Designing Machine Learning Systems
Machine learning has become a critical component of modern technology, shaping industries from healthcare and finance to marketing and entertainment. Yet, building effective machine learning systems is about more than just selecting the right algorithm; it requires a holistic approach that considers design, scalability, deployment, and ongoing maintenance. This book, Designing Machine Learning Systems, offers readers a comprehensive guide to creating resilient and scalable machine learning systems that can deliver real-world results. Whether you're an engineer, data scientist, or product manager, this book is designed to bridge the gap between theory and practice, emphasizing system design principles crucial for long-term success. Through a step-by-step approach, we explore key topics such as data engineering, model selection, and the deployment lifecycle. Each chapter provides insights into best practices, tools, and frameworks that simplify the process of taking machine learning from experimentation to production. With a focus on reliability, scalability, and performance, this book aims to equip readers with a practical toolkit to build robust machine learning systems capable of handling complex demands. By the end, readers will not only understand the technical foundations but also gain the confidence to design, deploy, and monitor machine learning systems that align with real-world business objectives.
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes
The earliest research into time-to-event outcomes can be dated back to the 17th century. Here the initial focus was predicting time until death, hence the term survival analysis. Applications of time-to-event outcomes are to be found in many walks of life, such as insurance, medicine, and even calculating when will a customer end their subscription. Recently, the machine learning community has made significant methodological advances in survival analysis that take advantage of the representation learning ability of deep neural networks. At this point, there is a proliferation of deep survival analysis models. In this monograph, the author provides a self-contained modern introduction to survival analysis. The focus is on predicting time-to-event outcomes at the individual data point level with the help of neural networks. They provide the reader with a working understanding of precisely what the basic time-to-event prediction problem is, how it differs from standard regression and classification, and how key "design patterns" have been used time after time to derive new time-to-event prediction models. The author also details two extensions of the basic time-to-event prediction setup, namely the competing risks setting and the dynamic setting. The monograph concludes with a discussion of a variety of topics such as fairness, causal reasoning, interpretability, and statistical guarantees. This timely monograph provides researchers and students with a succinct introduction to the use of time-to-event outcomes in modern artificial intelligence driven systems.
Microsoft 365 Copilot at Work
Learn to leverage Microsoft's new AI tool, Copilot, for enhanced productivity at work In Microsoft 365 Copilot At Work: Using AI to Get the Most from Your Business Data and Favorite Apps, a team of software and AI experts delivers a comprehensive guide to unlocking the full potential of Microsoft's groundbreaking AI tool, Copilot. Written for people new to AI, as well as experienced users, this book provides a hands-on roadmap for integrating Copilot into your daily workflow. You'll find the knowledge and strategies you need to maximize your team's productivity and drive success. The authors offer you a unique opportunity to gain a deep understanding of AI fundamentals, including machine learning, large language models, and generative AI versus summative AI. You'll also discover: How Copilot utilizes AI technologies to provide real-time intelligent assistance and revolutionize the way you work with Microsoft 365 apps Practical Implementation Strategies for project and change management, as well as practical guidance on rolling out Copilot within your organization Specific use cases, including Outlook, Teams, Excel, PowerPoint, and OneNote, and how Copilot can streamline tasks and boost efficiency across various Microsoft applications Take your Copilot proficiency to the next level with advanced AI concepts, usage monitoring, and custom development techniques. Delve into Microsoft Framework Accelerator, Copilot plugins, semantic kernels, and custom plugin development, empowering you to tailor Copilot to your organization's unique needs and workflows. Get ready to revolutionize your productivity with Microsoft 365 Copilot!
Demystifying Chaotic Cryptology
Unravel the secrets of chaotic cryptology as this groundbreaking book takes you on a journey through the intricate world of cryptographic chaos. Through meticulous meta-analysis and clear explanations, it demystifies complex cryptographic concepts, offering readers a profound understanding of this enigmatic field. Embark on an illuminating exploration of chaotic cryptology with this in-depth guide. Delving into the depths of cryptographic chaos, it unveils the mysteries surrounding complex encryption techniques. Through the lens of explainable meta-analysis, readers gain invaluable insights into the workings of chaotic cryptosystems, demystifying the seemingly inscrutable realm of cryptography. Unlock the potential of chaotic cryptology in cyber-oriented digital engineering with this groundbreaking guide. As the digital landscape evolves, the need for secure and lightweight encryption becomes paramount, especially in pervasive systems where resource constraints are prevalent. This book offers a comprehensive framework for leveraging chaotic cryptology in the design and engineering of ultra-lightweight ciphers tailored for pervasive systems. Drawing on the principles of chaos theory, it explores innovative approaches to cryptographic design, optimizing for both security and efficiency in resource-constrained environments. Through a blend of theoretical insights and practical applications, readers will learn how to harness chaotic dynamics to create robust encryption schemes capable of withstanding modern cyber threats while operating seamlessly in pervasive computing environments. Whether you are a seasoned cryptographer, a digital engineer, or a cybersecurity enthusiast, this book provides the tools and techniques needed to navigate the complexities of chaotic cryptology and engineer resilient, ultra-lightweight ciphers for the pervasive systems of tomorrow.
