Intelligence continues to undergo significant changes at a remarkable pace, notably developments related to “Big Data,” surveillance, and cyber. Intelligence today involves multiagency, multinational, multidisciplinary, multidomain information sharing and sense-making, conducted by commerce, academic, government, civil society, media, law enforcement, military, and nongovernmental/nonprofit organizations. Increasingly complex systems, including interrelated technical dimensions, are central to modern defense systems. Intelligence Engineering: Operating Beyond the Conventional provides a new framework for generating analysis, exploring how systems to system-of-systems can be harnessed both for and into the future. Intelligence engineering (IE) involves the use of scientific and technical knowledge to artfully create, operate, maintain, and dismantle complex devices, machines, structures, systems, and processes that support and/or disrupt human endeavor occurring in the intelligence context. Spanning both human and technical intelligence realms, IE includes the collection and analysis of information that is of military and/or political value, and that relates to international relations, defense, and national security. Strategic Futures, risk management across to resilience concerns, are similarly engaged.

The discipline of design studies applies various technologies, from basic theory to application systems, while intelligence engineering encompasses computer-aided industrial design, human-factor design, and greenhouse design, and plays a major part within design science. Intelligence engineering technologies also include topics from theory and application, such as computational technologies, sensing technologies, and video detection. This book presents the proceedings of DSIE2023, the 2023 International Symposium on Design Studies and Intelligence Engineering, held on 28 & 29 October 2023 in Hangzhou, China. The conference provides a platform for professionals and researchers from industry and academia to present and discuss recent advances in the fields of design studies and intelligence engineering. It also fosters cooperation among the organizations and researchers involved in these overlapping fields, and invites internationally renowned professors to further explore these topics in some depth, providing the opportunity for them to discuss the technical presentations with conference participants. In all, 275 submissions were received for the conference, 105 of which were accepted after thorough review by 3 or 4 referees for presentation at the conference and inclusion here. Providing a valuable overview of the latest developments, the book will be of interest to all those working in the fields of design studies and intelligence engineering.

An analysis of different concepts and case studies in engineering disciplines such as chemical, civil, electrical, telecommunications and mechanical engineering, demonstrating how engineering systems and processes can leverage the power of AI to drive and achieve the UN SDGs.

When men of knowledge impart this knowledge, I do not mean they will convince your reason. I mean they will awaken in you the faith that it is so. - Sri Krishna, Bhagavadgita BACKGROUND The use of computers has led to significant productivity increases in the en gineering industry. Most ofthe computer-aided engineering applications were . restricted to algorithmic computations, such as finite element programs and circuit analysis programs. However, a number ofproblems encountered in en gineering are not amenable to purely algorithmic solutions. These problems are often ill-structured; the term ill-structured problems is used here to de note problems that do not have a clearly defined algorithmic solution. An experienced engineer deals with these ill-structured problems using his/her judgment and experience. The knowledge-based systems (KBS) technology, which emerged out of research in artificial intelligence (AI), offers a method ologyto solve these ill-structuredengineering problems. The emergenceofthe KBS technology can be viewed as the knowledge revolution: other important events that led to increased productivity are the industrial revolution (17th century); the invention of the transistor and associated developments (first half of the 20th century); and the world-wide web (towards the end of the 20th century). Kurzweil, in a lecture at M. LT on December 3, 1987, linked the progress of automation to two industrial revolutions: the first industrial PREFACE xxxii revolution leveraged our physical capabilities, whereas the second industrial revolution - the knowledge revolution - is expected leverage oUr mental ca pabilities.

Artificial Intelligence (AI) is defined as the simulation of human intelligence through the mimicking of the human brain for analysis, modeling, and decision‐making. Science and engineering problem solving requires modeling of physical phenomena, and humans approach the solution of scientific and engineering problems differently from other problems. Artificial Intelligence for Science and Engineering Applications addresses the unique differences in how AI should be developed and used in science and engineering. Through the inclusion of definitions and detailed examples, this book describes the actual and realistic requirements as well as what characteristics must be avoided for correct and successful science and engineering applications of AI. This book: • Offers a brief history of AI and covers science and engineering applications • Explores the modeling of physical phenomena using AI • Discusses explainable AI (XAI) applications • Covers the ethics of AI in science and engineering • Features real‐world case studies Offering a probing view into the unique nature of scientific and engineering exploration, this book will be of interest to generalists and experts looking to expand their understanding of how AI can better tackle and advance technology and developments in scientific and engineering disciplines.

This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: ​ Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and techniques for xAI, Causality and Explainable AI; Explainable AI in Finance, cybersecurity, health-care and biomedicine. Part II: Surveys, benchmarks, visual representations and applications for xAI; xAI for decision-making and human-AI collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks; Actionable eXplainable AI, Semantics and explainability, and Explanations for Advice-Giving Systems. Part III: xAI for time series and Natural Language Processing; Human-centered explanations and xAI for Trustworthy and Responsible AI; Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI.

AI expert and consultant William Taylor provides a practical explanation of the parts of AI research that are ready for use by anyone with an engineering degree and that can help engineers do their jobs better.

This volume is a collection of 19 chapters on intelligent engineering systems written by respectable experts of the fields. The book consists of three parts. The first part is devoted to the foundational aspects of computational intelligence. It consists of 8 chapters that include studies in genetic algorithms, fuzzy logic connectives, enhanced intelligence in product models, nature-inspired optimization technologies, particle swarm optimization, evolution algorithms, model complexity of neural networks, and fitness landscape analysis. The second part contains contributions to intelligent computation in networks, presented in 5 chapters. The covered subjects include the application of self-organizing maps for early detection of denial of service attacks, combating security threats via immunity and adaptability in cognitive radio networks, novel modifications in WSN network design for improved SNR and reliability, a conceptual framework for the design of audio based cognitive infocommunication channels, and a case study on the advantages of fuzzy and anytime signal- and image processing techniques. Computational intelligence represents a widely spread interdisciplinary research area with many applications in various disciplines including engineering, medicine, technology, environment, among others. Therefore, third part of this book consists of 6 chapters on applications. This is a very important part of the volume because the reader can find in it a wide range of fields where computational intelligence plays a significant role.

The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance. Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/

Computational Intelligence and Blockchain in Complex Systems provides readers with a guide to understanding the dynamics of AI, Machine Learning, and Computational Intelligence in Blockchain, and how these rapidly developing technologies are revolutionizing a variety of interdisciplinary research fields and applications. The book examines the role of Computational Intelligence and Machine Learning in the development of algorithms to deploy Blockchain technology across a number of applications, including healthcare, insurance, smart grid, smart contracts, digital currency, precision agriculture, and supply chain. The authors cover the unique and developing intersection between cyber security and Blockchain in modern networks, as well as in-depth studies on cyber security challenges and multidisciplinary methods in modern Blockchain networks. Readers will find mathematical equations throughout the book as part of the underlying concepts and foundational methods, especially the complex algorithms involved in Blockchain security aspects for hashing, coding, and decoding. Computational Intelligence and Blockchain in Complex Systems provides readers with the most in-depth technical guide to the intersection of Computational Intelligence and Blockchain, two of the most important technologies for the development of next generation complex systems. - Covers the research issues and concepts of machine learning technology in blockchain - Provides in-depth information about handling and managing personal data by machine learning methods in blockchain - Helps readers understand the links between computational intelligence, blockchain, complex systems, and developing secure applications in multidisciplinary sectors