Two significant areas of study that are continually impacting various dimensions in computer science are computer vision and imaging. These technologies are rapidly enhancing how information and data is being exchanged and opening numerous avenues of advancement within areas such as multimedia and intelligent systems. The high level of applicability in computer vision and image processing requires significant research on the specific utilizations of these technologies. Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies is an essential reference source that discusses innovative developments in computational imaging for solving real-life issues and problems and addresses their execution in various disciplines. Featuring research on topics such as image modeling, remote sensing, and support vector machines, this book is ideally designed for IT specialists, scientists, researchers, engineers, developers, practitioners, industry professionals, academicians, and students seeking coverage on the latest developments and innovations in computer vision applications within the realm of multimedia systems.

Inverse problems such as imaging or parameter identification deal with the recovery of unknown quantities from indirect observations, connected via a model describing the underlying context. While traditionally inverse problems are formulated and investigated in a static setting, we observe a significant increase of interest in time-dependence in a growing number of important applications over the last few years. Here, time-dependence affects a) the unknown function to be recovered and / or b) the observed data and / or c) the underlying process. Challenging applications in the field of imaging and parameter identification are techniques such as photoacoustic tomography, elastography, dynamic computerized or emission tomography, dynamic magnetic resonance imaging, super-resolution in image sequences and videos, health monitoring of elastic structures, optical flow problems or magnetic particle imaging to name only a few. Such problems demand for innovation concerning their mathematical description and analysis as well as computational approaches for their solution.

Processing information and analyzing data efficiently and effectively is crucial for any company that wishes to stay competitive in its respective market. Nonlinear data presents new challenges to organizations, however, due to its complexity and unpredictability. The only technology that can properly handle this form of data is artificial neural networks. These modeling systems present a high level of benefits in analyzing complex data in a proficient manner, yet considerable research on the specific applications of these intelligent components is significantly deficient. Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature of artificial neural networks and their specific implementations within data analysis. While highlighting topics including propagation functions, optimization techniques, and learning methodologies, this book is ideally designed for researchers, statisticians, academicians, developers, scientists, practitioners, students, and educators seeking current research on the use of artificial neural networks in diagnosing and solving nonparametric problems.

This book presents a summary of artificial intelligence and machine learning techniques in its first two chapters. The remaining chapters of the book provide everything one must know about the basic artificial intelligence to modern machine intelligence techniques including the hybrid computational intelligence technique, using the concepts of several real-life solved examples, design of projects and research ideas. The solved examples with more than 200 illustrations presented in the book are a great help to instructors, students, non-AI professionals, and researchers. Each example is discussed in detail with encoding, normalization, architecture, detailed design, process flow, and sample input/output. Summary of the fundamental concepts with solved examples is a unique combination and highlight of this book.

This book examines the crucial role of psychoanalysis in understanding what AI means for us as speaking, sexed subjects. Drawing on Lacanian theory and recent clinical developments it explores what philosophy and critical theory of AI has hitherto neglected: enjoyment. Through the reconceptualization of Intelligence, the Artificial Object and the Sexual Abyss the book outlines the Sexbot as a figure who exists on the boundary of psychoanalysis and AI. Through this figure and the medium of film, the author subverts Kant's three Enlightenment questions and guides readers to transition from asking 'Does it think?' to 'Can it enjoy?' The book will appeal in particular to students and scholars of psychoanalysis, philosophy, film and media studies, critical theory, feminist theory and AI research.

This book explores the genesis of ransomware and how the parallel emergence of encryption technologies has elevated ransomware to become the most prodigious cyber threat that enterprises are confronting. It also investigates the driving forces behind what has been dubbed the 'ransomware revolution' after a series of major attacks beginning in 2013, and how the advent of cryptocurrencies provided the catalyst for the development and increased profitability of ransomware, sparking a phenomenal rise in the number and complexity of ransomware attacks. This book analyzes why the speed of technology adoption has been a fundamental factor in the continued success of financially motivated cybercrime, and how the ease of public access to advanced encryption techniques has allowed malicious actors to continue to operate with increased anonymity across the internet. This anonymity has enabled increased collaboration between attackers, which has aided the development of new ransomware attacks, and led to an increasing level of technical complexity in ransomware attacks. This book highlights that the continuous expansion and early adoption of emerging technologies may be beyond the capacity of conventional risk managers and risk management frameworks. Researchers and advanced level students studying or working in computer science, business or criminology will find this book useful as a reference or secondary text. Professionals working in cybersecurity, cryptography, information technology, financial crime (and other related topics) will also welcome this book as a reference.

