Artificial intelligence (AI) Books

Book SynopsisHuman-Robot Interaction: Safety, Standardization, and Benchmarking provides a comprehensive introduction to the new scenarios emerging where humans and robots interact in various environments and applications on a daily basis. The focus is on the current status and foreseeable implications of robot safety, approaching these issues from the standardization and benchmarking perspectives. Featuring contributions from leading experts, the book presents state-of-the-art research, and includes real-world applications and use cases. It explores the key leading sectorsârobotics, service robotics, and medical roboticsâand elaborates on the safety approaches that are being developed for effective human-robot interaction, including physical robot-human contacts, collaboration in task execution, workspace sharing, human-aware motion planning, and exploring the landscape of relevant standards and guidelines.Features Presenting aTable of Contents 1 The Role of Standardization in Technical Regulations André Pirlet 2 The intricate relationships between private standards and publicpolicymakingin the case of personal care robot. Who cares more? Eduard Fosch-Villaronga and Angelo Jr Golia 3 Standard Ontologies and HRI Sandro Rama Fiorini, Abdelghani Chibani, Tamas Haidegger, Joel Luis Carbonera, Craig Schlenoff, Jacek Malec, Edson Prestes, Paulo Gonçalves, S. Veera Ragavan, Howard Li, Hirenkumar Nakawala, Stephen Balakirsky, Sofiane Bouznad, Noauel Ayari, and Yacine Amirat 4 Robot Modularity and safety for Service Robots Hong Seong Park and Gurvinder Singh Virk 5 Human-robot shared workspace in aerospace factories Gilber Tang 6 Workspace sharing in mobile manipulation José Saenz 7 On rehabilitation robotics safety, benchmarking, standards Jan F. Veneman 8 A practical appraisal of ISO 13482 as a reference for an orphan robot category Paolo Barattini 9 Safety of Medical Robots, Regulation and Standards Kiyo Chinzei 10 The Other End of Human–Robot Interaction: Models for Safe and Efficient Tool–Tissue Interactions Arpad Takacs, Imre J. Rudas, Tamas Haidegger 11 Passive Bilateral Teleoperation with Safety Considerations Lorinc Marton 12 Human-Robot Interfaces in Autonomous Surgical Robots Paolo Fiorini and Riccardo Muradore

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Book SynopsisLand use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations.Features: Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques Provides analysis of land erosion and sedimenTable of ContentsIntroduction to Watershed Management. Characteristics of Watershed. Soil Erosion and Its Control. Water Harvesting. Water Quality Management in Watershed. Groundwater. Flood and Drought. Sediment Sampling and Transport. Runoff. Application of Artificial Intelligence for Prediction of Ground Water Fluctuation. Prediction of Flood Using Hybrid ANFIS-FFA Approaches in Barak River Basin. Prophecy of Sediment Load Using Hybrid AI Approaches at Various Gauge Station in Mahanadi River Basin, India. Scheming of Runoff Using Hybrid ANFIS for a Watershed: Western Odisha, India. Application of Hybrid Neural Network Techniques for Drought Forecasting.

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Book SynopsisThe eagerly anticipated updated resource on one of the most important areas of research and development: multi-agent systems Multi-agent systems allow many intelligent agents to interact with each other, and this field of study has advanced at a rapid pace since the publication of the first edition of this book, which was nearly a decade ago.Trade Review“Nevertheless, despite these minor issues, this book is highly recommended to all socio-economic agent-based modellers, beginners or otherwise. Wooldridge’s scope, rigor, and well-respected experience at the current coalface means there’s plenty in here of interest for old-timers, while beginners can skip some of the maths and more bleeding-edge theory and concentrate easily on the implementation without loosing much.” (Appl. Spatial Analysis, 2011) Table of ContentsPreface xiii Acknowledgements xxi Part I Setting the Scene 1 1 Introduction 3 1.1 The Vision Thing 6 1.2 Some Views of the Field 9 1.2.1 Agents as a paradigm for software engineering 9 1.2.2 Agents as a tool for understanding human societies 12 1.3 Frequently Asked Questions (FAQ) 12 Part II Intelligent Autonomous Agents 19 2 Intelligent Agents 21 2.1 Intelligent Agents 26 2.2 Agents and Objects 28 2.3 Agents and Expert Systems 30 2.4 Agents as Intentional Systems 31 2.5 Abstract Architectures for Intelligent Agents 34 2.6 How to Tell an Agent What to Do 38 3 Deductive Reasoning Agents 49 3.1 Agents as Theorem Provers 50 3.2 Agent-Oriented Programming 55 3.3 Concurrent MetateM 56 4 Practical Reasoning Agents 65 4.1 Practical Reasoning = Deliberation +Means–Ends Reasoning 65 4.2 Means–Ends Reasoning 69 4.3 Implementing a Practical Reasoning Agent 75 4.4 The Procedural Reasoning System 79 5 Reactive and Hybrid Agents 85 5.1 Reactive Agents 85 5.1.1 The subsumption architecture 86 5.1.2 PENGI 90 5.1.3 Situated automata 90 5.1.4 The agent network architecture 91 5.1.5 The limitations of reactive agents 92 5.2 Hybrid Agents 92 5.2.1 Touring Machines 94 5.2.2 InteRRaP 96 5.2.3 3T 98 5.2.4 Stanley 99 Part III Communication and Cooperation 105 6 Understanding Each Other 107 6.1 Ontology Fundamentals 108 6.1.1 Ontology building blocks 108 6.1.2 Anontology of ontologies 110 6.2 Ontology Languages 113 6.2.1 XML–adhoc ontologies 113 6.2.2 OWL–The web ontology language 114 6.2.3 KIF–ontologies in first-order logic 120 6.3 RDF 121 6.4 Constructing an Ontology 124 6.5 Software Tools for Ontologies 127 7 Communicating 131 7.1 Speech Acts 132 7.1.1 Austin 132 7.1.2 Searle 133 7.1.3 The plan-based theory of speech acts 134 7.1.4 Speech acts as rational action 135 7.2 Agent Communication Languages 136 7.2.1 KQML 136 7.2.2 The FIPA agent communication language 140 7.2.3 JADE 146 8 Working Together 151 8.1 Cooperative Distributed Problem Solving 151 8.2 Task Sharing and Result Sharing 153 8.2.1 Task sharing in the Contract Net 156 8.3 Result Sharing 159 8.4 Combining Task and Result Sharing 159 8.5 Handling Inconsistency 161 8.6 Coordination 162 8.6.1 Coordination through partial global planning 163 8.6.2 Coordination through joint intentions 165 8.6.3 Coordination by mutual modelling 170 8.6.4 Coordination by norms and social laws 173 8.7 Multiagent Planning and Synchronization 177 9 Methodologies 183 9.1 When is an Agent-Based Solution Appropriate? 183 9.2 Agent-Oriented Analysis and Design 184 9.2.1 The AAII methodology 184 9.2.2 Gaia 186 9.2.3 Tropos 187 9.2.4 Prometheus 188 9.2.5 Agent UML 188 9.2.6 Agents in Z 189 9.3 Pitfalls of Agent Development 190 9.4 Mobile Agents 193 10 Applications 201 10.1 Agents for Workflow and Business Process Management 201 10.2 Agents for Distributed Sensing 203 10.3 Agents for Information Retrieval and Management 205 10.4 Agents for Electronic Commerce 211 10.5 Agents for Human–Computer Interfaces 213 10.6 Agents for Virtual Environments 214 10.7 Agents for Social Simulation 214 10.8 Agents for X 218 Part IV Multiagent Decision Making 221 11 Multiagent Interactions 223 11.1 Utilities and Preferences 223 11.2 Setting the Scene 226 11.3 Solution Concepts and Solution Properties 229 11.3.1 Dominant strategies 230 11.3.2 Nash equilibria 230 11.3.3 Pareto efficiency 233 11.3.4 Maximizing social welfare 235 11.4 Competitive and Zero-Sum Interactions 235 11.5 The Prisoner’s Dilemma 236 11.5.1 The shadow of the future 240 11.5.2 Program equilibria 243 11.6 Other Symmetric 2 ×2Interactions 245 11.7 Representing Multiagent Scenarios 248 11.8 Dependence Relations in Multiagent Systems 249 12 Making Group Decisions 253 12.1 Social Welfare Functions and Social Choice Functions 253 12.2 Voting Procedures 255 12.2.1 Plurality 255 12.2.2 Sequential majority elections 257 12.2.3 The Borda count 260 12.2.4 The Slater ranking 260 12.3 Desirable Properties for Voting Procedures 261 12.3.1 Arrow’s theorem 263 12.4 Strategic Manipulation 264 13 Forming Coalitions 269 13.1 Cooperative Games 270 13.1.1 The core 272 13.1.2 The Shapley value 274 13.2 Computational and Representational Issues 277 13.3 Modular Representations 278 13.3.1 Induced subgraphs 278 13.3.2 Marginal contribution nets 280 13.4 Representations for Simple Games 281 13.4.1 Weighted voting games 282 13.4.2 Network flow games 285 13.5 Coalitional Games with Goals 287 13.6 Coalition Structure Formation 288 14 Allocating Scarce Resources 293 14.1 Classifying Auctions 294 14.2 Auctions for Single Items 295 14.2.1 English auctions 295 14.2.2 Dutch auctions 296 14.2.3 First-price sealed-bid auctions 296 14.2.4 Vickrey auctions 296 14.2.5 Expected revenue 297 14.2.6 Lies and collusion 298 14.2.7 Counter speculation 299 14.3 Combinatorial Auctions 299 14.3.1 Bidding languages 302 14.3.2 Winner determination 306 14.3.3 The VCG mechanism 308 14.4 Auctions in Practice 310 14.4.1 Online auctions 310 14.4.2 Adwords auctions 311 14.4.3 The trading agent competition 312 15 Bargaining 315 15.1 Negotiation Parameters 315 15.2 Bargaining for Resource Division 317 15.2.1 Patient players 317 15.2.2 Impatient players 320 15.2.3 Negotiation decision functions 321 15.2.4 Applications of alternating offers 323 15.3 Bargaining for Task Allocation 323 15.3.1 Themonotonic concession protocol 326 15.3.2 The Zeuthen strategy 327 15.3.3 Deception 329 15.4 Bargaining for Resource Allocation 330 16 Arguing 337 16.1 Types of Argument 338 16.2 Abstract Argumentation 338 16.2.1 Preferred extensions 339 16.2.2 Credulous and skeptical acceptance 341 16.2.3 Preferences in abstract argument systems 343 16.2.4 Values in abstract argument systems 344 16.3 Deductive Argumentation Systems 345 16.4 Dialogue Systems 348 16.5 Implemented Argumentation Systems 350 17 Logical Foundations 355 17.1 Logics for Knowledge and Belief 355 17.1.1 Possible-worlds semantics for modal logics 357 17.1.2 Normal modal logics 358 17.1.3 Normal modal logics as epistemic logics 361 17.1.4 Logical omniscience 363 17.1.5 Axioms for knowledge and belief 364 17.1.6 Multiagent epistemic logics 365 17.1.7 Common and distributed knowledge 367 17.2 Logics for Mental States 369 17.2.1 Cohen and Levesque’s intention logic 369 17.2.2 Modelling speech acts 371 17.3 Logics for Cooperation 373 17.3.1 Incomplete information 375 17.3.2 Cooperation logics for social choice 376 17.4 Putting Logic to Work 376 17.4.1 Logic in specification 377 17.4.2 Logic in implementation 378 17.4.3 Logic in verification 381 Part V Coda 391 A A History Lesson 393 B Afterword 405 Glossary of Key Terms 407 References 425 Index 453

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Book SynopsisMultiagent systems combine multiple autonomous entities, each having diverging interests or different information. This overview of the field offers a computer science perspective, but also draws on ideas from game theory, economics, operations research, logic, philosophy and linguistics. It will serve as a reference for researchers in each of these fields, and be used as a text for advanced undergraduate or graduate courses. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes andTrade Review'… an excellent volume … It is the first book I have read that brings together the relevant mathematical results from such a wide variety of underlying disciplines. The writing is very clear, and the production standard is excellent … an invaluable reference manual for graduate students and researchers working on these topics … The price is appropriate for a volume of this type, especially as the book serves both to educate the reader and to serve as a reference manual.' Journal of the Operational Research SocietyTable of Contents1. Distributed constraint satisfaction; 2. Distributed optimization; 3. Introduction to non-cooperative game theory; 4. Computing solution concepts of normal-form games; 5. Games with sequential actions; 6. Richer representations; 7. Learning and teaching; 8. Communication; 9. Aggregating preferences; 10. Protocols for strategic agents; 11. Protocols for multiagent resource allocation; 12. Teams of selfish agents; 13. Logics of knowledge and belief; 14. Beyond belief.

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Book SynopsisBringing together a comprehensive and diverse collection of research, theory, and thought, this volume builds a foundation for the new field of Augmented Cognition research and development. The first section introduces general Augmented Cognition methods and techniques, including physiological and neurophysiological measures such as EEG and fNIR; adaptive techniques; and sensors and algorithms for cognitive state estimation. The second section discusses Augmented Cognition applications such as simulation and training, intent-driven user interfaces, closed-loop command and control systems, then goes on to explore lessons learned to date, and future directions in Augmented Cognition-enabled HCI.Table of ContentsContents: Part I: Human Information Processing.Part II: Cognitive State Sensors.Part III: Augmented Cognition Technology.Part IV: Augmented Cognition and Advanced Computing.Part V: AugCog New Directions.

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Book SynopsisThe equation Mind = Machine is false. This pocket lexicon of neuromythology shows why. Taking a series of key words such as calculation, language, information and memory, Professor Tallis shows how their misuse has a lured a whole generation into accepting the computational model of the mind. First of all these words were used literally in the description of the human mind. Then computer scientists applied them metaphorically to the workings of their machines. And finally, their metaphorical status forgotten, the use of the terms was called as evidence of artificial intelligence in machines and the computational nature of conscious thought.

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Book SynopsisThere is an urgent need for responsible governance of Artificial Intelligence systems. This Handbook maps important features of responsible AI governance and demonstrates how to achieve and implement them at the regional, national and international level.Trade Review'… an indispensable and thought-provoking resource for shaping the future of AI and its societal impact.' Matija Franklin, PrometheusTable of ContentsIntroduction; Part I. Foundations of Responsible AI: 1. Artificial Intelligence – Key Technologies and Opportunities Wolfram Burgard; 2. Automating Supervision of AI Delegates Jaan Tallinn and Richard Ngo; 3. Artificial Moral Agents – Conceptual Issues and Ethical Controversy Catrin Misselhorn;4. Risk Imposition by Artificial Agents – The Moral Proxy Problem Johanna Thoma; 5. Artificial Intelligence and its Integration into the Human Lifeworld Christoph Durt; Part II. Current and Future Approaches to AI Governance: 6. Artificial Intelligence and the Past, Present and Future of Democracy Mathias Risse; 7. The New Regulation of the European Union on Artificial Intelligence – Fuzzy Ethics Diffuse into Domestic Law and Sideline International Law Thomas Burri; 8. Fostering the Common Good – An Adaptive Approach Regulating High-Risk AI-Driven Products and Services Thorsten Schmidt and Silja Voeneky; 9. China's Normative Systems for Responsible AI – From Soft Law to Hard Law Weixing Shen and Yun Liu; 10. Towards a Global Artificial Intelligence Charter Thomas Metzinger; 11. Intellectual Debt – With Great Power Comes Great Ignorance Jonathan Zittrain; Part III. Responsible AI Liability Schemes: 12. Liability for Artificial Intelligence – The Need to Address both Safety Risks and Fundamental Rights Risks Christiane Wendehorst; 13. Forward to the Past – A Critical Evaluation of the European Approach to Artificial Intelligence in Private International Law Jan von Hein; Part IV. Fairness and Non-Discrimination in AI Systems: 14. Differences that Make a Difference – Computational Profiling and Fairness to Individuals Wilfried Hinsch; 15. Discriminatory AI and the Law – Legal Standards for Algorithmic Profiling Antje von Ungern-Sternberg; Part V. Responsible Data Governance: 16. Artificial Intelligence and the Right to Data Protection Ralf Poscher; 17. Artificial Intelligence as a Challenge for Data Protection Law – And Vice Versa Boris Paal; 18. Data Governance and Trust – Lessons from South Korean Experiences Coping with COVID-19 Haksoo Ko, Sangchul Park and Yong Lim; Part VI. Responsible Corporate Governance of AI Systems: 19. From Corporate Governance to Algorithm Governance – Artificial Intelligence as a Challenge for Corporations and their Executives Jan Lieder; 20. Autonomization and Antitrust – On the Construal of the Cartel Prohibition in the Light of Algorithmic Collusion Stefan Thomas; 21. Artificial Intelligence in Financial Services – New Risks and the Need for More Regulation? Matthias Paul; Part VII. Responsible AI Healthcare and Neurotechnology Governance: 22. Medical AI – Key Elements at the International Level Fruzsina Molnár-Gábor and Johanne Giesecke; 23. 'Hey Siri, How Am I Doing?' – Legal Challenges for Artificial Intelligence Alter Egos in Healthcare Christoph Kroenke; 24. Neurorights – A Human-Rights Based Approach for Governing Neurotechnologies Philipp Kellmeyer; 25. AI-Supported Brain-Computer Interfaces and the Emergence of 'Cyberbilities' Boris Essmann and Oliver Mueller; Part VIII. Responsible AI for Security Applications and in Armed Conflict: 26. Artificial Intelligence, Law and National Security Ebrahim Afsah; 27. Morally Repugnant Weaponry? Ethical Responses to the Prospect of Autonomous Weapons Alex Leveringhaus; 28. On 'Responsible AI' in War – Exploring Preconditions for Respecting International Law in Armed Conflict Dustin A. Lewis.

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Book SynopsisThis collection is the first to comprehensively examine the implications of AI technology on legal and regulatory systems. Featuring experts from Europe and the US, this book will appeal to scholars of law, economics, and public policy, as well as readers generally interested in emerging legal questions related to algorithms.Trade Review'There is a shift in the academic debate from the 'if' to the 'how' AI should and could be regulated. This volume covers a broad range of fields, from robotics to copyrights and financial services, all united in one question: what would a regulatory framework that allows us to de-mystify algorithms and get to grips with the commercialisation of data look like? The regulatability of AI is the key issue of our times. The ten contributions provide dense up-to-date information and enticing inspiration in the search for societally acceptable solutions.' Hans W. Micklitz, European University Institute'A timely book that finely addresses a crucial issue in the age of digitalization - the governance of algorithms - and helps to identify a new and necessary field of legal studies.' Ugo Pagallo, University of Turin'The ubiquity of algorithms in many areas of our lives has become one of the burning issues of our time, with legislators and policy-makers around the world grappling with the many challenges associated with Artificial Intelligence and Algorithms. This development is significant for many disciplines, including law. This collection of essays examines many of the legal issues of AI and algorithms and illustrates just how complex an area this has become. It will be welcomed by any reader interested in understanding the many legal and ethical questions which need to be resolved.' Christian Twigg-Flesner, University of Warwick'The book accomplishes a difficult task. It is an excellent source for those who dive for the first time into the legal challenges that AI poses to law … The book is written in such a clear manner that it allows an interdisciplinary understanding. The authors and editors should be applauded for the clarity with which they explore an extremely complex subject.' Francisco de Elizalde, PrometheusTable of ContentsPreface; 1. Robotics and Artificial Intelligence: The Present and Future Visions Sami Haddadin and Dennis Knobbe; 2. Regulating AI and Robotics: Ethical and Legal Challenges Martin Ebers; 3. Regulating Algorithms – How to De-Mystify the Alchemy of Code? Mario Martini; 4: Automated Decision-Making under Article 22 GDPR: Towards a More Substantial Regime for Solely Automated Decision-Making Diana Sancho; 5. Robot Machines and Civil Liability Susana Navas; 6. Extra-contractual Liability for Wrongs Committed by Autonomous Systems Ruth Janal; 7. Control of Algorithms in Financial Markets – the Example of High Frequency Trading Gerald Spindler; 8. Creativity of Algorithms and Copyright Susana Navas; 9. 'Wake Neutrality' of Artificial Intelligence Devices Brian Subirana, Renwick Bivings and Sanjay Sarma; 10. The (envisaged) Legal Framework of Commercialisation of Digital Data within the EU Björn Steinrötter.