Blockchain Technology
Blockchain technology is considered a disruptive innovation that changes the ways companies and global processes operate. This technology has impressive powers to change this world for the better.This book examines the origins, emergence, challenges, and opportunities in the blockchain field, rethinking business strategy and readiness in the digital world and how blockchain technology would improve businesses. It provides a blockchain readiness model for managing supply chains and reviews enabling technologies such as AI, big data and organisational capabilities that support the adoption of blockchain technology. Through innovative design and simulation of a blockchain framework, it aims to enhance the traceability and transparency of business operations and supply chains. This includes developing key performance indicators for measuring the seamless integration of blockchain technology and achieving a successful outcome. It explores how blockchain technology enhances the green and sustainability aspects of businesses by comparing the sectors and discussing the potential for blockchain to promote a green and sustainable economy. This book concludes with research frontiers and blockchain applications in healthcare, international trade, and supply chain sectors.Key features Integrates both theoretical and practical perspectives Includes material that is informative for readers from diverse backgrounds and disciplines Explores blockchain technology practices and challenges in-depth across various sectors Offers up-to-date, critical insights on the design, management, and control of blockchain technology for businesses Written by experts with extensive experience in the field. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields including electrical engineering, electronics and communication engineering, computer engineering, and information technology.
AI Factory
This book provides insights into how to approach and utilise data science tools, technologies, and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI technologies and their interconnections. It includes descriptions of various technology and methodology approaches and their purpose and benefits when developing AI solutions in industrial contexts. In addition, this book summarises experiences from AI technology deployment projects from several industrial sectors.Features: Presents a compendium of methodologies and technologies in industrial AI and digitalisation. Illustrates the sensor-to-actuation approach showing the complete cycle, which defines and differentiates AI and digitalisation. Covers a broad range of academic and industrial issues within the field of asset management. Discusses the impact of Industry 4.0 in other sectors. Includes a dedicated chapter on real-time case studies. This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and machine learning (ML), engineering managers, operational and maintenance specialists, asset managers, and digital and AI manufacturing specialists.
Multi-Sensor and Multi-Temporal Remote Sensing
This book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the 'individual sample as mean' training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields.Key features: Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI) Discusses the role of training data to handle the heterogeneity within a class Supports multi-sensor and multi-temporal data processing through in-house SMIC software Includes case studies and practical applications for single class mapping This book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.
Hybrid Computational Intelligent Systems
Hybrid Computational Intelligent Systems - Modeling, Simulation and Optimization unearths the latest advances in evolving hybrid intelligent modeling and simulation of human-centric data-intensive applications optimized for real-time use, thereby enabling researchers to come up with novel breakthroughs in this ever-growing field.Salient features include the fundamentals of modeling and simulation with recourse to knowledge-based simulation, interaction paradigms, and human factors, along with the enhancement of the existing state of art in a high-performance computing setup. In addition, this book presents optimization strategies to evolve robust and failsafe intelligent system modeling and simulation.The volume also highlights novel applications for different engineering problems including signal and data processing, speech, image, sensor data processing, innovative intelligent systems, and swarm intelligent manufacturing systems.Features: A self-contained approach to integrating the principles of hybrid computational ntelligence with system modeling and simulation Well-versed foundation of computational intelligence and its application to real life engineering problems Elucidates essential background, concepts, definitions, and theories thereby putting forward a complete treatment on the subject Effective modeling of hybrid intelligent systems forms the backbone of almost every operative system in real-life Proper simulation of real-time hybrid intelligent systems is a prerequisite for deriving any real-life system solution Optimized system modeling and simulation enable real-time and failsafe operations of the existing hybrid intelligent system solutions Information presented in an accessible way for researchers, engineers, developers, and practitioners from academia and industry working in all major areas and interdisciplinary areas of hybrid computational intelligence and communication systems to evolve human-centered modeling and simulations of real-time data-intensive intelligent systems.
Artificial Intelligence in Cyber-Physical Systems
Artificial Intelligence (AI) and the Internet of Things (IoT) are growing rapidly in today's business world. In today's era, 25 billion devices, including machines, sensors, and cameras, are connected and continue to grow steadily. It is assumed that in 2025, 41.6 billion IoT devices will be connected, generating around 79.4 zettabytes of data.IoT and AI are intersecting in various scenarios. IoT-enabled devices are generating a huge amount of data, and with the help of AI, this data is used to build various intelligent models. These intelligent models are helpful in our daily lives and make the world smarter.Artificial Intelligence in Cyber Physical Systems: Principles and Applications addresses issues related to system safety, security, reliability, and deployment strategies in healthcare, military, transportation, energy, infrastructure, smart homes, and smart cities.
Decision Support System and Automated Negotiations
Decision support systems are developed for integrated pest and disease management and nutrition management using open-source technologies as java, android, and low-cost hardware devices like Arduino micro controller. This text discusses the techniques to convert agricultural knowledge in the context of ontology and assist grape growers by providing this knowledge through decision support system. The key features of the book are: Presents the design & development of an ontology-based decision support system for integrated crop management. Discusses the techniques to convert agricultural knowledge in text to ontology. Focuses on an extensive study of various e-Negotiation protocols for automated negotiations Provides an architecture for predicting the opponent's behaviour and various factors which affect the process of negotiation. The text is primarily written for graduate students, professionals, and academic researchers working in the fields of computer science and engineering, agricultural science, and information technology.
