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.
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
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.
Multi-Sensor and Multi-Temporal Remote Sensing
This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, fuzzy machine learning models supported by case studies.
AI Factory
This book provides insights on how to approach and utilise data science tools, technologies/methodologies related to artificial intelligence in industrial context including their essence and inter-connections. Description of technology/methodology approaches, their purpose and benefits when developing AI-solution is given with case studies.
Modern Optimization Methods for Decision Making Under Risk and Uncertainty
The book comprises original articles on topical issues in risk theory, rational decision making, statistical decisions, and control of stochastic systems.
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.
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.
Decision Support System and Automated Negotiations
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. It presents the design & development of an ontology-based decision support system for integrated crop management.
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.
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.
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 technical and societal evaluations to ensure a well-defined balance for societal outcomes. It explores ot topics such as Generative Artificial Intelligence, and Quantum Artificial Intelligence.
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.
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.
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.
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.
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
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
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.
Computational Intelligence Aided Systems for Healthcare Domain
The text 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.
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.
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.
Python for Engineers and Scientists
This text covers fundamentals of Python up to object-oriented concepts including regular expression, and applications in a single volume. It will an ideal text for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
Digital Image Processing
The book augurs to be a mix of theoretical and practical perceptions of the related concepts pertaining to image processing. The primary objectives orient to offer an overview to the elementary concepts and practices appropriate to DIP as well as to provide theoretical exposition.
Neural Networks, Machine Learning, and Image Processing
The text presents mathematical modeling techniques such as wavelet transform, differential calculus, 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 electrical, electronics and communication, and computer.
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.
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
This book provides an introduction to the most popular methods in machine learning. It covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including CNNs, reinforcement learning, and unsupervised learning focused on clustering.
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.
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. It concentrates on cutting-edge research in DL and GAN which includes creating new tools and methods for processing text, images, and audio.
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.
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
The text covers applications, algorithms, and tools of distributed information processing in the healthcare sector. 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.
Robotics and Smart Autonomous Systems
The text discusses fundamental, advanced concepts, applications of robotics, and autonomous systems. It also 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.
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.
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 second volume encompasses Chapter 15 to Chapter 25 and has the subtitle ``Reliability and Availability Modeling, Measuring and Workload, and Lifetime Data Analysis".
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 working examples, case studies, implementation and management of smart healthcare systems using AI.
Deep Learning in Gaming and Animations
The text discusses the core concepts and principles of deep learning in gaming and animation with applications in a single volume. It will be a useful reference text for graduate students, and professionals in diverse areas such as electrical engineering, electronics and communication engineering, computer science, gaming and animation.
Next Generation Communication Networks for Industrial Internet of Things Systems
The text presents concepts and research challenges in communication networking for Industrial internet of things systems. It will serve as an ideal reference text for senior undergraduate, graduate students, and researchers in diverse engineering domains including electrical, electronics and communications, and computer science.
Handbook of Iot and Blockchain
This handbook includes contributions from around the globe on recent advances and findings in the domain of Internet of Things (IoT) and Blockchain. Chapters will include theoretical analysis, practical implications and extensive surveys with analysis, on methods, algorithms, and processes, for new product development.
Artificial Intelligence for Internet of Things
The text comprehensively discusses design principles, modernization techniques, advanced developments in artificial intelligence. The text will be helpful for senior undergraduate, graduate students, and academic researchers in diverse engineering fields including electrical, electronics and communication, and computer.
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.