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Book SynopsisAI can assist the linguist in doing research on the structure of language. This Element illustrates this possibility by showing how a conversational AI based on a Large Language Model can assist the Construction Grammarian, and especially the Frame Semanticist.Table of ContentsIntroduction; 1. Safety Instructions: Risks and Limitations of LLMs and Generative AI; 2. Constructions; 3. Using an AI to Help Study Constructions; 4. Limitations of LLMs for Constructional Analysis; 5. Cognitive Frames and FrameNet; 6. Prompt Engineering for Building FrameNet; 7. Final safety instructions: Risks and limitations revisited; 8. Imagining the Future of Copilots for Linguists.

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Book SynopsisArtificial intelligence (AI) is increasingly impacting many aspects of people's lives across the globe, from relatively mundane technology to more advanced digital systems that can make their own decisions. While AI has great potential, it also holds great peril depending on how it is designed and used. AI for Diversity questions how AI technology can lead to inclusion or exclusion for diverse groups in society. The way data is selected, trained, used, and embedded into societies can have unfortunate consequences unless we critically investigate the dangers of systems left unchecked, and can lead to misogynistic, homophobic, racist, ageist, transphobic, or ableist outcomes. This book encourages the reader to take a step back to see how AI is impacting diverse groups of people and how diversity-awareness strategies can impact AI.Trade Review"The book is written in a really approachable way for non-specialists and will engage introductory and interdisciplinary audiences. The sections on gender and queering AI are particularly strong, and the book is a highly worthy and important contribution for those chapters alone." --Ashley Shew, Associate Professor, Virginia TechTable of Contents1.Opening the Black Box of AI. 2. Gendered AI: performativity, expectations, and sexism. 3. Queering AI: gender expression, identity, and binaries. 4. AI and Race: recognition, bias, and systemic issues. 5. Bodies and AI: Health, ageing, and disabilities. 6. AI and Class: socioeconomic issues reproduced by technology. 7. Intersectionality and Responsible AI.

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Book SynopsisArtificial intelligence (AI), machine learning, and advanced electronic circuits involve learning from every data input and using those inputs to generate new rules for future business analytics. AI and machine learning are now giving us new opportunities to use big data that we already had, as well as unleash a whole lot of new use cases with new data types. With the increasing use of AI dealing with highly sensitive information such as healthcare, adequate security measures are required to securely store and transmit this information. This book provides a broader coverage of the basic aspects of advanced circuits design and applications.AI for Big Data-Based Engineering Applications from Security Perspectives is an integrated source that aims at understanding the basic concepts associated with the security of advanced circuits. The content includes theoretical frameworks and recent empirical findings in the field to understand the associated principles, key challenges, and recent real-time applications of advanced circuits, AI, and big data security. It illustrates the notions, models, and terminologies that are widely used in the area of Very Large Scale Integration (VLSI) circuits, security, identifies the existing security issues in the field, and evaluates the underlying factors that influence system security. This work emphasizes the idea of understanding the motivation behind advanced circuit design to establish the AI interface and to mitigate security attacks in a better way for big data. This book also outlines exciting areas of future research where already existing methodologies can be implemented. This material is suitable for students, researchers, and professionals with research interest in AI for big dataâbased engineering applications, faculty members across universities, and software developers.

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Book SynopsisHow can we give data physical form?And how might those creations change the ways we experience data and the stories it can tell?Making with Data: Physical Design and Craft in a Data-Driven World provides a snapshot of the diverse practices contemporary creators are using to produce objects, spaces, and experiences imbued with data. Across 25+ beautifully-illustrated chapters, international artists, designers, and scientists each explain the process of creating a specific data-driven pieceâillustrating their practice with candid sketches, photos, and design artifacts from their own studios.The author website, featuring updates and more information about the projects behind the book, can be found here: https://makingwithdata.org/.Featuring influential voices in computer science, data science, graphic design, art, craft, and architecture, Making with Data is accessible and inspiring for entTrade Review"A mind-blowing collection! With the rich visual process descriptions, the creators invite us into their workshops and let us look over their shoulders. You will discover both an exhibition of wonderful data-inspired works as well as the backstories of each of these pieces. Whether hand-made, machine-controlled, or through natural processes, all the chapters show fascinating and bespoke creations of data objects. A much needed collection highlighting what is happening at the frontiers of art and sciences in this new field of data design."-- Giorgia Lupi, partner at Pentagram and author of Dear Data"What a much-needed book! Till, Sam, Lora, and Wes show us that data communication can be so much more than just visualization. There is a whole exciting world of data physicalization waiting to be explored, and the authors open the door for us and lead us through it with intelligent commentary. The book takes us to visit different artists, who explain their approaches and tools – from copper pipes to paper, from wood to electronics. It's a hugely inspiring tour. Reading this book will make you want to experiment with data in the realm of the physical."-- Lisa Charlotte Muth, data vis designer and writer at Datawrapper "This book has fresh inspirations from innovative artist-inventors who open up new possibilities for anyone who has data that tells a story. The screen is no longer the goal or the limit; freeing designers to explore more dimensions and shape deeper experiences to reach people with important messages about their health, communities, and climate. Data physicalizations break free into new dimensions where playful imaginations can use water, plastic, wood, or stone to fabricate data stories for public installations and private reflections. This book makes me want to turn on the laser cutter and restart the 3D printer to fabricate something startling, informative, and eye opening."-- Ben Shneiderman, Professor, Computer science, University of Maryland, USA"A collection of recent and diverse data-driven physical artifacts and sensorial experiences. Projects are beautifully illustrated and described in jargon-free language packed with practical information elucidating the design process, from the tools used to the context of their conception. Making with Data is an invaluable resource for educators and practitioners alike. It broadens our perspective of representing data by engaging all our senses."-- Isabel Meirelles, Professor, Faculty of Design, OCAD University, Toronto, Canada"“Designing with Data” is one of today’s key mantras. What next? Perhaps “Making with Data”, as argued by professors Huron, Nagel, Oehlberg and Willett. This timely book explores new ways data is penetrating our living environment and is crossing the boundary between the physical and the digital. Innovative fabrication methods lend materiality to data, as designers experiment with the use of laser cutters and 3D printers to transform maps and charts into tactile models and artworks. A compelling read for any data enthusiast!"-- Carlo Ratti, Director, MIT Senseable City Lab, USATable of Contents1. Handcraft - Introduction by Sheelagh Carpendale and Lora Oehlberg. 1.1 Snow Water Equivalent by Adrien Segal. 1.2 Life in Clay by Alice Thudt. 1.3 V-Pleat Data Origami by Sarah Hayes. 1.4 Anthropocene Footprints by Mieka West. 1.5 Endings by Loren Madsen. 2. Participation - Introduction by Georgia Panagiotidou and Andrew Vande Moere. 2.1 Cairn by Pauline Gourlet and Thierry Dassé. 2.2 SeeBoat by Laura Perovich. 2.3 Let’s Play with Data by Jose Duarte and EasyDataViz. 2.4 100% [City] by Rimini Protokoll (Helgard Haug, Stefan Kaegi, and Daniel Wetzel). 2.5 Data Strings by Daniel Pearson, Pau Garcia, and Alexandra de Requesens. 3. Digital Production - Introduction by Yvonne Jansen. 3.1 Chemo Singing Bowl by Stephen Barrass. 3.2 Wage Islands by Ekene Ijeoma. 3.3 Data That Feels Gravity by Volker Schweisfurth. 3.4 Orbacles by MINN_LAB Design Collective (Daniel F. Keefe, Ross Altheimer, Andrea J. Johnson, Mahdieh Mahmoudi, Patrick Moe, Maura Rockcastle, Marc Swackhamer, and Aaron Wittkamper). 3.5 Dataseeds by Nick Dulake and Ian Gwilt. 4 Actuation - Introduction by Pierre Dragicevic. 4.1 Tenison Road Charts by David Sweeney, Alex Taylor, and Siân Lindley. 4.2 LOOP by Kim Sauvé and Steven Houben. 4.3 AirFIELD by Nik Hafermaas, Dan Goods, and Jamie Barlow. 4.4 EMERGE by Jason Alexander, Faisal Taher, John Hardy, and John Vidler. 4.5 Zooids by Mathieu Le Goc, Charles Perin, Sean Follmer, Jean-Daniel Fekete, and Pierre Dragicevic. 5. Environment - Introduction by Dietmar Offenhuber. 5.1 Perpetual Plastic by Liina Klauss, Moritz Stefaner and Skye Morét. 5.2 Dataponics: Human-Vegetal Play by Robert Cercós. 5.3 Solar Totems by Charles Sowers. 5.4 Staubmarke (Dustmark) by Dietmar Offenhuber.

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Book SynopsisArtificial intelligence and the use of computational methods to extract information from data are providing adequate tools to monitor and predict water pollutants and water quality issues faster and more accurately. Smart sensors and machine learning models help detect and monitor dispersion and leakage of pollutants before they reach groundwater. With contributions from experts in academia and industries, who give a unified treatment of AI methods and their applications in water science, this book help governments, industries, and homeowners not only address water pollution problems more quickly and efficiently, but also gain better insight into the implementation of more effective remedial measures.FEATURES Provides cutting-edge AI applications in water sector. Highlights the environmental models used by experts in different countries. Discusses various types of models using AI and its tools for achieving sustainable development in water and groundwater. Includes case studies and recent research directions for environmental issues in water sector. Addresses future aspects and innovation in AI field related to watersustainability. This book will appeal to scientists, researchers, and undergraduate and graduate students majoring in environmental or computer science and industry professionals in water science and engineering, environmental management, and governmental sectors. It showcases artificial intelligence applications in detecting environmental issues, with an emphasis on the mitigation and conservation of water and underground resources.Table of ContentsIntroduction. Environmental Models for Sustainable Development. Role of Artificial Intelligence in Water Sector: Dependency on Automation Systems. Modeling and Prediction of Water Security Connected to Global Challenges. Simulation Models of Threatened Aquatic Ecosystems. Monitoring of Contaminants in Aquatic Ecosystems using Big Data. Mitigation of Water Shortage Issues: Water 4.0. Water Pollution Monitoring Using Artificial Intelligent: Basic Algorithm Design. Neural Networks in Wastewater Treatment Process. Circular Economy Models in Water and Wastewater. Integrated Water Resources Management: Perspectives and Challenges. Hydrological Modeling for Sustainable Groundwater Resources.

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Book SynopsisUrban Freight Analytics examines the key concepts associated with the development and application of decision support tools for evaluating and implementing city logistics solutions. New analytical methods are required for effectively planning and operating emerging technologies including the Internet of Things (IoT), Information and Communication Technologies (ICT), and Intelligent Transport Systems (ITS).The book provides a comprehensive study of modelling and evaluation approaches to urban freight transport. It includes case studies from Japan, the US, Europe, and Australia that illustrate the experiences of cities that have already implemented city logistics, including analytical methods that address the complex issues associated with adopting advanced technologies such as autonomous vehicles and drones in urban freight transport.Also considered are future directions in urban freight analytics, including hyperconnected city logistics based on the Physical ITable of ContentsPart I. Methods. 1. Introduction. 2. Data collection and analyses. 3. Geographic information systems and spatial analysis. 4. Optimisation. 5. Multi-agent simulation with machine learning. 6. Reliability and resilience. 7. Evaluation. Part II. Applications. 8. Autonomous Vehicles and Robots. 9. Access management and pricing. 10. Environmental sustainability. 11. Disruption of Networks. 12. Future directions.

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Book SynopsisWhere most introductory texts to the field of digital signal processing assume a degree of technical knowledge, this class-tested textbook provides a comprehensive introduction to the fundamentals of digital signal processing in a way that is accessible to all.Beginning from the first principles, readers will learn how signals are acquired, represented, analyzed and transformed by digital computers. Specific attention is given to digital sampling, discrete Fourier analysis and linear filtering in the time and frequency domains. All concepts are introduced practically and theoretically, combining intuitive illustrations, mathematical derivations and software implementations written in the Python programming language. Practical exercises are included at the end of each chapter to test reader knowledge.Written in a clear and accessible style, Digital Signals Theory is particularly aimed at students and general readers interested in audio and digital signal processiTable of ContentsSignals. Digital Sampling. Convolution. The Discrete Fourier Transform. Properties of the DFT. DFT Invertibility. Fast Fourier Transform. Time Frequency Representation. Frequency Domain Convolution. Infinite Impulse Response Filters. Analyzing IIR filters. Appendix.

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Book SynopsisToday, raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML), this data can be used to gain meaningful insights. In addition, as data is the new raw material for today's world, AI and ML will be applied in every industrial sector. Industry 4.0 mainly focuses on the automation of things. From that perspective, the oil and gas industry is one of the largest industries in terms of economy and energy.Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors, namely upstream, midstream, and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML, ranging from exploration, data management, extraction, processing, real-time data analysis, monitoring, cloud-based connectivity system, and conditions analysis, to the final delivery of the proTable of Contents1. A Comprehensive Review of Machine Application in the Oil and Gas Industry 2. AI and ML Application in the Upstream Sector of the Oil and Gas Industry 3. One Step Further in Upstream Sector 4. Midstream Sector with ML Models and Techniques 5. Downstream Sector with Machine Learning 6. Safety and Maintenance with AI and ML 7. Finance with ML and AI 8. Market and Trading in Oil and Gas (Petroleum) Industry 9. Future of Oil and Gas (Petroleum) Industry with AI

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Book SynopsisDigitalization is shaping our everyday lives, yet navigating the changes it entails can feel like trekking into the unknown, where both the possibilities and the consequences are unclear and difficult to grasp. Exploring how digitalization affects all aspects of our lives, from health to culture, this book aims to develop and strengthen the reader's ability to think critically about such developments.Written in a clear and concise manner with reference to science fiction and pop culture, this book presents potent theoretical perspectives for understanding digitalization processes as societal change. Various exercises are included throughout to encourage readers to critically explore digitalization in their own lives.Replete with illustrations and examples, this book is an accessible guide to digitalization in the modern societal context, appealing to students at the undergraduate level as well as general readership.Table of ContentsPrefaceSection 1Chapter 1: Getting lost in a the digital1.1 Limited or liberated by ubiquitous digital technology? 1.2 It Could Be Otherwise (ICBO) – the foundation of critical thinking1.3 Opening the black box1.4 A response to political and corporate solutionism1.5 Digitalization as a topic for Science and Technology Studies (STS) 1.6 A critical sociotechnical perspective1.7 The structure of the book1.8 ConclusionReferencesChapter 2: What is "digitalization," exactly? 2.1 Digitalization as technological fix2.2 Defining digitalization2.3 Defining digitalization as a political act in itself2.4 A digitalized world2.5 Digitalization as a sociotechnical process2.6 ConclusionReferencesSection 2Chapter 3: A sociotechnical perspective on digitalization3.1 What is a sociotechnical perspective on digitalization? 3.2 What do we mean by "technology"? 3.3 Technologies and their agency3.4 Why technological determinism is a dead end3.5 Technological reductionism3.6 How social determinism is equally problematic3.7 ConclusionReferencesChapter 4: Domestication: User perspectives on technology4.1 A user perspective on technology4.2 Domestication theory4.3 The dimensional model of domestication4.4 The history of domestication4.5 Strengths and weaknesses of domestication theory4.6 Re-domestication and dis-domestication4.7 What non-users can teach us about the use of technology4.8 Normativity and use4.9 ConclusionReferencesChapter 5: Script: Technology’s manual for use5.1 Script as technology’s manual5.2 The historical and theoretical position of script theory5.3 How do you do a script analysis? 5.4 Making scripts through technology development5.5 ConclusionReferencesChapter 6: Technologies as normality machines6.1 A thought experiment on a student app6.2 Technology as inclusion or exclusion? 6.3 Scripting the use and users to create differences6.4 The digital divide6.5 ConclusionReferencesChapter 7: Digital technologies in the past and present7.1 Becoming a communication society7.2 What comes after the communication society? 7.3 Digitalization and some sample diagnoses of the times7.4 ConclusionReferencesSection 3Chapter 8: Digitalization of health: Networks of care and technology8.1 In search of good health: Robots to the rescue? 8.2 Digital technology for better health? 8.3 Talking flowerpots: Welfare technology in the home8.4 Exergames: Gamifying health8.5 Support groups in social media: Communities for mental health8.6 Digitalization makes the actor network of health visible8.7 ConclusionReferencesChapter 9: Digitalization of work: Automation, responsibility, and reskilling9.1 Two visions of future work9.2 From animal laborans to homo faber9.3 Automating workers? 9.4 Who operates self-service checkouts? 9.5 The digital stopwatch and the attempt to automate care work9.6 Craftspeople at construction sites working with robots9.7 What will we do in the future—and how will we do it? 9.8 ConclusionReferencesChapter 10: Digitalization of control: Surveillance, automation, and algorithms10.1 Control through surveillance and digital tracking10.2 Control of animals using virtual fences10.3 Care, technology, and the desire for boundaries when surveilling children10.4 Predictive police algorithms: Surveillance of data sets and predictions of the future10.5 Life in a surveillance society: What digitalization does to surveillance10.6 ConclusionReferencesChapter 11: Digitalization of culture: Remix, community, and prosumers11.1 SKAM and transmedia storytelling11.2 Remix culture as the foundation of digital culture11.3 Understanding where remix culture comes from: Participatory culture and networked publics11.4 Memes: Collective creativity, both serious and humorous11.5 Fan fiction: When fans take ownership of the story11.6 Twitch.tv and livestreaming games: How innovative gamers made one of the world’s biggest platforms11.7 Discussion: Prosumers’ new cultural expressions11.8 ConclusionReferencesChapter 12: Digitalization of the self: Selfies, influencers and the quantified self12.1 Picture perfect? What "Instagram vs. reality" can teach us about being fakeness and authenticity online12.2 From anonymity to persistent identities on the internet12.3 Frontstage, backstage, and the cyborg’s theater12.4 Selfies: The cyborg’s self-portrait? 12.5 Influencers: The professionalized digital self12.6 The quantified self: Believing in a countable and optimized self12.7 Discussion: The cyborg’s expanded toolbox12.8 ConclusionReferencesSection 4Chapter 13: Digitalization summarized13.1 Part 1: A critical perspective on digitalization13.2 Part 2: Theoretical Tools13.3 Part 3: Empirical case studies13.4 Digitalization as social change13.5 A user perspective on digitalization13.6 Critical thinking about digitalizationChapter 14: Analytical cheat sheet: A guide for thinking critically about digitalization14.1 Interpretative flexibility14.2 Delegation14.3 Actor-network14.4 Script14.5 DomesticationChapter 15: Methods cheat sheet: How to study digitalization15.1 Research question: What are you going to find out? 15.2 Choosing method: How are you going to find it? 15.3 Tips for getting good data15.4 From data to analysis

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This comprehensive guide to agricultural robots is the ideal companion for any student or professional engineer looking to understand and develop autonomous vehicles to use on the modern farm.With world hunger one of the modern era's most pressing issues, autonomous agricultural vehicles are a key tool in tackling this problem. Smart farming can increase total factory productivity through designing autonomous vehicles based on specific needs, in addition to implementing smart systems into day-to-day operations. This book provides step-by-step guidance, from the theory behind autonomous vehicles, through to the design process and manufacture. Detailing all components of an autonomous agricultural vehicle, from sensors, controlling algorithms, communication and controlling units, the book covers topics such as artificial intelligence and machine learning. It also includes case studies, and a detailed guide to international policymaking in recent years.Suitable fo