This book provides an innovative, realistic and reliable solution to the common problem of Indian water and energy sector due to the onset of the Impact of Climate Change and Large-Scale Urbanization. Twelve Case Studies and One Review Paper that were included in this book depict the way soft computation techniques, simulation and decision-making framework can optimize the best solution from multiple solutions to the problems of water and energy management which corresponds to a novel symbiotic and synchronous nexus between water and the energy sector. All the studies included in this book are collected from all parts of India. The selected studies utilized the latest technologies like Multi-Criteria Decision Frame Work, Neural Networks and Nature-Based Optimization techniques to achieve diverse objectives from the prediction of climatic parameters to yield from ungauged watershed to performance optimization of Water Treatment Plant, Hydropower as well as futuristic alternative energy systems like Wave to Power Plants.

This book consolidates the current state of knowledge on implementing cooperating robot-based systems to increase the flexibility of manufacturing systems. It is based on the concrete experiences of experts, practitioners, and engineers in implementing cooperating robot systems for more flexible manufacturing systems. Thanks to the great variety of manufacturing systems that we had the opportunity to study, a remarkable collection of methods and tools has emerged. The aim of the book is to share this experience with academia and industry practitioners seeking to improve manufacturing practice. While there are various books on teaching principles for robotics, this book offers a unique opportunity to dive into the practical aspects of implementing complex real-world robotic applications. As it is used in this book, the term "cooperating robots" refers to robots that either cooperate with one another or with people. The book investigates various aspects of cooperation in the context of implementing flexible manufacturing systems. Accordingly, manufacturing systems are the main focus in the discussion on implementing such robotic systems. The book begins with a brief introduction to the concept of manufacturing systems, followed by a discussion of flexibility. Aspects of designing such systems, e.g. material flow, logistics, processing times, shop floor footprint, and design of flexible handling systems, are subsequently covered. In closing, the book addresses key issues in operating such systems, which concern e.g. decision-making, autonomy, cooperation, communication, task scheduling, motion generation, and distribution of control between different devices. Reviewing the state of the art and presenting the latest innovations, the book offers a valuable asset for a broad readership.

The world is changing at a fast pace, so is the Government and Governance style. Humans are bound to go for Algorithmic strategies rather than manual or electronic ones in different domains. This book introduces the Algorithmic Government or Government by Algorithm, which refers to authorizing machines in the Public Sector for automated decision-making based on Artificial Intelligence, Data Science, and other technologies. It is an emerging concept introduced globally and will be considered revolutionary in the future. The book covers concepts, applications, progress status, and potential use-cases of Algorithmic Government. This book serves as introductory material for the readers from technology, public policy, administration, and management fields.

Smart Technologies and Design for Healthy Built Environment connects smart technology to a healthy built environmentthat builds upon the sustainable building movement.It provides an overall summary of the state-of-the-art technologies that are applied in the built environment. The book covers a broad spectrum of smart technology categories ranging from dynamic operability, energy efficiency, self-regulating and self-learning systems, and responsive systems. The foreseeable challenges that are associated with smart technologies are discussed and outlined in the book. Firstly, this book provides a snapshot of state-of-the-art smart technologies being applied in the built environment. It covers a broad spectrum of smart technology categories, ranging from dynamic operability, energy efficiency, self-regulating and self-learning systems, to responsive systems. Secondly, this book provides in-depth analysis of the four primary components of health (biological, physical, physiological and psychological); their effects on wellbeing and cognitive performance are introduced as well. Thirdly, it connects smart technologies to those health-influencing factors by reviewing three completed smart building projects. This book can also serve as a basis for education and discussion among professionals and students of diverse backgrounds who are interested in smart technologies, smart building, and healthy building. Smart Technologies and Design for Healthy Built Environment serves as the basis for education and discussions among professionals and students who are interested in smart technologies, smart building and healthy building, as it bridges the gap between smart technologies and a healthy built environment. The book also provides a foundation for anyone who is interested in the impact of smart technology on the health of built environment.

