Artificial intelligence is shaking up economies around the world as well as society at large and is predicted to be either the best or worst thing to happen to humanity. This book looks at what exactly artificial intelligence is, how it can be classified, how it differentiates from other concepts such as machine learning, big data, blockchain, or the Internet-of-Things, and how it has evolved and might evolve over time. Providing a clear and unbiased picture of artificial intelligence, the book provides critical analyses of the advantages and disadvantages, opportunities and threats of AI progress for business and civilisation. Solutions and possible directions of how humanity might deal with rapid development and evolutions will be given and discussed, and consider regulation, employment, ethics, education and international cooperation. Unlike existing literature, this book provides a comprehensive overview of AI based on detailed analysis and insight. Finally, several real-life examples from various sectors and industries, including for profit organizations, higher education, and government, will substantiate and illustrate the presented concepts, classifications, and discussions. This book is of interest to researchers, educators, students, and practitioners alike who desire to understand AI in its broad lines and discover the latest research and studies within the field.

This book proposes a new critical relationship between computation and architecture, developing a history and theory of representation in architecture to understand and unleash potential means to open up creativity in the field. Historically, architecture has led spatial representation. Today, computation has established new representational paradigms that can be compared to spatial representations, such as the revolution of perspective in the Renaissance. Architects now use software, robotics, and fabrication tools with very little understanding and participation in how these tools influence, revolutionize, and determine both architecture and its construction today. Why does the discipline of architecture not have a higher degree of authorship in the conception and development of computational technologies that define spatial representation? This book critically explores the relationship between history, theory and cultural criticism. Lorenzo-Eiroa positions new understandings through parallel historical sections and theories of many revolutionary representational architecture canons displaced by conventional spatial projection. He identifies the architects, artists, mathematicians, and philosophers that were able to revolutionise their disciplines through the development of new technologies, new systems of representation, and new lenses to understand reality. This book frames the discussion by addressing new means to understand and expand architecture authorship in relation to survey, information, representation, higher dimensional space, Big Data, and Artificial Intelligence - in the pursuit of activating an architecture of information. This will be important reading for upper-level students and researchers of architecture and architectural theory, especially those with a keen interest in computational design and robotic fabrication.

Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making Offers many real-time case studies

This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights. This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book's detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.

Convergence of Blockchain, AI, and IoT: Concepts and Challenges discusses the convergence of three powerful technologies that play into the digital revolution and blur the lines between biological, digital, and physical objects. This book covers novel algorithms, solutions for addressing issues in applications, security, authentication, and privacy. The book provides an overview of the clinical scientific research enabling smart diagnosis equipment through AI. It presents the role these technologies play in augmented reality and blockchain, covers digital currency managed with bitcoin, and discusses deep learning and how it can enhance human thoughts and behaviors. Targeted audiences range from those interested in the technical revolution of blockchain, big data and the Internet of Things, to research scholars and the professional market.

Artificial Intelligence (AI) has augmented human activities and unlocked opportunities for many sectors of the economy. It is used for data management and analysis, decision making, and many other aspects. As with most rapidly advancing technologies, law is often playing a catch up role so the study of how law interacts with AI is more critical now than ever before. This book provides a detailed qualitative exploration into regulatory aspects of AI in industry. Offering a unique focus on current practice and existing trends in a wide range of industries where AI plays an increasingly important role, the work contains legal and technical analysis performed by 15 researchers and practitioners from different institutions around the world to provide an overview of how AI is being used and regulated across a wide range of sectors, including aviation, energy, government, healthcare, legal, maritime, military, music, and others. It addresses the broad range of aspects, including privacy, liability, transparency, justice, and others, from the perspective of different jurisdictions. Including a discussion of the role of AI in industry during the Covid-19 pandemic, the chapters also offer a set of recommendations for optimal regulatory interventions. Therefore, this book will be of interest to academics, students and practitioners interested in technological and regulatory aspects of AI.

