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.

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.

Tech visionary and co-founder of Manas AI Reid Hoffman shares his unique insider’s perspective on an AI-powered future, making the case for its potential to unlock a world of possibilities.

Superagency offers a roadmap for using AI inclusively and adaptively to improve our lives and create positive change. While acknowledging challenges like disinformation and potential job changes, the book focuses on AI’s immense potential to increase individual agency and create better outcomes for society as a whole.

Imagine AI tutors personalizing education for each child, researchers rapidly discovering cures for diseases like Alzheimer's and cancer, and AI advisors empowering people to navigate complex systems and achieve their goals. Hoffman and co-author, tech and culture writer Greg Beato envision a world where these possibilities, and many more, become a reality.

Superagency challenges conventional fears, inviting us to view the future through a lens of opportunity, rather than fear. It’s a call to action – to embrace AI with excitement and actively shape a world where human ingenuity and the power of AI combine to create something extraordinary.

Entrepreneur Reid Hoffman is a co-founder of LinkedIn, Inflection AI, and Manas AI. He is also host of the podcasts Possible and Masters of Scale.

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.

Over the last few years, the area of Natural Language Processing has drastically grown in recognition, not only within the research and development community, but also with industry professionals. As NLP continues to be discussed and researched, certain areas continue to grow and mature. As a result, the need for advanced research and information is in high demand. Emerging Applications of Natural Language Processing: Concepts and New Research provides pertinent and vital information that researchers, postgraduate, doctoral students, and practitioners are seeking. This book compiles the latest discoveries and advances in NLP methodologies and applications while expanding upon various topics regarding the future of NLP.

This book systematically presents the operating principles and technical characteristics of the main radio navigating systems (RNSs) that make it possible to adequately evaluate the corresponding scratch indexes and levels of air safety for air vehicles, the chief concern of the International Civil Aviation Organization (ICAO). The book discusses how RNS systems substantially determine navigation accuracy and reliability, and therefore air safety; in addition, it presents practical solutions to problems arising in the operation and development of RNS systems.

The emergence of artificial intelligence has created a vast amount of advancements within various professional sectors and has transformed the way organizations conduct themselves. The implementation of intelligent systems has assisted with developing traditional processes including decision making, risk management, and security. An area that requires significant attention and research is how these companies are becoming accustomed to computer intelligence and applying this technology to their everyday practices. Transdisciplinary Perspectives on Risk Management and Cyber Intelligence is a pivotal reference source that provides vital research on the application of intelligent systems within various professional sectors as well as the exploration of theories and empirical findings. While highlighting topics such as decision making, cognitive science, and knowledge management, this publication explores the management of risk and uncertainty using training exercises, as well as the development of managerial intelligence competency. This book is ideally designed for practitioners, educators, researchers, policymakers, managers, developers, analysts, politicians, and students seeking current research on modern approaches to the analysis and performance of cyber intelligence.

This book discusses the computational geometry, topology and physics of digital images and video frame sequences. This trio of computational approaches encompasses the study of shape complexes, optical vortex nerves and proximities embedded in triangulated video frames and single images, while computational geometry focuses on the geometric structures that infuse triangulated visual scenes. The book first addresses the topology of cellular complexes to provide a basis for an introductory study of the computational topology of visual scenes, exploring the fabric, shapes and structures typically found in visual scenes. The book then examines the inherent geometry and topology of visual scenes, and the fine structure of light and light caustics of visual scenes, which bring into play catastrophe theory and the appearance of light caustic folds and cusps. Following on from this, the book introduces optical vortex nerves in triangulated digital images. In this context, computational physics is synonymous with the study of the fine structure of light choreographed in video frames. This choreography appears as a sequence of snapshots of light reflected and refracted from surface shapes, providing a solid foundation for detecting, analyzing and classifying visual scene shapes.

This book presents a variety of perspectives on vision-based applications. These contributions are focused on optoelectronic sensors, 3D & 2D machine vision technologies, robot navigation, control schemes, motion controllers, intelligent algorithms and vision systems. The authors focus on applications of unmanned aerial vehicles, autonomous and mobile robots, industrial inspection applications and structural health monitoring. Recent advanced research in measurement and others areas where 3D & 2D machine vision and machine control play an important role, as well as surveys and reviews about vision-based applications. These topics are of interest to readers from diverse areas, including electrical, electronics and computer engineering, technologists, students and non-specialist readers. * Presents current research in image and signal sensors, methods, and 3D & 2D technologies in vision-based theories and applications; * Discusses applications such as daily use devices including robotics, detection, tracking and stereoscopic vision systems, pose estimation, avoidance of objects, control and data exchange for navigation, and aerial imagery processing; * Includes research contributions in scientific, industrial, and civil applications.