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Book SynopsisThis book discusses the basic ideas, underlying principles, mathematical formulations, analysis and applications of the different combinatorial problems under uncertainty and attempts to provide solutions for the same. Uncertainty influences the behaviour of the market to a great extent. Global pandemics and calamities are other factors which affect and augment unpredictability in the market. The intent of this book is to develop mathematical structures for different aspects of allocation problems depicting real life scenarios. The novel methods which are incorporated in practical scenarios under uncertain circumstances include the STAR heuristic approach, Matrix geometric method, Ranking function and Pythagorean fuzzy numbers, to name a few. Distinct problems which are considered in this book under uncertainty include scheduling, cyclic bottleneck assignment problem, bilevel transportation problem, multi-index transportation problem, retrial queuing, uncertain matrix games, optimalTable of ContentsPreface. About the Editors. Chapter 1 Estimation of Uncertainties for Multiserver Queuing Systems with Bernoulli Feedback. Chapter 2 Optimality for Fuzzy Transportation Problem under Ranking Method. Chapter 3 Solution of Bilevel Linear Fractional Transportation Problem with Pythagorean Fuzzy Numbers. Chapter 4 Optimal Production Evaluation of Cotton in Different Soil and Water Conditions in Sundarban of West Bengal under Hesitant Interval Fuzzy Environment Using Projection Measures. Chapter 5 A Novel Approach for Feature Detection in Vector Graphics. Chapter 6 On Uncertain Matrix Games Involving Linguistic Pythagorean Fuzzy Sets. Chapter 7 Cyclic Surgery Scheduling using Variations of Cohort Intelligence. Chapter 8 Cone Method for Uncertain Multiobjective Optimization Problems with Minmax Robustness. Chapter 9 Solving Multi-Index Transportation Problem with Axial Constraints Having Impaired Flow. Chapter 10 STAR Heuristic Method: A Novel Approach and Its Comparative Analysis with CI Algorithm to Solve CBAP in Healthcare. Chapter 11 Development and Optimization of Quadratic Programming Problems with Intuitionistic Fuzzy Parameters. Index

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Book SynopsisArtificial Intelligence for Capital Market throws light on the application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to black box syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book.Features: Showcases artificial intelligence in finance service industry Explains credit and risk analysis Elaborates on cryptocurrencies and blockchain technology Focuses on the optimal choice of asset pricing model Introduces testing of market efficiency and forecasting in the Indian stock market This book serves as a reference book for academicians, industry professionals, traders, finance managers and stock brokers. It may also be used as textbook for graduate level courses in financial services and financial analytics.Table of Contents1. Artificial Intelligence in the Financial Service Industry. 2. Machine Learning and Big Data in Finance Services. 3. Artificial Intelligence in Financial Services: Advantages and Disadvantages. 4. Upscaling Profits in Financial Market. 5. Credit and Risk Analysis in the Financial and Banking Sectors: An Investigation. 6. Cryptocurrencies and Blockchain Technology Applications. 7. Machine Learning and the Optimal Choice of Asset Pricing Model. 8. Testing for Market Efficiency Using News-Driven Sentiment: Evidence from Select NYSE Stocks. 9. Comparing Statistical, Deep Learning, and Additive Models for Forecasting in the Indian Stock Market. 10. Applications and Impact of Artificial Intelligence in the Finance Sector.

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Book SynopsisThis handbook covers smart manufacturing development, processing, modifications, and applications. It provides a complete understanding of the recent advancements in smart manufacturing through its various enabling manufacturing technologies, and how industries and organizations can find the needed information on how to implement smart manufacturing towards sustainability of manufacturing practices.Handbook of Smart Manufacturing: Forecasting the Future of Industry 4.0 covers all related advances in manufacturing such as the integration of reverse engineering with smart manufacturing, industrial internet of things (IIoT), and artificial intelligence approaches, including Artificial Neural Network, Markov Decision Process, and Heuristics Methodology. It offers smart manufacturing methods like 4D printing, micro-manufacturing, and processing of smart materials to assist the biomedical industries in the fabrication of human prostheses and implants. The handbook goes on to discusTable of Contents1. Smart Manufacturing and Industry 4.0: State-of-the-Art Review. 2. Study And Analysis Of Iot (Industry 4.0): A Review. 3. Recent advances in Cybersecurity in Smart Manufacturing Systems in the Industry. 4. Integration of Circular Supply Chain and Industry 4.0 to Enhance Smart Manufacturing Adoption. 5. Artificial Intelligence with Additive Manufacturing. 6. Robotic additive manufacturing vision towards smart manufacturing and envisage the trend with patent landscape. 7. Smart Materials for Smart Manufacturing. 8. Smart Biomaterials in Industry and Healthcare. 9. Ferroelectric polymer composites and evaluation of their properties. 10. 4D print today and envisaging the trend with patent landscape for versatile applications. 11. Investigating the work generation potential of SMA wire actuator. 12. Troubleshooting on the sample preparation during Fused Deposition Modelling. 13. Hybrid Additive Manufacturing Technologies. 14. Smart Manufacturing Using 4d Printing. 15. Developments in 4D Printing and Associated Smart Materials. 16. Role of smart manufacturing systems in improving electric vehicle production. 17. Safety management with application of Internet of Things, Artificial Intelligence and Machine Learning for Industry 4.0 environment.

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Book SynopsisThis title covers computer vision and machine learning (ML) advances that facilitate automation in diagnostic, therapeutic, and preventative healthcare. The book shows the development of algorithms and architectures for healthcare. Table of Contents1. Human–AI Relationship in Healthcare. 2. Deep Learning in Medical Image Analysis: Recent Models and Explainability. 3. An Overview of Functional Near-Infrared Spectroscopy and Explainable Artificial Intelligence in fNIRS. 4. An Explainable Method for Image Registration with Applications in Medical Imaging. 5. State-of-the-Art Deep Learning Method and Its Explainability for Computerized Tomography Image Segmentation. 6. Interpretability of Segmentation and Overall Survival for Brain Tumors. 7. Identification of MR Image Biomarkers in Brain Tumor Patients Using Machine Learning and Radiomics Features. 8. Explainable Artificial Intelligence in Breast Cancer Identification. 9. Interpretability of Self-Supervised Learning for Breast Cancer Image Analysis. 10. Predictive Analytics in Hospital Readmission for Diabetes Risk Patients. 11. Continuous Blood Glucose Monitoring Using Explainable AI Techniques. 12. Decision Support System for Facial Emotion-Based Progression Detection of Parkinson’s Patients. 13. Interpretable Machine Learning in Athletics for Injury Risk Prediction. 14. Federated Learning and Explainable AI in Healthcare.

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Book SynopsisCompletely updated, this new edition uniquely explains how to assess and handle technical risk, schedule risk, and cost risk efficiently and effectively for complex systems that include Artificial Intelligence, Machine Learning, and Deep Learning. It enables engineering professionals to anticipate failures and highlight opportunities to turn failure into success through the systematic application of Risk Engineering. What Every Engineer Should Know About Risk Engineering and Management, Second Edition discusses Risk Engineering and how to deal with System Complexity and Engineering Dynamics, as it highlights how AI can present new and unique ways that failures can take place. The new edition extends the term Risk Engineering introduced by the first edition, to Complex Systems in the new edition. The book also relates Decision Tree which was explored in the first edition to Fault Diagnosis in the new edition and introduces new chapters on System Complexity, AI, and Causal RiskTable of Contents1. Risk Engineering - Dealing with System Complexity and Engineering Dynamics. 2. Risk Identification - Understanding the Limits of Engineering Designs. 3. Risk Assessment - Extending Murphy’s Law. 4. Design for Risk Engineering - The Art of War Against Failures. 5. Risk Acceptability - Uncertainty in Perspective. 6. From Risk Engineering to Risk Management. 7. Cost Risk - Interacting with Engineering Economy. 8. Schedule Risk - Identifying and Controlling Critical Paths. 9. Integrated Risk Management and Computer Simulation.

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Book SynopsisThe idea of creating artificial humans can be found at the beginning of the human culture. Ancient myths contain the stories of artificial humans brought to life by gods. The word robot originates from a play that was about artificial humans made from artificial flesh that aims to serve real humans. With advancements in robotics, the materialization of this idea is more real than ever before. We are witnessing attempts to create humanoid robots that might be deployed in many spheres of our life - policing, healthcare, and even for love and sex.The book focuses on the ethical issues of human likeness of robots and human tendency to anthropomorphize. It is built on the assumption that design choices are not neutral, and they need to be discussed to align robots with human values. With robots operating in the physical world, they bring ideas and risks that should be addressed before widespread deployment. The book reviews specific issues and provides suggestions an

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Book SynopsisThe book guides the reader through the auditing and compliance process of the newly released ISO Artificial Intelligence standard. It provides tools and best practices on how to put together an AI management system that is certifiable and sheds light on ethical and legal challenges business leaders struggle with to make their AI system comply with existing laws and regulations, and the ethical framework of the organization.The book is unique because it provides implementation guidance on the new certification and conformity assessment process required by the new ISO Standard on Artificial Intelligence (ISO 42001:2023 Artificial Intelligence Management System) published by ISO in August 2023. This is the first book that addresses this issue.As a member of the US/ISO team who participated in the drafting of this standard during the last 3 years, the author has direct knowledge and insights that are critical to the implementation of the standard. He explains the context o

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Book SynopsisData as the Fourth Pillar reasons that data should be considered the Fourth Pillar of every enterprise, alongside people, processes, and technology. Aimed at Boards, CEOs, and CxOs, it provides a compelling case for why and how they should treat data as a strategic asset. It presents a comprehensive, success-by-design approach for enterprises, guiding them through a Maturity Framework to accelerate their data-centric journey.This book addresses the Why, the What, and the How of achieving this goal in measurable terms. It introduces key performance indicators (KPIs) such as Total Addressable Value (TAV) and Expected Addressable Value through data (EAV) to help measure the impact provided by the data pillar. The book also explores the symbiotic relationship between AI and data, illustrating how both enable and benefit from each other. A case study of Audi AG provides practical insights into the concepts and frameworks discussed.This book is an essential resource for business executives in both SMBs and large enterprises, helping them navigate a highly complex and hyper-competitive business landscape while accelerating business value for their stakeholder communities.

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Book SynopsisIn today's rapidly changing world, diplomacy is undergoing a revolutionary transformation. Imagine ambassadors using artificial intelligence to analyze millions of social media posts in real time, crisis responses guided by predictive analytics, and complex negotiations enhanced by unprecedented data-driven insights. This isn't the futureâit's diplomacy today, reimagined through AI.Drawing on over 21 years of experience integrating technology into foreign affairs, Donald Kilburg, a retired U.S. diplomat, reveals how AI is revolutionizing diplomatic engagement, crisis management, and public diplomacy. From enhancing communication strategies to optimizing consular services, each chapter presents a vivid exploration of AI's potential to amplify the effectiveness of diplomatic missions across the globe.Readers will discover practical strategies for implementing AI in diplomatic operations, gain insights into the future of AI-driven global governance, and learn whenâcruciallyânot to use AI at all. Through vivid case studies and real-world examples, this book illuminates both the opportunities and ethical complexities at the intersection of technology and international relations.Whether you're a diplomatic practitioner, a student of international affairs, or fascinated by technology's impact on global relationships, this groundbreaking guide charts the course for diplomacy's next evolutionâwhere human wisdom and artificial intelligence converge to address our world's most pressing challenges.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisIn this comprehensive textbook about robot grasping, readers will discover an integrated look at the major concepts and technical results in robot grasp mechanics. A large body of prior research, including key theories, graphical techniques, and insights on robot hand designs, is organized into a systematic review, using common notation and a common analytical framework. With introductory and advanced chapters that support senior undergraduate and graduate level robotics courses, this book provides a full introduction to robot grasping principles that are needed to model and analyze multi-finger robot grasps, and serves as a valuable reference for robotics students, researchers, and practicing robot engineers. Each chapter contains many worked-out examples, exercises with full solutions, and figures that highlight new concepts and help the reader master the use of the theories and equations presented.Trade Review'The Mechanics of Robot Grasping, by two of the world's leading experts, fills an important gap in the literature by providing the first comprehensive survey of the mathematical tools needed to model the physics of grasping. The book uses configuration space to consistently characterize equilibrium, immobilizing, and caging grasps, and clearly conveys important points such as the distinction between first-order and second-order form closure. The book also contains new material on the effects of gravity, compliance, and hand mechanism design. Grasping remains a Grand Challenge for robots and this book provides the solid foundation for progress for students and researchers in the years ahead.' Ken Goldberg, University of California, Berkeley'This is a book on robotic hand grasping from new view points. Different from other books on grasping, this book concretely explains the equilibrium grasp, the immobilizing grasp and the caging grasp. In addition, I have never seen a book discussing the equilibrium stance of legged robots in relation to the equilibrium grasp. Classical topics on grasping mechanics are also covered in this book.' Kensuke Harada, Osaka University, JapanTable of Contents1. Introduction and overview; Part I. Basic Geometry of the Grasping Process: 2. Rigid-body configuration space; 3. Configuration space tangent and cotangent vectors; 4. Rigid body equilibrium grasps; 5. A catalog of equilibrium grasps; Part II. Frictionless Rigid Body Grasps and Stances: 6. Introduction to secure grasps; 7. First-order immobilizing grasps; 8. Second-order immobilizing grasps; 9. Minimal immobilizing grasps; 10. Multi-finger caging grasps; 11. Frictionless hand supported stances under gravity; Part III. Frictional Rigid-Body Grasps, Fixtures, and Stances: 12. Wrench resistant grasps; 13. Grasp quality functions; 14. Hand supported stances under gravity – Part I; 15. Hand supported stances under gravity – Part II; Part IV. Grasping Mechanisms: 16. The kinematics and mechanics of grasping mechanisms; 17. Grasp manipulability; 18. Hand mechanism compliance; Appendices; Index.

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Book SynopsisSentiment analysis is the computational study of people''s opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and alTrade Review'As a whole, this book serves as a useful introduction to sentiment analysis along with in-depth discussions of linguistic phenomena related to sentiments, opinions, and emotions. Although many sentiment analysis methods are based on machine learning as in other NLP [Natural Language Processing] tasks, sentiment analysis is much more than just a classification or regression problem, because the natural language constructs used to express opinions, sentiments, and emotions are highly sophisticated, including sentiment shift, implicated expression, sarcasm, and so on. Liu has described these issues and problems very clearly. Readers will find this book to be inspiring and it will arouse their interests in sentiment analysis.' Jun Zhao, Chinese Academy of SciencesTable of Contents1. Introduction; 2. The Problem of Sentiment Analysis; 3. Document Sentiment Classification; 4. Sentence Subjectivity and Sentiment Classification; 5. Aspect Sentiment Classification; 6. Aspect and Entity Extraction; 7. Sentiment Lexicon Generation; 8. Analysis of Comparative Opinions; 9. Opinion Summarization and Search; 10. Analysis of Debates and Comments; 11. Mining Intents; 12. Detecting Fake or Deceptive Opinions; 13. Quality of Reviews; 14. Conclusions.

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Book SynopsisAlgorithms have transformed our society, upsetting the concepts of work, property, government, even humanity. We rejoice that they make life easier, but fear that they will enslave us. Going beyond visions of good vs evil, this book takes a new look at our time, the age of algorithms. Algorithms will be what we want them to be: it's up to us.Trade Review'... written by two computer scientists offering a most accessible view on both what algorithms are (the book starts with a clearest analogy between algorithms and recipes) and how algorithms are severely changing human life.' Simona Chiodo, Metascience'This short and interesting book provides a non-technical introduction to the age of algorithms. The book is worth reading many times even by those unfamiliar with algorithms or computer science.' S.V. Nagaraj, The SIGACT NewsTable of Contents1. Algorithms intrigue, algorithms disturb; 2. What is an algorithm?; 3. Algorithms, computers, and programs; 4. What algorithms do; 5. What algorithms don't do; 6. Computational thinking; 7. The end of employment; 8. The end of work; 9. The end of property; 10. Governing in the age of algorithms; 11. An algorithm in the community; 12. The responsibility of algorithms; 13. Personal data and privacy; 14. Fairness, transparency, and diversity; 15. Computers and ecology; 16. Computer science education; 17. The augmented human; 18. Can an algorithm be intelligent?; 19. Can an algorithm have feelings? 20. Time to choose.

