Robotics
This textbook introduces robotic theories and technologies, bridging the gap between classic theories and real-world applications of robotic manipulators. A valuable resource to broad readers, including those who have a limited background in general engineering and wish to explore non-conventional applications of robotic manipulators.
Data Science for Civil Engineering
This book explains use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation. Features: Details information on essential mathematics required to implement civil engineering applications using data science techniques. Discusses broad background of data science and its fundamentals. Focusses on structural engineering, transportation systems, water resource management, geomatics, and environmental engineering. Includes python programming libraries to solve complex problems. Addresses various real-world applications of data science based civil engineering use cases. This book aims at senior undergraduate students in Civil Engineering and Applied Data Science.
Machine Learning for Neuroscience
This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience.
ConVision- A Context-based Deep Learning Algorithm for Efficient Lane Detection
Dive into the cutting-edge world of artificial intelligence with ""ConVision- A Context-based Deep Learning Algorithm for Efficient Lane Detection."" This comprehensive volume brings together six groundbreaking research studies, each meticulously designed and implemented with IEEE-level formulas, simulations, and equations. Discover how theoretical insights are seamlessly transformed into practical applications, enhancing various AI domains. From sophisticated simulations to robust implementations, this book showcases innovative approaches that push the boundaries of AI technology. Whether you're a researcher, practitioner, or enthusiast, this book offers a deep understanding of AI's core principles and real-world impact. Authored by experts in the field, this volume is a must-read for anyone seeking to stay at the forefront of AI research and application. Get ready to explore the future of artificial intelligence and its transformative potential. Praise for Advances in Artificial Intelligence: ""A remarkable collection of innovative AI research that bridges the gap between theory and practice."" - Dr. Jane Smith, AI Researcher ""Essential reading for anyone involved in AI development and application."" - TechReview Magazine Target Audience: Researchers, Practitioners, AI Enthusiasts, and Academics.
High Security Network for IOT using Edge Computing
Edge computing and the internet of things (IOT) have revolutionised the manner in which data is handled and analysed, making it possible to cut down on latency while simultaneously increasing energy efficiency. Yet, assuring the integrity of these systems and the security of their data continues to be a difficulty. This research proposes a unique system that addresses these difficulties in edge computing and IOT contexts. It mixes Base64 encoding, trustworthy algorithms, and virtual force mapping by trust value. This architecture not only boosts security and dependability by prioritising trustworthy components and preserving data while it is being sent, but it also adds significantly to reductions in energy usage, which is a huge win for everyone involved. The technique that has been provided provides a strong solution for safe, efficient, and reliable data processing in edge computing and IOT systems. As a result, it paves the way for future improvements in these sectors.
Route Optimization and Congestion Control in Wireless Network
The network is made up of two types of components: nodes and communication lines. The nodes typically handle the network protocols and provide switching capabilities. A node is usually itself a computer (general or special) which runs specific network software. The communication lines may take many different shapes and forms, even in the same network. Examples include: copper wire cables, optical fiber, radio channels, and telephone lines. By applying mathematics to a problem of the real world mostly means, at first, modeling the problem mathematically, may be with hard restrictions, idealizations, or simplifications, then solving the mathematical problem, and finally drawing conclusions about the real problem based on the solutions of the mathematical problem. Since about 60 years, a shift of paradigms has taken place in some sense; the opposite way has come into fashion. The point is that the world has done well even in times when nothing about mathematical modeling was known. The one of the alternate ways is evolutionary computation, which encompasses three main components- Evolution strategies, Genetic Algorithms and Evolution programs. Genetic Algorithms encode a potential solution to a specific problem on a simple chromosome like data structure and apply recombination operators to several structures so as to preserve critical information. Shortest path routing algorithms are well established problem and addressed by many researchers in different ways. One such alternative is to use a GA-based routing algorithm. However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. We propose to use this huge stochastic optimization tool for Optimum Path Routing Problem. GA may be used for optimization of searching process for optimum path routing in a network for optimization of both the distance and the congestion problem in a network. The proposed book "Congestion control in Wireless Networks" has been implemented in MATLAB 7.0.