This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak. The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results.

The healthcare industry is starting to adopt digital twins to improve personalized medicine, healthcare organization performance, and new medicine and devices. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations as well as drug and device manufacturers. Digital twins are digital representations of human physiology built on computer models. The use of digital twins in healthcare is revolutionizing clinical processes and hospital management by enhancing medical care with digital tracking and advancing modelling of the human body. These tools are of great help to researchers in studying diseases, new drugs, and medical devices. Digital Twins and Healthcare: Trends, Techniques, and Challenges facilitates the advancement and knowledge dissemination in methodologies and applications of digital twins in the healthcare and medicine fields. This book raises interest and awareness of the uses of digital twins in healthcare in the research community. Covering topics such as deep neural network, edge computing, and transfer learning method, this premier reference source is an essential resource for hospital administrators, pharmacists, medical professionals, IT consultants, students and educators of higher education, librarians, and researchers.

Medical internet of things (IoT)-based applications are being utilized in several industries and have been shown to provide significant advantages to users in critical health applications. Artificial intelligence (AI) plays a key role in the growth and success of medical IoT applications and IoT devices in the medical sector. To enhance revenue, improve competitive advantage, and increase consumer engagement, the use of AI with medical IoT should be encouraged in the healthcare and medical arena. Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications provides greater knowledge of how AI affects healthcare and medical efficacy in order to improve outputs. It focuses on a thorough and comprehensive introduction to machine learning. Covering topics such as patient treatment, cyber-physical systems, and telemedicine, this premier reference source is a dynamic resource for hospital administrators, medical professionals, government officials, students and faculty of higher education, librarians, researchers, and academicians.

This collection represents the primary reference work for researchers and students in the area of Temporal Reasoning in Artificial Intelligence. Temporal reasoning has a vital role to play in many areas, particularly Artificial Intelligence. Yet, until now, there has been no single volume collecting together the breadth of work in this area. This collection brings together the leading researchers in a range of relevant areas and provides an coherent description of the breadth of activity concerning temporal reasoning in the filed of Artificial Intelligence.
Key Features:
- Broad range: foundations; techniques and applications
- Leading researchers around the world have written the chapters
- Covers many vital applications
- Source book for Artificial Intelligence, temporal reasoning
- Approaches provide foundation for many future software systems
- Broad range: foundations; techniques and applications
- Leading researchers around the world have written the chapters
- Covers many vital applications
- Source book for Artificial Intelligence, temporal reasoning
- Approaches provide foundation for many future software systems

This volume presents several machine intelligence technologies, developed over recent decades, and illustrates how they can be combined in application. One application, the detection of dementia from patterns in speech, is used throughout to illustrate these combinations. This application is a classic stationary pattern detection task, so readers may easily see how these combinations can be applied to other similar tasks. The expositions of the methods are supported by the basic theory they rest upon, and their application is clearly illustrated. The book's goal is to allow readers to select one or more of these methods to quickly apply to their own tasks. Includes a variety of machine intelligent technologies and illustrates how they can work together Shows evolutionary feature subset selection combined with support vector machines and multiple classifiers combined Includes a running case study on intelligent processing relating to Alzheimer's / dementia detection, in addition to several applications of the machine hybrid algorithms

The general focus of this book is on multimodal communication, which captures the temporal patterns of behavior in various dialogue settings. After an overview of current theoretical models of verbal and nonverbal communication cues, it presents studies on a range of related topics: paraverbal behavior patterns in the classroom setting; a proposed optimal methodology for conversational analysis; a study of time and mood at work; an experiment on the dynamics of multimodal interaction from the observer's perspective; formal cues of uncertainty in conversation; how machines can know we understand them; and detecting topic changes using neural network techniques. A joint work bringing together psychologists, communication scientists, information scientists and linguists, the book will be of interest to those working on a wide range of applications from industry to home, and from health to security, with the main goals of revealing, embedding and implementing a rich spectrum of information on human behavior.