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Book SynopsisEngineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown's 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithTable of ContentsAcknowledgments xi Introduction xiii Part I Systems and Artificial Intelligence 1 1 Artificial Intelligence, Science Fiction, and Fear 3 1.1 The Danger of AI 3 1.2 The Human Analogy 5 1.3 The Systems Analogy 6 1.4 Killer Robots 7 1.5 Watching the Watchers 9 1.6 Cybersecurity in a World of Fallible Humans 12 1.7 Imagining Failure 17 1.8 The New Role of Data: The Green School Bus Problem 23 1.9 Data Requirements 25 1.9.1 Diversity 26 1.9.2 Augmentation 28 1.9.3 Distribution 29 1.9.4 Synthesis 30 1.10 The Data Lifecycle 31 1.11 AI Systems and People Systems 41 1.12 Making an AI as Safe as a Human 45 References 48 2 We Live in a World of Systems 49 2.1 What Is a System? 49 2.2 Natural Systems 51 2.3 Engineered Systems 53 2.4 Human Activity Systems 54 2.5 Systems as a Profession 54 2.5.1 Systems Engineering 54 2.5.2 Systems Science 55 2.5.3 Systems Thinking 55 2.6 A Biological Analogy 56 2.7 Emergent Behavior: What Makes a System, a System 56 2.8 Hierarchy in Systems 60 2.9 Systems Engineering 64 3 The Intelligence in the System: How Artificial Intelligence Really Works 71 3.1 What Is Artificial Intelligence? 71 3.1.1 Myth 1: AI SystemsWork Just Like the Brain Does 72 3.1.2 Myth 2: As Neural Networks Grow in Size and Speed, They Get Smarter 72 3.1.3 Myth 3: Solving a Hard or Complex Problem Shows That an AI Is Nearing Human Intelligence 73 3.2 Training the Deep Neural Network 75 3.3 Testing the Neural Network 76 3.4 Annie Learns to Identify Dogs 76 3.5 How Does a Neural NetworkWork? 80 3.6 Features: Latent and Otherwise 81 3.7 Recommending Movies 82 3.8 The One-Page Deep Neural Network 84 4 Intelligent Systems and the People they Love 97 4.1 Can Machines Think? 97 4.2 Human Intelligence vs. Computer Intelligence 98 4.3 The Chinese Room: Understanding, Intentionality, and Consciousness 99 4.4 Objections to the Chinese Room Argument 104 4.4.1 The Systems Reply to the CRA 104 4.4.2 The Robot Reply 104 4.4.3 The Brain Simulator Reply 105 4.5 Agreement on the CRA 107 4.5.1 Analyzing the Systems Reply: Can the Room Understand when Searle Does Not? 109 4.6 Implementation of the Chinese Room System 114 4.7 Is There a Chinese-Understanding Mind in the Room? 115 4.7.1 Searle and Block on Whether the Chinese Room Can Understand 116 4.8 Chinese Room: Simulator or an Artificial Mind? 118 4.8.1 Searle on Strong AI Motivations 120 4.8.2 Understanding and Simulation 121 4.9 The Mind of the Programmer 127 4.10 Conclusion 133 References 135 Part II Systems Engineering for Intelligent Systems 137 5 Designing Systems by Drawing Pictures and Telling Stories 139 5.1 Requirements and Stories 139 5.2 Stories and Pictures: A Better Way 141 5.3 How Systems Come to Be 141 5.4 The Paradox of Cost Avoidance 145 5.5 Communication and Creativity in Engineering 147 5.6 Seeing the Real Needs 148 5.7 Telling Stories 150 5.8 Bringing a Movie to Life 153 5.9 Telling System Stories and the Combination Pitch 157 5.10 The Combination Pitch 159 5.11 Stories in Time 160 5.12 Roles and Personas 161 6 Use Cases: The Superpower of Systems Engineering 165 6.1 The Main Purpose of Systems Engineering 165 6.2 Getting the Requirements Right: A Parable 166 6.2.1 A Parable of Systems Engineering 168 6.3 Building a Home: A Journey of Requirements and Design 170 6.4 Where Requirements Come From and a Koan 173 6.4.1 A Requirements Koan 177 6.5 The Magic of Use Cases 177 6.6 The Essence of a Use Case 181 6.7 Use Case vs. Functions: A Parable 184 6.8 Identifying Actors 186 6.8.1 Actors Are Outside the System 187 6.8.2 Actors Interact with the System 187 6.8.3 Actors Represent Roles 188 6.8.4 Finding the Real Actors 188 6.8.5 Identifying Nonhuman Actors 191 6.8.6 DoWe Have ALL the Actors? 193 6.9 Identifying Use Cases 193 6.10 Use Case Flows of Events 196 6.10.1 BalancingWork Up-Front with Speed 199 6.10.2 Use Case Flows and Scenarios 201 6.10.3 Writing Alternate Flows 202 6.10.4 Include and Extend with Use Cases 203 6.11 Examples of Use Cases 205 6.11.1 Example Use Case 1: Request Customer Service from Acme Library Support 205 6.11.2 Example Use Case 2: Ensure Network Stability 206 6.11.3 Example Use Case 3: Search for Boat in Inventory 206 6.12 Use Cases with Human Activity Systems 207 6.13 Use Cases as a Superpower 208 References 208 7 Picturing Systems with Model Based Systems Engineering 209 7.1 How Humans Build Things 209 7.2 C: Context 212 7.2.1 Actors for the VX 213 7.2.2 Actors for the Home System 216 7.3 U: Usage 217 7.4 S: States and Modes 221 7.5 T: Timing 224 7.6 A: Architecture 225 7.7 R: Realization 230 7.8 D: Decomposition 234 7.9 Conclusion 238 8 A Time for Timeboxes and the Use of Usage Processes 239 8.1 Problems in Time Modeling: Concurrency, False Precision, and Uncertainty 240 8.1.1 Concurrency 240 8.1.2 False Precision 240 8.1.3 Uncertainty 241 8.2 Processes and Use Cases 242 8.3 Modeling: Two Paradigms 243 8.3.1 The Key Observation 244 8.3.2 Source of the Problem 246 8.4 Process and System Paradigms 247 8.5 A Closer Examination of Time 248 8.6 The Need for a New Approach 251 8.7 The Timebox 252 8.8 Timeboxes with Timelines 257 8.8.1 Thinking in Timeboxes 257 8.9 The Usage Process 258 8.10 Pilot Project Examples 262 8.10.1 Pilot Project: The Hunt for Red October 262 8.10.2 Pilot Project: FAA 265 8.10.3 Pilot Project: IBM Agile Process 267 8.11 Summary: A New Paradigm Modeling Approach 269 8.11.1 The Impact of New Paradigm Models 270 8.11.2 The Future of New Paradigm Models 271 References 272 Part III Systems Thinking for Intelligent Systems 275 9 Solving Hard Problems with Systems Thinking 277 9.1 Human Activity Systems and Systems Thinking 277 9.2 The Central Insight of Systems Thinking 279 9.3 Solving Problems with Systems Thinking 281 9.3.1 Identify a Problem 281 9.3.2 Find the Real Problem 282 9.3.3 Identify the System 284 9.4 Understanding the System 285 9.4.1 Rocks Are Hard 288 9.4.2 Heart and Soul 290 9.4.3 Confusing Cause and Effect 292 9.4.4 Logical Fallacies 296 9.5 System Archetypes 298 9.5.1 Tragedy of the Commons 299 9.5.2 The Rich Get Richer 300 9.6 Intervening in a System 302 9.7 Testing Implementing Intervention Incrementally 315 9.8 Systems Thinking and theWorld 316 10 People Systems: A New Way to Understand the World 317 10.1 Reviewing Types of Systems 317 10.2 People Systems 318 10.3 People Systems and Psychology 320 10.4 Endowment Effect 323 10.5 Anchoring 324 10.6 Functional Architecture of a Person 325 10.7 Example: The Problem of Pollution 327 10.8 Speech Acts 332 10.8.1 People System Archetypes 337 10.8.1.1 Demand Slowing 339 10.8.1.2 Customer Service 340 10.9 Seeking Quality 341 10.10 Job Hunting as a People System 344 10.10.1 Who Are You? 345 10.10.2 What Do You Want to Do? 345 10.10.3 For Whom? 347 10.10.4 Pick a Few 348 10.10.5 Go Straight to the Hiring Manager 349 10.10.6 Follow Through 351 10.10.7 Broaden Your View 352 10.10.8 Step Two 352 10.11 Shared Service Monopolies 354 References 356 Index 357

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Book SynopsisThe 24 chapters in this book provides a deep overview of robotics and the application of AI and IoT in robotics. It contains the exploration of AI and IoT based intelligent automation in robotics. The various algorithms and frameworks for robotics based on AI and IoT are presented, analyzed, and discussed. This book also provides insights on application of robotics in education, healthcare, defense and many other fields which utilize IoT and AI. It also introduces the idea of smart cities using robotics.Table of ContentsPreface xvii 1 Introduction to Robotics 1Srinivas Kumar Palvadi, Pooja Dixit and Vishal Dutt 1.1 Introduction 1 1.2 History and Evolution of Robots 3 1.3 Applications 6 1.4 Components Needed for a Robot 7 1.5 Robot Interaction and Navigation 10 1.5.1 Humanoid Robot 11 1.5.2 Control 11 1.5.3 Autonomy Levels 12 1.6 Conclusion 12 References 13 2 Techniques in Robotics for Automation Using AI and IoT 15Sandeep Kr. Sharma, N. Gayathri, S. Rakesh Kumar and Rajiv Kumar Modanval 2.1 Introduction 16 2.2 Brief History of Robotics 16 2.3 Some General Terms 17 2.4 Requirements of AI and IoT for Robotic Automation 20 2.5 Role of AI and IoT in Robotics 21 2.6 Diagrammatic Representations of Some Robotic Systems 23 2.7 Algorithms Used in Robotics 25 2.8 Application of Robotics 27 2.9 Case Studies 30 2.9.1 Sophia 30 2.9.2 ASIMO 30 2.9.3 Cheetah Robot 30 2.9.4 IBM Watson 31 2.10 Conclusion 31 References 31 3 Robotics, AI and IoT in the Defense Sector 35Rajiv Kumar Modanval, S. Rakesh Kumar, N. Gayathri and Sandeep Kr. Sharma 3.1 Introduction 36 3.2 How Robotics Plays an Important Role in the Defense Sector 36 3.3 Review of the World’s Current Robotics Capabilities in the Defense Sector 38 3.3.1 China 38 3.3.2 United State of America 39 3.3.3 Russia 40 3.3.4 India 41 3.4 Application Areas of Robotics in Warfare 43 3.4.1 Autonomous Drones 43 3.4.2 Autonomous Tanks and Vehicles 44 3.4.3 Autonomous Ships and Submarines 45 3.4.4 Humanoid Robot Soldiers 47 3.4.5 Armed Soldier Exoskeletons 48 3.5 Conclusion 50 3.6 Future Work 50 References 50 4 Robotics, AI and IoT in Medical and Healthcare Applications 53Pooja Dixit, Manju Payal, Nidhi Goyal and Vishal Dutt 4.1 Introduction 53 4.1.1 Basics of AI 53 4.1.1.1 AI in Healthcare 54 4.1.1.2 Current Trends of AI in Healthcare 55 4.1.1.3 Limits of AI in Healthcare 56 4.1.2 Basics of Robotics 57 4.1.2.1 Robotics for Healthcare 57 4.1.3 Basics of IoT 59 4.1.3.1 IoT Scenarios in Healthcare 60 4.1.3.2 Requirements of Security 61 4.2 AI, Robotics and IoT: A Logical Combination 62 4.2.1 Artificial Intelligence and IoT in Healthcare 62 4.2.2 AI and Robotics 63 4.2.2.1 Limitation of Robotics in Medical Healthcare 66 4.2.3 IoT with Robotics 66 4.2.3.1 Overview of IoMRT 67 4.2.3.2 Challenges of IoT Deployment 69 4.3 Essence of AI, IoT, and Robotics in Healthcare 70 4.4 Future Applications of Robotics, AI, and IoT 71 4.5 Conclusion 72 References 72 5 Towards Analyzing Skill Transfer to Robots Based on Semantically Represented Activities of Humans 75Devi.T, N. Deepa, S. Rakesh Kumar, R. Ganesan and N. Gayathri 5.1 Introduction 76 5.2 Related Work 77 5.3 Overview of Proposed System 78 5.3.1 Visual Data Retrieval 79 5.3.2 Data Processing to Attain User Objective 80 5.3.3 Knowledge Base 82 5.3.4 Robot Attaining User Goal 83 5.4 Results and Discussion 83 5.5 Conclusion 85 References 85 6 Healthcare Robots Enabled with IoT and Artificial Intelligence for Elderly Patients 87S. Porkodi and D. Kesavaraja 6.1 Introduction 88 6.1.1 Past, Present, and Future 88 6.1.2 Internet of Things 88 6.1.3 Artificial Intelligence 89 6.1.4 Using Robotics to Enhance Healthcare Services 89 6.2 Existing Robots in Healthcare 90 6.3 Challenges in Implementation and Providing Potential Solutions 90 6.4 Robotic Solutions for Problems Facing the Elderly in Society 98 6.4.1 Solutions for Physical and Functional Challenges 98 6.4.2 Solutions for Cognitive Challenges 98 6.5 Healthcare Management 99 6.5.1 Internet of Things for Data Acquisition 99 6.5.2 Robotics for Healthcare Assistance and Medication Management 102 6.5.3 Robotics for Psychological Issues 103 6.6 Conclusion and Future Directions 103 References 104 7 Robotics, AI, and the IoT in Defense Systems 109Manju Payal, Pooja Dixit, T.V.M. Sairam and Nidhi Goyal 7.1 AI in Defense 110 7.1.1 AI Terminology and Background 110 7.1.2 Systematic Sensing Applications 111 7.1.3 Overview of AI in Defense Systems 112 7.2 Overview of IoT in Defense Systems 114 7.2.1 Role of IoT in Defense 116 7.2.2 Ministry of Defense Initiatives 117 7.2.3 IoT Defense Policy Challenges 117 7.3 Robotics in Defense 118 7.3.1 Technical Challenges of Defense Robots 120 7.4 AI, Robotics, and IoT in Defense: A Logical Mix in Context 123 7.4.1 Combination of Robotics and IoT in Defense 123 7.4.2 Combination of Robotics and AI in Defense 124 7.5 Conclusion 126 References 127 8 Techniques of Robotics for Automation Using AI and the IoT 129Kapil Chauhan and Vishal Dutt 8.1 Introduction 130 8.2 Internet of Robotic Things Concept 131 8.3 Definitions of Commonly Used Terms 132 8.4 Procedures Used in Making a Robot 133 8.4.1 Analyzing Tasks 133 8.4.2 Designing Robots 134 8.4.3 Computerized Reasoning 134 8.4.4 Combining Ideas to Make a Robot 134 8.4.5 Making a Robot 134 8.4.6 Designing Interfaces with Different Frameworks or Robots 134 8.5 IoRT Technologies 135 8.6 Sensors and Actuators 137 8.7 Component Selection and Designing Parts 138 8.7.1 Robot and Controller Structure 140 8.8 Process Automation 141 8.8.1 Benefits of Process Automation 141 8.8.2 Incorporating AI in Process Automation 141 8.9 Robots and Robotic Automation 142 8.10 Architecture of the Internet of Robotic Things 142 8.10.1 Concepts of Open Architecture Platforms 143 8.11 Basic Abilities 143 8.11.1 Discernment Capacity 143 8.11.2 Motion Capacity 144 8.11.3 Manipulation Capacity 144 8.12 More Elevated Level Capacities 145 8.12.1 Decisional Self-Sufficiency 145 8.12.2 Interaction Capacity 145 8.12.3 Cognitive Capacity 146 8.13 Conclusion 146 References 146 9 An Artificial Intelligence-Based Smart Task Responder: Android Robot for Human Instruction Using LSTM Technique 149T. Devi, N. Deepa, SP. Chokkalingam, N. Gayathri and S. Rakesh Kumar 9.1 Introduction 150 9.2 Literature Review 152 9.3 Proposed System 152 9.4 Results and Discussion 157 9.5 Conclusion 161 References 162 10 AI, IoT and Robotics in the Medical and Healthcare Field 165V. Kavidha, N. Gayathri and S. Rakesh Kumar 10.1 Introduction 165 10.2 A Survey of Robots and AI Used in the Health Sector 167 10.2.1 Surgical Robots 167 10.2.2 Exoskeletons 168 10.2.3 Prosthetics 170 10.2.4 Artificial Organs 171 10.2.5 Pharmacy and Hospital Automation Robots 172 10.2.6 Social Robots 173 10.2.7 Big Data Analytics 175 10.3 Sociotechnical Considerations 176 10.3.1 Sociotechnical Influence 176 10.3.2 Social Valence 177 10.3.3 The Paradox of Evidence-Based Reasoning 178 10.4 Legal Considerations 180 10.4.1 Liability for Robotics, AI and IoT 180 10.4.2 Liability for Physicians Using Robotics, AI and IoT 181 10.4.3 Liability for Institutions Using Robotics, AI and IoT 182 10.5 Regulating Robotics, AI and IoT as Medical Devices 183 10.6 Conclusion 185 References 185 11 Real-Time Mild and Moderate COVID-19 Human Body Temperature Detection Using Artificial Intelligence 189K. Logu, T. Devi, N. Deepa, S. Rakesh Kumar and N. Gayathri 11.1 Introduction 190 11.2 Contactless Temperature 191 11.2.1 Bolometers (IR-Based) 192 11.2.2 Thermopile Radiation Sensors (IR-Based) 193 11.2.3 Fiber-Optic Pyrometers 193 11.2.4 RGB Photocell 194 11.2.5 3D Sensor 195 11.3 Fever Detection Camera 196 11.3.1 Facial Recognition 197 11.3.2 Geometric Approach 198 11.3.3 Holistic Approach 198 11.3.4 Model-Based 198 11.3.5 Vascular Network 199 11.4 Simulation and Analysis 200 11.5 Conclusion 203 References 203 12 Drones in Smart Cities 205Manju Payal, Pooja Dixit and Vishal Dutt 12.1 Introduction 206 12.1.1 Overview of the Literature 206 12.2 Utilization of UAVs for Wireless Network 209 12.2.1 Use Cases for WN Using UAVs 209 12.2.2 Classifications and Types of UAVs 210 12.2.3 Deployment of UAVS Using IoT Networks 213 12.2.4 IoT and 5G Sensor Technologies for UAVs 214 12.3 Introduced Framework 217 12.3.1 Architecture of UAV IoT 217 12.3.2 Ground Control Station 218 12.3.3 Data Links 218 12.4 UAV IoT Applications 223 12.4.1 UAV Traffic Management 223 12.4.2 Situation Awareness 223 12.4.3 Public Safety/Saving Lives 225 12.5 Conclusion 227 References 227 13 UAVs in Agriculture 229DeepanshuSrivastava, S. RakeshKumar and N. Gayathri 13.1 Introduction 230 13.2 UAVs in Smart Farming and Take-Off Panel 230 13.2.1 Overview of Systems 230 13.3 Introduction to UGV Systems and Planning 234 13.4 UAV-Hyperspectral for Agriculture 236 13.5 UAV-Based Multisensors for Precision Agriculture 239 13.6 Automation in Agriculture 242 13.7 Conclusion 245 References 245 14 Semi-Automated Parking System Using DSDV and RFID 247Mayank Agrawal, Abhishek Kumar Rawat, Archana, SandhyaKatiyar and Sanjay Kumar 14.1 Introduction 247 14.2 Ad Hoc Network 248 14.2.1 Destination-Sequenced Distance Vector (DSDV) Routing Protocol 248 14.3 Radio Frequency Identification (RFID) 249 14.4 Problem Identification 250 14.5 Survey of the Literature 250 14.6 PANet Architecture 251 14.6.1 Approach for Semi-Automated System Using DSDV 252 14.6.2 Tables for Parking Available/Occupied 253 14.6.3 Algorithm for Detecting the Empty Slots 255 14.6.4 Pseudo Code 255 14.7 Conclusion 256 References 256 15 Survey of Various Technologies Involved in Vehicle-to-Vehicle Communication 259Lisha Kamala K., Sini Anna Alex and Anita Kanavalli 15.1 Introduction 259 15.2 Survey of the Literature 260 15.3 Brief Description of the Techniques 262 15.3.1 ARM and Zigbee Technology 262 15.3.2 VANET-Based Prototype 262 15.3.2.1 Calculating Distance by Considering Parameters 263 15.3.2.2 Calculating Speed by Considering Parameters 263 15.3.3 Wi-Fi–Based Technology 263 15.3.4 Li-Fi–Based Technique 264 15.3.5 Real-Time Wireless System 266 15.4 Various Technologies Involved in V2V Communication 267 15.5 Results and Analysis 267 15.6 Conclusion 268 References 268 16 Smart Wheelchair 271Mekala Ajay, Pusapally Srinivas and Lupthavisha Netam 16.1 Background 271 16.2 System Overview 275 16.3 Health-Monitoring System Using IoT 275 16.4 Driver Circuit of Wheelchair Interfaced with Amazon Alexa 276 16.5 MATLAB Simulations 277 16.5.1 Obstacle Detection 277 16.5.2 Implementing Path Planning Algorithms 278 16.5.3 Differential Drive Robot for Path Following 280 16.6 Conclusion 282 16.7 Future Work 282 Acknowledgment 283 References 283 17 Defaulter List Using Facial Recognition 285Kavitha Esther, Akilindin S.H., Aswin S. and Anand P. 17.1 Introduction 286 17.2 System Analysis 287 17.2.1 Problem Description 287 17.2.2 Existing System 287 17.2.3 Proposed System 287 17.3 Implementation 289 17.3.1 Image Pre-Processing 289 17.3.2 Polygon Shape Family Pre-Processing 289 17.3.3 Image Segmentation 289 17.3.4 Threshold 289 17.3.5 Edge Detection 291 17.3.6 Region Growing Technique 291 17.3.7 Background Subtraction 291 17.3.8 Morphological Operations 291 17.3.9 Object Detection 292 17.4 Inputs and Outputs 292 17.5 Conclusion 292 References 293 18 Visitor/Intruder Monitoring System Using Machine Learning 295G. Jenifa, S. Indu, C. Jeevitha and V. Kiruthika 18.1 Introduction 296 18.2 Machine Learning 296 18.2.1 Machine Learning in Home Security 297 18.3 System Design 297 18.4 Haar-Cascade Classifier Algorithm 298 18.4.1 Creating the Dataset 298 18.4.2 Training the Model 299 18.4.3 Recognizing the Face 299 18.5 Components 299 18.5.1 Raspberry Pi 299 18.5.2 Web Camera 300 18.6 Experimental Results 300 18.7 Conclusion 302 Acknowledgment 302 References 303 19 Comparison of Machine Learning Algorithms for Air Pollution Monitoring System 305Tushr Sethi and R. C. Thakur 19.1 Introduction 305 19.2 System Design 306 19.3 Model Description and Architecture 307 19.4 Dataset 308 19.5 Models 310 19.6 Line of Best Fit for the Dataset 312 19.7 Feature Importance 313 19.8 Comparisons 315 19.9 Results 318 19.10 Conclusion 318 References 321 20 A Novel Approach Towards Audio Watermarking Using FFT and CORDIC-Based QR Decomposition 323Ankit Kumar, Astha Singh, Shiv Prakash and Vrijendra Singh 20.1 Introduction and Related Work 324 20.2 Proposed Methodology 326 20.2.1 Fast Fourier Transform 328 20.2.2 CORDIC-Based QR Decomposition 329 20.2.3 Concept of Cyclic Codes 331 20.2.4 Concept of Arnold’s Cat Map 331 20.3 Algorithm Design 331 20.4 Experiment Results 334 20.5 Conclusion 337 References 338 21 Performance of DC-Biased Optical Orthogonal Frequency Division Multiplexing in Visible Light Communication 339S. Ponmalar and Shiny J.J. 21.1 Introduction 340 21.2 System Model 341 21.2.1 Transmitter Block 341 21.2.2 Receiver Block 342 21.3 Proposed Method 342 21.3.1 Simulation Parameters for OptSim 343 21.3.2 Block Diagram of DCO-OFDM in OptSim 343 21.4 Results and Discussion 344 21.5 Conclusion 352 References 353 22 Microcontroller-Based Variable Rate Syringe Pump for Microfluidic Application 355G. B. Tejashree, S. Swarnalatha, S. Pavithra, M. C. Jobin Christ and N. Ashwin Kumar 22.1 Introduction 356 22.2 Related Work 357 22.3 Methodology 358 22.3.1 Hardware Design 359 22.3.2 Hardware Interface with Software 360 22.3.3 Programming and Debugging 361 22.4 Result 362 22.5 Inference 363 22.5.1 Viscosity (η) 365 22.5.2 Time Taken 365 22.5.3 Syringe Diameter 366 22.5.4 Deviation 366 22.6 Conclusion and Future Works 366 References 368 23 Analysis of Emotion in Speech Signal Processing and Rejection of Noise Using HMM 371S. Balasubramanian 23.1 Introduction 372 23.2 Existing Method 373 23.3 Proposed Method 374 23.3.1 Proposed Module Description 375 23.3.2 MFCC 376 23.3.3 Hidden Markov Models 379 23.4 Conclusion 382 References 383 24 Securing Cloud Data by Using Blend Cryptography with AWS Services 385Vanchhana Srivastava, Rohit Kumar Pathak and Arun Kumar 24.1 Introduction 385 24.1.1 AWS 387 24.1.2 Quantum Cryptography 388 24.1.3 ECDSA 389 24.2 Background 389 24.3 Proposed Technique 392 24.3.1 How the System Works 393 24.4 Results 394 24.5 Conclusion 396 References 396 Index 399

