Artificial intelligence (AI) Books

Book SynopsisAn essential handbook for our time of astonishing technological transformation by the world''s leading AI poet.In 2018, Sasha Stiles found herself wondering what the rise of large language models might mean for writers - and for creativity at large. To probe the possibilities, she began translating over a decade of analog poems and research into a personalized AI model, augmenting human voice with next-gen imagination.Crafted jointly by Stiles and her poetic alter ego, and first published in hardcover in 2021, Technelegy is a prescient artifact of the pre-ChatGPT era - a collection of generative poems nestled in their own training data - and an unprecedented experiment fusing past and future, woman and machine, verse and code, elegy and wordplay, in search of answers to the urgent question: what does it mean to be human in a nearly posthuman world?

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Book SynopsisArtificial intelligence changes everything. This book encourages readers to consider the challenges of the digital transformation driven by Artificial Intelligence. The reader will discover why this transformation is to be regarded as the greatest cultural revolution since the invention of mass printing and how it can be shaped positively in a value-oriented way. The author pursues the thesis that intelligent objects on the internet, as well as physical objects, are attaining their own consciousness. Using many examples, he shows how these digital companions become our digital partners. This non-fiction book provides many suggestions for one's own living and working environment and is full of examples of how artificial intelligence systems can be implemented. The reader learns what is already possible today and what can be expected in the next ten to twenty years. The book is of interest to anyone interested in AI and the digital transformation - from those responsible in companies, public institutions, and in politics, to all teachers and parents who want to understand what the next generation can expect.Trade Review“This book might be worth reading for someone who wants to inform themselves about the challenges of coming digital transformations (including AI) in popular style … .” (G. Haring, Computing Reviews, August 11, 2022)Table of ContentsChapter 1 . It's all about us.- Chapter 2 . The Objects of This World Become Intelligent .- chapter 3. How did Artificial Intelligence come into being and where do we stand today? .- Chapter 4. Can Machines have their own Consciousness? .- Chapter 5. The Homo Zappiens .- Chapter 6. The Inverse Gutenberg Revolution .- Chapter 7. The Age of Hybrid Intelligence .- Chapter 8. The Digital System Landscape .- Chapter 9. On the Way to new Business models .- Chapter 10. Artificial Intelligence is a Gamechanger of all Jobs .- Chapter 11. Everything is linked to everything and becomes transparent .- Chapter 12. The ethical and legal Implications .- Chapter 13. Guidelines for the necessary Redesign of our regulatory Systems in Industry and Society .

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Book SynopsisThis book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given.A particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain’s three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well.Neuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.Table of Contents1 Introduction to Neuromorphic Computing Systems.- 2 Neuromorphic System Design Fundamentals.- 3 Learning in Neuromorphic Systems.- 4 Emerging Memory Devices for Neuromorphic Systems.- 5 Communication Networks for Neuromorphic Systems.- 6 Fault-Tolerant Neuromorphic System Design.- 7 Reconfigurable Neuromorphic Computing System.- 8 Case Study: Real Hardware-Software Design of 3D-NoC-based Neuromorphic System.- 9 Survey of Neuromorphic Systems.

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Book SynopsisThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. Table of Contents1. Data Science Process2. Dimensionality Reduction - Creating Manageable Training Datasets3. Classical Algorithms - Overview4. Regression Analysis5. Decision Tree6. Ensemble - Bagging and Boosting7. K-Nearest Neighbors8. Naive Bayes9. Support Vector Machines: A supervised learning algorithm for Classification and Regression10. Clustering Overview11. Centroid-based Clustering12. Connectivity-based Clustering13. Gaussian Mixture Model14. Density-based15. BIRCH16. CLARANS17. Affinity Propagation Clustering18. STING19. CLIQUE20. Artificial Neural Networks21. ANN-based Applications22. Automated Tools23. Data Scientist’s Ultimate Workflow

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Book SynopsisAI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers.Table of Contents1.Introduction to AI in Learning – Designing the Future.- Part I: AI expanding learning and wellbeing throughout life.- 2.Artificial Intelligence Innovations for Multimodal Learning, Interfaces, and Analytics.- 3.Curiosity and Interactive Learning in Artificial Systems.- 4.Assessing and Tracking Students’ Wellbeing through an Automated Scoring System: Schoolday Wellbeing Model.- 5.Learning from Intelligent Social Agents as Social and Intellectual Mirrors.- 6.An AI-Powered Teacher Assistant for Student Problem Behavior Diagnosis.- 7.Analysis and Improvement of Classroom Teaching Based on Artificial Intelligence.- Part II. AI in Games and Simulations.- 8.Perspectives and Metaphors of Learning: A Commentary on James Lester’s Narrative-centered AI-based Environments.- 9.Learning Career Knowledge: Can AI Simulation and Machine Learning Improve Career Plans and Educational Expectations?.- 10.Learning clinical reasoning through gaming in nursing education – Future scenarios of game metrics and AI.- 11.AI-Supported Simulation-Based Learning: Learners’ Emotional Experiences and Self-Regulation in Challenging Situations.- Part III. AI Technologies for education and Intelligent Tutoring Systems.- 12.Training Hard Skills in Virtual Reality: Developing a Theoretical Framework for AI-based Immersive Learning.-13.Multiple users’ experiences of an AI-aided educational platform for teaching and learning. 14.Deep Learning in Automatic Math Word Problem Solvers. 15.Recent Advances in Intelligent Textbooks for Better Learning.- Part IV. AI and Ethical Challenges in New Learning Environments.- 16.Ethical Guidelines for Artificial Intelligence-based Learning: A Transnational Study between in China and Finland.- 17.Artificial Intelligence Ethics from the Perspective of Educational Technology Companies and Schools.- 18.Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game: A thought experiment on AI Ethics.- 19.Four surveillance technologies creating challenges for education.- 20.Reflections on the contributions and future scenarios in AI-based learning.

