7.2.2 Preliminaries . 123 7.2.3 System Model and Security Model 124 7.3 Objectives and Workflow . 126 7.3.1 Objectives . 126 7.3.2 Workflow . 128 7.4 Predicate Encryption based Anomaly Detection . 129 7.4.1 Procedures 129 ix 7.4.2 Development of Predicate . 131 7.4.3 Deployment of Anomaly Detection 132 7.5 Case Study and Evaluation . 134 7.5.1 Overhead . 134 7.5.2 Detection . 136 7.6 Summary 137 8 Adversarial Examples: Challenges and Solutions 139 8.1 Adversarial Examples . 139 8.1.1 Problem Formulation in Machine Learning 140 8.1.2 Creation of Adversarial Examples . 141 8.1.3 Targeted and Non-Targeted Attacks . 141 8.1.4 Black-Box and White-Box Attacks 142 8.1.5 Defenses against Adversarial Examples 142 8.2 Adversarial Attacks in Security Applications 143 8.2.1 Malware 143 8.2.2 Cyber Intrusions . 143 8.3 Case Study: Improving Adversarial Attacks Against Malware Detectors 144 8.3.1 Background 144 8.3.2 Adversarial Attacks on Malware Detectors 145 8.3.3 MalConv Architecture 147 8.3.4 Research Idea 148 8.4 Case Study: A Metric for Machine Learning Vulnerability to Adversarial Examples . 149 8.4.1 Background 149 8.4.2 Research Idea 150 8.5 Case Study: Protecting Smart Speakers from Adversarial Voice Commands . 153 8.5.1 Background 153 8.5.2 Challenges 154 8.5.3 Directions and Tasks 155 8.6 Summary 157 xi

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