To endow computers with common sense is one of the major long-term goals of artificial intelligence research. One approach to this problem is to formalize commonsense reasoning using mathematical logic. Commonsense Reasoning: An Event Calculus Based Approach is a detailed, high-level reference on logic-based commonsense reasoning. It uses the event calculus, a highly powerful and usable tool for commonsense reasoning, which Erik Mueller demonstrates as the most effective tool for the broadest range of applications. He provides an up-to-date work promoting the use of the event calculus for commonsense reasoning, and bringing into one place information scattered across many books and papers. Mueller shares the knowledge gained in using the event calculus and extends the literature with detailed event calculus solutions that span many areas of the commonsense world. The Second Edition features new chapters on commonsense reasoning using unstructured information including the Watson system, commonsense reasoning using answer set programming, and techniques for acquisition of commonsense knowledge including crowdsourcing.

The application of artificial intelligence technology to 5G wireless communications is now appropriate to address the design of optimized physical layers, complicated decision-making, network management, and resource optimization tasks within networks. In exploring 5G wireless technologies and communication systems, artificial intelligence is a powerful tool and a research topic with numerous potential fields of application that require further study. Applications of Artificial Intelligence in Wireless Communication Systems explores the applications of artificial intelligence for the optimization of wireless communication systems, including channel models, channel state estimation, beamforming, codebook design, signal processing, and more. Covering key topics such as neural networks, deep learning, and wireless systems, this reference work is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

By specializing in a vertical market, companies can better understand their customers and bring more insight to clients in order to become an integral part of their businesses. This approach requires dedicated tools, which is where artificial intelligence (AI) and machine learning (ML) will play a major role. By adopting AI software and services, businesses can create predictive strategies, enhance their capabilities, better interact with customers, and streamline their business processes. This edited book explores novel concepts and cutting-edge research and developments towards designing these fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking. The chapters focus on models and theoretical approaches to guarantee automation in large multi-scale implementations of AI and ML systems; protocol designs to ensure AI systems meet key requirements for future services such as latency; and optimisation algorithms to leverage the trusted distributed and efficient complex architectures. The book is of interest to researchers, scientists, and engineers working in the fields of ICTs, networking, AI, ML, signal processing, HCI, robotics and sensing. It could also be used as supplementary material for courses on AI, machine and deep learning, ICTs, networking signal processing, robotics and sensing.

Unmanned Aerial Vehicle (UAV) has extended the freedom to operate and monitor the activities from remote locations. It has advantages of flying at low altitude, small size, high resolution, lightweight, and portability. UAV and artificial intelligence have started gaining attentions of academic and industrial research. UAV along with machine learning has immense scope in scientific research and has resulted in fast and reliable outputs. Deep learning-based UAV has helped in real time monitoring, data collection and processing, and prediction in the computer/wireless networks, smart cities, military, agriculture and mining. This book covers artificial techniques, pattern recognition, machine and deep learning - based methods and techniques applied to different real time applications of UAV. The main aim is to synthesize the scope and importance of machine learning and deep learning models in enhancing UAV capabilities, solutions to problems and numerous application areas. This book is ideal for researchers, scientists, engineers and designers in academia and industry working in the fields of computer science, computer vision, pattern recognition, machine learning, imaging, feature engineering, UAV and sensing.

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.

Throughout the world, artificial intelligence is reshaping businesses, trade interfaces, economic activities, and society as a whole. In recent years, scholarly research on artificial intelligence has emerged from a variety of empirical and applied domains of knowledge. Computer scientists have developed advanced deep learning algorithms to leverage its utility in a variety of fields such as medicine, energy, travel, education, banking, and business management. Although a growing body of literature is shedding light on artificial intelligence-enabled difficulties, there is still much to be gained by applying fresh theory-driven techniques to this vital topic. Revolutionizing Business Practices Through Artificial Intelligence and Data-Rich Environments provides a comprehensive understanding of the business systems, platforms, procedures, and mechanisms that underpin different stakeholders' experiences with reality-enhancing technologies and their transformative application in management. The book also identifies areas in various business processes where artificial intelligence intervention would not only transform the business but would also make the business more sustainable. Covering key topics such as blockchain, business automation, and manufacturing, this reference work is ideal for computer scientists, business owners, managers, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

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.

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.

It is crucial that forensic science meets challenges such as identifying hidden patterns in data, validating results for accuracy, and understanding varying criminal activities in order to be authoritative so as to hold up justice and public safety. Artificial intelligence, with its potential subsets of machine learning and deep learning, has the potential to transform the domain of forensic science by handling diverse data, recognizing patterns, and analyzing, interpreting, and presenting results. Machine Learning and deep learning frameworks, with developed mathematical and computational tools, facilitate the investigators to provide reliable results. Further study on the potential uses of these technologies is required to better understand their benefits. Aiding Forensic Investigation Through Deep Learning and Machine Learning Frameworks provides an outline of deep learning and machine learning frameworks and methods for use in forensic science to produce accurate and reliable results to aid investigation processes. The book also considers the challenges, developments, advancements, and emerging approaches of deep learning and machine learning. Covering key topics such as biometrics, augmented reality, and fraud investigation, this reference work is crucial for forensic scientists, law enforcement, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.