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Book SynopsisThis book offers a structured overview and a comprehensive guide to the emerging field of Autonomous Intelligent Cyber Defense Agents (AICA). The book discusses the current technical issues in autonomous cyber defense and offers information on practical design approaches. The material is presented in a way that is accessible to non-specialists, with tutorial information provided in the initial chapters and as needed throughout the book. The reader is provided with clear and comprehensive background and reference material for each aspect of AICA.Today’s cyber defense tools are mostly watchers. They are not active doers. They do little to plan and execute responses to attacks, and they don’t plan and execute recovery activities. Response and recovery – core elements of cyber resilience – are left to human cyber analysts, incident responders and system administrators. This is about to change. The authors advocate this vision, provide detailed guide to how such a vision can be realized in practice, and its current state of the art.This book also covers key topics relevant to the field, including functional requirements and alternative architectures of AICA, how it perceives and understands threats and the overall situation, how it plans and executes response and recovery, how it survives threats, and how human operators deploy and control AICA. Additionally, this book covers issues of testing, risk, and policy pertinent to AICA, and provides a roadmap towards future R&D in this field.This book targets researchers and advanced students in the field of cyber defense and resilience. Professionals working in this field as well as developers of practical products for cyber autonomy will also want to purchase this book.Table of ContentsChapter. 1. Autonomous Intelligent Cyber-defense Agent: Introduction and OverviewChapter. 2. Alternative Architectural ApproachesChapter. 3. Perception of EnvironmentChapter. 4. Perception of Cyber ThreatsChapter. 5. Situational Understanding and DiagnosticsChapter. 6. Learning about the AdversaryChapter. 7. Response PlanningChapter. 8. Recovery PlanningChapter. 9. Strategic Cyber CamouflageChapter. 10. Adaptivity & AntifragilityChapter. 11. Negotiation and CollaborationChapter. 12. Human InteractionsChapter. 13. Testing and MeasurementsChapter. 14. Deployment and OperationChapter. 15. Command in AICA-intensive OperationsChapter. 16. Risk ManagementChapter. 17. Policy IssuesChapter. 18. Development ChallengesChapter. 19. Case Study A: A Prototype Autonomous Intelligent Cyber-defense AgentChapter. 20. Case Study B: AI Agents for Tactical EdgeChapter. 21. Case Study C: the Sentinel Agents

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Book SynopsisChapter 1 The Landscape.- Chapter 2 "Explainable AI: needs, opportunities and challenges".- Chapter 3 Intrinsic Explainable Models.- Chapter 4 Model-agnostic methods for XAI.- Chapter 5 Explaining Deep Learning Models.- Chapter 6 Additive Models for Interpretability.- Chapter 7 Adversarial Machine Learning and Explainability.-Chapter 8 Explainability of Language Models (XAI and LLM).- Chapter 9 Making science with Machine Learning and XAI.- Chapter 10 AGI, LLM, XAI.- Chapter 11 "A proposal for a sustainable model of Explainable AI.

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Book SynopsisForeword.- Introduction.- Navigating the AI Frontier: Emerging Trends and Governance Implications.- How to Evaluate GenAI Models: Unlocking Their Potential for Your Unique Tasks.- Taming AI Hallucinations: Innovations in Retrieval-Augmented Generation and Evaluation.- Invisible Exposure: The Privacy Risks of AI Models.- Unmasking Deepfakes: Navigating Challenges and Solutions in the Age of AI-Driven Manipulation.- Unmasking the Security Risks of Generative AI: Threats and Strategies for Defense.- Unmasking Model Bias: Building Fair, Reliable, and Trustworthy Models.- Unveiling the Black Box: Enhancing AI/ML Model Explainability for Transparency and Trust.- Model Monitoring: Lessons from Recent Failures.- Navigating Change Management: Lessons and Best Practices for Managing AI/GenAI Models in a Dynamic Landscape.- AI Governance: Preparing for the Rise of Agentic AI.- Epilogue.

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Book Synopsis5G, the emerging technology in mobile communication, is expected to deliver an important and decisive impact on several of the UN’s Sustainable Development Goals where universal accessibility to ICTs remains a serious concern. However, cyber security has emerged as a serious challenge, not least because of the increased accessibility and broader usage with associated vulnerability. Developing countries have additional challenges associated with both the expected faster build-up of accessibility and lack of qualified competencies within cyber security. Discussion of these challenges is the overall theme and motivation for this book.Technical topics discussed in the book include: 5G in rural networks Critical infrastructures Open RAN Protection of privacy Cybersecurity and machine learning Cybersecurity and disaster monitoring Table of Contents1. Expansion-security Tradeoffs in the Pathway to Rural 5G Networks 2. Cybersecurity Threats to 5G's Critical Infrastructure 3. Critical Infrastructure Security: Issues, Challenges and 5G Solutions in Indian Perspective 4. OpenRAN and Security in an Emerging Country Context 5. Deployment of 5G in Emerging Economies: Cyber-Security Challenges and Potentials for Ghana 6. Mapping the Iranian Policy Network for Protecting Users’ Data on Platforms 7. Empirical Investigation of the Drives of 5G and Mediating Role of Users' Attitude to Achieve Word of Mouth and Willingness: A Case Study of Microfinance Institutions of Zambia 8. Study of Cyber Security in 5G using a Machine Learning Algorithm for Protecting Financial Transactions – Developing World Cases 9. Integrating InSAR, GNSS, IoT, 5G, and Cybersecurity for Earthquakes/Tremor Monitoring and Forecasting in Abuja, Nigeria

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Book SynopsisThis book presents a unique examination of mobile robots and embedded systems, from introductory to intermediate level. It is structured in three parts, dealing with Embedded Systems (hardware and software design, actuators, sensors, PID control, multitasking), Mobile Robot Design (driving, balancing, walking, and flying robots), and Mobile Robot Applications (mapping, robot soccer, genetic algorithms, neural networks, behavior-based systems, and simulation). The book is written as a text for courses in computer science, computer engineering, IT, electronic engineering, and mechatronics, as well as a guide for robot hobbyists and researchers.Table of ContentsPart I, Embedded Systems and Programming Environments.- Robots and Controllers.- Central Processing Unit.- Sensors.- Actuators.- Actuators.- Control.- Multitasking.- Wireless Communication.- Raspberry Pi.- Arduino.- Part II, Mobile Robot Design.- Driving Robots.- Omnidirectional Robots.- Balancing Robots.- Walking Robots.- Autonomous Planes.- Autonomous Vessels and Underwater Vehicles.- Robot Manipulators.- Simulation Systems.- Part III, Mobile Robot Applications.- Localization and Navigation.- Maze Exploration.- Map Generation.- Real-Time Image Processing.- Robot Soccer.- Neural Networks.- Evolutionary Computing Approaches.- Behavior-Based Systems.- Evolution of Walking Gaits.- Automotive Systems.- Outlook.- Appendices, Index.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisScience has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature.Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information.This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory.

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Book SynopsisChapter 1. What is learning-based sports analytics?.- Chapter 2. Data acquisition with computer vision.- Chapter 3. Predictive analysis and play evaluation with machine learning.- Chapter 4. Potential play evaluation with learning-based agent modeling.- Chapter 5. Future perspectives and ecosystems.

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Book SynopsisA dramatic new future of work in which managers assemble exactly the expertise they need—within minutes.Gone are the days of static organizational charts and staffing based on the manager’s rolodex and intuition. Now you can recruit any expertise you need from a global online network within minutes: an on-demand, on-the-spot expert at the exact moment that you need their help. You can right-size their involvement: some of those experts give a second-opinion or a moment of brainstorming, while others join as full-fledged team members for a sustained collaborative effort.This is the future promised by Flash Teams, a model that The New York Times has already praised for its “revolutionary potential”: a world where experts are available anytime and everywhere, where remote work has become a norm, and where AI is in the loop to guide team decisions. In Flash Teams, award-winning management scholar Melissa Valentine and computer scientist Michael Bernstein chart the opportunities of flash teams and navigate the challenges that teams and managers will face. They distill lessons from their own work assembling and managing flash teams on demand that every manager can learn from so they can successfully use flash teams in their own organizations.Drawing on original research and industry examples, this book will help readers to:•   Recognize and source needed expertise within minutes•   Anticipate and plan team designs with AI•   Redesign team structures on the fly as projects evolveIndustries are already being transformed by this new approach to teaming. Flash Teams arms leaders, managers, and entrepreneurs with the tools they need to accomplish their goals with confidence, speed, and agility.

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Book SynopsisThe industry bible for graphic arts professionals, with pricing guidelines and information on business, ethical, and legal issues in a new, updated edition with essential information on AI, NFTs, social media, and more.

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Book SynopsisArtificial intelligence is everywhere - from self-driving cars, to image generation from text, to the unexpected power of language systems like ChatGPT - yet few people seem to know how it all really works. How AI Works unravels the mysteries of artificial intelligence, without the complex math and unnecessary jargon. You'll learn: the relationship between artificial intelligence, machine learning, and deep learning; the history behind AI and why the artificial intelligence revolution is happening now; how decades of work in symbolic AI failed and opened the door for the emergence of neural networks; what neural networks are, how they are trained, and why all the wonder of modern AI boils down to a simple, repeated unit that knows how to multiply input numbers to produce an output number, and the implications of large language models, like ChatGPT and Bard, on our society - nothing will be the same again. AI isn't magic. If you've ever wondered how it works, what it can do, or why theTrade Review"Succinct and clear-cut . . . a beacon for those wanting to delve into the mesmerizing world of AI, offering a detailed yet comprehensible overview of the evolutionary trajectory of machine learning technologies, from their rudimentary forms to the pioneering entities of today."—Antoine Tardif, CEO, Unite.AI“A tour de force of the rich history of artificial intelligence . . . For beginners, it demystifies AI and is a perfect resource to get up to date with more than six decades of research and development. . . . Even AI experts will gain a fresh perspective, enhancing their understanding and ability to articulate complex concepts."—Ben Dickson, Software Engineer, Editor of TechTalks"Kneusel does a superb job of explaining the evolution and intricacies of artificial intelligence with a combination of clarity and wit . . . Anyone who wants to understand the concepts of artificial intelligence, as well as its limitations and possible pitfalls, along with the tremendous potential it has to transform technology and society over time should read How AI Works."—Tony Bradley, Editor-in-Chief, TechSpective“A friendly and personal peek behind the curtain of modern AI. Ronald T. Kneusel tells the story of how the field grew, and surveys the ideas that are powering the AI revolution. From this book, you'll learn not only how AI works today, but its limits, its capabilities, and where it might take us tomorrow.”—Andrew Glassner, Author of Deep Learning: A Visual Approach"A must-read for anyone wishing to dig into AI without getting lost in the weeds. Kneusel has succeeded in explaining how AI works to a layperson like myself."—Kenneth Gass, Honorary Curator of Geology, Milwaukee Public Museum“After reading this book, I have a better understanding of the ML tools I have already used in my work . . . I recommend this book to anyone who works with software systems, including management, and anyone who just wants to know what AI actually does under the hood.”—Daniel Kosey, CISSP, Cybersecurity Engineer"If all the hype and reporting about AI has piqued your curiosity, but you don’t want to wade through the math and equations, Ron Kneusel’s latest book is for you. . . . You will no doubt learn a lot as you make your way from the earliest ideas about AI to today’s best models that are knocking at the door of True AI."—David Gorodetzky, Senior Scientist, Machine Learning and Remote Sensing"An excellent starting point for the enthusiast and anyone who wants to know what AI is and will be. . . . The author has another hit science book on a fascinating and apropos topic."—David S. Mazel, Principal/Manager Systems Engineer, Regulus-Group "AI isn't magic. If you've ever wondered how it works, what it can do, or why there's so much hype, How AI Works will teach you everything you want to know."—Midwest Book ReviewTable of ContentsAcknowledgmentsPrefaceChapter 1: And Away We Go: An AI OverviewChapter 2: Why Now? A History of AIChapter 3: Classical Models: Old-School Machine LearningChapter 4: Neural Networks: Brain-Like AIChapter 5: Convolutional Neural Networks: AI Learns to SeeChapter 6: Generative AI: AI Gets Creative Chapter 7: Large Language Models: True AI at Last?Chapter 8: Musings: The Implications of AIGlossaryResourcesIndex

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Book SynopsisTable of ContentsForeword vii Preface xi Acknowledgments xv Introduction 3 Chapter 1: A Primer on Modern AI 13 Chapter 2: Fair and Impartial 23 Chapter 3: Robust and Reliable 43 Chapter 4: Transparent 61 Chapter 5: Explainable 75 Chapter 6: Secure 93 Chapter 7: Safe 111 Chapter 8: Privacy 127 Chapter 9: Accountable 147 Chapter 10: Responsible 161 Chapter 11: Trustworthy AI in Practice 175 Chapter 12: Looking Forward 187 Notes 191 Index 199

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Book SynopsisKevin Wheeler is a HR, talent acquisition and L&D consultant specializing in advising global firms on how to improve their recruitment function. Based in Fremont, California, he is a frequent keynote speaker and industry writer.Bas van de Haterd is a professional speaker, consultant and trainer with a particular focus on using technology to improve recruitment. Based in Soest, The Netherlands, he has more than 20 years' experience and his consulting clients include Dutch Rail, Dutch Post and Twente University.Trade Review"With Talent Acquisition Excellence, Kevin and Bas offer a timely and astute look at how we are in a revolutionary period in recruiting. Blending keen insights with compelling examples, this book illuminates the future for talent leaders seeking to drive innovation and results. Required reading for anyone aiming to master modern recruiting's tools and tactics." * Toby Culshaw, Head of Talent Intelligence, Amazon Stores *"People teams worldwide are wrestling with the implications of divergent yet intersecting trends in the gig economy, shift to remote, cloud & collaboration, profound social and cultural change, the arrival of transformation technology in generative artificial intelligence, all amidst an unstable and increasingly worrisome geopolitical environment. This is a timely book from Kevin Wheeler and Bas van de Haterd, two of the most original and iconoclastic thinkers in the world of TA / HR, who pull together these diverse threads into a concise, imaginative and persuasive narrative." * Hung Lee, Curator Recruiting Brainfood *"This book is an insightful exploration of how the world of work is evolving and how the field of recruiting needs to adapt alongside it. Few authors can bring this to light with the credibility and bonafide of Kevin Wheeler and Bas van de Haterd. The depth of their collective experience is on display throughout each page as they explore the evolving dynamics of the workplace, seamlessly blending insights on technology, human psychology and the art of recruitment. The book covers an impressive spectrum, from exploring the role of algorithms and analytics in shaping recruitment strategies to the human-centric aspects of workforce planning and talent attraction. It is a guide, a reflection of decades of earned wisdom and a strategic tool all rolled into one." * Lars Schmidt, Founder & CEO Amplify Talent *"In Talent Acquisition Excellence, Bas van de Haterd and Kevin Wheeler set out a compelling and powerful template for talent acquisition in the age of AI, analytics and complex talent ecosystems. I particularly enjoyed the scenarios laid out in the book, which provide a compelling treatise on how strategy, technology, process and data can be interwoven together to build a world-class talent acquisition function. A must-read for recruiters, HR professionals and business leaders alike." * David Green, Managing Partner at Insight222, co-author of Excellence in People Analytics, and host of the Digital HR Leaders podcast. *"Talent Acquisition Excellence is a pragmatic guide for a TA or HR leader on what to consider when building a future-proof TA function from scratch. The authors use results and scientific data to understand what's integral to a best practice TA function and are not afraid to conjecture on how this will play out in the future with a mixture of recent real-life examples and near future projections to inspire HR and TA leaders. I welcome the thought-provoking discussion this forward-looking book will inspire and influence Talent Acquisition development for years to come." * Andrea Marston, Senior Director Global Talent Acquisition, VMware *"Kevin and Bas offer a balanced perspective that underscores how the universal themes and best practices of Talent Acquisition transcend industry sectors, languages, and borders across the globe. It's a treasure trove of wisdom, filled with real-world examples and insights. A must-read for TA leaders seeking practical answers and thought-provoking insights." * Shelley Billinghurst & Serge Boudreau, The Recruitment Flex Podcast *"Embark on a transformative odyssey alongside the visionary insights of Bas and Kevin, the oracles of talent acquisition. In their meticulously crafted guide, business leaders will discover the strategic power of talent acquisition, gaining profound insights into the future of recruitment. Peer into the crystal ball of emerging trends, technologies and strategies that will sculpt the hiring landscape. Stay ahead, triumph in the war for talent, elevate your leadership, make informed decisions and propel your organization to unprecedented success!" * Tash Johnston, Talent Acquisition Leader *"A timely and profound exploration of how to attract and retain top talent in today's rapidly evolving work environment. Talent Acquisition Excellence offers a masterclass in recruitment strategy, providing state-of-art perspectives and guidance." * Ross Dawson, Futurist, Keynote Speaker and Author of "Thriving on Chaos", Chairman, Advanced Human Technologies Group *"In an industry typically heavily influenced by vendors setting the tone for what TA excellence means, Kevin & Bas have curated an independent & refreshing take on navigating our ever-evolving landscape of recruiting & hiring practices. Your new 'go-to' recruiting resource is a blueprint for innovative approaches for success in sourcing, engaging, & securing top talent. This book is packed with punchy narratives, cool charts & contemporary insights that have practical application. leveraging real-world scenarios, useful tools and meaningful data it's a comprehensive guide that will resonate and provoke thought, as well as enable recruiters and hiring partners. A must-read for anyone aiming to keep ahead of the curve or elevate their recruitment game in the times ahead!" * Tracy Quinn, APAC Regional Head of Talent Acquisition *"Talent Acquisition Excellence is a masterful orientation to the current state of the art in TA with a big look at what we can expect next. Kevin and Bas combine their broad knowledge of international trends, and Kevin, as always, considers the grand sweep of history before making predictions about the future. It's practical, smart and full of concrete examples. I wish I had this book ten years ago!" * Dart Lindsley, Strategic Advisor, People Experience, Google, Speaker and host of Work For Humans *"This is a vital book for any talent acquisition professional looking to get ahead of the curve. These are incredibly disruptive times for the recruiting function with challenging talent markets, exponential technological advances, and fast-changing business needs that make it impossible to know what skills will be needed and when. Talent Acquisition Excellence provides a roadmap to the radically changed talent acquisition function of the future. The book is visionary but at the same time also accessible, pragmatic and practical." * Matt Alder, Host, The Recruiting Future Podcast *"Talent Acquisition Excellence provides a clear roadmap for building a more effective and efficient talent supply chain that is capable of meeting your organization's current and future talent needs. This book is a must read for any HR leader looking for practical advice and real-world examples for how to transform the recruiting function into a strategic capability." * Thomas Bertels, Founder and President, Purpose Works Consulting *"Talent Acquisition Excellence by Kevin Wheeler and Bas van de Haterd is a game-changer in recruitment literature. It stands out with its clear, no-nonsense approach, directly addressing how digital tools and analytics can revolutionize hiring. The authors, renowned experts in the field, effectively combine recruitment fundamentals with emerging technologies. The book offers unique insights and real-world case studies, making complex concepts accessible and applicable. This book is especially valuable for HR professionals and recruiters looking to leverage technology for smarter, more effective talent acquisition. Readers can expect to gain practical strategies and see tangible improvements in their recruitment outcomes. It's a concise and impactful read, essential for staying ahead in the modern recruitment landscape." * Felix Wetzel, Vice President, Product Management, Cielo *Table of Contents Chapter - 00: Introduction – what’s happening to work?; Chapter - 01: The emerging talent ecosystem; Chapter - 02: Technology and talent acquisition; Chapter - 03: Talent supply chain thinking; Chapter - 04: Talent attraction; Chapter - 05: Selection; Chapter - 06: RPO or in-house?; Chapter - 07: A radical new model for talent acquisition; Chapter - 08: ‘What hath God wrought?’;

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Book SynopsisWith this practical book, practitioners, students, and academics in both finance and data science will learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading.

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Book SynopsisRecent technological advances have resulted in a new generation of powerful text processing and generation tools, that produce text very similar to what a human could do. Such tools have the potential to change the way we work, learn and teach – and this book enables you to understand and be part of this exciting development. Did you wonder how tools like ChatGPT or Bard work, but do not have a technical background? Then this is the right book for you! To discuss and better understand what such technologies are capable of and how this will impact our lives and our society, a basic background knowledge about text processing and generation technologies is required. In particular, the book discusses the following questions: How did the field of automated text processing and generation evolve over the last years, and what happened to allow the incredible recent advances? Are chatbots such as ChatGPT or Bard truly understanding humans? What pitfalls exist and how are stereotypes of the society reflected in such models? What is the potential of such technology, and how will the digital society of the future look like in terms of human-chatbot-collaboration? The book is aimed for a general audience, briefly explaining mathematical or technical background when necessary. After having read this book, you will be confident to participate in public discussions about how this new generation of language models will impact society. You will be aware of the risks and pitfalls these technologies can bring along, and how to deal responsibly when making use of tools built from AI technology in general.Trade Review“Mascha Kurpicz-Briki’s book takes readers under the hood of today’s chatbots by explaining how LLMs work and looks through the hype. ... Her guide to how LLMs work steps through all of the need-to-know concepts underpinning natural language processing. And by the end of the journey, readers will have a clear understanding of what it takes for computers to process written text.” (James Tyrrell, T_HQ technology and business, techhq.com, December 20, 2023)Table of Contents1. Introduction.- 2. An Introduction to Machine Learning.- 3. Processing Written Language.- 4. Do Chatbots Have Emotions?.- 5. The True and the False.- 6. Stereotypes in Language Models.- 7. The Future of Humans and Language Models.

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Book SynopsisWhy and when to use a chatbot.- Good human writing.- Avoiding Typical Errors in Human Writing: Can Chatbots Help?.- Beyond common uses: the power and utility of chatbots.- How to sound natural and unbiased.- Exploiting, rather than rejecting, a chatbot's negative feedback on your work.- AI detectors and humanizers.- Hallucination, plausibility, confirmation bias, and over-reliance on chatbots.- Paraphrasing and plagiarism.- When are chatbots possibly better than humans.

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Book SynopsisThe future of AI is here.The world is transfixed by the marvel (and possible menace) of ChatGPT and other generative AI tools. It's clear Gen AI will transform the business landscape, but when and how much remain to be seen. Meanwhile, your smartest competitors are already navigating the risks and reaping the rewards of these new technologies. They're experimenting with new business models around generating text, images, and code at astonishing speed. They're automating customer interactions in ways never before possible. And they're augmenting human creativity in order to innovate faster. How can you take advantage of generative AI and avoid having your business disrupted?Generative AI: The Insights You Need from Harvard Business Review will help you understand the potential of these new technologies, pick the right Gen AI projects, and reinvent your business for the new age of AI.Business is changing. Will you adapt or be left behind?Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues—blockchain, cybersecurity, AI, and more—each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow.You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas—and prepare you and your company for the future.

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Book Synopsis'This colourful page-turner puts artificial intelligence into a human perspective . . . Metz explains this transformative technology and makes the quest thrilling.' Walter Isaacson, author of Steve Jobs____________________________________________________This is the inside story of a small group of mavericks, eccentrics and geniuses who turned Artificial Intelligence from a fringe enthusiasm into a transformative technology. It's the story of how that technology became big business, creating vast fortunes and sparking intense rivalries. And it's the story of breakneck advances that will shape our lives for many decades to come - both for good and for ill. ________________________________________________'One day soon, when computers are safely driving our roads and speaking to us in complete sentences, we'll look back at Cade Metz's elegant, sweeping Genius Makers as their birth story - the Genesis for an age of sentient machines.' Brad Stone, author of The Everything Store and The Upstarts'A ringside seat at what may turn out to be the pivotal episode in human history . . . easy and fun to read . . . undeniably charming.' ForbesTrade ReviewIn Genius Makers, Cade Metz delivers the definitive take on how AI technology came to be and what its arrival will mean for us humans. The book relies on tireless reporting and delightful writing to bring to life one of the most surprising and important stories of our time. If you want to read one book to understand AI, this is the one. -- Ashlee Vance, New York Times bestselling author of ELON MUSKThis colourful page-turner puts artificial intelligence into a human perspective. Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling. -- Walter Isaacson, #1 New York Times bestselling author of LEONARDO DA VINCI, STEVE JOBS and THE INNOVATORSCade Metz has produced an enthralling narrative of the advance of artificial intelligence. He describes the key personalities, the seminal meetings and the crucial breakthroughs with his customary eye for detail, building them into a dramatic history of this era-defining technology. -- Kai-Fu Lee, author of AI SUPERPOWERSThis is the inside story of how AI entered Google, Facebook, and the rest of high tech. It is also the story of how Silicon Valley and its mega-bucks infiltrated AI and changed its course. Chock full of behind-the-scenes anecdotes and wry humour - we learn the true tale of the technology that is transforming humanity. -- Oren Etzioni, chief executive, Allen Institute for Artificial IntelligenceOne day soon, when computers are safely driving our roads and speaking to us in complete sentences, we'll look back at Cade Metz's elegant, sweeping Genius Makers as their birth story - the Genesis for an age of sentient machines. -- Brad Stone, author of THE EVERYTHING STORE and THE UPSTARTS

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Book SynopsisDeep learning from the ground up using R and the powerful Keras library! In Deep Learning with R, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. about the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. what's inside Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation about the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.

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Book SynopsisTable of Contents1. Programming in Python 2. Machine Learning Basics 3. Regression 4. Classification 5. Dimensionality reduction 6. Clustering 7. Ensemble methods 8. Feature extraction and selection 9. Introduction to Deep Learning 10. Neural Networks 11. Convolutional Neural Networks

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

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Book SynopsisWe live in an incredible period in history. The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before.But our love of computers should not cloud our thinking about their limitations.We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are useless in judging whether the unearthed patterns are sensible because computers do not think the way humans think.We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to maTrade ReviewAI is eating the world! Or is it? Read the AI Delusion to find out. Gary Smith provides us with a rich tapestry of stories, studies, and science to elucidate this topic in a fun and accessible fashion. Learning about AI, data, and science has never been more enjoyable! * Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, and Professor of Computer Science at the University of Washington *Gary Smith demolishes the hype, the exaggerations, and the unrealistic expectations that have surrounded artificial intelligence and data mining. Combining vivid narratives with insightful analysis, the book is both highly informative and enormously entertaining. * Ernest Davis, Professor of Computer Science, New York University *You won't need a degree in linear algebra or multivariate calculus to understand The AI Delusion — a no-nonsense look at the limitations of Big Data. * Andrew Sloves, Former Managing Director at JP Morgan *This refreshing, amusing and frank book dispels many myths about the nature of AI when compared with human intelligence, with a stimulating range of examples. * David Lorimer, Paradigm Explorer *A remarkable book: deeply thoughtful but highly readable, full of practical examples to illustrate Smith's powerful computational critique of the proliferation of AI, big data, and machine learning in our daily lives. Truly essential reading. * Frank Pasquale, author The Black Box Society *Professor Gary Smith demonstrates why artificial intelligence doesn't live up to the hype. He uses a wide variety of real-world examples to illustrate the risks of taking humans out of the decision-making process. * Karl J. Meyer, Partner at Kleiner Perkins Caufield & Byers *Big data is increasingly being used to make big decisions, and that's a good thing, as long as we keep aware of how things can go wrong, as Gary Smith explains in this fun new book. * Andrew Gelman, Director of the Applied Statistics Center at Columbia University *Data professionals and consumers can benefit from Smith's entertaining and accessible demonstration that more computing power and more data do not imply more intelligence. We need to have more confidence in our human intellect. Humans may have common sense and an appreciation of context. Computers uniformly have none. * Eric Engberg, Data Scientist and Software Engineer, Wells Fargo *Prof Smith delivers a strong defense of the scientific method - theory before data - and clearly demonstrates the limitations of 'AI' and 'Big Data'. * Chris Nelson, CFO Universal Studios Hollywood *Smith's book goes a long way towards dispelling the BS about AI. * Roger Schank, Professor Emeritus, Northwestern University *Remarkable ... This book so deserves to be widely read. * Jonathan Cowie, Concatenation *Table of Contents1: Intelligent or Obedient? 2: Doing Without Thinking 3: Symbols Without Context 4: Bad Data 5: Patterns in Randomness 6: If You Torture the Data Long Enough 7: The Kitchen Sink 8: Old Wine in New Bottles 9: Take Two Aspirin 10: Beat the Market I 11: Beat the Market II 12: We're Watching You

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Book SynopsisA concise, accessible introduction to robots, what they can do, what they can''t, and what their increasing encroachment into our lives might mean for us Since the turn of the millennium a quiet revolution has been underway. Millions of autonomous robots with some level of intelligence are now in domestic use, mainly as vacuum cleaners. Driverless cars - which are nothing less than autonomous robots - are starting to appear on our streets. There is a huge effort underway in industry and universities to develop the next generation of more intelligent, autonomous, mobile robots. Accompanying these arrivals has been a steady stream of inflammatory articles in the media raising concerns over the impending spectre of super-intelligent robots, along with stories about how most jobs will soon be lost to robots.Here, using the Question-and-Answer format, Phil Husbands gives a balanced and broad introduction to robotics and the current state of the field, analysing where it has come from, and wTrade ReviewThe book is accessible, and readers can expect to learn much from it. Husbands has given us a historically informed introduction to robotics, rooted in technological reality and dismissing futuristic hype. * Simon Balle, Metascience *the book is accessible, and readers can expect to learn much from it. Husbands has given us a historically informed introduction to robotics, rooted in technological reality and dismissing futuristic hype. * Simon Balle, Metascience *Table of ContentsPreface 1: Robots are Here 2: The Basics 3: Some History 4: Inside the Machine 5: Robot Fantasies: Robots in Popular Culture 6: Intelligence, Super-Intelligence and Cyborgs 7: Robots at Work 8: Robot Ethics 9: Robot Futures

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Book SynopsisTable of Contents1. Introduction to Text and Sentiment Data Analysis 2. Natural Language Processing and Sentiment Analysis: Perspectives from Computational Intelligence 3. Applications and Challenges of Sentiment Analysis in Real Life Scenarios 4. Emotions Recognition of Students from Online and Offline Texts 5. Online Social Network Sensing Models 6. Identifying Sentiments of Hate Speech using Deep Learning 7. An Annotation System to Summarize Medical Corpus using Sentiment based Models 8. Deep learning-based Dataset Recommendation System by employing Emotions 9. Hybrid Deep Learning Architecture Performance on Large English Sentiment Text Data: Merits and Challenges 10. Human-centered Sentiment Analysis 11. An Interactive Tutoring System for Older Adults - Learning with New Apps 12. Irony and Sarcasm Detection 13. Concluding Remarks

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Book SynopsisTable of Contents1. Introduction to Digital Twin 2. Under-Actuated Digital Twin’s Robotic Hands with Tactile Sensing Capabilities for Well-being 3. Digital Twin for Healthcare Immersive Services 4. Challenges of Digital Twin in Healthcare 5. Architecture Reference Models of Digital Twins for Healthcare 6. Artificial Intelligence Models in Digital Twins for Health and Well-being 7. COVIDMe: A Digital Twin for COVID-19 self-assessment and detection 8. Improve Human Living Environment and Human Health by Environmental Digital Twins Technology 9. Role of smart technologies in detecting cognitive impairment and enhancing assisted living 10. Digital Twins and Cybersecurity in Healthcare systems 11. Potential applications of Digital Twin in Medical care 12. Digital Twin in Prognostics and Health Management System 13. Digital Twin for Cardiology 14. Applications of digital twins to migraine disease 15. Digital Twins for Nutrition 16. Digital Twins for Allergies

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Book SynopsisTable of Contents1. IoT: Fundamentals and challenges 2. Security issues of IoT 3. IoT-based Healthcare Systems 4. Emerging e-Health IoT Applications 5. Blockchain: Concept and Emergence 6. Application of Blockchain for security 7. Role of Blockchain in IoT based healthcare systems 8. Decentralized management of healthcare IoT devices 9. Blockchain based insurance and healthcare 10. Blockchain adoption strategies

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Book SynopsisWhat is artificial intelligence? How is artificial intelligence used in game development?Game development lives in its own technical world. It has its own idioms, skills, and challenges. Thatâs one of the reasons games are so much fun to work on. Each game has its own rules, its own aesthetic, and its own trade-offs, and the hardware it will run on keeps changing. AI for Games is designed to help you understand one element of game development: artificial intelligence (AI).Table of ContentsAuthor. Introduction. 1 What Is AI? 2 Model of Game AI. 3 Algorithms and Data Structures. 4 Game AI. 5 Techniques. 6 Supporting Technologies. Index.

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Book SynopsisThis book addresses the topics related to artificial intelligence, the Internet of Things, blockchain technology, and machine learning. It brings together researchers, developers, practitioners, and users interested in cybersecurity and forensics. The first objective is to learn and understand the need for and impact of advanced cybersecurity and forensics and its implementation with multiple smart computational technologies. This objective answers why and how cybersecurity and forensics have evolved as one of the most promising and widely-accepted technologies globally and has widely-accepted applications. The second objective is to learn how to use advanced cybersecurity and forensics practices to answer computational problems where confidentiality, integrity, and availability are essential aspects to handle and answer. This book is structured in such a way so that the field of study is relevant to each readerâs major or interests. It aims to help each reader see the relevance of cybersecurity and forensics to their career or interests. This book intends to encourage researchers to develop novel theories to enrich their scholarly knowledge to achieve sustainable development and foster sustainability. Readers will gain valuable knowledge and insights about smart computing technologies using this exciting book.This book:â Includes detailed applications of cybersecurity and forensics for real-life problemsâ Addresses the challenges and solutions related to implementing cybersecurity in multiple domains of smart computational technologies â Includes the latest trends and areas of research in cybersecurity and forensicsâ Offers both quantitative and qualitative assessments of the topics Includes case studies that will be helpful for the researchersProf. Keshav Kaushik is Assistant Professor in the Department of Systemics, School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India.Dr. Shubham Tayal is Assistant Professor at SR University, Warangal, India.Dr. Akashdeep Bhardwaj is Professor (Cyber Security & Digital Forensics) at the University of Petroleum & Energy Studies (UPES), Dehradun, India.Dr. Manoj Kumar is Assistant Professor (SG) (SoCS) at the University of Petroleum and Energy Studies, Dehradun, India.Table of Contents1. Detection of Cross-Site Scripting and Phishing Website Vulnerabilities Using Machine Learning. 2. A Review: Security and Privacy Defensive Techniques for Cyber Security Using Deep Neural Networks (DNNs). 3. DNA-Based Cryptosystem for Connected Objects and IoT Security. 4. A Role of Digital Evidence: Mobile Forensics Data. 5. Analysis of Kernel Vulnerabilities Using Machine Learning. 6. Cyber Threat Exploitation and Growth during COVID-19 Times. 7. An Overview of the Cybersecurity in Smart Cities in the Modern Digital Age. 8. The Fundamentals and Potential for Cyber Security of Machine Learning in the Modern World. 9. Qualitative and Quantitative Evaluation of Encryption Algorithms. 10. Analysis and Investigation of Advanced Malware Forensics. 11. Network Intrusion Detection System Using Naïve Bayes Classification Technique for Anomaly Detection. 12. Data Security Analysis in Mobile Cloud Computing for Cyber Security. 13. A Comprehensive Review of Investigations of Suspects of Cyber Crimes. 14. Fault Analysis Techniques in Lightweight Ciphers for IoT Devices.

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Book SynopsisThis book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging Table of ContentsMachine learning and deep learning in agriculture, Descriptive and predictive analytics of agricultural data using machine learning algorithms, Discrimination between weed and crop via image analysis using machine learning algorithm, Bio-inspired optimization algorithms for machine learning in agriculture applications, Agricultural modernization with forecasting stages and machine learning, Classification of segmented image using increased global contrast for Paddy plant disease, IOT in agriculture: Survey on technology, challenges and future scope, Role of IoT in sustainable farming, Smart farming: Crop models and decision support systems using IOT, Smart irrigation in farming using internet of things, Automation systems in agriculture via IOT, A complete automated solution for farm field and garden nurture using internet of things, Machine intelligence techniques for agricultural production: Case study with tomato leaf disease detection, Clock signal and its attribute for agriculture.

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