Modern Optimization Methods for Decision Making Under Risk and Uncertainty
The book comprises original articles on topical issues of risk theory, rational decision making, statistical decisions, and control of stochastic systems. The articles are the outcome of a series international projects involving the leading scholars in the field of modern stochastic optimization and decision making. The structure of stochastic optimization solvers is described. The solvers in general implement stochastic quasi-gradient methods for optimization and identification of complex nonlinear models. These models constitute an important methodology for finding optimal decisions under risk and uncertainty. While a large part of current approaches towards optimization under uncertainty stems from linear programming (LP) and often results in large LPs of special structure, stochastic quasi-gradient methods confront nonlinearities directly without need of linearization. This makes them an appropriate tool for solving complex nonlinear problems, concurrent optimization and simulation models, and equilibrium situations of different types, for instance, Nash or Stackelberg equilibrium situations. The solver finds the equilibrium solution when the optimization model describes the system with several actors. The solver is parallelizable, performing several simulation threads in parallel. It is capable of solving stochastic optimization problems, finding stochastic Nash equilibria, and of composite stochastic bilevel problems where each level may require the solution of stochastic optimization problem or finding Nash equilibrium. Several complex examples with applications to water resources management, energy markets, pricing of services on social networks are provided. In the case of power system, regulator makes decision on the final expansion plan, considering the strategic behavior of regulated companies and coordinating the interests of different economic entities. Such a plan can be an equilibrium - a planned decision where a company cannot increase its expected gain unilaterally.
Diagnosis of Neurological Disorders Based on Deep Learning Techniques
This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data preprocessing including scaling, correction, trimming, and normalization is also included. Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders. Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative, neurodevelopmental, and psychiatric disorders. Helps build, train, and deploy different types of deep architectures for diagnosis. Explores data preprocessing techniques involved in diagnosis. Includes real-time case studies and examples. This book is aimed at graduate students and researchers in biomedical imaging and machine learning.
Iapp Cipp / Us Certified Information Privacy Professional Study Guide
Prepare for success on the IAPP CIPP/US exam and further your career in privacy with this effective study guide - now includes a downloadable supplement to get you up to date on the current CIPP exam for 2024-2025! Information privacy has become a critical and central concern for small and large businesses across the United States. At the same time, the demand for talented professionals able to navigate the increasingly complex web of legislation and regulation regarding privacy continues to increase. Written from the ground up to prepare you for the United States version of the Certified Information Privacy Professional (CIPP) exam, Sybex's IAPP CIPP/US Certified Information Privacy Professional Study Guide also readies you for success in the rapidly growing privacy field. You'll efficiently and effectively prepare for the exam with online practice tests and flashcards as well as a digital glossary. The concise and easy-to-follow instruction contained in the IAPP/CIPP Study Guide covers every aspect of the CIPP/US exam, including the legal environment, regulatory enforcement, information management, private sector data collection, law enforcement and national security, workplace privacy and state privacy law, and international privacy regulation. Provides the information you need to gain a unique and sought-after certification that allows you to fully understand the privacy framework in the US Fully updated to prepare you to advise organizations on the current legal limits of public and private sector data collection and use Includes 1 year free access to the Sybex online learning center, with chapter review questions, full-length practice exams, hundreds of electronic flashcards, and a glossary of key terms, all supported by Wiley's support agents who are available 24x7 via email or live chat to assist with access and login questions Perfect for anyone considering a career in privacy or preparing to tackle the challenging IAPP CIPP exam as the next step to advance an existing privacy role, the IAPP CIPP/US Certified Information Privacy Professional Study Guide offers you an invaluable head start for success on the exam and in your career as an in-demand privacy professional.
Model-Based Enterprise
Model-Based Enterprise describes Model-Based Enterprise (MBE) and Model-Based Definition (MBD) in detail, focusing on how to obtain significant business value from MBE.This book presents MBE from technical and business perspectives, focusing on process improvement, productivity, quality, and obtaining greater value from our information and how we work. The evolution of MBD and MBE, from computer-aided design (CAD) topics to current approaches and to their future roles, is discussed. Following the progression from manual drawings to 2D CAD, 3D CAD, and to digital data and digital information models, MBE is presented as the method to achieve productivity and profitability by understanding the cost of how we work and refining our approaches to creating and using information. Many MBD and MBE implementations have changed how we work but yield little real business value - processes changed, engineering drawings were replaced with 3D models, but the organization achieved minor benefits from their efforts. This book provides methods to become an MBE and achieve the full value possible from digital transformation.Model-Based Enterprise is essential reading for anyone who creates or uses product-related information in original equipment manufacturers (OEMs) and suppliers, in the private sector, and in government procurement and development activities. This book is also essential for students in all engineering disciplines, manufacturing, quality, information management, product lifecycle management (PLM), and related business disciplines.