This book categorizes the wide area of data-driven model-free controllers, reveals the exact benefits of such controllers, gives the in-depth theory and mathematical proofs behind them, and finally discusses their applications. Each chapter includes a section for presenting the theory and mathematical definitions of one of the above mentioned algorithms. The second section of each chapter is dedicated to the examples and applications of the corresponding control algorithms in practical engineering problems. This book proposes to avoid complex mathematical equations, being generic as it includes several types of data-driven model-free controllers, such as Iterative Feedback Tuning controllers, Model-Free Controllers (intelligent PID controllers), Model-Free Adaptive Controllers, model-free sliding mode controllers, hybrid model-free and model-free adaptive-Virtual Reference Feedback Tuning controllers, hybrid model-free and model-free adaptive fuzzy controllers and cooperative model-free controllers. The book includes the topic of optimal model-free controllers, as well. The optimal tuning of model-free controllers is treated in the chapters that deal with Iterative Feedback Tuning and Virtual Reference Feedback Tuning. Moreover, the extension of some model-free control algorithms to the consensus and formation-tracking problem of multi-agent dynamic systems is provided. This book can be considered as a textbook for undergraduate and postgraduate students, as well as a professional reference for industrial and academic researchers, attracting the readers from both industry and academia.

"The more we know about smart and intelligent systems and their use, the more productive organizations can become, and the more quality of life will improve."-Gavriel Salvendy, President Academy of Science, Engineering and Medicine of Florida, University Distinguished Professor University of Central Florida" "Robots, drones, self-driving cars, and personal assistants are only some of the 'intelligent' and 'smart' systems which are populating our world and changing the way we use technology to carry out our everyday activities, bringing about both exciting opportunities for human-technology symbiosis, as well as compelling design and development challenges. Through a carefully selected choice of chapters, authored by top scientists in the field, this book, edited by Abbas Moallem, sheds light on fundamental aspects of intelligent and smart systems, investigating the role and impact of affective and psychophysiological computing, machine learning, cybersecurity, agent transparency, and human-agent teaming in the shaping of this new interaction paradigm, as well as the human factors involved in their application in critical domains such as health, education, and manufacturing in the emerging technological landscape."-Constantine Stephanidis, Professor of Computer Science, University of Crete, Distinguished member of Foundation for Research and Technology - Hellas (FORTH) In today's digital world, the words "smart" and intelligent" are now used to label devices, machinery, systems, and even environments. What is a "smart" system? Is "smart" synonymous with "intelligent"? If not, what does an "intelligent system" mean? Are all smart systems intelligent? This book tries to answer these questions by summarizing the existing research in various areas and providing new research findings. Smart and Intelligent Systems: The Human Elements in Artificial Intelligence, Robotics, and Cybersecurity presents new areas of smart and intelligent system design. It defines smart and intelligent systems, offers a human factors approach, discusses networking applications, and combines the human element with smart and intelligent systems. This book is perfect for engineering students in data sciences and artificial intelligence and practitioners at all levels in the fields of human factors and ergonomics, systems engineering, computer science, software engineering, and robotics.

This book examines the fundamentals and technologies of Artificial Intelligence (AI) and describes their tools, challenges, and issues. It also explains relevant theory as well as industrial applications in various domains, such as healthcare, economics, education, product development, agriculture, human resource management, environmental management, and marketing. The book is a boon to students, software developers, teachers, members of boards of studies, and researchers who need a reference resource on artificial intelligence and its applications and is primarily intended for use in courses offered by higher education institutions that strive to equip their graduates with Industry 4.0 skills. FEATURES: Gender disparity in the enterprises involved in the development of AI-based software development as well as solutions to eradicate such gender bias in the AI world A general framework for AI in environmental management, smart farming, e-waste management, and smart energy optimization The potential and application of AI in medical imaging as well as the challenges of AI in precision medicine AI's role in the diagnosis of various diseases, such as cancer and diabetes The role of machine learning models in product development and statistically monitoring product quality Machine learning to make robust and effective economic policy decisions Machine learning and data mining approaches to provide better video indexing mechanisms resulting in better searchable results ABOUT THE EDITORS: Prof. Dr. P. Kaliraj is Vice Chancellor at Bharathiar University, Coimbatore, India. Prof. Dr. T. Devi is Professor and Head of the Department of Computer Applications, Bharathiar University, Coimbatore, India.

