Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. The Handbook of Research on Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

This timely book presents a detailed analysis of the role of law and regulation in the utilisation of Artificial Intelligence (AI) in the media sector. As well as contributing to the wider discussion on law and AI, the book also digs deeper by exploring pressing issues at the intersections of AI, media, and the law. Chapters critically re-examine various rights and responsibilities from the perspectives of incentives for accountable utilisation of AI in the industry. Featuring chapters from leading scholars in the field, Artificial Intelligence and the Media provides a timely and in-depth research-based contribution to complex themes - especially at the interface of new technology (including AI) with media and regulation. Analysing both legislative and ethical solutions, chapters explore what "AI" and "accountability" mean in terms of media practices, principles, and power relations, as well as how to address the AI revolution with informed law and policy in order to incentivise accountable utilisation of AI and to reduce negative societal impacts. Offering ideas for further research in the area, this book is key reading for academics and researchers in the fields of information and media law, regulation, and technology law. It may also interest media law practitioners, with research-based guidance for everyday practices and tools to prepare for future developments in the area.

Decision-making is a frequent problem in today's financial, business, and industrial world. Thus, fuzzy expert systems are increasingly being used to solve decision-making problems by attempting to solve a part or whole of a practical problem. These expert systems have proven that they can solve problems in various domains where human expertise is required, including the field of agriculture. Fuzzy Expert Systems and Applications in Agricultural Diagnosis is a crucial source that examines the use of fuzzy expert systems for prediction and problem solving in the agricultural industry. Featuring research on topics such as nutrition management, sustainable agriculture, and defuzzification, this book is ideally designed for farmers, researchers, scientists, academics, students, policymakers, and development practitioners seeking the latest research in technological tools that support crop disease diagnosis.

This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods. The former is easier to do due to the availability of appropriate software. To understand the methods it is necessary to have some mathematical knowledge which is explained in the book with the help of figures and descriptions of the theory behind the software. In addition, the book includes numerical examples to guide readers on the working of existing popular software. The use of mathematics is reduced and simplified for non-experts using established methods, which also helps in avoiding mistakes in application and interpretation. Finally, the book enables the reader to understand and conceptualize the overall flow of brain imaging data analysis, particularly for statisticians and data-scientists unfamiliar with this area. The state of the art method described in the book has a multivariate approach developed by the authors' team. Since brain imaging data, generally, has a highly correlated and complex structure with large amounts of data, categorized into big data, the multivariate approach can be used as dimension reduction by following the application of statistical methods. The R package for most of the methods described is provided in the book. Understanding the background theory is helpful in implementing the software for original and creative applications and for an unbiased interpretation of the output. The book also explains new methods in a conceptual manner. These methodologies and packages are commonly applied in life science data analysis. Advanced methods to obtain novel insights are introduced, thereby encouraging the development of new methods and applications for research into medicine as a neuroscience.

Fractional Order Systems: Optimization, Control, Circuit Realizations and Applications consists of 21 contributed chapters by subject experts. Chapters offer practical solutions and novel methods for recent research problems in the multidisciplinary applications of fractional order systems, such as FPGA, circuits, memristors, control algorithms, photovoltaic systems, robot manipulators, oscillators, etc. This book is ideal for researchers working in the modeling and applications of both continuous-time and discrete-time dynamics and chaotic systems. Researchers from academia and industry who are working in research areas such as control engineering, electrical engineering, mechanical engineering, computer science, and information technology will find the book most informative.

There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these "game changers," governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society. This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation. The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.

Communication based on the internet of things (IoT) generates huge amounts of data from sensors over time, which opens a wide range of applications and areas for researchers. The application of analytics, machine learning, and deep learning techniques over such a large volume of data is a very challenging task. Therefore, it is essential to find patterns, retrieve novel insights, and predict future behavior using this large amount of sensory data. Artificial intelligence (AI) has an important role in facilitating analytics and learning in the IoT devices. Applying AI-Based IoT Systems to Simulation-Based Information Retrieval provides relevant frameworks and the latest empirical research findings in the area. It is ideal for professionals who wish to improve their understanding of the strategic role of trust at different levels of the information and knowledge society and trust at the levels of the global economy, networks and organizations, teams and work groups, information systems, and individuals as actors in the networked environments. Covering topics such as blockchain visualization, computer-aided drug discovery, and health monitoring, this premier reference source is an excellent resource for business leaders and executives, IT managers, security professionals, data scientists, students and faculty of higher education, librarians, hospital administrators, researchers, and academicians.

