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.

With artificial neural network research being one of the new directions for new generation computers, current research suggests that open-box artificial higher order neural networks (HONNs) play an important role in this new direction.Artificial Higher Order Neural Networks for Modeling and Simulation introduces artificial Higher Order Neural Networks (HONNs) to professionals working in the fields of modeling and simulation, and explains that HONN is an open-box artificial neural network tool as compared to traditional artificial neural networks. Including details of the most popular HONN models, this book provides an opportunity for practitioners in the field of modeling and simulations to understand and know how to use HONNS in their area of expertise.

This book highlights numerical models as powerful tools for the optimal design of Micro-Electro-Mechanical Systems (MEMS). Most MEMS experts have a background in electronics, where circuit models or behavioral models (i.e. lumped-parameter models) of devices are preferred to field models. This is certainly convenient in terms of preliminary design, e.g. in the prototyping stage. However, design optimization should also take into account fine-sizing effects on device behavior and therefore be based on distributed-parameter models, such as finite-element models. The book shows how the combination of automated optimal design and field-based models can produce powerful design toolboxes for MEMS. It especially focuses on illustrating theoretical concepts with practical examples, fostering comprehension through a problem-solving approach. By comparing the results obtained using different methods, readers will learn to identify their respective strengths and weaknesses. In addition, special emphasis is given to evolutionary computing and nature-inspired optimization strategies, the effectiveness of which has already been amply demonstrated. Given its scope, the book provides PhD students, researchers and professionals in the area of computer-aided analysis with a comprehensive, yet concise and practice-oriented guide to MEMS design and optimization. To benefit most from the book, readers should have a basic grasp of electromagnetism, vector analysis and numerical methods.

AI is going to change your world – but don’t panic.
 
As AI becomes more widespread in the workplace and in society, what impact will it have on your job, your life and the world around you? If AI can take on more and more of the tasks people perform at work, and do them more efficiently, where does that leave human beings?
 
Taking the Anxiety out of AI  explains how to live with AI, how to benefit from it, and how to avoid being replaced by it. The book explores the differences between human intelligence and artificial intelligence, considers what tasks will always be performed better by humans, and sets out possible futures in which humans and AI work together. It provides tools to work out how AI will affect your role, what skills you need to learn, and which mindsets will equip you to thrive in the future. The book concludes with a guide to current AI programs and how to use them.
 
Whether you have experience with AI, or simply want to learn more about it, this book is an invaluable guide for navigating your future.

In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification, and predition of control and recognition scenarios. Due to the fact that HONNs have been proven to be faster, more accurate, and easier to explain than traditional neural networks, their applications are limitless. Applied Artificial Higher Order Neural Networks for Control and Recognition explores the ways in which higher order neural networks are being integrated specifically for intelligent technology applications. Emphasizing emerging research, practice, and real-world implementation, this timely reference publication is an essential reference source for researchers, IT professionals, and graduate-level computer science and engineering students.

Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.

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.

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.

This volume provides an extensive overview of the Ethics of Artificial Intelligence for the Sustainable Development Goals. The authors are experts contributing with perspectives from different fields. The comprehensive collection of chapters illustrates the pressing governance problems related to using AI for the SDGs, and case studies describing how AI is advancing and can advance the achievement of the Goals. Students, scholars, and practitioners working on AI for SDGs, the ethical governance of AI, sustainability, and the fourth revolution can find this book a helpful reference.

Sensor technologies play a large part in modern life, as they are present in things like security systems, digital cameras, smartphones, and motion sensors. While these devices are always evolving, research is being done to further develop this technology to help detect and analyze threats, perform in-depth inspections, and perform tracking services. Optoelectronics in Machine Vision-Based Theories and Applications provides innovative insights on theories and applications of optoelectronics in machine vision-based systems. It also covers topics such as applications of unmanned aerial vehicle, autonomous and mobile robots, medical scanning, industrial applications, agriculture, and structural health monitoring. This publication is a vital reference source for engineers, technology developers, academicians, researchers, and advanced-level students seeking emerging research on sensor technologies and machine vision.

Before the integration of expert systems in biomedical science, complex problems required human expertise to solve them through conventional procedural methods. Advancements in expert systems allow for knowledge to be extracted when no human expertise is available and increases productivity through quick diagnosis. Expert System Techniques in Biomedical Science Practice is an essential scholarly resource that contains innovative research on the methods by which an expert system is designed to solve complex problems through the automation of decision making through the use of if-then-else rules rather than conventional procedural methods. Featuring coverage on a broad range of topics such as image processing, bio-signals, and cognitive AI, this book is a vital reference source for computer engineers, information technologists, biomedical engineers, data-processing specialists, medical professionals, and industrialists within the fields of biomedical engineering, pervasive computing, and natural language processing.

As organizations, businesses, and other institutions work to move forward during a new era of ubiquitous modern technology, new computing and technology implementation strategies are necessary to harness the shared knowledge of individuals to advance their organizations as a whole. Intelligent and Knowledge-Based Computing for Business and Organizational Advancements examines the emerging computing paradigm of Collective Intelligence (CI). The global contributions contained in this publication will prove to be essential to both researchers and practitioners in the computer and information science communities as these populations move toward a new period of fully technology-integrated business.