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Book SynopsisLearn how AI and data science are upending the worlds of biology and medicine In Silico Dreams: How Artificial Intelligence and Biotechnology Will Create the Medicines of the Future delivers an illuminating and fresh perspective on the convergence of two powerful technologies: AI and biotech. Accomplished genomics expert, executive, and author Brian Hilbush offers readers a brilliant exploration of the most current work of pioneering tech giants and biotechnology startups who have already started disrupting healthcare. The book provides an in-depth understanding of the sources of innovation that are driving the shift in the pharmaceutical industry away from serendipitous therapeutic discovery and toward engineered medicines and curative therapies. In this fascinating book, you'll discover: An overview of the rise of data science methods and the paradigm shift in biology that led to the in silico revolutionAn outline of the fundamental breakthroughs in AI and deep learning and their applications across medicineA compelling argument for the notion that AI and biotechnology tools will rapidly accelerate the development of therapeuticsA summary of innovative breakthroughs in biotechnology with a focus on gene editing and cell reprogramming technologies for therapeutic developmentA guide to the startup landscape in AI in medicine, revealing where investments are poised to shape the innovation base for the pharmaceutical industry Perfect for anyone with an interest in scientific topics and technology, In Silico Dreams also belongs on the bookshelves of decision-makers in a wide range of industries, including healthcare, technology, venture capital, and government.Table of ContentsIntroduction xvii Chapter 1 The Information Revolution’s Impact on Biology 1 A Biological Data Avalanche at Warp Speed 5 Tracking SARS-CoV-2 with Genomic Epidemiology 11 Biology’s Paradigm Shift Enables In Silico Biology 17 Transitions and Computation in Cancer Research 18 Structural Biology and Genomics 24 Sequencing the Human Genome 27 Computational Biology in the Twenty-First Century 33 Applications of Human Genome Sequencing 35 Analyzing Human Genome Sequence Information 37 Omics Technologies and Systems Biology 40 Chapter 2 A New Era of Artificial Intelligence 53 AI Steps Out of the Bronx 55 From Neurons and Cats Brains to Neural Networks 58 Machine Learning and the Deep Learning Breakthrough 66 Deep Learning Arrives for AI 73 Deep Neural Network Architectures 75 Deep Learning’s Beachhead on Medicine: Medical Imaging 78 Limitations on Artificial Intelligence 83 Chapter 3 The Long Road to New Medicines 91 Medicine’s Origins: The Role of Opium Since the Stone Age 96 Industrial Manufacturing of Medicines 102 Paul Ehrlich and the Birth of Chemotherapeutic Drug Discovery 108 The Pharmaceutical Industry: Drugs and War—New Medicines in the Twentieth Century 112 From Synthetic Antibiotics to the Search for New Drugs from the Microbial World 116 Developing Therapeutics for Cancer 119 Antifolates and the Emergence of DNA Synthesis Inhibitors 120 Antibiotics as Cancer Chemotherapeutic Drugs 123 Immunotherapy 125 The Pharmaceutical Business Model in the Twenty-First Century 126 R&D Productivity Challenges Within the Pharmaceutical Industry 131 Sources of Pharmaceutical Innovation: Biotechnology and New Therapeutic Modalities 135 Chapter 4 Gene Editing and the New Tools of Biotechnology 145 Molecular Biology and Biological Information Flow 150 Manipulating Gene Information with Recombinant DNA Technology 154 Genetics, Gene Discovery, and Drugs for Rare Human Diseases 160 Second-Generation Biotechnology Tools: CRISPR- Cas9 and Genome Editing Technologies 167 Human Genome Editing and Clinical Trials 171 Biotechnology to the Rescue: Vaccine Development Platforms Based on Messenger RNA 179 Chapter 5 Healthcare and the Entrance of the Technology Titans 189 Digital Health and the New Healthcare Investment Arena 191 Assessing the Tech Titans as Disruptors in Healthcare 195 Alphabet: Extending Its Tentacles Into Healthcare with Google and Other Bets 196 Apple Inc: Consumer Technology Meets Healthcare 200 Amazon: Taking Logistics to the Next Level for Delivering Healthcare 204 Echoes of the Final Frontier 207 Chapter 6 AI-Based Algorithms in Biology and Medicine 211 Recognizing the Faces of Cancer 217 Tumor Classification Using Deep Learning with Genomic Features 222 AI for Diseases of the Nervous System: Seeing and Changing the Brain 229 Regulatory Approval and Clinical Implementation: Twin Challenges for AI-Based Algorithms in Medicine 234 Chapter 7 AI in Drug Discovery and Development 245 A Brief Survey of In Silico Methods in Drug Discovery 247 Virtual Screening with Cheminformatics and HTS Technologies 250 AI Brings a New Toolset for Computational Drug Design 252 AI-Based Virtual Screening Tools 257 Generative Models for De Novo Drug Design 257 A New Base of Innovation for the Pharmaceutical Industry 259 Atomwise 261 Recursion Pharmaceuticals 262 Deep Genomics 262 Relay Therapeutics 263 Summary 265 Chapter 8 Biotechnology, AI, and Medicine’s Future 269 Building Tools to Decipher Molecular Structures and Biological Systems 272 AlphaFold: Going Deep in Protein Structure Prediction 274 Predicting Genome 3D Organization and Regulatory Elements 276 AI Approaches to Link Genetics-Based Targets to Disease 277 Quantum Computing for In Silico Chemistry and Biology 278 Neuroscience and AI: Modeling Brain and Behavior 280 Brain Information Processing and Modularity: Climbing a Granite Wall 283 Engineering Medicines with Biotechnology and AI 289 Glossary 295 Index 303

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Book SynopsisTable of ContentsPreface xv 1 In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins 1Manisha Sritharan and Asita Elengoe 1.1 Introduction 2 1.2 Methodology 4 1.2.1 Sequence of Protein 4 1.2.2 Homology Modeling 4 1.2.3 Physiochemical Characterization 4 1.2.4 Determination of Secondary Models 4 1.2.5 Determination of Stability of Protein Structures 4 1.2.6 Identification of Active Site 4 1.2.7 Preparation of Ligand Model 5 1.2.8 Docking of Target Protein and Phytocompound 5 1.3 Results and Discussion 5 1.3.1 Determination of Physiochemical Characters 5 1.3.2 Prediction of Secondary Structures 7 1.3.3 Verification of Stability of Protein Structures 7 1.3.4 Identification of Active Sites 14 1.3.5 Target Protein-Ligand Docking 14 1.4 Conclusion 18 References 18 2 Medical Data Classification in Cloud Computing Using Soft Computing With Voting Classifier: A Review 23Saurabh Sharma, Harish K. Shakya and Ashish Mishra 2.1 Introduction 24 2.1.1 Security in Medical Big Data Analytics 24 2.1.1.1 Capture 24 2.1.1.2 Cleaning 25 2.1.1.3 Storage 25 2.1.1.4 Security 26 2.1.1.5 Stewardship 26 2.2 Access Control–Based Security 27 2.2.1 Authentication 27 2.2.1.1 User Password Authentication 28 2.2.1.2 Windows-Based User Authentication 28 2.2.1.3 Directory-Based Authentication 28 2.2.1.4 Certificate-Based Authentication 28 2.2.1.5 Smart Card–Based Authentication 29 2.2.1.6 Biometrics 29 2.2.1.7 Grid-Based Authentication 29 2.2.1.8 Knowledge-Based Authentication 29 2.2.1.9 Machine Authentication 29 2.2.1.10 One-Time Password (OTP) 30 2.2.1.11 Authority 30 2.2.1.12 Global Authorization 30 2.3 System Model 30 2.3.1 Role and Purpose of Design 31 2.3.1.1 Patients 31 2.3.1.2 Cloud Server 31 2.3.1.3 Doctor 31 2.4 Data Classification 32 2.4.1 Access Control 32 2.4.2 Content 33 2.4.3 Storage 33 2.4.4 Soft Computing Techniques for Data Classification 34 2.5 Related Work 36 2.6 Conclusion 42 References 43 3 Research Challenges in Pre-Copy Virtual Machine Migration in Cloud Environment 45Nirmala Devi N. and Vengatesh Kumar S. 3.1 Introduction 46 3.1.1 Cloud Computing 46 3.1.1.1 Cloud Service Provider 47 3.1.1.2 Data Storage and Security 47 3.1.2 Virtualization 48 3.1.2.1 Virtualization Terminology 49 3.1.3 Approach to Virtualization 50 3.1.4 Processor Issues 51 3.1.5 Memory Management 51 3.1.6 Benefits of Virtualization 51 3.1.7 Virtual Machine Migration 51 3.1.7.1 Pre-Copy 52 3.1.7.2 Post-Copy 52 3.1.7.3 Stop and Copy 53 3.2 Existing Technology and Its Review 54 3.3 Research Design 56 3.3.1 Basic Overview of VM Pre-Copy Live Migration 57 3.3.2 Improved Pre-Copy Approach 58 3.3.3 Time Series–Based Pre-Copy Approach 60 3.3.4 Memory-Bound Pre-Copy Live Migration 62 3.3.5 Three-Phase Optimization Method (TPO) 62 3.3.6 Multiphase Pre-Copy Strategy 64 3.4 Results 65 3.4.1 Finding 65 3.5 Discussion 69 3.5.1 Limitation 69 3.5.2 Future Scope 70 3.6 Conclusion 70 References 71 4 Estimation and Analysis of Prediction Rate of Pre-Trained Deep Learning Network in Classification of Brain Tumor MRI Images 73Krishnamoorthy Raghavan Narasu, Anima Nanda, Marshiana D., Bestley Joe and Vinoth Kumar 4.1 Introduction 74 4.2 Classes of Brain Tumors 75 4.3 Literature Survey 76 4.4 Methodology 78 4.5 Conclusion 93 References 95 5 An Intelligent Healthcare Monitoring System for Coma Patients 99Bethanney Janney J., T. Sudhakar, Sindu Divakaran, Chandana H. and Caroline Chriselda L. 5.1 Introduction 100 5.2 Related Works 102 5.3 Materials and Methods 104 5.3.1 Existing System 104 5.3.2 Proposed System 105 5.3.3 Working 105 5.3.4 Module Description 106 5.3.4.1 Pulse Sensor 106 5.3.4.2 Temperature Sensor 107 5.3.4.3 Spirometer 107 5.3.4.4 OpenCV (Open Source Computer Vision) 108 5.3.4.5 Raspberry Pi 108 5.3.4.6 USB Camera 109 5.3.4.7 AVR Module 109 5.3.4.8 Power Supply 109 5.3.4.9 USB to TTL Converter 110 5.3.4.10 EEG of Comatose Patients 110 5.4 Results and Discussion 111 5.5 Conclusion 116 References 117 6 Deep Learning Interpretation of Biomedical Data 121T.R. Thamizhvani, R. Chandrasekaran and T.R. Ineyathendral 6.1 Introduction 122 6.2 Deep Learning Models 125 6.2.1 Recurrent Neural Networks 125 6.2.2 LSTM/GRU Networks 127 6.2.3 Convolutional Neural Networks 128 6.2.4 Deep Belief Networks 130 6.2.5 Deep Stacking Networks 131 6.3 Interpretation of Deep Learning With Biomedical Data 132 6.4 Conclusion 139 References 140 7 Evolution of Electronic Health Records 143G. Umashankar, Abinaya P., J. Premkumar, T. Sudhakar and S. Krishnakumar 7.1 Introduction 143 7.2 Traditional Paper Method 144 7.3 IoMT 144 7.4 Telemedicine and IoMT 145 7.4.1 Advantages of Telemedicine 145 7.4.2 Drawbacks 146 7.4.3 IoMT Advantages with Telemedicine 146 7.4.4 Limitations of IoMT With Telemedicine 147 7.5 Cyber Security 147 7.6 Materials and Methods 147 7.6.1 General Method 147 7.6.2 Data Security 148 7.7 Literature Review 148 7.8 Applications of Electronic Health Records 150 7.8.1 Clinical Research 150 7.8.1.1 Introduction 150 7.8.1.2 Data Significance and Evaluation 151 7.8.1.3 Conclusion 151 7.8.2 Diagnosis and Monitoring 151 7.8.2.1 Introduction 151 7.8.2.2 Contributions 152 7.8.2.3 Applications 152 7.8.3 Track Medical Progression 153 7.8.3.1 Introduction 153 7.8.3.2 Method Used 153 7.8.3.3 Conclusion 154 7.8.4 Wearable Devices 154 7.8.4.1 Introduction 154 7.8.4.2 Proposed Method 155 7.8.4.3 Conclusion 155 7.9 Results and Discussion 155 7.10 Challenges Ahead 157 7.11 Conclusion 158 References 158 8 Architecture of IoMT in Healthcare 161A. Josephin Arockia Dhiyya 8.1 Introduction 161 8.1.1 On-Body Segment 162 8.1.2 In-Home Segment 162 8.1.3 Network Segment Layer 163 8.1.4 In-Clinic Segment 163 8.1.5 In-Hospital Segment 163 8.1.6 Future of IoMT? 164 8.2 Preferences of the Internet of Things 165 8.2.1 Cost Decrease 165 8.2.2 Proficiency and Efficiency 165 8.2.3 Business Openings 165 8.2.4 Client Experience 166 8.2.5 Portability and Nimbleness 166 8.3 loMT Progress in COVID-19 Situations: Presentation 167 8.3.1 The IoMT Environment 168 8.3.2 IoMT Pandemic Alleviation Design 169 8.3.3 Man-Made Consciousness and Large Information Innovation in IoMT 170 8.4 Major Applications of IoMT 171 References 172 9 Performance Assessment of IoMT Services and Protocols 173A. Keerthana and Karthiga 9.1 Introduction 174 9.2 IoMT Architecture and Platform 175 9.2.1 Architecture 176 9.2.2 Devices Integration Layer 177 9.3 Types of Protocols 177 9.3.1 Internet Protocol for Medical IoT Smart Devices 177 9.3.1.1 HTTP 178 9.3.1.2 Message Queue Telemetry Transport (MQTT) 179 9.3.1.3 Constrained Application Protocol (CoAP) 180 9.3.1.4 AMQP: Advanced Message Queuing Protocol (AMQP) 181 9.3.1.5 Extensible Message and Presence Protocol (XMPP) 181 9.3.1.6 DDS 183 9.4 Testing Process in IoMT 183 9.5 Issues and Challenges 185 9.6 Conclusion 185 References 185 10 Performance Evaluation of Wearable IoT-Enabled Mesh Network for Rural Health Monitoring 187G. Merlin Sheeba and Y. Bevish Jinila 10.1 Introduction 188 10.2 Proposed System Framework 190 10.2.1 System Description 190 10.2.2 Health Monitoring Center 192 10.2.2.1 Body Sensor 192 10.2.2.2 Wireless Sensor Coordinator/Transceiver 192 10.2.2.3 Ontology Information Center 195 10.2.2.4 Mesh Backbone-Placement and Routing 196 10.3 Experimental Evaluation 200 10.4 Performance Evaluation 201 10.4.1 Energy Consumption 201 10.4.2 Survival Rate 201 10.4.3 End-to-End Delay 202 10.5 Conclusion 204 References 204 11 Management of Diabetes Mellitus (DM) for Children and Adults Based on Internet of Things (IoT) 207Krishnakumar S., Umashankar G., Lumen Christy V., Vikas and Hemalatha R.J. 11.1 Introduction 208 11.1.1 Prevalence 209 11.1.2 Management of Diabetes 209 11.1.3 Blood Glucose Monitoring 210 11.1.4 Continuous Glucose Monitors 211 11.1.5 Minimally Invasive Glucose Monitors 211 11.1.6 Non-Invasive Glucose Monitors 211 11.1.7 Existing System 211 11.2 Materials and Methods 212 11.2.1 Artificial Neural Network 212 11.2.2 Data Acquisition 213 11.2.3 Histogram Calculation 213 11.2.4 IoT Cloud Computing 214 11.2.5 Proposed System 215 11.2.6 Advantages 215 11.2.7 Disadvantages 215 11.2.8 Applications 216 11.2.9 Arduino Pro Mini 216 11.2.10 LM78XX 217 11.2.11 MAX30100 218 11.2.12 LM35 Temperature Sensors 218 11.3 Results and Discussion 219 11.4 Summary 222 11.5 Conclusion 222 References 223 12 Wearable Health Monitoring Systems Using IoMT 225Jaya Rubi and A. Josephin Arockia Dhivya 12.1 Introduction 225 12.2 IoMT in Developing Wearable Health Surveillance System 226 12.2.1 A Wearable Health Monitoring System with Multi-Parameters 227 12.2.2 Wearable Input Device for Smart Glasses Based on a Wristband-Type Motion-Aware Touch Panel 228 12.2.3 Smart Belt: A Wearable Device for Managing Abdominal Obesity 228 12.2.4 Smart Bracelets: Automating the Personal Safety Using Wearable Smart Jewelry 228 12.3 Vital Parameters That Can Be Monitored Using Wearable Devices 229 12.3.1 Electrocardiogram 230 12.3.2 Heart Rate 231 12.3.3 Blood Pressure 232 12.3.4 Respiration Rate 232 12.3.5 Blood Oxygen Saturation 234 12.3.6 Blood Glucose 235 12.3.7 Skin Perspiration 236 12.3.8 Capnography 238 12.3.9 Body Temperature 239 12.4 Challenges Faced in Customizing Wearable Devices 240 12.4.1 Data Privacy 240 12.4.2 Data Exchange 240 12.4.3 Availability of Resources 241 12.4.4 Storage Capacity 241 12.4.5 Modeling the Relationship Between Acquired Measurement and Diseases 242 12.4.6 Real-Time Processing 242 12.4.7 Intelligence in Medical Care 242 12.5 Conclusion 243 References 244 13 Future of Healthcare: Biomedical Big Data Analysis and IoMT 247Tamiziniyan G. and Keerthana A. 13.1 Introduction 248 13.2 Big Data and IoT in Healthcare Industry 250 13.3 Biomedical Big Data Types 251 13.3.1 Electronic Health Records 252 13.3.2 Administrative and Claims Data 252 13.3.3 International Patient Disease Registries 252 13.3.4 National Health Surveys 253 13.3.5 Clinical Research and Trials Data 254 13.4 Biomedical Data Acquisition Using IoT 254 13.4.1 Wearable Sensor Suit 254 13.4.2 Smartphones 255 13.4.3 Smart Watches 255 13.5 Biomedical Data Management Using IoT 256 13.5.1 Apache Spark Framework 257 13.5.2 MapReduce 258 13.5.3 Apache Hadoop 258 13.5.4 Clustering Algorithms 259 13.5.5 K-Means Clustering 259 13.5.6 Fuzzy C-Means Clustering 260 13.5.7 DBSCAN 261 13.6 Impact of Big Data and IoMT in Healthcare 262 13.7 Discussions and Conclusions 263 References 264 14 Medical Data Security Using Blockchain With Soft Computing Techniques: A Review 269Saurabh Sharma, Harish K. Shakya and Ashish Mishra 14.1 Introduction 270 14.2 Blockchain 272 14.2.1 Blockchain Architecture 272 14.2.2 Types of Blockchain Architecture 273 14.2.3 Blockchain Applications 274 14.2.4 General Applications of the Blockchain 276 14.3 Blockchain as a Decentralized Security Framework 277 14.3.1 Characteristics of Blockchain 278 14.3.2 Limitations of Blockchain Technology 280 14.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science 281 14.4.1 Data Science in Healthcare 281 14.5 Literature Review: Medical Data Security in Cloud Storage 281 14.6 Conclusion 286 References 287 15 Electronic Health Records: A Transitional View 289Srividhya G. 15.1 Introduction 289 15.2 Ancient Medical Record, 1600 BC 290 15.3 Greek Medical Record 291 15.4 Islamic Medical Record 291 15.5 European Civilization 292 15.6 Swedish Health Record System 292 15.7 French and German Contributions 293 15.8 American Descriptions 293 15.9 Beginning of Electronic Health Recording 297 15.10 Conclusion 298 References 298 Index 301