Artificial Intelligence: Technologies, Applications, and Challenges is an invaluable resource for readers to explore the utilization of Artificial Intelligence, applications, challenges, and its underlying technologies in different applications areas. Using a series of present and future applications, such as indoor-outdoor securities, graphic signal processing, robotic surgery, image processing, character recognition, augmented reality, object detection and tracking, intelligent traffic monitoring, emergency department medical imaging, and many more, this publication will support readers to get deeper knowledge and implementing the tools of Artificial Intelligence. The book offers comprehensive coverage of the most essential topics, including: Rise of the machines and communications to IoT (3G, 5G). Tools and Technologies of Artificial Intelligence Real-time applications of artificial intelligence using machine learning and deep learning. Challenging Issues and Novel Solutions for realistic applications Mining and tracking of motion based object data image processing and analysis into the unified framework to understand both IoT and Artificial Intelligence-based applications. This book will be an ideal resource for IT professionals, researchers, under or post-graduate students, practitioners, and technology developers who are interested in gaining insight to the Artificial Intelligence with deep learning, IoT and machine learning, critical applications domains, technologies, and solutions to handle relevant challenges.

Co-edited by an experienced roboticist and author as well as an experienced academic Addresses the legal aspect of autonomous driving and ADAS Presents the application of ADAS in autonomous vehicle parking systems

provides a thorough understanding of the integration of computational intelligence with information retrieval includes discussion on protecting and analysing big data on cloud platforms provides a plethora of theoretical as well as experimental research, along with surveys and impact studies

Includes detailed applications of cybersecurity and forensics for real life problems Addresses the challenges and solutions related to implementation of cybersecurity in multiple domains of smart computational technologies Includes the latest trends and area of research in cybersecurity and forensics Offers both quantitative and qualitative assesmnet of the topics Includes case studies that will be helpful for the researchers

This edited book describes how newly emerging Artificial Intelligence (AI) technologies will provide unprecedented opportunities to penetrate technology and automation into everything we do, and at the same time, provide a huge playing field for businesses to develop newer models to capture market share. It establishes a milestone in understanding global transformational changes occurring in the manufacturing and corporate world due to AI and tries to find powerful and sophisticated solutions that will improve and streamline operations. Reinventing Manufacturing and Business Processes Through Artificial Intelligence will be of interest to students, researchers, and professionals of the AI community as well as interdisciplinary researchers.

First book to look at legal and regulatory aspects of personal care robots

Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

Provides the details of state-of-the-art machine learning methods used in VLSI Design. Discusses hardware implementation and device modeling pertaining to machine learning algorithms. Explores machine learning for various VLSI architectures and reconfigurable computing. Illustrate latest techniques for device size and feature optimization. Highlight latest case studies and reviews of the methods used for hardware implementation.

Covers AI and ML for digital health and wellbeing in society 5.0 Presents how putting Knowledge Management systems in place can facilitate the flow of information and result in better and more informed decisions. Discusses applications of distruptive technologies in digital health and wellbeing Offers examples of implementation of Knowledge Management along with case studies Addresses the challenges associated with practical implementation and adoption of technologies in healthcare

Introduces design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt and adapt. Free from endless derivations equations are presented and explained strategically, explaining why it is imperative to use them and how they will help in your task at hand. Illustrations and simple explanation help readers visualize and absorb easily, difficult to understand concepts.

Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.

Artificial intelligence will not necessarily create a super-intelligent "human robot"; however, it is very probable that intelligent robots and intelligent informats will bring about a form of super-globalization, in which money and goods are prioritized over people and democracy and where the widespread use of casual labour - that is, short-term contracts - will become the most common form of employment relationship. It is also very likely that artificial intelligence will bring about what is known as singularity. This term is used to describe a situation where intelligent robots, from a rational and logical perspective, are smarter than humans, i.e. the development of AI. This book explores the impact that these intelligent robots and intelligent informats will have on social and societal development. The author tackles the question of singularity from three distinct standpoints: technological singularity - the intelligence of machines compared to that of humans - which he argues will bring about a qualitatively new labour market; economic singularity - the consequences for work relationships, value creation and employment - which he asserts will promote full automation, result in precarious contracts with low salaries, and, in some countries, possibly lead to the introduction of a universal basic income; and social singularity - the consequences of technological and economic singularity for democratic processes, bureaucratic procedures for exercising authority and control, and the direction in which society will develop, in addition to the emergence of new social institutions - which Johannessen says will promote a transition from representative democracy to genuine democracy. The book will appeal to academics, researchers and students of economic sociology and political economy, as well as those focusing upon the emerging innovation economy. It will also find an audience among professionals and policymakers keen to understand the impact the Fourth Industrial Revolution will have on organizations, individuals and society at large.