The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria decision tools can make deep neural networks more interpretable - and even, in many cases, more efficient. Fuzzy logic together with multi-criteria decision-making tools provides very powerful tools for modeling human thinking. Based on their common theoretical basis, we propose a consistent framework for modeling human thinking by using the tools of all three fields: fuzzy logic, multi-criteria decision-making, and deep learning to help reduce the black-box nature of neural models; a challenge that is of vital importance to the whole research community.

This book highlights essential concepts in connection with the traditional bat algorithm and its recent variants, as well as its application to find optimal solutions for a variety of real-world engineering and medical problems. Today, swarm intelligence-based meta-heuristic algorithms are extensively being used to address a wide range of real-world optimization problems due to their adaptability and robustness. Developed in 2009, the bat algorithm (BA) is one of the most successful swarm intelligence procedures, and has been used to tackle optimization tasks for more than a decade. The BA's mathematical model is quite straightforward and easy to understand and enhance, compared to other swarm approaches. Hence, it has attracted the attention of researchers who are working to find optimal solutions in a diverse range of domains, such as N-dimensional numerical optimization, constrained/unconstrained optimization and linear/nonlinear optimization problems. Along with the traditional BA, its enhanced versions are now also being used to solve optimization problems in science, engineering and medical applications around the globe.

This book includes a collection of expanded papers from the 2019 Sino-German Symposium on AI-supported educational technologies, which was held in Wuhan, China, March, 2019. The contributors are distinguished researchers from computer science and learning science. The contributions are organized in four sections: (1) Overviews and systematic perspectives , (2) Example Systems, (3) Algorithms, and (4) Insights gained from empirical studies. For example, different data mining and machine learning methods to quantify different profiles of a learner in different learning situations (including interaction patterns, cognitive modes, knowledge skills, interests and emotions etc.) as well as connections to measurements in psychology and learning sciences are discussed in the chapters.

These proceedings presents the state-of-the-art in spoken dialog systems with applications in robotics, knowledge access and communication. It addresses specifically: 1. Dialog for interacting with smartphones; 2. Dialog for Open Domain knowledge access; 3. Dialog for robot interaction; 4. Mediated dialog (including crosslingual dialog involving Speech Translation); and,5. Dialog quality evaluation. These articles were presented at the IWSDS 2012 workshop.

This book consists of refereed selected papers from the International Conference on Security & Privacy - ICSP 2020. The book is focused on the state-of-the-art developments of network security, secure cryptographic protocols, post-quantum cryptography, quantum cryptography, block-chain and cryptocurrency, IoT security and privacy, cloud security, machine learning in cybersecurity, and other disciplines related to security and privacy. In this book, a wide variety of basic security primitives are discussed along with recent developments in some advanced topics like functional encryption, two-party/multi-party computation, bitcoin, cryptocurrency, and post-quantum security.

As adult learners and educators pioneer the use of technology in the new century, attention has been focused on developing strategic approaches to effectively integrate adult learning and technology in different learning environments. Integrating Adult Learning and Technologies for Effective Education: Strategic Approaches provides innovative instructional approaches as well as relevant theoretical frameworks and the latest empirical research findings in the area of adult learning and technology. This compelling body of research aids professionals in improving instructional training strategies to educate traditional and non-traditional students in today's information age.

This book introduces readers to some of the most significant advances in core computer science-based technologies. At the dawn of the 4th Industrial Revolution, the field of computer science-based technologies is growing continuously and rapidly, and is developing both in itself and in terms of its applications in many other disciplines. Written by leading experts and consisting of 18 chapters, the book is divided into seven parts: (1) Computer Science-based Technologies in Education, (2) Computer Science-based Technologies in Risk Assessment and Readiness, (3) Computer Science-based Technologies in IoT, Blockchains and Electronic Money, (4) Computer Science-based Technologies in Mobile Computing, (5) Computer Science-based Technologies in Scheduling and Transportation, (6) Computer Science-based Technologies in Medicine and Biology, and (7) Theoretical Advances in Computer Science with Significant Potential Applications in Technology. Featuring an extensive list of bibliographic references at the end of each chapter to help readers probe further into the application areas of interest to them, this book is intended for professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also useful for those from other disciplines wanting to become well versed in some of the latest computer science-based technologies.