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Book SynopsisBIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data miniTable of ContentsPreface xiii 1 An Introduction to Big Data Analytics Techniques in Healthcare 1Anil Audumbar Pise 1.1 Introduction 1 1.2 Big Data in Healthcare 3 1.3 Areas of Big Data Analytics in Medicine 5 1.4 Healthcare as a Big Data Repository 9 1.5 Applications of Healthcare Big Data 10 1.6 Challenges in Big Data Analytics 16 1.7 Big Data Privacy and Security 17 1.8 Conclusion 18 1.9 Future Work 18 2 Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia 21Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam and Mohammed Siddique 2.1 Introduction 22 2.2 Literature Review 23 2.3 Methodology and Data Source 25 2.4 Implementation and Results 28 2.5 Conclusion 44 3 Pre-Trained CNN Models in Early Alzheimer's Prediction Using Post-Processed MRI 47Kalyani Gunda and Pradeepini Gera 3.1 Introduction 48 3.2 Experimental Study 51 3.3 Data Exploration 55 3.4 OASIS Dataset Pre-Processing 61 3.5 Alzheimer's 4-Class-MRI Features Extraction 69 3.6 Alzheimer 4-Class MRI Image Dataset 69 3.7 RMSProp (Root Mean Square Propagation) 80 3.8 Activation Function 81 3.9 Batch Normalization 81 3.10 Dropout 81 3.11 Result--I 82 3.12 Conclusion and Future Work 89 4 Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging 97Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal 4.1 Introduction 98 4.2 Basics of Proposed Methods 100 4.3 Experimental Results and Discussion 107 4.4 Conclusion 115 5 Analysis of Healthcare Systems Using Computational Approaches 119Hemanta Kumar Bhuyan and Subhendu Kumar Pani 5.1 Introduction 120 5.2 AI & ML Analysis in Health Systems 124 5.3 Healthcare Intellectual Approaches 127 5.4 Precision Approaches to Medicine 133 5.5 Methodology of AI, ML With Healthcare Examples 134 5.6 Big Analytic Data Tools 136 5.7 Discussion 141 5.8 Conclusion 142 6 Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy 147Shrikaant Kulkarni 6.1 Introduction 148 6.2 AI Methods 149 6.3 Turing Test 156 6.4 Barriers to Technologies 157 6.5 Advantages of AI for Behavioral & Mental Healthcare 157 6.6 Enhanced Self-Care & Access to Care 158 6.7 Other Considerations 160 6.8 Expert Systems in Mental & Behavioral Healthcare 161 6.9 Dynamical Approaches to Clinical AI and Expert Systems 165 6.10 Conclusion 173 6.11 Future Prospects 175 7 A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19) 187Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad and Mukkamala S.N.V. Jitendra 7.1 Introduction 188 7.2 Related Work 189 7.3 Proposed Frameworks 190 7.4 Results and Discussion 194 7.5 Conclusion 201 8 An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information 205Sowjanya Naidu K. and Srinivasa L. Chakravarthy 8.1 Introduction 206 8.2 Related Work 212 8.3 Need for Blockchain in Healthcare 216 8.4 Proposed Frameworks 219 8.5 Use Cases 223 8.6 Discussions 229 8.7 Challenges and Limitations 231 8.8 Future Work 231 8.9 Conclusion 232 9 An Epidemic Graph's Modeling Application to the COVID-19 Outbreak 237Hemanta Kumar Bhuyan and Subhendu Kumar Pani 9.1 Introduction 237 9.2 Related Work 239 9.3 Theoretical Approaches 240 9.4 Frameworks 243 9.5 Evaluation of COVID-19 Outbreak 246 9.6 Conclusions and Future Works 250 10 Big Data and Data Mining in e-Health: Legal Issues and Challenges 257Amita Verma and Arpit Bansal Object of Study 257 10.1 Introduction 258 10.2 Big Data and Data Mining in e-Health 260 10.3 Big Data and e-Health in India 262 10.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health 263 10.5 Big Data and Issues of Privacy in e-Health 271 10.6 Conclusion and Suggestions 272 11 Basic Scientific and Clinical Applications 275Manna Sheela Rani Chetty and Kiran Babu C. V. 11.1 Introduction 275 11.2 Case Study-1: Continual Learning Using ML for Clinical pplications 283 11.3 Case Study-2 286 11.4 Case Study-3: ML Will Improve the Radiology Patient Experience 289 11.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization 292 11.6 Case Study-5: ML will Benefit All Medical Imaging 'ologies' 295 11.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data 298 11.8 Conclusion 300 12 Healthcare Branding Through Service Quality 305Saraju Prasad and Sunil Dhal 12.1 Introduction to Healthcare 305 12.2 Quality in Healthcare 307 12.3 Service Quality 311 12.4 Conclusion and Road Ahead 315 References 316 Index 321

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Book SynopsisHANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reaTable of ContentsPreface xvii 1 An Introduction to Knowledge Engineering and Data Analytics 1D. Karthika and K. Kalaiselvi 1.1 Introduction 2 1.1.1 Online Learning and Fragmented Learning Modeling 2 1.2 Knowledge and Knowledge Engineering 5 1.2.1 Knowledge 5 1.2.2 Knowledge Engineering 5 1.3 Knowledge Engineering as a Modelling Process 6 1.4 Tools 7 1.5 What are KBSs? 8 1.5.1 What is KBE? 8 1.5.2 When Can KBE Be Used? 10 1.5.3 CAD or KBE? 12 1.6 Guided Random Search and Network Techniques 13 1.6.1 Guide Random Search Techniques 13 1.7 Genetic Algorithms 14 1.7.1 Design Point Data Structure 15 1.7.2 Fitness Function 15 1.7.3 Constraints 16 1.7.4 Hybrid Algorithms 16 1.7.5 Considerations When Using a GA 16 1.7.6 Alternative to Genetic-Inspired Creation of Children 17 1.7.7 Alternatives to GA 18 1.7.8 Closing Remarks for GA 18 1.8 Artificial Neural Networks 19 1.9 Conclusion 19 References 20 2 A Framework for Big Data Knowledge Engineering 21Devi T. and Ramachandran A. 2.1 Introduction 22 2.1.1 Knowledge Engineering in AI and Its Techniques 23 2.1.1.1 Supervised Model 23 2.1.1.2 Unsupervised Model 23 2.1.1.3 Deep Learning 24 2.1.1.4 Deep Reinforcement Learning 24 2.1.1.5 Optimization 25 2.1.2 Disaster Management 25 2.2 Big Data in Knowledge Engineering 26 2.2.1 Cognitive Tasks for Time Series Sequential Data 27 2.2.2 Neural Network for Analyzing the Weather Forecasting 27 2.2.3 Improved Bayesian Hidden Markov Frameworks 28 2.3 Proposed System 30 2.4 Results and Discussion 32 2.5 Conclusion 33 References 36 3 Big Data Knowledge System in Healthcare 39P. Sujatha, K. Mahalakshmi and P. Sripriya 3.1 Introduction 40 3.2 Overview of Big Data 41 3.2.1 Big Data: Definition 41 3.2.2 Big Data: Characteristics 42 3.3 Big Data Tools and Techniques 43 3.3.1 Big Data Value Chain 43 3.3.2 Big Data Tools and Techniques 45 3.4 Big Data Knowledge System in Healthcare 45 3.4.1 Sources of Medical Big Data 51 3.4.2 Knowledge in Healthcare 53 3.4.3 Big Data Knowledge Management Systems in Healthcare 55 3.4.4 Big Data Analytics in Healthcare 56 3.5 Big Data Applications in the Healthcare Sector 59 3.5.1 Real Time Healthcare Monitoring and Altering 59 3.5.2 Early Disease Prediction with Big Data 59 3.5.3 Patients Predictions for Improved Staffing 61 3.5.4 Medical Imaging 61 3.6 Challenges with Healthcare Big Data 62 3.6.1 Challenges of Big Data 62 3.6.2 Challenges of Healthcare Big Data 62 3.7 Conclusion 64 References 64 4 Big Data for Personalized Healthcare 67Dhanalakshmi R. and Jose Anand 4.1 Introduction 68 4.1.1 Objectives 68 4.1.2 Motivation 69 4.1.3 Domain Description 70 4.1.4 Organization of the Chapter 70 4.2 Related Literature 71 4.2.1 Healthcare Cyber Physical System Architecture 71 4.2.2 Healthcare Cloud Architecture 71 4.2.3 User Authentication Management 72 4.2.4 Healthcare as a Service (HaaS) 72 4.2.5 Reporting Services 73 4.2.6 Chart and Trend Analysis 73 4.2.7 Medical Data Analysis 73 4.2.8 Hospital Platform Based On Cloud Computing 74 4.2.9 Patient’s Data Collection 74 4.2.10 H-Cloud Challenges 75 4.2.11 Healthcare Information System and Cost 75 4.3 System Analysis and Design 75 4.3.1 Proposed Solution 76 4.3.2 Software Components 76 4.3.3 System Design 76 4.3.4 Architecture Diagram 77 4.3.5 List of Modules 78 4.3.6 Use Case Diagram 81 4.3.7 Sequence Diagram 81 4.3.8 Class Diagram 82 4.4 System Implementation 83 4.4.1 User Interface 83 4.4.2 Storage Module 84 4.4.3 Notification Module 85 4.4.4 Middleware 86 4.4.5 OTP Module 87 4.5 Results and Discussion 88 4.6 Conclusion 90 References 90 5 Knowledge Engineering for AI in Healthcare 93A. Thirumurthi Raja and B. Mahalakshmi 5.1 Introduction 94 5.2 Overview 95 5.2.1 Knowledge Representation 95 5.2.2 Types of Knowledge in Artificial Intelligence 96 5.2.3 Relation Between Knowledge and Intelligence 97 5.2.4 Approaches to Knowledge Representation 97 5.2.5 Requirements for Knowledge Representation System 98 5.2.6 Techniques of Knowledge Representation 98 5.2.6.1 Logical Representation 99 5.2.6.2 Semantic Network Representation 99 5.2.6.3 Frame Representation 99 5.2.6.4 Production Rules 100 5.2.7 Process of Knowledge Engineering 101 5.2.8 Knowledge Discovery Process 106 5.3 Applications of Knowledge Engineering in AI for Healthcare 106 5.3.1 AI Supports in Clinical Decisions 107 5.3.2 AI-Assisted Robotic Surgery 107 5.3.3 Enhance Primary Care and Triage 108 5.3.4 Clinical Judgments or Diagnosis 108 5.3.5 Precision Medicine 109 5.3.6 Drug Discovery 109 5.3.7 Deep Learning to Diagnose Diseases 110 5.3.8 Automating Administrative Tasks 111 5.3.9 Reducing Operational Costs 112 5.3.10 Virtual Nursing Assistants 113 5.4 Conclusion 113 References 114 6 Business Intelligence and Analytics from Big Data to Healthcare 115Maheswari P., A. Jaya and João Manuel R. S. Tavares 6.1 Introduction 116 6.1.1 Impact of Healthcare Industry on Economy 116 6.1.2 Coronavirus Impact on the Healthcare Industry 117 6.1.3 Objective of the Study 117 6.1.4 Limitations of the Study 117 6.2 Related Works 118 6.3 Conceptual Healthcare Stock Prediction System 120 6.3.1 Data Source 122 6.3.2 Business Intelligence and Analytics Framework 122 6.3.2.1 Simple Machine Learning Model 122 6.3.2.2 Time Series Forecasting 123 6.3.2.3 Complex Deep Neural Network 123 6.3.3 Predicting the Stock Price 124 6.4 Implementation and Result Discussion 124 6.4.1 Apollo Hospitals Enterprise Limited 125 6.4.2 Cadila Healthcare Ltd 125 6.4.3 Dr. Reddy’s Laboratories 128 6.4.4 Fortis Healthcare Limited 130 6.4.5 Max Healthcare Institute Limited 131 6.4.6 Opto Circuits Limited 131 6.4.7 Panacea Biotec 135 6.4.8 Poly Medicure Ltd 136 6.4.9 Thyrocare Technologies Limited 138 6.4.10 Zydus Wellness Ltd 138 6.5 Comparisons of Healthcare Stock Prediction Framework 141 6.6 Conclusion and Future Enhancement 143 References 143 Books 145 Web Citation 145 7 Internet of Things and Big Data Analytics for Smart Healthcare 147Sathish Kumar K., Om Prakash P.G., Alangudi Balaji N. and Robertas Damaševičius 7.1 Introduction 148 7.2 Literature Survey 149 7.3 Smart Healthcare Using Internet of Things and Big Data Analytics 151 7.3.1 Smart Diabetes Prediction 151 7.3.2 Smart ADHD Prediction 154 7.4 Security for Internet of Things 159 7.4.1 K(Binary) ECC FSM 159 7.4.2 NAF Method 160 7.4.3 K-NAF Multiplication Architecture 161 7.4.4 K(NAF) ECC FSM 161 7.5 Conclusion 164 References 165 8 Knowledge-Driven and Intelligent Computing in Healthcare 167R. Mervin, Dinesh Mavalaru and Tintu Thomas 8.1 Introduction 168 8.1.1 Basics of Health Recommendation System 169 8.1.2 Basics of Ontology 169 8.1.3 Need of Ontology in Health Recommendation System 170 8.2 Literature Review 171 8.2.1 Ontology in Various Domain 172 8.2.2 Ontology in Health Recommendation System 174 8.3 Framework for Health Recommendation System 175 8.3.1 Domain Ontology Creation 176 8.3.2 Query Pre-Processing 178 8.3.3 Feature Selection 179 8.3.4 Recommendation System 180 8.4 Experimental Results 182 8.5 Conclusion and Future Perspective 183 References 183 9 Secure Healthcare Systems Based on Big Data Analytics 189A. Angel Cerli, K. Kalaiselvi and Vijayakumar Varadarajan 9.1 Introduction 190 9.2 Healthcare Data 193 9.2.1 Structured Data 193 9.2.2 Unstructured Data 194 9.2.3 Semi-Structured Data 194 9.2.4 Genomic Data 194 9.2.5 Patient Behavior and Sentiment Data 194 9.2.6 Clinical Data and Clinical Notes 194 9.2.7 Clinical Reference and Health Publication Data 195 9.2.8 Administrative and External Data 195 9.3 Recent Works in Big Data Analytics in Healthcare Data 195 9.4 Healthcare Big Data 197 9.5 Privacy of Healthcare Big Data 198 9.6 Privacy Right by Country and Organization 200 9.7 How Blockchain is Big Data Usable for Healthcare 200 9.7.1 Digital Trust 200 9.7.2 Smart Data Tracking 202 9.7.3 Ecosystem Sensible 202 9.7.4 Switch Digital 202 9.7.5 Cybersecurity 203 9.7.6 Sharing Interoperability and Data 203 9.7.7 Improving Research and Development (R&D) 206 9.7.8 Drugs Fighting Counterfeit 206 9.7.9 Patient Mutual Participation 206 9.7.10 Internet Access by Patient to Longitudinal Data 206 9.7.11 Data Storage into Off Related to Confidentiality and Data Scale 207 9.8 Blockchain Threats and Medical Strategies Big Data Technology 207 9.9 Conclusion and Future Research 208 References 208 10 Predictive and Descriptive Analysis for Healthcare Data 213Pritam R. Ahire and Rohini Hanchate 10.1 Introduction 214 10.2 Motivation 215 10.2.1 Healthcare Analysis 215 10.2.2 Predictive Analytics 217 10.2.3 Predictive Analytics Current Trends 217 10.2.3.1 Importance of PA 217 10.2.4 Descriptive Analysis 218 10.2.4.1 Descriptive Statistics 218 10.2.4.2 Categories of Descriptive Analysis 219 10.2.5 Method of Modeling 221 10.2.6 Measures of Data Analytics 221 10.2.7 Healthcare Data Analytics Platforms and Tools 223 10.2.8 Challenges 225 10.2.9 Issues in Predictive Healthcare Analysis 226 10.2.9.1 Integrating Separate Data Sources 226 10.2.9.2 Advanced Cloud Technologies 226 10.2.9.3 Privacy and Security 227 10.2.9.4 The Fast Pace of Technology Changes 227 10.2.10 Applications of Predictive Analysis 227 10.2.10.1 Improving Operational Efficiency 227 10.2.10.2 Personal Medicine 228 10.2.10.3 Population Health and Risk Scoring 228 10.2.10.4 Outbreak Prediction 228 10.2.10.5 Controlling Patient Deterioration 228 10.2.10.6 Supply Chain Management 228 10.2.10.7 Potential in Precision Medicine 229 10.2.10.8 Cost Savings From Reducing Waste and Fraud 229 10.3 Conclusion 229 References 229 11 Machine and Deep Learning Algorithms for Healthcare Applications 233K. France, A. Jaya and Doru Tiliute 11.1 Introduction 234 11.2 Artificial Intelligence, Machine Learning, and Deep Learning 234 11.3 Machine Learning 236 11.3.1 Supervised Learning 236 11.3.2 Unsupervised Learning 238 11.3.3 Semi-Supervised 238 11.3.4 Reinforcement Learning 238 11.4 Advantages of Using Deep Learning on Top of Machine Learning 239 11.5 Deep Learning Architecture 239 11.6 Medical Image Analysis using Deep Learning 242 11.7 Deep Learning in Chest X-Ray Images 243 11.8 Machine Learning and Deep Learning in Content-Based Medical Image Retrieval 246 11.9 Image Retrieval Performance Metrics 249 11.10 Conclusion 250 References 250 12 Artificial Intelligence in Healthcare Data Science with Knowledge Engineering 255S. Asha, Kanchana Devi V. and G. Sahaja Vaishnavi 12.1 Introduction 256 12.2 Literature Review 260 12.3 AI in Healthcare 266 12.4 Data Science and Knowledge Engineering for COVID-19 268 12.5 Proposed Architecture and Its Implementation 270 12.5.1 Implementation 270 12.5.1.1 Data Collection 270 12.5.1.2 Understanding Class and Dependencies 270 12.5.1.3 Pre-Processing 272 12.5.1.4 Sampling 273 12.5.1.5 Model Fixing 273 12.5.1.6 Analysis of Real-Time Datasets 273 12.5.1.7 Machine Learning Algorithms 276 12.6 Conclusions and Future Work 278 References 280 13 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System 285Agasba Saroj S. J., B. Saleena and B. Prakash 13.1 Introduction 285 13.1.1 Motivation 287 13.2 Ongoing Research on Intelligent Decision Support System 289 13.3 Methodology and Architecture of the Intelligent Rule-Based System 291 13.3.1 Proposed System Design 292 13.3.2 Algorithms Used 293 13.3.2.1 Forward Chaining 293 13.3.2.2 Backward Chaining 294 13.4 Creating a Rule-Based System using Prolog 295 13.5 Results and Discussions 304 13.6 Conclusion 306 13.7 Acknowledgments 307 References 307 14 Big Data in Healthcare: Management, Analysis, and Future Prospects 309A. Akila, R. Parameswari and C. Jayakumari 14.1 Introduction 309 14.2 Breast Cancer: Overview 310 14.3 State-of-the-Art Technology in Treatment of Cancer 311 14.3.1 Chemotherapy 311 14.3.2 Radiotherapy 311 14.4 Early Diagnosis of Breast Cancer: Overview 312 14.4.1 Advantages and Risks Associated with the Early Detection of Breast Cancer 312 14.4.2 Diagnosis the Breast Cancer 313 14.5 Literature Review 314 14.6 Machine Learning Algorithms 315 14.6.1 Principal Component Analysis Algorithms 316 14.6.2 K-Means Algorithm 317 14.6.3 K-Nearest Neighbor Algorithm 317 14.6.4 Logistic Regression Algorithm 318 14.6.5 Support Vector Machine Algorithm 318 14.6.6 AdaBoost Algorithm 319 14.6.7 Neural Networks Algorithm 319 14.6.8 Random Forest Algorithm 319 14.7 Result and Discussion 320 14.7.1 Performance Metrics 320 14.7.1.1 ROC Curve 320 14.7.1.2 Accuracy 321 14.7.1.3 Precision and Recall 321 14.7.1.4 F1-Score 322 14.8 Experimental Result and Discussion 322 14.9 Conclusion 324 References 325 15 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System 327G. Jaculine Priya and S. Saradha 15.1 Introduction 327 15.2 Machine Learning in Healthcare 329 15.3 Types of Learnings in Machine Learning 331 15.3.1 Supervised Learning 332 15.3.2 Unsupervised Algorithms 333 15.3.3 Semi-Supervised Learning 334 15.3.4 Reinforcement Learning 334 15.4 Types of Machine Learning Algorithms 334 15.4.1 Classification 335 15.4.2 Bayes Classification 335 15.4.3 Association Analysis 335 15.4.4 Correlation Analysis 336 15.4.5 Cluster Analysis 336 15.4.6 Outlier Analysis 336 15.4.7 Regression Analysis 337 15.4.8 K-Means 337 15.4.9 Apriori Algorithm 337 15.4.10 K Nearest Neighbor 337 15.4.11 Naive Bayes 338 15.4.12 AdaBoost 338 15.4.13 Support Vector Machine 338 15.4.14 Classification and Regression Trees 339 15.4.15 Linear Discriminant Analysis 339 15.4.16 Logistic Regression 339 15.4.17 Linear Regression 339 15.4.18 Principal Component Analysis 339 15.5 Machine Learning for Information Extraction 340 15.5.1 Natural Language Processing 340 15.6 Predictive Analysis in Healthcare 341 15.7 Conclusion 342 References 342 16 Knowledge Fusion Patterns in Healthcare 345N. Deepa and N. Kanimozhi 16.1 Introduction 346 16.2 Related Work 348 16.3 Materials and Methods 349 16.3.1 Classification of Data Fusion 349 16.3.2 Levels and Its Working in Healthcare Ecosystems 351 16.3.2.1 Initial Level Data Access (ILA) 351 16.3.2.2 Middle Level Access (MLA) 352 16.3.2.3 High Level Access (HLA) 352 16.4 Proposed System 352 16.4.1 Objective 353 16.4.2 Sample Dataset 355 16.5 Results and Discussion 355 16.6 Conclusion and Future Work 361 References 362 17 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells 365J.K. Adedeji, T.O. Owolabi and R.S. Fayose 17.1 Introduction 366 17.2 Materials and Methods 367 17.2.1 Data Acquisition and Pre-Processing 367 17.2.2 Sickle Cells Normalization Image 368 17.2.3 Gradient Calculation 369 17.2.4 Gradient Descent Step 371 17.2.5 Insight to Previous Methods Adopted in Convolutional Neural Networks 372 17.2.6 Segments of Convolutional Neural Networks 372 17.2.6.1 Convolutional Layer 372 17.2.6.2 Pooling Layer 373 17.2.6.3 Fully Connected Layer 374 17.2.6.4 Softmax Layer 374 17.2.7 Basic Transformations of Convolutional Neural Networks in Healthcare 374 17.2.8 Algorithm Review and Comparison 376 17.2.9 Feedforward 376 17.3 Results and Discussion 377 17.3.1 Results on Suitability for Applications in Healthcare 377 17.3.2 Class Prediction 377 17.3.3 The Model Sanity Checking 377 17.3.4 Analysis of the Epoch and Training Losses 378 17.3.5 Discussion and Healthcare Interpretations 379 17.3.6 Load Data 379 17.3.7 Image Pre-Processing 380 17.3.8 Building and Training the Classifier 381 17.3.9 Saving the Checkpoint Suitable for Healthcare 382 17.3.10 Loading the Checkpoint 383 17.4 Conclusion 383 References 383 18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare 387Niha K. and Aisha Banu W. 18.1 Introduction 388 18.2 Related Work 389 18.3 Convolutional Layer 389 18.4 Pooling Layer 390 18.5 Fully Connected Layer 390 18.6 Recurrent Neural Network 391 18.7 LSTM and GRU 392 18.8 Materials and Methods 397 18.8.1 Pre-Processing Strategy Selection 397 18.8.2 Feature Extraction and Classification 400 18.9 Results and Discussions 406 18.10 Conclusion 408 18.11 Acknowledgement 409 References 409 Index 413

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Book SynopsisIntroduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model Development There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning implementation in chemical safety and health research and to provide guidance for implementing machine learning techniques and algorithms into chemical safety and health research. Written by an international team of authors and edited by renowned experts in the areas of process safety and occupational and environmental health, sample topics covered within the work include: An introduction to the fundamentals of machine learning, including regression, classification and cross-validatTable of ContentsList of Contributors xiii Preface xvii 1 Introduction 1 Pingfan Hu and Qingsheng Wang 1.1 Background 2 1.2 Current State 5 1.2.1 Flammability Characteristics Prediction Using Quantitative Structure–Property Relationship 5 1.2.2 Consequence Prediction Using Quantitative Property–Consequence Relationship 6 1.2.3 Machine Learning in Process Safety and Asset Integrity Management 6 1.2.4 Machine Learning for Process Fault Detection and Diagnosis 7 1.2.5 Intelligent Method for Chemical Emission Source Identification 7 1.2.6 Machine Learning and Deep Learning Applications in Medical Image Analysis 7 1.2.7 Predictive Nanotoxicology: Nanoinformatics Approach to Toxicity Analysis of Nanomaterials 8 1.2.8 Machine Learning in Environmental Exposure Assessment 8 1.2.9 Air Quality Prediction Using Machine Learning 8 1.3 Software and Tools 9 1.3.1 R 9 1.3.2 Python 12 References 13 2 Machine Learning Fundamentals 19 Yan Yan 2.1 What Is Learning? 19 2.1.1 Machine Learning Applications and Examples 20 2.1.2 Machine Learning Tasks 21 2.2 Concepts of Machine Learning 22 2.3 Machine Learning Paradigms 24 2.4 Probably Approximately Correct Learning 25 2.4.1 Deterministic Setting 26 2.4.2 Stochastic Setting 29 v 0005453285.3D 5 30/8/2022 8:51:33 PM 2.5 Estimation and Approximation 31 2.6 Empirical Risk Minimization 32 2.6.1 Empirical Risk Minimizer 32 2.6.2 VC-dimension Generalization Bound 33 2.6.3 General Loss Functions 34 2.7 Regularization 35 2.7.1 Regularized Loss Minimization 35 2.7.2 Constrained and Regularized Problem 36 2.7.3 Trade-off Between Estimation and Approximation Error 37 2.8 Maximum Likelihood Principle 38 2.8.1 Maximum Likelihood Estimation 39 2.8.2 Cross Entropy Minimization 40 2.9 Optimization 41 2.9.1 Linear Regression: An Example 42 2.9.2 Closed-form Solution 42 2.9.3 Gradient Descent 43 2.9.4 Stochastic Gradient Descent 45 References 46 3 Flammability Characteristics Prediction Using QSPR Modeling 47 Yong Pan and Juncheng Jiang 3.1 Introduction 47 3.1.1 Flammability Characteristics 47 3.1.2 QSPR Application 48 3.1.2.1 Concept of QSPR 48 3.1.2.2 Trends and Characteristics of QSPR 48 3.2 Flowchart for Flammability Characteristics Prediction 49 3.2.1 Dataset Preparation 51 3.2.2 Structure Input and Molecular Simulation 52 3.2.3 Calculation of Molecular Descriptors 53 3.2.4 Preliminary Screening of Molecular Descriptors 54 3.2.5 Descriptor Selection and Modeling 55 3.2.6 Model Validation 57 3.2.6.1 Model Fitting Ability Evaluation 57 3.2.6.2 Model Stability Analysis 59 3.2.6.3 Model Predictivity Evaluation 60 3.2.7 Model Mechanism Explanation 61 3.2.8 Summary of QSPR Process 61 3.3 QSPR Review for Flammability Characteristics 62 3.3.1 Flammability Limits 62 3.3.1.1 LFLT and LFL 62 3.3.1.2 UFLT and UFL 64 3.3.2 Flash Point 65 3.3.3 Auto-ignition Temperature 68 3.3.4 Heat of Combustion 69 vi Contents 0005453285.3D 6 30/8/2022 8:51:33 PM 3.3.5 Minimum Ignition Energy 70 3.3.6 Gas-liquid Critical Temperature 70 3.3.7 Other Properties 72 3.4 Limitations 72 3.5 Conclusions and Future Prospects 73 References 73 4 Consequence Prediction and Quantitative Property–Consequence Relationship Models 81 Zeren Jiao and Qingsheng Wang 4.1 Introduction 81 4.2 Conventional Consequence Prediction Methods 82 4.2.1 Empirical Method 82 4.2.2 Computational Fluid Dynamics (CFD) Method 83 4.2.3 Integral Method 84 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models 84 4.4 Quantitative Property–Consequence Relationship Models 86 4.4.1 Consequence Database 88 4.4.2 Property Descriptors 89 4.4.3 Machine Learning and Deep Learning Algorithms 89 4.5 Challenges and Future Directions 90 References 91 5 Machine Learning in Process Safety and Asset Integrity Management 93 Ming Yang ,Hao Sun and Rustam Abubarkirov 5.1 Opportunities and Threats 93 5.2 State-of-the-Art Reviews 95 5.2.1 Artificial Neural Networks (ANNs) 95 5.2.2 Principal Component Analysis (PCA) 97 5.2.3 Genetic Algorithm (GA) 97 5.3 Case Study of Asset Integrity Assessment 98 5.4 Data-Driven Model of Asset Integrity Assessment 105 5.4.1 Condition Monitoring Data Collection 106 5.4.2 Data Processing and Storage 106 5.4.3 Data Mining for Risk Quantification and Monitoring Control 107 5.4.4 AIM Application 107 5.4.5 The Application of the Framework 108 5.5 Conclusion 109 References 109 6 Machine Learning for Process Fault Detection and Diagnosis 113 Rajeevan Arunthavanathan, Salim Ahmed, Faisal Khan and Syed Imtiaz 6.1 Background 113 6.2 Machine Learning Approaches in Fault Detection and Diagnosis 114 6.3 Supervised Methods for Fault Detection and Diagnosis 115 Contents vii 0005453285.3D 7 30/8/2022 8:51:33 PM 6.3.1 Neural Network 115 6.3.1.1 Neural Network Theory and Algorithm 115 6.3.1.2 Neural Network Learning for Fault Classification 117 6.3.1.3 Algorithm for Fault Classification Using Neural Network 118 6.3.2 Support Vector Machine 118 6.3.2.1 Support Vector Machine Theory and Algorithm 118 6.3.3 Support Vector Machine Model Selection and Algorithm 120 6.3.4 Support Vector Machine Multiclass Classification 121 6.4 Unsupervised Learning Models for Fault Detection and Diagnosis 122 6.4.1 K-Nearest Neighbors 122 6.4.2 One-Class Support Vector Machine 123 6.4.3 One-Class Neural Network 124 6.4.4 Comparison Between Deep Learning with Machine Learning in Fault Detection and Diagnosis 126 6.5 Intelligent FDD Using Machine Learning 127 6.5.1 Model Development 127 6.5.2 Data Collection 129 6.5.2.1 Model Development Steps 129 6.5.2.2 Result Comparison 130 6.6 Concluding Remarks 134 References 134 7 Intelligent Method for Chemical Emission Source Identification 139 Denglong Ma 7.1 Introduction 139 7.1.1 Development of Detecting Gas Emission 139 7.1.2 Development of Source Term Identification 140 7.2 Intelligent Methods for Recognizing Gas Emission 141 7.2.1 Leakage Recognition of Sequestrated CO2 in the Atmosphere 141 7.2.1.1 Gas Leakage Recognition for CO2 Geological Sequestration 142 7.2.1.2 Case Studies for CO2 Recognition 144 7.2.2 Emission Gas Identification with Artificial Olfactory 149 7.2.2.1 Features of Responses in AOS 150 7.2.2.2 Support Vector Machine Models for Gas Identification 150 7.2.2.3 Deep Learning Models for Gas Identification 155 7.3 Intelligent Methods for Identifying Emission Sources 158 7.3.1 Source Estimation with Intelligent Optimization Method 158 7.3.1.1 Principle of Source Estimation with Optimization Method 158 7.3.1.2 Case Studies of Source Estimation with Optimization Method 159 7.3.2 Source Estimation with MRE-PSO Method 159 7.3.2.1 Principle of PSO-MRE for Source Estimation 161 7.3.2.2 Case Studies 163 7.3.3 Source Estimation with PSO-Tikhonov Regulation Method 164 7.3.3.1 Principle of PSO-Tikhonov Regularization Hybrid Method 164 7.3.3.2 Case Study 167 viii Contents 0005453285.3D 8 30/8/2022 8:51:33 PM 7.3.4 Source Estimation with MCMC-MLA Method 168 7.3.4.1 Forward Gas Dispersion Model Based on MLA 168 7.3.4.2 Source Estimation with MCMC-MLA Method 169 7.3.4.3 Case Study 172 7.4 Conclusions and Future Work 173 7.4.1 Conclusions 173 7.4.2 Limitations and Future Work 177 References 178 8 Machine Learning and Deep Learning Applications in Medical Image Analysis 183 Pingfan Hu, Changjie Cai, Yu Feng and Qingsheng Wang 8.1 Introduction 183 8.1.1 Machine Learning in Medical Imaging 183 8.1.2 Deep Learning in Medical Imaging 183 8.2 CNN-Based Models for Classification 184 8.2.1 ResNet50 184 8.2.2 YOLOv4 (Darknet53) 185 8.2.3 Grad-CAM 186 8.3 Case Study 186 8.3.1 Background 186 8.3.2 Study Design 187 8.3.3 Training and Testing Database Preparation 187 8.3.4 Results 190 8.3.4.1 Classification Performance of the Modified ResNet50 Model 190 8.3.4.2 Classification Performance of the YOLOv4 Model 190 8.3.4.3 Post-Processing Via Grad-CAM Model and HSV 193 8.3.5 Conclusion 194 8.4 Limitations and Future Work 194 References 195 9 Predictive Nanotoxicology: Nanoinformatics Approach to Toxicity Analysis of Nanomaterials 199 Bilal M. Khan and Yoram Cohen 9.1 Predictive Nanotoxicology 199 9.1.1 Introduction 199 9.1.2 Nano Quantitative Structure–Activity Relationship (QSAR) 200 9.1.3 Importance of Data for Nanotoxicology 204 9.2 Machine Learning Modeling for Predictive Nanotoxicology 205 9.2.1 Overview 205 9.2.2 Unsupervised Learning 211 9.2.2.1 Data Exploration Via Self-Organizing Maps (SOMs) 211 9.2.2.2 Evaluating Associations among Sublethal Toxicity Responses 214 9.2.3 Supervised Learning 215 9.2.3.1 Random Forest Models 216 Contents ix 0005453285.3D 9 30/8/2022 8:51:33 PM 9.2.3.2 Support Vector Machines 216 9.2.3.3 Bayesian Networks 216 9.2.3.4 Supervised Classification and Regression-Based Models for Nano-(Q)SARs 218 9.2.4 Predictive Nano-(Q)SARs for the Assessment of Causal Relationships 220 9.3 Development of Machine Learning Based Models for Nano-(Q)SARs 224 9.3.1 Overview 224 9.3.1.1 Data-Driven Models 224 9.3.1.2 Mechanistic/Theoretical Models 225 9.3.2 Data Generation, Collection, and Preprocessing 225 9.3.3 Descriptor Selection 226 9.3.4 Model Selection and Training 229 9.3.5 Model Validation 230 9.3.5.1 Descriptor Importance 231 9.3.5.2 Applicability Domain 231 9.3.6 Model Diagnosis and Debugging 231 9.4 Nanoinformatics Approaches to Predictive Nanotoxicology 234 9.5 Summary 235 References 238 10 Machine Learning in Environmental Exposure Assessment 251 Gregory L. Watson 10.1 Introduction 251 10.2 Environmental Exposure Modeling 252 10.3 Machine Learning Exposure Models 254 10.4 Model Evaluation 257 10.5 Case Study 258 10.6 Other Topics 260 10.6.1 Bias and Fairness 260 10.6.2 Wearable Sensors 260 10.6.3 Interpretability 260 10.6.4 Extreme Events 260 10.7 Conclusion 261 References 261 11 Air Quality Prediction Using Machine Learning 267 Lan Gao, Changjie Cai and Xiao-Ming Hu 11.1 Introduction 267 11.2 Air Quality and Climate Data Acquisition 269 11.2.1 Earth Satellite Observation Datasets 269 11.2.1.1 Basics of Earth Satellite Observations 269 11.2.1.2 Earth Satellite Products 270 11.2.2 Ground-Based In Situ Observation Datasets 276 11.2.2.1 Basics of the Ground-Based In Situ Observations 276 11.2.2.2 Ground-Based In Situ Products 277 11.3 Applications of Machine Learning in Air Quality Study 279 x Contents 0005453285.3D 10 30/8/2022 8:51:34 PM 11.3.1 Shallow Learning 280 11.3.2 Deep Learning 280 11.4 An Application Practice Example 281 11.4.1 Satellite Data Acquisition and Variable Selections 282 11.4.2 Machine Learning and Deep Learning Algorithms 282 References 283 12 Current Challenges and Perspectives 289 Changjie Cai and Qingsheng Wang 12.1 Current Challenges 289 12.1.1 Data Development and Cleaning 289 12.1.2 Hardware Issues 290 12.1.3 Data Confidentiality 290 12.1.4 Other Challenges 291 12.2 Perspectives 291 12.2.1 Real-Time Monitoring and Forecast of Chemical Hazards 291 12.2.2 Toolkits for Dummies 292 12.2.3 Physics-Informed Machine Learning 292 References 293 Index 000