Artificial Intelligence
This book provides an examination of cutting-edge research and developments in the field of artificial intelligence. It seeks to extend the view in both technical and societal evaluations to ensure a well-defined balance for societal outcomes. It explores hot topics such as generative artificial intelligence, artificial intelligence in law, education, and climate change.Artificial Intelligence: Technical and Societal Advancements seeks to bridge the gap between theory and practical applications of AI by giving readers insight into recent advancements. It offers readers a deep dive into the transformative power of AI for the present and future world. As artificial intelligence continues to revolutionize various sectors, the book discusses applications from healthcare to finance and from entertainment to industrial areas. It discusses the technical aspects of intelligent systems and the effects of these aspects on humans. To this point, this book considers technical advancements while discussing the societal pros and cons in terms of human-machine interaction in critical applications. The authors also stress the importance of deriving policies and predictions about how to make future intelligent systems compatible with humans through a necessary level of human management. Finally, this book provides the opinions and views of researchers and experts (from public/private sector) including educators, lawyers, policymakers, managers, and business-related representatives.The target readers of this book include academicians; researchers; experts; policymakers; educators; and B.S., M.S., and Ph.D. students in the context of target problem fields. It can be used accordingly as a reference source and even supportive material for artificial intelligence-oriented courses.
Computational Techniques for Text Summarization based on Cognitive Intelligence
The book is concerned with contemporary methodologies used for automatic text summarization. It proposes interesting approaches to solve well-known problems on text summarization using computational intelligence (CI) techniques including cognitive approaches. A better understanding of the cognitive basis of the summarization task is still an open research issue; an extent of its use in text summarization is highlighted for further exploration. With the ever-growing text, people in research have little time to spare for extensive reading, where summarized information helps for a better understanding of the context at a shorter time.This book helps students and researchers to automatically summarize the text documents in an efficient and effective way. The computational approaches and the research techniques presented guides to achieve text summarization at ease. The summarized text generated supports readers to learn the context or the domain at a quicker pace. The book is presented with reasonable amount of illustrations and examples convenient for the readers to understand and implement for their use. It is not to make readers understand what text summarization is, but for people to perform text summarization using various approaches. This also describes measures that can help to evaluate, determine, and explore the best possibilities for text summarization to analyse and use for any specific purpose. The illustration is based on social media and healthcare domain, which shows the possibilities to work with any domain for summarization. The new approach for text summarization based on cognitive intelligence is presented for further exploration in the field.
Algorithm and Design Complexity
Computational complexity is critical in analysis of algorithms and is important to be able to select algorithms for efficiency and solvability. Algorithm and Design Complexity initiates with discussion of algorithm analysis, time-space trade-off, symptotic notations, and so forth. It further includes algorithms that are definite and effective, known as computational procedures. Further topics explored include divide-and-conquer, dynamic programming, and backtracking.Features: Includes complete coverage of basics and design of algorithms Discusses algorithm analysis techniques like divide-and-conquer, dynamic programming, and greedy heuristics Provides time and space complexity tutorials Reviews combinatorial optimization of Knapsack problem Simplifies recurrence relation for time complexity This book is aimed at graduate students and researchers in computers science, information technology, and electrical engineering.
Computing, Communication and Intelligence
The International Conference on Cutting-edge Technology in Computing, Communications, and Intelligence- (ICCTCCI-2024) focuses on the application of smart technology and materials for smarter industrial production. The ICCTCCI-2024 provides common platform for presentation of original research findings, exchange of ideas and dissemination of innovative, practical development experiences in different aspects and fields of industry. It also focuses on the event organized with the objective of bringing together academicians, scientists, researchers from industry, research scholars, and students working in different industrial domains and applied applications.
Epileptic Seizure Prediction Using Electroencephalogram Signals
This book presents an innovative method of EEG-based feature extraction and classification of seizures using EEG signals. It describes the methodology required for EEG analysis, seizure detection, seizure prediction, and seizure classification. It contains a compilation of techniques described in the literature and emphasizes newly proposed techniques. The book includes a brief discussion of existing methods for epileptic seizure diagnosis and prediction and introduces new efficient methods specifically for seizure prediction. Focuses on the mathematical models and machine learning algorithms from a perspective of clinical deployment of EEG-based epileptic seizure prediction Discusses recent trends in seizure detection, prediction, and classification methodologies Provides engineering solutions to severity or risk analysis of detected seizures at remote places Presents wearable solutions to seizure prediction Includes details of the use of deep learning for epileptic seizure prediction using EEG This book acts as a reference for academicians and professionals who are working in the field of computational biomedical engineering and are interested in the domain of EEG-based disease prediction.
