Artificial intelligence (AI) Books

Book SynopsisSandalWoodNet:A Blockchain based Supply chain for Trusted Auction Biding and Fairtrade of Sandalwood.- The machine learning methods in rating cognitive state of elderly people.- Trajectory Approximation of Video Based on Phase Correlation for Forward Facing Camera.- Implementation of 2-D U-Net on the GrapesNet Dataset.- Ensemble system for cybersecurity threat detection.- Spectral Based Mango Disease Classification: Intro-duction of a Novel Dataset.- Examining Strategies and Technological Aspects of Taxation Among Micro, Small, and Medium Enterprises in Indonesia.- Unraveling the Motivations behind Moonlighting: A Phe-nomenological Exploration of Multi-Job Holders' Lived Experiences through Thematic Analysis.- Factors Influencing the Intention to Use Virtual and Augmented Reality in Supply Chain: Technology-Organization-Environment Framework Development.- Value creation with natural products in the food value chain.- Deep Learning-Based Speaker Identification for Individuals with Voice Disorders.- Efficiency Enhancement of Online Gaming Environments using Cloud Computing.- Skin Disease Prediction Based On Anomaly Detection.- Early Earthquake Prediction Using Machine Learning Algorithm.- An AI Framework for Estimating Obesity Levels Using Randomly Optimized Machine Learning Models Based on Dietary and Physical Conditions.- An AI Framework for Estimating Obesity Levels Using Randomly Optimized Machine Learning Models Based on Dietary and Physical Conditions.- Analysis of the environmental aspect of global trends determining the transformation of fiscal mechanisms in the context of new technological paradigms.- A Novel Random PIN Based Authentication Scheme.- The Influence of Environmental, Social and Governance (ESG) on Mergers and Acquisitions: Due Diligence and Integration.- A feature-driven method for adaptive and perfective maintenance.- A Secure E-commerce Shopping Cart Design with Multi-Factor Authentication, Tokenized Payment Processing, and Open-Source Integration for Enhanced User Experience and Reduced Development Time.- Evaluating Solar Energy Potential in the City Through Data Sciences.- AI-Driven Solutions for Regression Testing: Insights from Bangladesh Software Industry.- Fortifying Mobile Wallets : An Assessment of Security Measures in Modern Payment Platforms.

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Book SynopsisThinking Machines, Thinking Minds: AI & Cognitive Science.- Trends, Challenges and New Frontiers of Arti?cial Intelligence.- Biological Neural Network, Arti?cial Neural Network and Cellular Automata.- CA Rule Design for Proteomics and Linguistics.- CAML Model for Computational Biology.- CAML model for Computational Linguistics.- Cellular Automata based Binarized Machine Learning Model.

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Book SynopsisUntersuchung pädagogischer Ansätze zur Entwicklung von Beschäftigungsfähigkeiten im Gefolge der COVID-19-Pandemie.- Zusammenhang zwischen arbeitsintegriertem Lernen und der Diskussion über Beschäftigungsfähigkeiten in der Post-COVID-19-Ära.- Integration von Belangen der Innenraumluftqualität in die Bildungsgemeinschaft im Rahmen der Zusammenarbeit mit dem Campus Bizia Lab der Universität des Baskenlandes.- Ein Überblick über Methoden zur Kontrolle und Schätzung der Kapazität bei der COVID-19-Pandemie anhand von Punktwolken- und Bilddaten.- Vorhersage der COVID-19-Ausbreitung im Iran, in Italien und in Mexiko unter Verwendung neuartiger nichtlinearer autoregressiver neuronaler Netzwerke und ARIMA-basierter Hybridmodelle.- A Comparative Study of Deep-Learning Models for COVID-19 Diagnosis based on X-ray Images.- Equipping European higher education teachers for successful and sustainable e-learning with home remote work.- Anticipating and Preparing for Future Change and Uncertainty: Building Adaptive Pathways.- Ein nachhaltiges Ernährungsverhalten im Zeitalter des Klimawandels.- Die Entwicklung einer intelligenten, abstimmbaren Vollspektrum-LED-Beleuchtungstechnologie, die Covid-19-Infektionen verhindern und behandeln kann, um die Widerstandsfähigkeit und Lebensqualität der Gesellschaft zu verbessern.- Energieeffiziente Technologien für die Ultra-Niedrigtemperatur-Kühlung.