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Book SynopsisTable of ContentsPreface xiii About the Companion Website xix 1 Teaching Methods for This Textbook 1 Synopsis 1 1.1 Education in Civil and Environmental Engineering 1 1.2 Machine Learning as an Educational Material 2 1.3 Possible Pathways for Course/Material Delivery 3 1.4 Typical Outline for Possible Means of Delivery 7 Chapter Blueprint 8 Questions and Problems 8 References 8 2 Introduction to Machine Learning 11 Synopsis 11 2.1 A Brief History of Machine Learning 11 2.2 Types of Learning 12 2.3 A Look into ML from the Lens of Civil and Environmental Engineering 15 2.4 Let Us Talk a Bit More about ML 17 2.5 ML Pipeline 18 2.6 Conclusions 27 Definitions 27 Chapter Blueprint 29 Questions and Problems 29 References 30 3 Data and Statistics 33 Synopsis 33 3.1 Data and Data Science 33 3.2 Types of Data 34 3.3 Dataset Development 37 3.4 Diagnosing and Handling Data 37 3.5 Visualizing Data 38 3.6 Exploring Data 59 3.7 Manipulating Data 66 3.8 Manipulation for Computer Vision 68 3.9 A Brief Review of Statistics 68 3.10 Conclusions 76 4 Machine Learning Algorithms 81 Synopsis 81 4.1 An Overview of Algorithms 81 4.2 Conclusions 127 5 Performance Fitness Indicators and Error Metrics 133 Synopsis 133 5.1 Introduction 133 5.2 The Need for Metrics and Indicators 134 5.3 Regression Metrics and Indicators 135 5.4 Classification Metrics and Indicators 142 5.5 Clustering Metrics and Indicators 142 5.6 Functional Metrics and Indicators* 151 5.7 Other Techniques (Beyond Metrics and Indicators) 154 5.8 Conclusions 159 6 Coding-free and Coding-based Approaches to Machine Learning 169 Synopsis 169 6.1 Coding-free Approach to ML 169 6.2 Coding-based Approach to ML 280 6.3 Conclusions 322 7 Explainability and Interpretability 327 7 Synopsis 327 7.1 The Need for Explainability 327 7.2 Explainability from a Philosophical Engineering Perspective* 329 7.3 Methods for Explainability and Interpretability 331 7.4 Examples 335 7.5 Conclusions 428 8 Causal Discovery and Causal Inference 433 Synopsis 433 8.1 Big Ideas Behind This Chapter 433 8.2 Re-visiting Experiments 434 8.3 Re-visiting Statistics and ML 435 8.4 Causality 436 8.5 Examples 451 8.6 A Note on Causality and ML 475 8.7 Conclusions 475 9 Advanced Topics (Synthetic and Augmented Data, Green ML, Symbolic Regression, Mapping Functions, Ensembles, and AutoML) 481 Synopsis 481 9.1 Synthetic and Augmented Data 481 9.2 Green ML 488 9.3 Symbolic Regression 498 9.4 Mapping Functions 529 9.5 Ensembles 539 9.6 AutoML 548 9.7 Conclusions 552 10 Recommendations, Suggestions, and Best Practices 559 Synopsis 559 10.1 Recommendations 559 10.2 Suggestions 564 10.3 Best Practices 566 11 Final Thoughts and Future Directions 573 Synopsis 573 11.1 Now 573 11.2 Tomorrow 573 11.3 Possible Ideas to Tackle 575 11.4 Conclusions 576 References 576 Index 577

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Book SynopsisA ROADMAP FOR ENABLING INDUSTRY 4.0 BY ARTIFICAIAL INTELLIGENCE The book presents comprehensive and up-to-date technological solutions to the main aspects regarding the applications of artificial intelligence to Industry 4.0. The industry 4.0 vision has been discussed for quite a while and the enabling technologies are now mature enough to turn this vision into a grand reality sooner rather than later. The fourth industrial revolution, or Industry 4.0, involves the infusion of technology-enabled deeper and decisive automation into manufacturing processes and activities. Several information and communication technologies (ICT) are being integrated and used towards attaining manufacturing process acceleration and augmentation. This book explores and educates the recent advancements in blockchain technology, artificial intelligence, supply chains in manufacturing, cryptocurrencies, and their crucial impact on realizing the Industry 4.0 goals. The book thus provides a conceptual framework Table of ContentsPreface xv 1 Artificial Intelligence—The Driving Force of Industry 4.0 1 Hesham Magd, Henry Jonathan, Shad Ahmad Khan and Mohamed El Geddawy 1.1 Introduction 2 1.2 Methodology 2 1.3 Scope of AI in Global Economy and Industry 4.0 3 1.3.1 Artificial Intelligence—Evolution and Implications 4 1.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy 5 1.3.3 The Driving Forces for Industry 4.0 7 1.4 Artificial Intelligence—Manufacturing Sector 8 1.4.1 AI Diversity—Applications to Manufacturing Sector 9 1.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0 12 1.5 Conclusion 13 References 14 2 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview 17 Sachi Pandey, Vijay Laxmi and Rajendra Prasad Mahapatra 2.1 Introduction 17 2.2 Industrial Transformation/Value Chain Transformation 18 2.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT 19 2.2.2 Second Scenario: Selling Outcome (User Demand)– Based Services Using IIoT 20 2.3 IIoT Reference Architecture 20 2.4 IIoT Technical Concepts 22 2.5 IIoT and Cloud Computing 26 2.6 IIoT and Security 27 References 29 3 Artificial Intelligence of Things (AIoT) and Industry 4.0– Based Supply Chain (FMCG Industry) 31 Seyyed Esmaeil Najafi, Hamed Nozari and S. A. Edalatpanah 3.1 Introduction 32 3.2 Concepts 33 3.2.1 Internet of Things 33 3.2.2 The Industrial Internet of Things (IIoT) 34 3.2.3 Artificial Intelligence of Things (AIoT) 35 3.3 AIoT-Based Supply Chain 36 3.4 Conclusion 40 References 40 4 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 43 Alireza Goli, Amir-Mohammad Golmohammadi and S. A. Edalatpanah 4.1 Introduction 44 4.2 Literature Review 45 4.2.1 Summary of the First Three Industrial Revolutions 45 4.2.2 Emergence of Industry 4.0 45 4.2.3 Some of the Challenges of Industry 4.0 47 4.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting 48 4.4 Proposed Approach 50 4.4.1 Mathematical Model 50 4.4.2 Advantages of the Proposed Model 51 4.5 Discussion and Conclusion 52 References 53 5 Integrating IoT and Deep Learning—The Driving Force of Industry 4.0 57 Muhammad Farrukh Shahid, Tariq Jamil Saifullah Khanzada and Muhammad Hassan Tanveer 5.1 Motivation and Background 58 5.2 Bringing Intelligence Into IoT Devices 60 5.3 The Foundation of CR-IoT Network 62 5.3.1 Various AI Technique in CR-IoT Network 63 5.3.2 Artificial Neural Network (ANN) 63 5.3.3 Metaheuristic Technique 64 5.3.4 Rule-Based System 64 5.3.5 Ontology-Based System 65 5.3.6 Probabilistic Models 65 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network 65 5.5 Realization of CR-IoT Network in Daily Life Examples 69 5.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study 70 5.7 Conclusion 75 References 75 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment 79 Mahendra Prasad Nath, Sushree Bibhuprada B. Priyadarshini, Debahuti Mishra and Brojo Kishore Mishra 6.1 Introduction 80 6.2 Overview of Blockchain 83 6.3 Components of Blockchain 85 6.3.1 Data Block 85 6.3.2 Smart Contracts 87 6.3.3 Consensus Algorithms 87 6.4 Safety Issues in Blockchain Technology 88 6.5 Usage of Big Data Framework in Dynamic Supply Chain System 91 6.6 Machine Learning and Big Data 94 6.6.1 Overview of Shallow Models 95 6.6.1.1 Support Vector Machine (SVM) 95 6.6.1.2 Artificial Neural Network (ANN) 95 6.6.1.3 K-Nearest Neighbor (KNN) 95 6.6.1.4 Clustering 96 6.6.1.5 Decision Tree 96 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems 96 6.7.1 Replenishment Planning 96 6.7.2 Optimizing Orders 97 6.7.3 Arranging and Organizing 97 6.7.4 Enhanced Demand Structuring 97 6.7.5 Real-Time Management of the Supply Chain 97 6.7.6 Enhanced Reaction 98 6.7.7 Planning and Growth of Inventories 98 6.8 IoT-Enabled Blockchains 98 6.8.1 Securing IoT Applications by Utilizing Blockchain 99 6.8.2 Blockchain Based on Permission 101 6.8.3 Blockchain Improvements in IoT 101 6.8.3.1 Blockchain Can Store Information Coming from IoT Devices 101 6.8.3.2 Secure Data Storage with Blockchain Distribution 101 6.8.3.3 Data Encryption via Hash Key and Tested by the Miners 102 6.8.3.4 Spoofing Attacks and Data Loss Prevention 102 6.8.3.5 Unauthorized Access Prevention Using Blockchain 103 6.8.3.6 Exclusion of Centralized Cloud Servers 103 6.9 Conclusions 103 References 104 7 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet 111 Yash Gupta, Shilpi Sharma, Naveen Rajan P. and Nadia Mohamed Kunhi 7.1 Introduction 112 7.2 Related Work 113 7.3 Methodology 114 7.3.1 Splitting of Data (Test/Train) 116 7.3.2 Prophet Model 116 7.3.3 Data Cleaning 119 7.3.4 Model Implementation 119 7.4 Results 120 7.4.1 Comparing Forecast to Actuals 121 7.4.2 Adding Holidays 122 7.4.3 Comparing Forecast to Actuals with the Cleaned Data 122 7.5 Conclusion and Future Scope 122 References 125 8 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems 127 Manas Kumar Yogi and A.S.N. Chakravarthy 8.1 Introduction 128 8.2 Related Work 131 8.3 Proposed Mechanism 133 8.4 Experimental Results 135 8.5 Future Directions 137 8.6 Conclusion 138 References 138 9 Environmental and Industrial Applications Using Internet of Things (IoT) 141 Manal Fawzy, Alaa El Din Mahmoud and Ahmed M. Abdelfatah 9.1 Introduction 142 9.2 IoT-Based Environmental Applications 146 9.3 Smart Environmental Monitoring 147 9.3.1 Air Quality Assessment 147 9.3.2 Water Quality Assessment 148 9.3.3 Soil Quality Assessment 150 9.3.4 Environmental Health-Related to COVID- 19 Monitoring 150 9.4 Applications of Sensors Network in Agro-Industrial System 151 9.5 Applications of IoT in Industry 153 9.5.1 Application of IoT in the Autonomous Field 153 9.5.2 Applications of IoT in Software Industries 155 9.5.3 Sensors in Industry 156 9.6 Challenges of IoT Applications in Environmental and Industrial Applications 157 9.7 Conclusions and Recommendations 159 Acknowledgments 159 References 159 10 An Introduction to Security in Internet of Things (IoT) and Big Data 169 Sushree Bibhuprada B. Priyadarshini, Suraj Kumar Dash, Amrit Sahani, Brojo Kishore Mishra and Mahendra Prasad Nath 10.1 Introduction 170 10.2 Allusion Design of IoT 172 10.2.1 Stage 1—Edge Tool 172 10.2.2 Stage 2—Connectivity 172 10.2.3 Stage 3—Fog Computing 173 10.2.4 Stage 4—Data Collection 173 10.2.5 Stage 5—Data Abstraction 173 10.2.6 Stage 6—Applications 173 10.2.7 Stage 7—Cooperation and Processes 174 10.3 Vulnerabilities of IoT 174 10.3.1 The Properties and Relationships of Various IoT Networks 174 10.3.2 Device Attacks 175 10.3.3 Attacks on Network 175 10.3.4 Some Other Issues 175 10.3.4.1 Customer Delivery Value 175 10.3.4.2 Compatibility Problems With Equipment 176 10.3.4.3 Compatibility and Maintenance 176 10.3.4.4 Connectivity Issues in the Field of Data 176 10.3.4.5 Incorrect Data Collection and Difficulties 177 10.3.4.6 Security Concern 177 10.3.4.7 Problems in Computer Confidentiality 177 10.4 Challenges in Technology 178 10.4.1 Skepticism of Consumers 178 10.5 Analysis of IoT Security 179 10.5.1 Sensing Layer Security Threats 180 10.5.1.1 Node Capturing 180 10.5.1.2 Malicious Attack by Code Injection 180 10.5.1.3 Attack by Fake Data Injection 180 10.5.1.4 Sidelines Assaults 181 10.5.1.5 Attacks During Booting Process 181 10.5.2 Network Layer Safety Issues 181 10.5.2.1 Attack on Phishing Page 181 10.5.2.2 Attacks on Access 182 10.5.2.3 Attacks on Data Transmission 182 10.5.2.4 Attacks on Routing 182 10.5.3 Middleware Layer Safety Issues 182 10.5.3.1 Attack by SQL Injection 183 10.5.3.2 Attack by Signature Wrapping 183 10.5.3.3 Cloud Attack Injection with Malware 183 10.5.3.4 Cloud Flooding Attack 183 10.5.4 Gateways Safety Issues 184 10.5.4.1 On-Boarding Safely 184 10.5.4.2 Additional Interfaces 184 10.5.4.3 Encrypting End-to-End 184 10.5.5 Application Layer Safety Issues 185 10.5.5.1 Theft of Data 185 10.5.5.2 Attacks at Interruption in Service 185 10.5.5.3 Malicious Code Injection Attack 185 10.6 Improvements and Enhancements Needed for IoT Applications in the Future 186 10.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) 189 10.8 Conclusion 192 References 193 11 Potential, Scope, and Challenges of Industry 4.0 201 Roshan Raman and Aayush Kumar 11.1 Introduction 202 11.2 Key Aspects for a Successful Production 202 11.3 Opportunities with Industry 4.0 204 11.4 Issues in Implementation of Industry 4.0 206 11.5 Potential Tools Utilized in Industry 4.0 207 11.6 Conclusion 210 References 210 12 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges 215 Roshan Raman and Aditya Ranjan 12.1 Introduction 216 12.2 Changing Market Demands 217 12.2.1 Individualization 218 12.2.2 Volatility 218 12.2.3 Efficiency in Terms of Energy Resources 218 12.3 Recent Technological Advancements 219 12.4 Industrial Revolution 4.0 221 12.5 Challenges to Industry 4.0 224 12.6 Conclusion 225 References 226 13 The Role of Multiagent System in Industry 4.0 227 Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan and Rudra Pratap Ojha 13.1 Introduction 228 13.2 Characteristics and Goals of Industry 4.0 Conception 228 13.3 Artificial Intelligence 231 13.3.1 Knowledge-Based Systems 232 13.4 Multiagent Systems 234 13.4.1 Agent Architectures 234 13.4.2 Jade 238 13.4.3 System Requirements Definition 239 13.4.4 HMI Development 240 13.5 Developing Software of Controllers Multiagent Environment Behavior Patterns 240 13.5.1 Agent Supervision 240 13.5.2 Documents Dispatching Agents 241 13.5.3 Agent Rescheduling 242 13.5.4 Agent of Executive 242 13.5.5 Primary Roles of High-Availability Agent 243 13.6 Conclusion 244 References 244 14 An Overview of Enhancing Encryption Standards for Multimedia in Explainable Artificial Intelligence Using Residue Number Systems for Security 247 Akeem Femi Kadri, Micheal Olaolu Arowolo, Ayisat Wuraola Yusuf-Asaju, Kafayat Odunayo Tajudeen and Kazeem Alagbe Gbolagade 14.1 Introduction 248 14.2 Reviews of Related Works 250 14.3 Materials and Methods 258 14.3.1 Multimedia 258 14.3.2 Artificial Intelligence and Explainable Artificial Intelligence 261 14.3.3 Cryptography 262 14.3.4 Encryption and Decryption 265 14.3.5 Residue Number System 266 14.4 Discussion and Conclusion 268 References 268 15 Market Trends with Cryptocurrency Trading in Industry 4.0 275 Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal and Vivek Kumar Verma 15.1 Introduction 276 15.2 Industry Overview 276 15.2.1 History (From Barter to Cryptocurrency) 276 15.2.2 In the Beginning Was Bitcoin 278 15.3 Cryptocurrency Market 279 15.3.1 Blockchain 279 15.3.1.1 Introduction to Blockchain Technology 279 15.3.1.2 Mining 280 15.3.1.3 From Blockchain to Cryptocurrency 281 15.3.2 Introduction to Cryptocurrency Market 281 15.3.2.1 What is a Cryptocurrency? 281 15.3.2.2 Cryptocurrency Exchanges 283 15.4 Cryptocurrency Trading 283 15.4.1 Definition 283 15.4.2 Advantages 283 15.4.3 Disadvantages 284 15.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain 285 15.5.1 Need for a Fee-Driven System 285 15.5.2 Ethereum Structure 286 15.5.3 How is the Gas Fee Calculated? 287 15.5.3.1 Why are Ethereum Gas Prices so High? 287 15.5.3.2 Carbon Neutrality 287 15.6 Conclusion 291 References 292 16 Blockchain and Its Applications in Industry 4.0 295 Ajay Sudhir Bale, Tarun Praveen Purohit, Muhammed Furqaan Hashim and Suyog Navale 16.1 Introduction 296 16.2 About Cryptocurrency 296 16.3 History of Blockchain and Cryptocurrency 298 16.4 Background of Industrial Revolution 300 16.4.1 The First Industrial Revolution 301 16.4.2 The Second Industrial Revolution 301 16.4.3 The Third Industrial Revolution 302 16.4.4 The Fourth Industrial Revolution 302 16.5 Trends of Blockchain 303 16.6 Applications of Blockchain in Industry 4.0 304 16.6.1 Blockchain and the Government 304 16.6.2 Blockchain in the Healthcare Sector 304 16.6.3 Blockchain in Logistics and Supply Chain 306 16.6.4 Blockchain in the Automotive Sector 307 16.6.5 Blockchain in the Education Sector 308 16.7 Conclusion 309 References 310 Index 315

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