This book focuses on reversible steganography and authentication via transform encoding, fully discussing in detail the reversibility computation of six transformation techniques: DFT, DCT, wavelets, Z, binomial and grouplet, as well as chaos-based authentication. The book also describes algorithmic approaches based on all transformations along with implementation details and results. Further topics include embedding and extraction into the spatial domain, tuning using GA-based approaches and embedding into imaginary coefficients of the Z domain. Featuring detailed algorithms for encryption and descriptions of all techniques, including embedding techniques for all transform-based steganographic processes, the book also explores the adjustment of pixel values after embedding and presents numerical examples of reversible computations. In the context of chaos-based authentication, it also describes testing the quality of generator is using Monobit, Serial and Poker tests. The book then outlines 15 test cases recommended by NIST fifteen test cases, along with their implementation on six evolutionary algorithms for neural cryptographic systems in the context of wireless computations - TPM, KSOMSCT, DHLPSCT, CHDLPSCT, CTHLPSCT and CGTHLPSCT - and verifies their satisfiability based on the implementations of these six techniques. Lastly it presents various metrics of image processing systems. This book is a valuable reference resource for research scholars, PG/UG students and practicing engineers

This book is the world's first book on 6G Mobile Wireless Networks that aims to provide a comprehensive understanding of key drivers, use cases, research requirements, challenges and open issues that are expected to drive 6G research. In this book, we have invited world-renowned experts from industry and academia to share their thoughts on different aspects of 6G research. Specifically, this book covers the following topics: 6G Use Cases, Requirements, Metrics and Enabling Technologies, PHY Technologies for 6G Wireless, Reconfigurable Intelligent Surface for 6G Wireless Networks, Millimeter-wave and Terahertz Spectrum for 6G Wireless, Challenges in Transport Layer for Tbit/s Communications, High-capacity Backhaul Connectivity for 6G Wireless, Cloud Native Approach for 6G Wireless Networks, Machine Type Communications in 6G, Edge Intelligence and Pervasive AI in 6G, Blockchain: Foundations and Role in 6G, Role of Open-source Platforms in 6G, and Quantum Computing and 6G Wireless. The overarching aim of this book is to explore the evolution from current 5G networks towards the future 6G networks from a service, air interface and network perspective, thereby laying out a vision for 6G networks. This book not only discusses the potential 6G use cases, requirements, metrics and enabling technologies, but also discusses the emerging technologies and topics such as 6G PHY technologies, reconfigurable intelligent surface, millimeter-wave and THz communications, visible light communications, transport layer for Tbit/s communications, high-capacity backhaul connectivity, cloud native approach, machine-type communications, edge intelligence and pervasive AI, network security and blockchain, and the role of open-source platform in 6G. This book provides a systematic treatment of the state-of-the-art in these emerging topics and their role in supporting a wide variety of verticals in the future. As such, it provides a comprehensive overview of the expected applications of 6G with a detailed discussion of their requirements and possible enabling technologies. This book also outlines the possible challenges and research directions to facilitate the future research and development of 6G mobile wireless networks.

Jump-start your career as a data scientist--learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn't cover SQL broadly. Instead, you'll learn the subset of SQL skills that data analysts and data scientists use frequently. You'll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner's perspective, moving your data scientist career forward!

Two of the most important factors contributing to national and international economy are processing of information for accurate financial forecasting and decision making as well as processing of information for efficient control of manufacturing systems for increased productivity. The associated problems are very complex and conventional methods often fail to produce acceptable solutions. Moreover, businesses and industries always look for superior solutions to boost profitability and productivity. In recent times, artificial neural networks have demonstrated promising results in solving many real-world problems in these domains, and these techniques are increasingly gaining business and industry acceptance among the practitioners. ""Artificial Neural Networks in Finance and Manufacturing"" presents many state-of-the-art and diverse applications to finance and manufacturing, along with underlying neural network theories and architectures. It offers researchers and practitioners the opportunity to access exciting and cutting-edge research focusing on neural network applications, combining two aspects of economic domain in a single and consolidated volume.

This book strikes a healthy balance between theory and applications, ensuring that it doesn't offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R. The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike.