Integration of Cloud Computing with Emerging Technologies
This book gives a complete overview of cloud computing: its importance, its trends, innovations, and its amalgamation with other technologies.Key Features: In-depth explanation of emerging technologies utilizing cloud computing Supplemented with visuals, flow charts, and diagrams Real-time examples included Caters to beginners, as well as advanced researchers, by explaining implications, innovations, issues, and challenges of cloud computing Highlights the need for cloud computing and the true benefits derived by its application and integration in emerging technologies Simple, easy language
Artificial Intelligence in Telemedicine
This book explores the role of artificial Intelligence in Telemedicine. It explains the concepts through the detailed study and processing of biosignals, physiological parameters, and medical images. The book focuses on computational algorithms in telemedicine for the processing of biosignals, physiological parameters, and medical Images. The book is presented in two section. The first section presents the role of computational algorithms in the processing of biosignal and medical images for disease diagnosis and treatment planning. Noise removal in ECG signal using an improved adaptive learning approach, classification of ECG signals using CNN for cardiac arrhythmia detection, EEG signal analysis for stroke detection, and EMG signal analysis for gesture classification were discussed in this section. Application of CNN in pertussis Diagnosis by temperature monitoring, physician handwriting recognition using deep learning model, melanoma detection using ABCD parameters, and transfer learning enabled heuristic approach for pneumonia detection was also discussed in this section The second section focus on the role of IoT and artificial intelligence in the healthcare sector. IoT in smart health care and applications of artificial intelligence in disease diagnosis and prediction was discussed in this section. The importance of 5G/6G in the pandemic scenario for telemedicine applications, wireless capsule endoscopy image compression, leukemia detection from the microscopic cell images, and genomic signal processing using numerical mapping techniques was also discussed in this section. This book can be used by a wide range of users including students, research scholars, faculty, and practitioners in the field of engineering for applications in biomedical signal, image analysis, and diagnosis.
Computer Organization, Design, and Architecture
This unique and classroom-proven text provides a hands-on introduction to the design of computer systems. It depicts, step by step, the design and programming of a simple but complete hypothetical computer, followed by detailed architectural features of existing computer systems as enhancements to the structure of the simple computer.
Smart Cities
This book discusses the basic principles of sustainable development in a smart city ecosystem to better serve the life of citizens. It examines smart city systems driven by emerging IoT-powered technologies and the other dependent platforms.Smart Cities: AI, IoT Technologies, Big Data Solutions, Cloud Platforms, and Cybersecurity Techniques discusses the design and implementation of the core components of the smart city ecosystem. The editors discuss the effective management and development of smart city infrastructures, starting with planning and integrating complex models and diverse frameworks into an ecosystem. Specifically the chapters examine the core infrastructure elements, including activities of the public and private services as well as innovative ICT solutions, computer vision, IoT technologies, data tools, cloud services, AR/VR technologies, cybersecurity techniques, treatment solution of the environmental water pollution, and other intelligent devices for supporting sustainable living in the smart environment.The chapters also discuss machine vision models and implementation as well as real-time robotic applications. Upon reading the book, users will be able to handle the challenges and improvements of security for smart systems, and will have the know-how to analyze and visualize data using big data tools and visualization applications. The book will provide the technologies, solutions as well as designs of smart cities with advanced tools and techniques for students, researchers, engineers, and academics.
Advancing Medical Assistance - Hungarian-Language Medical Chatbot with Artificial Intelligence
ARM64 Assembly Language and Arcitecture
With the proliferation of IOT, and embedded systems RISC (Reduced Instruction Set Computer) processors are ubiquitous. More recently many computer manufacturers have started to deploy 64-bit ARM based processors within their products. A detailed knowledge of ARM systems can be a distinct advantage for software engineers in today's fast-moving technological marketplace. This book is aimed at computer science students and hobbyists wishing to learn about assembly language concepts and programming using the ARM64 architecture. Experienced programmers that are familiar with high-level language coding, wishing to learn more about system level programming may also find the content beneficial.
Machine Learning Algorithms and Applications in Engineering
Continuous-Time Signals and Systems
Drawing on author's 30+ years of teaching experience, "Continuous-Time Signals and Systems: A MATLAB Integrated Approach" represents a novel and comprehensive approach to understanding signals and systems theory. Many textbooks use MATLAB as a computational tool, but Alkin's text employs MATLAB both computationally and pedagogically to provide interactive, visual reinforcement of fundamental concepts important in the study of continuous- time signals and systems.In addition to 210 traditional end-of-chapter problems and 168 solved examples, the book includes hands-on MATLAB modules consisting of: 77 MATLAB-based homework problems and projects (coordinated with the traditional end-of-chapter problems) 106 live scripts and GUI-based interactive apps that animate key figures and bring core concepts to life Downloadable MATLAB code for most of the solved examples 64 fully detailed MATLAB exercises that involve step by step development of code to simulate the relevant signal and/or system being discussed, including some case studies on topics such as synthesizers, simulating instrument sounds, pulse-width modulation, etc. The ebook+ version includes clickable links that allow running MATLAB code associated with solved examples and exercises in a browser, using the online version of MATLAB. It also includes audio files for some of the examples. Each module or application is linked to a specific segment of the text to ensure seamless integration between learning and doing. The aim is to not simply give the student just another toolbox of MATLAB functions, but to use the development of MATLAB code as part of the learning process, or as a litmus test of students' understanding of the key concepts. All relevant MATLAB code is freely available from the publisher. In addition, a solutions manual, figures, presentation slides and other ancillary materials are available for instructors with qualifying course adoption.