Autism spectrum disorder (ASD) is known as a neuro-disorder in which a person may face problems in interaction and communication with people, amongst other challenges. As per medical experts, ASD can be diagnosed at any stage or age but is often noticeable within the first two years of life. If caught early enough, therapies and services can be provided at this early stage instead of waiting until it is too late. ASD occurrences appear to have increased over the last couple of years leading to the need for more research in the field. It is crucial to provide researchers and clinicians with the most up-to-date information on the clinical features, etiopathogenesis, and therapeutic strategies for patients as well as to shed light on the other psychiatric conditions often associated with ASD. In addition, it is equally important to understand how to detect ASD in individuals for accurate diagnosing and early detection. Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder discusses the early detection and diagnosis of autism spectrum disorder enabled by artificial intelligence technologies, applications, and therapies. This book will focus on the early diagnosis of ASD through artificial intelligence, such as deep learning and machine learning algorithms, for confirming diagnosis or suggesting the need for further evaluation of individuals. The chapters will also discuss the use of artificial intelligence technologies, such as medical robots, for enhancing the communication skills and the social and emotional skills of children who have been diagnosed with ASD. This book is ideally intended for IT specialists, data scientists, academicians, scholars, researchers, policymakers, medical practitioners, and students interested in how artificial intelligence is impacting the diagnosis and treatment of autism spectrum disorder.

The internet of things (IoT) is quickly growing into a large industry with a huge economic impact expected in the near future. However, the users' needs go beyond the existing web-like services, which do not provide satisfactory intelligence levels. Ambient intelligence services in IoT environments is an emerging research area that can change the way that technology and services are perceived by the users. Ambient Intelligence Services in IoT Environments: Emerging Research and Opportunities is a unique source that systemizes recent trends and advances for service development with such key technological enablers of modern ICT as ambient intelligence, IoT, web of things, and cyber-physical systems. The considered concepts and models are presented using a smart spaces approach with a particular focus on the Smart-M3 platform, which is now shaping into an open source technology for creating ontology-based smart spaces and is shifting towards the development of web of things applications and socio-cyber-physical systems. Containing coverage on a broad range of topics such as fog computing, smart environments, and virtual reality, multitudes of researchers, students, academicians, and professionals will benefit from this timely reference.

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics. Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source. This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.

Food is a necessary aspect of human life, and agriculture is crucial to any country's global economy. Because the food business is essential to both a country's economy and global economy, artificial intelligence (AI)-based smart solutions are needed to assure product quality and food safety. The agricultural sector is constantly under pressure to boost crop output as a result of population growth. This necessitates the use of AI applications. Artificial Intelligence Applications in Agriculture and Food Quality Improvement discusses the application of AI, machine learning, and data analytics for the acceleration of the agricultural and food sectors. It presents a comprehensive view of how these technologies and tools are used for agricultural process improvement, food safety, and food quality improvement. Covering topics such as diet assessment research, crop yield prediction, and precision farming, this premier reference source is an essential resource for food safety professionals, quality assurance professionals, agriculture specialists, crop managers, agricultural engineers, food scientists, computer scientists, AI specialists, students, libraries, government officials, researchers, and academicians.

The Easy Introduction to Machine Learning (Ml) for Nontechnical People--In Business and Beyond Artificial Intelligence for Business is your plain-English guide to Artificial Intelligence (AI) and Machine Learning (ML): how they work, what they can and cannot do, and how to start profiting from them. Writing for nontechnical executives and professionals, Doug Rose demystifies AI/ML technology with intuitive analogies and explanations honed through years of teaching and consulting. Rose explains everything from early "expert systems" to advanced deep learning networks. First, Rose explains how AI and ML emerged, exploring pivotal early ideas that continue to influence the field. Next, he deepens your understanding of key ML concepts, showing how machines can create strategies and learn from mistakes. Then, Rose introduces current powerful neural networks: systems inspired by the structure and function of the human brain. He concludes by introducing leading AI applications, from automated customer interactions to event prediction. Throughout, Rose stays focused on business: applying these technologies to leverage new opportunities and solve real problems. Compare the ways a machine can learn, and explore current leading ML algorithms Start with the right problems, and avoid common AI/ML project mistakes Use neural networks to automate decision-making and identify unexpected patterns Help neural networks learn more quickly and effectively Harness AI chatbots, virtual assistants, virtual agents, and conversational AI applications