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Book SynopsisThis book proposes new technologies and discusses future solutions for ICT design infrastructures, as reflected in high-quality papers presented at the 8th International Conference on ICT for Sustainable Development (ICT4SD 2023), held in Goa, India, on August 3–4, 2023. The book covers the topics such as big data and data mining, data fusion, IoT programming toolkits and frameworks, green communication systems and network, use of ICT in smart cities, sensor networks and embedded system, network and information security, wireless and optical networks, security, trust, and privacy, routing and control protocols, cognitive radio and networks, and natural language processing. Bringing together experts from different countries, the book explores a range of central issues from an international perspective.Table of ContentsTraffic Classification of Software Defined Networks using Machine LearningRice Leaf Disease Detection Using Different Models And Comparative Analysis5G Mobile System Drawbacks and LimitationsSurvey on implementation of Machine Learning in Cloud SecurityDeep learning approach to predict crop yield using IOT sensor dataClassification of Pomegranate Fruit Disease using CNNNICE: Navagraha Iconography Classification EngineAutograder: A Feature Based Quantitative Essay Grad-ing System Using BERTBluetooth Automated Wheel ChairSCADA in HealthcareData Security in a Cloud Environment using Cryptographic MechanismsSecure data education: leveraging big data for enhanced academic performance and student success in educational institutionsDrought Monitoring and Assessment Through Remote Sensing Data in Bundelkhand Area of Madhya PradeshSmart glasses designed using ESP32- cam coupled with Google lensHardware Architecture of Reinforcement Learning for Edge DevicesStrengthening the Privacy of Blockchain with Zero Knowledge Proof Case Study: Online Exam Student VerificationMeta-analysis of popular encryption and hashing algorithmsCloud based Internet of things architecture for hydroponics farm automationAMAX-AR: A Way to Maximize Augmented RealitySlack time analysis for APB Timer using Genus Synthesis toolDictionary-Based PLS Approach to Pharmacokinetic Mapping in DCE-MRI using Tofts ModeUser Experience Evaluation for Pre-Primary Children using an Augmented Reality Animal Themed Phonics SystemBlockchain-based Secure Cloud Data Management: A Novel Approach for Data Privacy and IntegrityA Queuing Model for Single Phase Server Breakdown Using Markov Chains with Random TransitionAudience Targeting – Identify Gap in audience targeting for Storage revenue YTYRecent Advances in Semantic Segmentation for Sports AnalyticsIntelligent Decision Analysis to Stimulate Student Learning

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Book SynopsisChapter 1: Introduction to RHEL AI.- Chapter 2: Setting Up.- Chapter 3: Exploring Core Components.- Chapter 4: Advanced Features and Optimization.- Chapter 5: Developing Custom AI Applications.- Chapter 6: Monitoring and Maintenance.- Chapter 7: Use Cases and Best Practices.- Chapter 8: Future Trends.- Chapter 9: Community and Support Ecosystem.

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Book SynopsisPart I Agenda 2030.- Chapter 1: The 17 UN Sustainable Development Goals.- Part II Earth.- Chapter 2: Scarcity of Resources.- Chapter 3: Decrease in Green Spaces.- Chapter 4: Deforestation Monitoring in Siberia.- Chapter 5: Efficient Fertilizer Production Using Quantum Computing.- Chapter 6: Digital Farming.- Chapter 7: Degradation of Biodiversity.- Chapter 8: Waste Disposal.- Chapter 9: SAS Viya for Forecasting Sponsorships for Children.- Chapter 10: Smart Cities.- Chapter 11: AI-Driven Traffic Management.- Chapter 12: Healthcare.- Chapter 13: OPAL: An Innovative Tool for Environmentally Conscious Development.- Chapter 14: Combating Plastic Pollution in Ghana.- Part III Air.

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Book SynopsisChapter 1: Introduction to Generative AI.- Chapter 2: GenAI and the Financial Services Sector.- Chapter 3: GenAI Strategy: A Blueprint for Successful Adoption.- Chapter 4: Architecting and Building a GenAI Application.- Chapter 5: Risk and Compliance Managment with GenAI.- Chapter 6: Retail Banking with GenAI.- Chapter 7: Investment Banking with GenAI.- Chapter 8: Wealth and Asset Management wth GenAI.- Chapter 9: Implementation, Operations, and Maintenance of GenAI Applications.- Chapter 10: Emerging Trends, Summary, and Next Steps.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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