The discovery and development of new computational methods have expanded the capabilities and uses of simulations. With agent-based models, the applications of computer simulations are significantly enhanced. Multi-Agent-Based Simulations Applied to Biological and Environmental Systems is a pivotal reference source for the latest research on the implementation of autonomous agents in computer simulation paradigms. Featuring extensive coverage on relevant applications, such as biodiversity conservation, pollution reduction, and environmental risk assessment, this publication is an ideal source for researchers, academics, engineers, practitioners, and professionals seeking material on various issues surrounding the use of agent-based simulations.

The main purpose of this book is not only to present recent studies and advances in the field of social science research, but also to stimulate discussion on related practical issues concerning statistics, mathematics, and economics. Accordingly, a broad range of tools and techniques that can be used to solve problems on these topics are presented in detail in this book, which offers an ideal reference work for all researchers interested in effective quantitative and qualitative tools. The content is divided into three major sections. The first, which is titled "Social work", collects papers on problems related to the social sciences, e.g. social cohesion, health, and digital technologies. Papers in the second part, "Education and teaching issues," address qualitative aspects, education, learning, violence, diversity, disability, and ageing, while the book's final part, "Recent trends in qualitative and quantitative models for socio-economic systems and social work", features contributions on both qualitative and quantitative issues. The book is based on a scientific collaboration, in the social sciences, mathematics, statistics, and economics, among experts from the "Pablo de Olavide" University of Seville (Spain), the "University of Defence" of Brno (Czech Republic), the "G. D'Annunzio" University of Chieti-Pescara (Italy) and "Alexandru Ioan Cuza University" of Iasi (Romania). The contributions, which have been selected using a peer-review process, examine a wide variety of topics related to the social sciences in general, while also highlighting new and intriguing empirical research conducted in various countries. Given its scope, the book will appeal, in equal measure, to sociologists, mathematicians, statisticians and philosophers, and more generally to scholars and specialists in related fields.

The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe-Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.

This book contains selected papers from SEB-18, the Tenth International Conference on Sustainability in Energy and Buildings, which was organised by KES International and Griffith University and held in Gold Coast, Australia in June 2018. SEB-18 invited contributions on a range of topics related to sustainable buildings and renewable energy, and explored innovative topics regarding intelligent buildings and cities. Applicable areas included the sustainable design and of buildings, neighbourhoods and cities (built and natural environment); optimisation and modelling techniques; smart energy systems for smart cities; green information communications technology; and a broad range of solar, wind, wave and other renewable energy topics. The aim of the conference was to bring together researchers and government and industry professionals to discuss the future of energy in buildings, neighbourhoods and cities from a theoretical, practical, implementation and simulation perspective. In addition, SEB-18 offered an exciting opportunity to present, interact, and learn about the latest research in Sustainability in Energy and Buildings.

This book highlights the emerging field of intelligent computing and developing smart systems. It includes chapters discussing the outcome of challenging research related to distributed computing, smart machines and their security related research, and also covers next-generation communication techniques and the networking technologies that have the potential to build the future communication infrastructure. Bringing together computing, communications and other aspects of intelligent and smart computing, it contributes to developing a roadmap for future research on intelligent systems.

Revealing the unfolding story of Artificial Intelligence, Richard Susskind presents a short non-technical guide that challenges us to think differently about AI. Susskind brings AI out of computing laboratories, big tech companies, and start-ups - and into everyday life.

In recent years, and certainly since the launch of ChatGPT, there has been massive public and professional interest in Artificial Intelligence. But people are confused about what AI is, what it can and cannot do, what is yet to come, and whether AI is good or bad for humanity and civilisation - whether it will provide solutions to mankind's major challenges or become our gravest existential threat. There is also confusion about how we should regulate AI and where we should draw moral boundaries on its use.

In How To Think About AI, Richard Susskind draws on his experience of working on AI since the early 1980s. For Susskind, balancing the benefits and threats of artificial intelligence is the defining challenge of our age. He explores the history of AI and possible scenarios for its future. His views on AI are not always conventional. He positions ChatGPT and generative AI as no more than the latest chapter in the ongoing story of AI and claims we are still at the foothills of developments. He argues that to think responsibly about the impact of AI requires us to look well beyond todayâs technologies, suggesting that not-yet-invented technologies will have far greater impact on us in the 2030s than the tools we have today. This leads Susskind to discuss the possibility of conscious machines, magnificent new AI-enabled virtual worlds, and the impact of AI on the evolution of biological humans.