Deep Reinforcement Learning Hands-On - Third Edition
Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methodsPurchase of the print or Kindle book includes a free PDF eBookKey Features: - Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation- Develop deep RL models, improve their stability, and efficiently solve complex environments- New content on RL from human feedback (RLHF), MuZero, and transformersBook Description: Reward yourself and take this journey into RL with the third edition of Deep Reinforcement Learning Hands-On. The book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the field, this deep reinforcement learning book will equip you with the practical know-how of RL and the theoretical foundation to understand and implement most modern RL papers.The book retains its strengths by providing concise and easy-to-follow explanations. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods.If you want to learn about RL using a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition is your ideal companionWhat You Will Learn: - Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs- Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG- Implement RL algorithms using PyTorch and modern RL libraries- Build and train deep Q-networks to solve complex tasks in Atari environments- Speed up RL models using algorithmic and engineering approaches- Leverage advanced techniques like proximal policy optimization (PPO) for more stable trainingWho this book is for: This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it's also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and financeTable of Contents- What Is Reinforcement Learning?- OpenAI Gym - Deep Learning with PyTorch- The Cross-Entropy Method- Tabular Learning and the Bellman Equation- Deep Q-Networks- Higher-Level RL Libraries- DQN Extensions - Ways to Speed up RL- Stocks Trading Using RL- Policy Gradients - an Alternative- Actor-Critic Methods - A2C and A3C- The TextWorld Environment- Web Navigation- Continuous Action Space- Trust Regions - PPO, TRPO, ACKTR, and SAC- Black-Box Optimization in RL- Advanced Exploration- RL with Human Feedback- MuZero- RL in Discrete Optimization- Multi-agent RL- RL in Robotics
Causal Inference in R
Master the fundamentals to advanced techniques of causal inference through a practical, hands-on approach with extensive R code examples and real-world applicationsKey Features: - Explore causal analysis with hands-on R tutorials and real-world examples- Grasp complex statistical methods by taking a detailed, easy-to-follow approach- Equip yourself with actionable insights and strategies for making data-driven decisions- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Determining causality in data is difficult due to confounding factors. Written by an applied scientist specializing in causal inference with over a decade of experience, Causal Inference in R provides the tools and methods you need to accurately establish causal relationships, improving data-driven decision-making.This book helps you get to grips with foundational concepts, offering a clear understanding of causal models and their relevance in data analysis. You'll progress through chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. You'll discover techniques for establishing causality, from classic approaches to contemporary methods, such as propensity score matching and instrumental variables. Each chapter is enriched with detailed case studies and R code snippets, enabling you to implement concepts immediately. Beyond technical skills, this book also emphasizes critical thinking in data analysis to empower you to make informed, data-driven decisions. The chapters enable you to harness the power of causal inference in R to uncover deeper insights from data.By the end of this book, you'll be able to confidently establish causal relationships and make data-driven decisions with precision.What You Will Learn: - Get a solid understanding of the fundamental concepts and applications of causal inference- Utilize R to construct and interpret causal models- Apply techniques for robust causal analysis in real-world data- Implement advanced causal inference methods, such as instrumental variables and propensity score matching- Develop the ability to apply graphical models for causal analysis- Identify and address common challenges and pitfalls in controlled experiments for effective causal analysis- Become proficient in the practical application of doubly robust estimation using RWho this book is for: This book is for data practitioners, statisticians, and researchers keen on enhancing their skills in causal inference using R, as well as individuals who aspire to make data-driven decisions in complex scenarios. It serves as a valuable resource for both beginners and experienced professionals in data analysis, public policy, economics, and social sciences. Academics and students looking to deepen their understanding of causal models and their practical implementation will also find it highly beneficial.Table of Contents- Introducing Causal Inference- Unraveling Confounding and Associations- Initiating R with a Basic Causal Inference Example- Constructing Causality Models with Graphs- Navigating Causal Inference through Directed Acyclic Graphs- Employing Propensity Score Techniques- Employing Regression Approaches for Causal Inference- Executing A/B Testing and Controlled Experiments- Implementing Doubly Robust Estimation- Analyzing Instrumental Variables- Investigating Mediation Analysis- Exploring Sensitivity Analysis- Scrutinizing Heterogeneity in Causal Inference- Harnessing Causal Forests and Machine Learning Methods- Implementing Causal Discovery in R
Computational Intelligence Aided Systems for Healthcare Domain
This book covers recent advances in artificial intelligence, smart computing, and their applications in augmenting medical and health care systems. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical.This book: Presents architecture, characteristics, and applications of artificial intelligence and smart computing in health care systems Highlights privacy issues faced in health care and health informatics using artificial intelligence and smart computing technologies Discusses nature-inspired computing algorithms for the brain-computer interface Covers graph neural network application in the medical domain Provides insights into the state-of-the-art artificial intelligence and smart computing enabling and emerging technologies This book discusses recent advances and applications of artificial intelligence and smart technologies in the field of healthcare. It highlights privacy issues faced in health care and health informatics using artificial intelligence and smart computing technologies. It covers nature-inspired computing algorithms such as genetic algorithms, particle swarm optimization algorithms, and common scrambling algorithms to study brain-computer interfaces. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.
Reinforcement Learning for Cyber-Physical Systems
This book introduces reinforcement learning, and provides novel ideas and use cases to demonstrate the benefits of using reinforcement learning for Cyber Physical Systems. Two important case studies on applying reinforcement learning to cybersecurity problems are included.
Artificial Intelligence and Deep Learning for Computer Network
Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aims to systematically collect quality research spanning AI, ML, and deep learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights on the applicability of the theoretical similitudes, otherwise a rarity in many such books.Features: A diverse collection of important and cutting-edge topics covered in a single volume. Several chapters on cybersecurity, an extremely active research area. Recent research results from leading researchers and some pointers to future advancements in methodology. Detailed experimental results obtained from standard data sets. This book serves as a valuable reference book for students, researchers, and practitioners who wish to study and get acquainted with the application of cutting-edge AI, ML, and DL techniques to network management and cyber security.