This book has a focus on the development and deployment of the Industrial Internet of Things (IIoT) paradigm, discussing frameworks, methodologies, benefits and limitations, as well as providing case studies of employing the IoT vision in the industrial domain. IIoT is becoming an attractive business reality for many organisations such as manufacturing, logistics, oil and gas, energy and other utilities, mining, aviation, and many more. The opportunities for this paradigm are huge, and according to one report, the IIoT market is predicted to reach $125 billion by 2021. The driving philosophy behind the IIoT is that smart machines are better than humans at accurately capturing, analysing and communicating real-time data. The underlying technologies include distributed computing, machine learning, artificial intelligence, and machine-to-machine communication, with a typical IIoT system consisting of intelligent systems (applications, controllers, sensors, and security mechanisms), data communication infrastructure (cloud computing, edge computing, etc.), data analytics (to support business intelligence and corporate decision making), and most importantly the human element. The promised benefits of the IIoT include enhanced safety, better reliability, smart metering, inventory management, equipment tracking, and facilities management. There are, however, numerous issues that are also becoming the focus of active research, such as concerns regarding service availability, data security, and device communication. Lack of ubiquitous interoperability between heterogeneous devices is also a major concern. This book intends to fill a gap in the IIoT literature by providing the scientific contributions and latest developments from researchers and practitioners of international repute, focusing on frameworks, methodologies, benefits, and inherent issues/barriers to connected environments, especially in industrial settings. The intended audience includes network specialists, hardware engineers, and security experts who wish to adopt newer approaches for device connectivity, IoT security, and sensor-based devices design. University level students, researchers and practitioners will also find the latest innovation in technology and newer approaches relevant to the IIoT from a distributed computing perspective.

The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to organize mechanical and personal systems. The Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms is an essential scholarly resource on current algorithms that have been inspired by the natural world. Featuring coverage on diverse topics such as cellular automata, simulated annealing, genetic programming, and differential evolution, this reference publication is ideal for scientists, biological engineers, academics, students, and researchers that are interested in discovering what models from nature influence the current technology-centric world.

Facial recognition software has improved by leaps and bounds over the past few decades, with error rates decreasing significantly within the past ten years. Though this is true, conditions such as poor lighting, obstructions, and profile-only angles have continued to persist in preventing wholly accurate readings. Face Recognition in Adverse Conditions examines how the field of facial recognition takes these adverse conditions into account when designing more effective applications by discussing facial recognition under real world PIE variations, current applications, and the future of the field of facial recognition research. The work is intended for academics, engineers, and researchers specializing in the field of facial recognition.

This edited volume explores the intersection between philosophy and computing. It features work presented at the 2016 annual meeting of the International Association for Computing and Philosophy. The 23 contributions to this volume neatly represent a cross section of 40 papers, four keynote addresses, and eight symposia as they cut across six distinct research agendas. The volume begins with foundational studies in computation and information, epistemology and philosophy of science, and logic. The contributions next examine research into computational aspects of cognition and philosophy of mind. This leads to a look at moral dimensions of man-machine interaction as well as issues of trust, privacy, and justice. This multi-disciplinary or, better yet, a-disciplinary investigation reveals the fruitfulness of erasing distinctions among and boundaries between established academic disciplines. This should come as no surprise. The computational turn itself is a-disciplinary and no former discipline, whether scientific, artistic, or humanistic, has remained unchanged. Rigorous reflection on the nature of these changes opens the door to inquiry into the nature of the world, what constitutes our knowledge of it, and our understanding of our place in it. These investigations are only just beginning. The contributions to this volume make this clear: many encourage further research and end with open questions.

Hybrid learning is now the single-greatest trend in education today due to the numerous educational advantages when both traditional classroom learning and e-learning are implemented collectively. The Handbook of Research on Hybrid Learning Models: Advanced Tools, Technologies, and Applications collects emerging research and pedagogies related to the convergence of teaching and learning methods. This significant Handbook of Research provides researchers, scholars, students, and professionals access to the latest knowledge related to hybrid learning, discovered and written by an international gathering of e-learning experts.

In today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method. Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.

As the healthcare industry continues to expand, it must utilize technology to ensure efficiencies are maintained. Healthcare needs to move in a direction where computational methods and algorithms can relieve the routine work of medical doctors, leaving them more time to carry out more important and skilled tasks such as surgery. Computational Methods and Algorithms for Medicine and Optimized Clinical Practice discusses some of the most interesting aspects of theoretical and applied research covering complementary facets of computational methods and algorithms to achieve greater efficiency and support medical personnel. Featuring research on topics such as healthcare reform, artificial intelligence, and disease detection, this book will particularly appeal to medical professionals and practitioners, hospitals, administrators, students, researchers, and academicians.

This book bridges the communication gap between neuroscientists and engineers through the unifying theme of correlation-based learning

Developing brain-style signal processing or machine learning algorithms has attracted many sharp minds from a range of disciplines. Now, coauthored by four researchers with varying backgrounds in signal processing, neuroscience, psychology, and computer science, Correlative Learning unifies the many cross-fertilized ideas in computational neuroscience and signal processing in a common language that will help engineers understand and appreciate the human brain as a highly sophisticated biosystem for building more intelligent machines.

First, the authors present the necessary neuroscience background for engineers, and then go on to relate the common intrinsic structures of the learning mechanisms of the brain to signal processing, machine learning, kernel learning, complex-valued domains, and the ALOPEX learning paradigm.

This correlation-based approach to building complex, reliable (robust), and adaptive systems is vital for engineers, researchers, and graduate students from various fields of science and engineering. Figures, tables, worked examples, and case studies illustrate how to use computational tools for either helping to understand brain functions or fitting specific engineering applications, and a comprehensive bibliography covering over 1,000 references from major publications is included for further reading.

This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.