Digital Signal Processing
Digital Signal Processing: Fundamentals, Applications, and Deep Learning, Fourth Edition introduces students to the fundamental principles of digital signal processing (DSP) while also providing a working knowledge that they take with them into their engineering careers. Many instructive, worked examples are used to illustrate the material, and the use of mathematics is minimized for an easier grasp of concepts. As such, this title is also useful as a reference for non-engineering students and practicing engineers. This book goes beyond DSP theory, showing the implementation of algorithms in hardware and software. Additional topics covered include DSP for artificial intelligence, adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as pulse-code modulation, 繕-law, adaptive differential pulse-code modulation, multi-rate DSP, oversampling analog-to-digital conversion, sub-band coding, wavelet transform, and neural networks.
Multi-Criteria Decision Making Theory and Applications in Sustainable Healthcare
Multi-Cloud Administration Guide
As businesses increasingly adopt cloud-first strategies, managing workloads across multiple cloud platforms becomes a critical challenge. This comprehensive book provides practical solutions and in-depth knowledge to efficiently operate in a multi-cloud world. Learn to leverage frameworks from AWS, Azure, GCP, and Alibaba Cloud to maximize the benefits of multi-cloud environments. Understand cloud networking, software-defined networking, and microservices to optimize cloud connectivity. Develop a robust data strategy to ensure data quality, security, and integrity across multiple cloud platforms. Discover how automation and AI can help maintain compliance with governmental and industry regulations in the cloud. Designed for cloud architects, IT administrators, and technical managers, this book is also valuable for anyone looking to deepen their understanding of cloud technologies and multi-cloud strategies. FEATURES- Uses frameworks from AWS, Azure, GCP, and Alibaba Cloud to maximize the benefits of multi-cloud environments- Provides practical instructions and real-world examples for managing multi-cloud environments - Features insights into cloud-native technologies, serverless functions, and container orchestration with Kubernetes- Explores the details of multi-cloud connectivity, storage, compute, data management, security, and compliance- Includes companion files with code samples and color figures available for downloading
Information Society and Media Development in Modern Mongolia
This book provides an account of Mongolian information society from the perspective of critical media studies. The converged media sphere in modern Mongolia mirrors and shapes political communication, economic outlook, institutional norms, and Mongolian identity. When placing Mongolia on the global information society map, the arguments in the book juxtapose information society tenets and structural constraints like the small market, communist past, and mining-dependent economy. Today, people in Mongolia take advantage of the mobility, speed, and spatiality of the internet, as the Mongolians of old once saddled their horses and galloped across the grassy steps of Eurasia.
Emerging Trends in Computer Science and Its Application
The conference brought together a diverse group of scholars, researchers, and industry professionals to engage in meaningful discussions and share insights on cutting-edge trends in artificial intelligence, machine learning, data science, and their multifaceted applications. This collaboration and knowledge exchange fostered an environment of innovation, making the conference a successful and impactful event for all participants. It aimed to highlight these significant advancements and serve as a valuable resource for researchers, academicians, and practitioners who wish to stay informed about the recent innovations and methodologies shaping the landscape of computational intelligence. By showcasing a wide range of research topics and practical implementations, it not only addressed the current challenges but also inspired new ideas and approaches for future research.
Deep Learning and Reinforcement Learning
Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these technologies have gained widespread attention and applications in fields such as natural language processing, medical image analysis, and Internet of Things (IoT) device recognition. This book, Deep Learning and Reinforcement Learning examines the latest research achievements of these technologies and provides a reference for researchers, engineers, students, and other interested readers. It helps readers understand the opportunities and challenges faced by deep learning and reinforcement learning and how to address them, thus improving the research and application capabilities of these technologies in related fields.
Convergence of Deep Learning and Artificial Intelligence in Internet of Things
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!
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.
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.
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.
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.
Cisa Certified Information Systems Auditor Study Guide
Prepare for success on the 2024 CISA exam and further your career in security and audit with this effective study guide The CISA Certified Information Systems Auditor Study Guide: Covers 2024-2029 Exam Objectives provides comprehensive and accessible test preparation material for the updated CISA exam, which now consists of 150 questions testing knowledge and ability on real-life job practices leveraged by expert professionals. 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 2024-2029 CISA Study Guide covers every aspect of the exam. This study guide helps readers prepare for questions across the five domains on the test: Information System Auditing Process; Governance and Management of IT; Information Systems Acquisition, Development, and Implementation; Information Systems Operation and Business Resilience; and Protection of Information Assets. This study guide shows readers how to: Understand principles, best practices, and pitfalls of cybersecurity, which is now prevalent in virtually every information systems role Protect and control information systems and offer conclusions on the state of an organization's IS/IT security, risk, and control solutions Identify critical issues and recommend enterprise-specific practices to support and safeguard the governance of information and related technologies Prove not only competency in IT controls, but also an understanding of how IT relates to business 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 The CISA Certified Systems Auditor Study Guide: Covers 2024-2029 Exam Objectives is an essential learning resource for all students and professionals preparing for the 2024 version of the CISA exam from ISACA.
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.
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.
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.
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
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.
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.
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.