Network Forensics
This book primarily focuses on providing deep insight into the concepts of network security, network forensics, botnet forensics, ethics and incident response in global perspectives. It also covers the dormant and contentious issues of the subject in most scientific and objective manner. Various case studies addressing contemporary network forensics issues are also included in this book to provide practical know - how of the subject. Network Forensics: A privacy & Security provides a significance knowledge of network forensics in different functions and spheres of the security. The book gives the complete knowledge of network security, all kind of network attacks, intention of an attacker, identification of attack, detection, its analysis, incident response, ethical issues, botnet and botnet forensics. This book also refer the recent trends that comes under network forensics. It provides in-depth insight to the dormant and latent issues of the acquisition and system live investigation too.Features: Follows an outcome-based learning approach. A systematic overview of the state-of-the-art in network security, tools, Digital forensics. Differentiation among network security, computer forensics, network forensics and botnet forensics. Discussion on various cybercrimes, attacks and cyber terminologies. Discussion on network forensics process model. Network forensics tools and different techniques Network Forensics analysis through case studies. Discussion on evidence handling and incident response. System Investigations and the ethical issues on network forensics. This book serves as a reference book for post graduate and research investigators who need to study in cyber forensics. It can also be used as a textbook for a graduate level course in Electronics & Communication, Computer Science and Computer Engineering.
Python for Engineers and Scientists
The text focuses on the basics of Python programming fundamentals and introduction to present-day applications in technology and the upcoming state-of-art trends in a comprehensive manner. The text is based on Python 3.x and it covers the fundamentals of Python with object-oriented concepts having numerous worked-out examples. It provides a learning tool for the students of beginner level as well as for researchers of advanced level. Each chapter contains additional examples that explain the usage of methods/functions discussed in the chapter. It provides numerous programming examples along with their outputs.The book: Includes programming tips to highlight the important concepts and help readers avoid common programming errors Provides programming examples along with their outputs to ensure the correctness and help readers in mastering the art of writing efficient Python programs Contains MCQs with their answers; conceptual questions and programming questions; and solutions to some selected programming questions, for every chapter Discusses applications like time zone converter and password generators at the end Covers fundamental of Python up to object oriented concepts including regular expression The book offers a simple and lucid treatment of concepts supported with illustrations for easy understanding, provides numerous programming examples along with their outputs, and includes programming tips to highlight the important concepts. It will be a valuable resource for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
Human Factors in Engineering
This book addresses aspects of human factors in engineering and provides a detailed discussion of novel approaches, systems engineering tools, artificial cognitive systems, and intelligent technologies and automation. It presents applications in diverse areas, including digital manufacturing, transportation, infrastructure development, and cybersecurity.This book: - Merges the engineering perspective with the human factors and social dimension of computing and artificial intelligence-based technologies. - Covers technological development of human factors engineering and the human dimension in applications across all areas of modern society.- Relates to human behavior in the context of technology and systems interactions.- Discusses the design and the appropriation of 3D printing techniques in the management of an innovative product system.- Presents systems engineering tools, user experience methodologies, artificial cognitive systems, intelligent technologies, and automation.The text is for students, professionals, and researchers in the fields of ergonomics, human factors, industrial engineering, and manufacturing engineering.
Neural Networks, Machine Learning, and Image Processing
The text comprehensively discusses the latest mathematical modelling techniques and their applications in various areas such as fuzzy modelling, signal processing, neural network, machine learning, image processing, and their numerical analysis. It further covers image processing techniques like Viola-Jones Method for face detection and fuzzy approach for person video emotion. It will serve as an ideal reference text for graduate students and academic researchers in the fields of mechanical engineering, electronics, communication engineering, computer engineering, and mathematics. This book: Discusses applications of neural networks, machine learning, image processing, and mathematical modeling. Provides simulations techniques in machine learning and image processing-based problems. Highlights artificial intelligence and machine learning techniques in the detection of diseases. Introduces mathematical modeling techniques such as wavelet transform, modeling using differential equations, and numerical techniques for multi-dimensional data. Includes real-life problems for better understanding. The book presents mathematical modeling techniques such as wavelet transform, differential equations, and numerical techniques for multi-dimensional data. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields such as mechanical, electronics and communication and computer.
Digital Image Processing
The book provides a mix of theoretical and practical perceptions of the related concepts pertaining to image processing. The primary objectives are to offer an overview to the elementary concepts and practices appropriate to digital image processing as well as to provide theoretical exposition. It starts with an expanded coverage of the fundamentals to provide a more comprehensive and cohesive coverage of the topics including but not limited to: Applications and tools for image processing, and fundamentals with several implementation examples Concepts of image formation OpenCV installation with step-by-step screen shots Concepts behind intensity, brightness and contrast, color models Ways by which noises are created in an image and the possible remedial measures Edge detection, image segmentation, classification, regression, classification algorithms Importance of frequency domain in image processing field Relevant code snippets and the MATLAB(R) codes, and several interesting sets of simple programs in OpenCV and Python to aid learning and for complete understanding The video lectures for specific topics through YouTube enable easy inference for the readers to apply the learnt theory into practice. The addition of contents at the end of each chapter such as quizzes and review questions fully prepare the readers for further study.Graduate students, post graduate students, researchers, and anyone in general interested in image processing, computer vision, machine learning domains etc. can find this book an excellent starting point for information and an able ally.
Intelligent Internet of Things for Smart Healthcare Systems
The book focuses on developments in artificial intelligence (AI) and internet of things (IoT) integration for smart healthcare, with an emphasis on current methodologies and frameworks for the design, growth, implementation, and creative use of such convergence technologies to provide insight into smart healthcare service demands. Concepts like signal recognition, computation, internet of health stuff, and so forth and their applications are covered. Development in connectivity and intelligent networks allowing for social adoption of ambient intelligence is also included.Features: -Introduces Intelligent IoT as applicable to the key areas of smart healthcare.-Discusses computational intelligence and IoT-based optimizations of smart healthcare systems-Explores effective management of healthcare systems using dedicated IoT-based infrastructures-Includes dedicated chapters on securing patient's confidential data -Reviews diagnosis of critical diseases from medical imaging using advanced deep learning-based approachesThis book is aimed at researchers, professionals, and graduate students in intelligent systems, big data, cloud computing, information security, and healthcare systems.
Machine Learning
Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding, enabling further exploration Presents worked out suitable programming examples, thus ensuring conceptual, theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth, within limits of what can be taught in a short period of time. Thus, the book can provide foundations that will empower a student to read advanced books and research papers.
Computational Statistical Methodologies and Modeling for Artificial Intelligence
This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering.The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence
Modern Time Series Forecasting with Python - Second Edition
Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architecturesKey Features: - Apply ML and global models to improve forecasting accuracy through practical examples- Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS- Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions- Purchase of the print or Kindle book includes a free eBook in PDF formatBook Description: Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you're working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you'll learn preprocessing, feature engineering, and model evaluation. As you progress, you'll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.What You Will Learn: - Build machine learning models for regression-based time series forecasting- Apply powerful feature engineering techniques to enhance prediction accuracy- Tackle common challenges like non-stationarity and seasonality- Combine multiple forecasts using ensembling and stacking for superior results- Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series- Evaluate and validate your forecasts using best practices and statistical metricsWho this book is for: This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.Table of Contents- Introducing Time Series- Acquiring and Processing Time Series Data- Analyzing and Visualizing Time Series Data- Setting a Strong Baseline Forecast - Time Series Forecasting as Regression - Feature Engineering for Time Series Forecasting- Target Transformations for Time Series Forecasting - Forecasting Time Series with Machine Learning Models - Ensembling and Stacking- Global Forecasting Models - Introduction to Deep Learning- Building Blocks of Deep Learning for Time Series- Common Modeling Patterns for Time Series- Attention and Transformers for Time Series(N.B. Please use the Read Sample option to see further chapters)
Generative Adversarial Networks and Deep Learning
This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio.A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation, text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc.Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning.The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum
Data-Driven Intelligence in Wireless Networks
This book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence. This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks. The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.
Advances in Human-Machine Interaction, Artificial Intelligence, and Robotics
The aim of the following reprint is to explore and showcase the recent advances and interdisciplinary approaches within the field of human-robot interaction (HRI) and artificial intelligence (AI). This reprint features a selection of distinguished studies, ranging from the development of new methodologies in co-manipulation using electromyography to innovative uses of augmented reality in museums. It includes research that enhances human-AI communication, the adoption of Industry 4.0 technologies in education, and cutting-edge developments in defect detection in industrial settings. These contributions from global experts illuminate the multifaceted nature of technological progress in AI and robotics. By integrating diverse fields such as healthcare, manufacturing, and smart environments, this collection not only pushes the boundaries of current technology but also lays the groundwork for future innovations that could revolutionize our interaction with machines. Each study within this reprint provides a forward-looking perspective on how these technologies can be further refined and effectively integrated into society to improve everyday life and operational efficiency. This reprint is a journey into the potential of HRI and AI to transform our world, ensuring that technology serves as a bridge to more intuitive, inclusive, and sustainable human experiences.
Security, Privacy, and Trust in WBANs and E-Healthcare
Wireless Body Area Networks (WBANs) are vulnerable to cyberattacks and security breaches that could unlock the door for cybercriminals to penetrate hospital networks. This book covers the fundamental concepts of security and privacy in WBANs including security requirements, issues, and challenges.Security, Privacy, and Trust in WBANs and E-Healthcare highlights the taxonomy of threats and attacks in WBANs and Internet of Medical Things (IoMT) and presents all technical aspects related to the security and privacy of WBANs. In addition to outlining viable solutions that take into account constrained resources at WBAN end-devices, hybrid network architecture, application characteristics, and communication protocols, the book covers the core concepts of WBAN security, privacy, and trust. It describes both theoretical and practical aspects for those working in security in the WBAN and IoMT, emphasizing the most significant potential WBAN security issues and challenges. The book also covers intrusion detection and security risk assessments in WBANs as well as lightweight security solutions for WBANs, blockchain-based solutions for WBANs, and authentication and access control in WBANs through various applications and case studies.This book is highly relevant to the graduate/postgraduate students, academicians, security system designers, security analysts, computer scientists, engineers, researchers, digital forensic experts, and other personnel working in information security, IoMT, and WBAN.
Applications of Blockchain and Artificial Intelligence in Finance and Governance
In this book, the authors delve into the intricacies of this dynamic intersection, offering a comprehensive exploration of the transformative potential of these cutting-edge technologies.
Smart Distributed Embedded Systems for Healthcare Applications
This book discusses the applications and optimization of emerging smart technologies in the field of healthcare. It further explains different modeling scenarios of the latest technologies in the healthcare system and compares the results to better understand the nature and progress of diseases in the human body, which would ultimately lead to early diagnosis and better treatment and cure of diseases with the help of distributed technology. Covers the implementation models using technologies such as artificial intelligence, machine learning, and deep learning with distributed systems for better diagnosis and treatment of diseases. Gives in-depth review of technological advancements like advanced sensing technologies such as plasmonic sensors, usage of RFIDs, and electronic diagnostic tools in the field of healthcare engineering. Discusses possibilities of augmented reality and virtual reality interventions for providing unique solutions in medical science, clinical research, psychology, and neurological disorders. Highlights the future challenges and risks involved in the application of smart technologies such as cloud computing, fog computing, IOT, and distributed computing in healthcare. Confers to utilize the AI and ML and associated aids in healthcare sectors in the post-Covid 19 period to revitalize the medical setup. Contributions included in the book will motivate technological developers and researchers to develop new algorithms and protocols in the healthcare field. It will serve as a vast platform for gaining knowledge regarding healthcare delivery, health- care management, healthcare in governance, and health monitoring approaches using distributed environments. It will serve as an ideal reference text for graduate students and researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical fields.
Quantum Machine Learning
This text presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming.
Robotics and Smart Autonomous Systems
The text discusses fundamental, advanced concepts and applications of robotics and autonomous systems. It further discusses important topics, such as robotics techniques in the manufacturing sector, applications of smart autonomous systems in the healthcare sector, resource optimization in mobile robotics, and smart autonomous transport systems.Features Covers design and application aspects of robotic systems for implementing the concepts of smart manufacturing with reduced human intervention, better accuracy, and enhanced production capacity Discusses techniques including supervised learning, unsupervised learning, and reinforced learning with real-life examples Highlights a unified intermodal approach for automated transportation including cars, trucks, ships, and port management Explains the mechanical design of planetary rovers, and the mechanical design of space manipulators, actuators, and sensors Presents programming tools and platforms for autonomous robotic systems The book is primarily written for senior undergraduates, graduate students, and academic researchers in fields including electrical engineering, electronics and communications engineering, computer science and engineering, and automotive engineering.
Practical Artificial Intelligence for Internet of Medical Things
This book covers the fundamentals, applications, algorithms, protocols, emerging trends, problems, and research findings in the field of AI and IoT in smart healthcare. It includes case studies, implementation and management of smart healthcare systems using AI. Chapters focus on AI applications in Internet of Healthcare Things, provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and AI, with the real-world examples. This book is aimed at Researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics.Features: Focus on the Internet of Healthcare Things and innovative solutions developed for use in the application of healthcare services Discusses artificial intelligence applications, experiments, core concepts, and cutting-edge themes Demonstrates new approaches to analyzing medical data and identifying ailments using AI to improve overall quality of life Introduces fundamental concepts for designing the Internet of Healthcare Things solutions Includes pertinent case studies and applications This book is aimed at researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics.
Performance, Reliability, and Availability Evaluation of Computational Systems, Volume 2
This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance, reliability, and availability evaluation subjects. The volumes focus on computing systems, although the methods may also be applied to other systems. The first volume covers Chapter 1 to Chapter 14, whose subtitle is ``Performance Modeling and Background". The second volume encompasses Chapter 15 to Chapter 25 and has the subtitle ``Reliability and Availability Modeling, Measuring and Workload, and Lifetime Data Analysis".This text is helpful for computer performance professionals for supporting planning, design, configuring, and tuning the performance, reliability, and availability of computing systems. Such professionals may use these volumes to get acquainted with specific subjects by looking at the particular chapters. Many examples in the textbook on computing systems will help them understand the concepts covered in each chapter. The text may also be helpful for the instructor who teaches performance, reliability, and availability evaluation subjects. Many possible threads could be configured according to the interest of the audience and the duration of the course. Chapter 1 presents a good number of possible courses programs that could be organized using this text.Volume II is composed of the last two parts. Part III examines reliability and availability modeling by covering a set of fundamental notions, definitions, redundancy procedures, and modeling methods such as Reliability Block Diagrams (RBD) and Fault Trees (FT) with the respective evaluation methods, adopts Markov chains, Stochastic Petri nets and even hierarchical and heterogeneous modeling to represent more complex systems. Part IV discusses performance measurements and reliability data analysis. It first depicts some basic measuring mechanisms applied in computer systems, then discusses workload generation. After, we examine failure monitoring and fault injection, and finally, we discuss a set of techniques for reliability and maintainability data analysis.
Next Generation Communication Networks for Industrial Internet of Things Systems
This book presents Internet of Things (IoT) technology and security-related solutions that employ intelligent data processing technologies and machine learning (ML) approaches for data analytics. It presents practical scenarios from the industry for the application of the internet of things in various domains. Next Generation Communication Networks for Industrial Internet of Things Systems presents concepts and research challenges in communication networking for Industrial internet of things systems. Features: Discusses process monitoring, environmental monitoring, control, and maintenance monitoring. Covers data collection and communication protocols in a comprehensive manner. Highlights the internet of things industrial applications, and industrial revolution 4.0. Presents 5G-enabled internet of things technology and architecture. Showcases artificial intelligence techniques in the IoT networks. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas of electrical engineering, electronics, and communications engineering, computer engineering, and information technology.
Deep Learning in Gaming and Animations
Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges.This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation.It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.
Introduction to the Cyber Ranges
With the rising cybercrimes, a well-trained cybersecurity workforce in an organization has become a necessity. This book aims to provide substantial theoretical knowhow on cyber ranges, their architectural design, along with a case study of existing cyber ranges in leading urban sectors like military, academic and commercial.