Development of Efficient Bio-Inspired Computing Algorithm for Image Classification
Agriculture is a cornerstone of India's economy, deeply intertwined with the nation's food security and the livelihoods of millions. Accounting for approximately 15% of India's GDP, the agricultural sector supports a vast majority of the rural population through employment and income generation. India's diverse crop production, ranging from rice and wheat to sugarcane and cotton, not only drives economic growth but also ensures food stability and self-sufficiency, mitigating the need for imports and protecting against global food crises. Beyond its economic contributions, agriculture fosters rural development by enhancing income levels and promoting rural entrepreneurship, thus lifting communities out of poverty and supporting inclusive growth. The classification of plants within this sector is vital, providing a systematic approach to understanding the vast array of plant species in India. This knowledge aids farmers and agricultural experts in making informed decisions on cultivation, pest management, and resource allocation, while also conserving native species and preserving traditional farming practices. This research delves into the intersection of agriculture and advanced computing techniques, with a focus on the following objectives: 1. Analyzing and evaluating various bio-inspired computing algorithms for plant image segmentation and classification. 2. Proposing a novel bio-inspired optimization algorithm and testing its efficacy on standard benchmarks. 3. Developing a new image classification method based on existing or new bio-inspired algorithms. 4. Comparing the performance of the proposed classification approach against current methods using a plant image dataset. This work aims to enhance agricultural efficiency and sustainability through innovative computational techniques, ultimately benefiting India's vast agricultural landscape.
Artificial Mind
Beyond AI--Artificial Mind with True Intelligence. How that could emerge.Billionaire computer scientist David Sterling was no stranger to controversy but his latest criticisms of AI at its very technological foundation struck a nerve at a time when AI was taking the world by storm-hinting at a new generation of AI that could upend everything. He went so far as to claim AI had absolutely zero intelligence of its own, labeling it as a knowledge system.Before he could share his groundbreaking discovery, Sterling was found-by none other than his old friend, MIT professor William Foster-murdered.Now, haunted by the past and driven by sense of duty, Foster partners with Anastasia Ivanov, a gifted computer scientist, to uncover Sterling's lost research. But powerful forces are closing in-ruthless players determined to claim the discovery for themselves, no matter the cost.The truth could change the world.The price could be their lives.Can Foster and Anastasia survive long enough to unlock the future?Author BioThe author is a retired Canadian computer scientist and engineer. He received his Ph.D. from McGill University, Montreal, Canada. He held various architecture leadership positions at two of the world's largest telecommunication equipment makers, some startup companies, and a mid-sized international corporation of thousands of engineers as its Chief Architect. He played a pivotal role in the design and development of numerous products in supercomputing, server virtualization, and telecommunication. His extensive work in telecommunication spanned the areas of electrical and optical, as well as wireless and wireline.The author has traveled frequently around the globe, partly for pleasure but mostly for providing technical guidance to product development at his companies' R&D sites in North America, Europe, and Asia.
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. It provides content for reference.
Artificial Intelligence in Telemedicine
This book explores the role of computational algorithms in telemedicine. It explains the concepts through the detailed study and processing of biosignals, physiological parameters, and medical images. The book focuses on describing applications, encouraging advances, and valuable discoveries of telemedicine.
Smart Embedded Systems
"Smart Embedded Systems: Advances and Applications" is a comprehensive guide that demystifies the complex world of embedded technology.
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.
Handbook on Federated Learning
Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.
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.
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.
Integrating LoRa and Direction APIs for High-Performance Machine Vision in Smart Cars
Edge-AI in Healthcare
The book provides comprehensive research ideas about Edge-AI technology that can assist doctors in making better data-driven decisions. It provides insights for improving the healthcare industry by examining future trends, simplifying decision making and investigating structured and unstructured data.
Artificial Intelligence on Dark Matter and Dark Energy
As we prod the cosmos at large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book resorts to artificial intelligence for answers and describes the outcome of this quest in terms of an ur-universe.
Machine Learning Algorithms and Applications in Engineering
Discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.
Artificial Intelligence in Radiation Oncology and Biomedical Physics
This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics.
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.
Convergence of Deep Learning and Artificial Intelligence in Internet of Things
The text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the internet of things. It further discusses applications of smart technologies in diverse sectors such as production, manufacturing, transport, and healthcare.
Machine Learning for Healthcare
This book will provide in depth information about handling and managing healthcare data by Machine Learning methods. It will express the long-standing challenges in healthcare informatics and provide rational orientations on how to deal with them.
Computer Organisation and Architecture
This book provides a reasonable balance between the theoretical concepts of computer organization and its practical implementation. It describes successful solution approaches in the form of abstract and real models as well as the related algorithms.
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.
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 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.
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.
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.
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.
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.
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.