Artificial intelligence has become an invaluable tool in modern society and can be utilized across fields such as healthcare, travel, education, and construction. There are numerous benefits for companies, industries, and governments when adopting this technology into their daily operations as it continues to evolve to support the needs of society. Further study on the challenges and strategies of implementation is required in order to ensure the technology is employed to its full potential. Artificial Intelligence for Societal Development and Global Well-Being considers the various uses, best practices, and success factors of artificial intelligence across fields and industries and discusses critical ways in which the technology must be developed further for the good of society. Covering a range of topics such as smart devices, artificial neural networks, and natural intelligence, this reference work is crucial for scientists, librarians, business owners, government officials, entrepreneurs, scholars, researchers, practitioners, instructors, and students.

Emerging technologies continue to affect a variety of industries, making processes more effective and efficient. However, they also impact society by promoting opportunities to encourage social change and socioeconomic advancement. Blockchain is one that is already influencing third world countries and disrupting the globe. Blockchain Technology for Global Social Change is an essential research publication that provides insight into advancements being made in blockchain and some potential applications of the technology that can improve the lives of individuals in emerging markets. This publication covers a range of topics such as digital government, health systems, and urbanization and is ideal for policymakers, academicians, researchers, sociologists, government officials, economists, and financial experts seeking current and relevant research on evolving blockchain technologies.

In this book, translated into English for the first time, Lelio Demichelis takes on a modern perspective of the concept/process of alienation. This concept-much more profound and widespread today than first described and denounced by Marx-has largely been forgotten and erased. Using the characters of Narcissus, Pygmalion and Prometheus, the author reinterprets and updates Marx, Nietzsche, Anders, Foucault and, in particular, critical theory and the Frankfurt School views on an administered society (where everything is automated and engineered, manifest today in algorithms, AI, machine learning and social networking) showing that, in a world where old and new forms of alienation come together, man is increasingly led to delegate (i.e. alienate) sovereignty, freedom, responsibility and the awareness of being alive.

Multimedia represents information in novel and varied formats. One of the most prevalent examples of continuous media is video. Extracting underlying data from these videos can be an arduous task. From video indexing, surveillance, and mining, complex computational applications are required to process this data. Intelligent Analysis of Multimedia Information is a pivotal reference source for the latest scholarly research on the implementation of innovative techniques to a broad spectrum of multimedia applications by presenting emerging methods in continuous media processing and manipulation. This book offers a fresh perspective for students and researchers of information technology, media professionals, and programmers.

Autonomic networking aims to solve the mounting problems created by increasingly complex networks, by enabling devices and service-providers to decide, preferably without human intervention, what to do at any given moment, and ultimately to create self-managing networks that can interface with each other, adapting their behavior to provide the best service to the end-user in all situations. This book gives both an understanding and an assessment of the principles, methods and architectures in autonomous network management, as well as lessons learned from, the ongoing initiatives in the field. It includes contributions from industry groups at Orange Labs, Motorola, Ericsson, the ANA EU Project and leading universities. These groups all provide chapters examining the international research projects to which they are contributing, such as the EU Autonomic Network Architecture Project and Ambient Networks EU Project, reviewing current developments and demonstrating how autonomic management principles are used to define new architectures, models, protocols, and mechanisms for future network equipment.

Artificial intelligence (AI) and knowledge management can create innovative digital solutions and business opportunities in Asia from circular and green economies to technological disruption, innovation, and smart cities. It is essential to understand the impact and importance of AI and knowledge management within the digital economy for future development and for fostering the best practices within 21st century businesses. The Handbook of Research on Artificial Intelligence and Knowledge Management in Asia's Digital Economy offers conceptual frameworks, empirical studies, and case studies that help to understand the latest developments in artificial intelligence and knowledge management, as well as its potential for digital transformation and business opportunities in Asia. Covering topics such as augmented reality. Convolutional neural networks, and digital transformation, this major reference work generates enriching debate on the challenges and opportunities for economic growth and inclusion in the region among business executives and leaders, IT managers, policymakers, government officials, students and educators of higher education, researchers, and academicians.