Introduces a new web-based optimizer for Geometric algebra algorithms; Supports many programming languages as well as hardware; Covers the advantages of High-dimensional algebras; Includes geometrically intuitive support of quantum computing

Machine Translation (MT) has become widely used throughout the world as a medium of communication between those who live in different countries and speak different languages. However, translation between distant languages constitutes a challenge for machines. Therefore, translation evaluation is poised to play a significant role in the process of designing and developing effective MT systems. This book evaluates three prominent MT systems, including Google Translate, Microsoft Translator, and Sakhr, each of which provides translation between English and Arabic. In the book Almahasees scrutinizes the capacity of the three systems in dealing with translation between English and Arabic in a large corpus taken from various domains, including the United Nation (UN), the World Health Organization (WHO), the Arab League, Petra News Agency reports, and two literary texts: The Old Man and the Sea and The Prophet. The evaluation covers holistic analysis to assess the output of the three systems in terms of Translation Automation User Society (TAUS) adequacy and fluency scales. The text also looks at error analysis to evaluate the systems' output in terms of orthography, lexis, grammar, and semantics at the entire-text level and in terms of lexis, grammar, and semantics at the collocation level. The research findings contained within this volume provide important feedback about the capabilities of the three MT systems with respect to English<>Arabic translation and paves the way for further research on such an important topic. This book will be of interest to scholars and students of translation studies and translation technology.

Physical and behavioral biometric technologies such as fingerprinting, facial recognition, voice identification, etc. have enhanced the level of security substantially in recent years. Governments and corporates have employed these technologies to achieve better customer satisfaction. However, biometrics faces major challenges in reducing criminal, terrorist activities and electronic frauds, especially in choosing appropriate decision-making algorithms. To face this challenge, new developments have been made, that amalgamate biometrics with artificial intelligence (AI) in decision-making modeling. Advanced software algorithms of AI, processing information offered by biometric technology, achieve better results. This has led to growth in the biometrics technology industry, and is set to increase the security and internal control operations manifold. This book provides an overview of the existing biometric technologies, decision-making algorithms and the growth opportunity in biometrics. The book proposes a throughput model, which draws on computer science, economics and psychology to model perceptual, informational sources, judgmental processes and decision choice algorithms. It reviews how biometrics might be applied to reduce risks to individuals and organizations, especially when dealing with digital-based media.

Disruptive Trends in Computer Aided Diagnosis collates novel techniques and methodologies in the domain of content based image classification and deep learning/machine learning techniques to design efficient computer aided diagnosis architecture. It is aimed to highlight new challenges and probable solutions in the domain of computer aided diagnosis to leverage balancing of sustainable ecology. The volume focuses on designing efficient algorithms for proposing CAD systems to mitigate the challenges of critical illnesses at an early stage. State-of-the-art novel methods are explored for envisaging automated diagnosis systems thereby overriding the limitations due to lack of training data, sample annotation, region of interest identification, proper segmentation and so on. The assorted techniques addresses the challenges encountered in existing systems thereby facilitating accurate patient healthcare and diagnosis. Features: An integrated interdisciplinary approach to address complex computer aided diagnosis problems and limitations. Elucidates a rich summary of the state-of-the-art tools and techniques related to automated detection and diagnosis of life threatening diseases including pandemics. Machine learning and deep learning methodologies on evolving accurate and precise early detection and medical diagnosis systems. Information presented in an accessible way for students, researchers and medical practitioners. The volume would come to the benefit of both post-graduate students and aspiring researchers in the field of medical informatics, computer science and electronics and communication engineering. In addition, the volume is also intended to serve as a guiding factor for the medical practitioners and radiologists in accurate diagnosis of diseases.