Uncertainty in Data Envelopment Analysis: Fuzzy and Belief Degree-Based Uncertainties introduces methods to investigate uncertain data in DEA models, providing a deeper look into two types of uncertain DEA methods: Fuzzy DEA and Belief Degree Based Uncertainty DEA, which are based on uncertain measures. These models aim to solve problems encountered by classical data analysis in cases where inputs and outputs of systems and processes are volatile and complex, making measurement difficult. Classical data envelopment analysis (DEA) models use crisp data in order to measure inputs and outputs of a given system. Crisp input and output data are fundamentally indispensable in the conventional DEA models. If these models contain complex-uncertain data, then they will become more important and practical for decision-makers.

There is a significant deficiency among contemporary medicine practices reflected by experts making medical decisions for a large proportion of the population for which no or minimal data exists. Fortunately, our capacity to procure and apply such information is rapidly rising. As medicine becomes more individualized, the implementation of health IT and data interoperability become essential components to delivering quality healthcare. Quality Assurance in the Era of Individualized Medicine is a collection of innovative research on the methods and utilization of digital readouts to fashion an individualized therapy instead of a mass-population-directed strategy. While highlighting topics including assistive technologies, patient management, and clinical practices, this book is ideally designed for health professionals, doctors, nurses, hospital management, medical administrators, IT specialists, data scientists, researchers, academicians, and students.

Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book.

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications covers timely topics, including the neural network (NN), particle swarm optimization (PSO), evolutionary algorithm (GA), fuzzy sets (FS) and rough sets (RS), etc. Furthermore, the book highlights recent research on representative techniques to elaborate how a data-centric system formed a powerful platform for the processing of cloud hosted multimedia big data and how it could be analyzed, processed and characterized by CI. The book also provides a view on how techniques in CI can offer solutions in modeling, relationship pattern recognition, clustering and other problems in bioengineering. It is written for domain experts and developers who want to understand and explore the application of computational intelligence aspects (opportunities and challenges) for design and development of a data-centric system in the context of multimedia cloud, big data era and its related applications, such as smarter healthcare, homeland security, traffic control trading analysis and telecom, etc. Researchers and PhD students exploring the significance of data centric systems in the next paradigm of computing will find this book extremely useful.

Internet of things (IoT) is an emerging research field that is rapidly becoming an important part of our everyday lives including home automation, smart buildings, smart things, and more. This is due to cheap, efficient, and wirelessly-enabled circuit boards that are enabling the functions of remote sensing/actuating, decentralization, autonomy, and other essential functions. Moreover, with the advancements in embedded artificial intelligence, these devices are becoming more self-aware and autonomous, hence making decisions themselves. Current research is devoted to the understanding of how decision support systems are integrated into industrial IoT. Decision Support Systems and Industrial IoT in Smart Grid, Factories, and Cities presents the internet of things and its place during the technological revolution, which is taking place now to bring us a better, sustainable, automated, and safer world. This book also covers the challenges being faced such as relations and implications of IoT with existing communication and networking technologies; applications like practical use-case scenarios from the real world including smart cities, buildings, and grids; and topics such as cyber security, user privacy, data ownership, and information handling related to IoT networks. Additionally, this book focuses on the future applications, trends, and potential benefits of this new discipline. This book is essential for electrical engineers, computer engineers, researchers in IoT, security, and smart cities, along with practitioners, researchers, academicians, and students interested in all aspects of industrial IoT and its applications.

Recent advances in socio-cognitive and affective computing require further study as countless benefits and opportunities have emerged from these innovative technologies that may be useful in a number of contexts throughout daily life. In order to ensure these technologies are appropriately utilized across sectors, the challenges and strategies for adoption as well as potential uses must be thoroughly considered. Principles and Applications of Socio-Cognitive and Affective Computing discusses several aspects of affective interactions and concepts in affective computing, the fundamentals of emotions, and emerging research and exciting techniques for bridging the emotional disparity between humans and machines, all within the context of interactions. The book also considers problem and solution guidelines emerging in cognitive computing, thus summarizing the roadmap of current machine computational intelligence techniques for affective computing. Covering a range of topics such as social interaction, robotics, and virtual reality, this reference work is crucial for scientists, engineers, industry professionals, academicians, researchers, scholars, practitioners, instructors, and students.

In today's modernized world, the field of healthcare has seen significant practical innovations with the implementation of computational intelligence approaches and soft computing methods. These two concepts present various solutions to complex scientific problems and imperfect data issues. This has made both very popular in the medical profession. There are still various areas to be studied and improved by these two schemes as healthcare practices continue to develop. Computational Intelligence and Soft Computing Applications in Healthcare Management Science is an essential reference source that discusses the implementation of soft computing techniques and computational methods in the various components of healthcare, telemedicine, and public health. Featuring research on topics such as analytical modeling, neural networks, and fuzzy logic, this book is ideally designed for software engineers, information scientists, medical professionals, researchers, developers, educators, academicians, and students.

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.

This comprehensive book systematically introduces Dynamic Data Driven Simulation (DDDS) as a new simulation paradigm that makes real-time data and simulation model work together to enable simulation-based prediction/analysis.The text is significantly dedicated to introducing data assimilation as an enabling technique for DDDS. While data assimilation has been studied in other science fields (e.g., meteorology, oceanography), it is a new topic for the modeling and simulation community.This unique reference text bridges the two study areas of data assimilation and modelling and simulation, which have been developed largely independently from each other.