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This book presents a series of applications of different techniques found in Industry 4.0 with relation to productivity, continuous improvement, quality, decision systems, software development, and automation systems. The techniques used throughout this book allow the reader to replicate the results obtained towards different types of companies that wish to undertake in the new era of the digital industrial revolution. This book can also help students from different areas of engineering to understand how the use of new technologies is applied to solve current relevant problems and how they give the possibility of constant innovation in the different industrial sectors. This is accomplished through the analysis of illustrative case studies, descriptive methodologies and structured insights that are provided through the different considered techniques.
This book includes chapters related to the analysis of cultural differences as a tool to enrich tacit knowledge and make processes more efficient, the factors that influence job satisfaction and the value of social capital as a competitive strategy to achieve productivity and competitiveness of organizations, in addition to research of the utmost importance to discover the facets of the diamond with respect to the symbolic capital of the organizations where Generation Z will work and how it will discover the best time to establish an innovation ecosystem that will influence its work trajectory. Industry 4.0 requires a major paradigm shift, since human capital is a source of competitive advantage. Being competitive enables to a company, a region, a society or a country the power to advance in different areas, contributing to the benefit of a social group, therefore, and organizations need to make efforts that lead to adding value and generate a competitive advantage. Industrial applications based on artificial intelligence can change our lives in just one generation. The chapters in this book show progress and challenges related to real-world applications, as well as the need to strengthen human capital to achieve systemic and comprehensive competitiveness required in the XXI century.
This book is intended to help management and other interested parties such as engineers, to understand the state of the art when it comes to the intersection between AI and Industry 4.0 and get them to realise the huge possibilities which can be unleashed by the intersection of these two fields. We have heard a lot about Industry 4.0, but most of the time, it focuses mainly on automation. In this book, the authors are going a step further by exploring advanced applications of Artificial Intelligence (AI) techniques, ranging from the use of deep learning algorithms in order to make predictions, up to an implementation of a full-blown Digital Triplet system. The scope of the book is to showcase what is currently brewing in the labs with the hope of migrating these technologies towards the factory floors. Chairpersons and CEOs must read these papers if they want to stay at the forefront of the game, ahead of their competition, while also saving huge sums of money in the process.
This five-volume set clearly manifests the great significance of these key technologies for the new economies of the new millennium. The discussions provide a wealth of practical ideas intended to foster innovation in thought and, consequently, in the further development of technology. Together, they comprise a significant and uniquely comprehensive reference source for research workers, practitioners, computer scientists, academics, students, and others on the international scene for years to come.
This book represents the experience of successful researchers from four continents on a broad range of intelligent systems, and it hints how to avoid anticipated conflicts and problems during multidisciplinary innovative research from Industry 4.0 and/or Internet of Things through modern machine learning, and software agent applications to open data science big data/advance analytics/visual analytics/text mining/web mining/knowledge discovery/deep data mining issues. The considered intelligent part is essential in most smart/control systems, cyber security, bioinformatics, virtual reality, robotics, mathematical modelling projects, and its significance rapidly increases in other technologies. Theoretical foundations of fuzzy sets, mathematical and non-classical logic also are rapidly developing.
Intelligent systems research is a multidisciplinary field that focuses on the development of systems that can perceive, reason, and act autonomously. This can include areas such as machine learning, artificial intelligence, natural language processing, and robotics. The goal of intelligent systems research is to develop systems that can understand, learn from, and adapt to their environment, to perform tasks that would typically require human intelligence. Business and innovation research is an interdisciplinary field that examines how organizations can create, develop, and implement new ideas, products, and services. This can include areas such as organizational behaviour, strategic management, and marketing. The goal of business and innovation research is to understand how organizations can foster an environment that encourages creativity and innovation, and how they can develop and implement new ideas in a way that leads to success. Intelligent Systems, Business and Innovation Research is a research area that brings together these two fields to study the use of intelligent systems and technologies in the business context to drive innovation, improve operational efficiency and effectiveness of the organization. The book aims to understand how intelligent systems can be used in business applications, how to design and implement them, how to manage the associated challenges, and how to leverage them to foster innovation, create new business models, and gain competitive advantage. It offers guidance on how to navigate potential conflicts and challenges that may arise during multidisciplinary research in areas such as Industry 4.0, Internet of Things, modern machine learning, software agent applications, and data science. The book focuses on the various fields in which intelligent systems play a critical role in enabling the development of advanced technologies that can perform tasks that would typically require human intelligence. For example, in smart/control systems, intelligent algorithms can be used to optimize the performance of machines and devices, while in cyber security, they can be used to protect networks and data from cyber-attacks. In bioinformatics, intelligent systems can be used to analyse large amounts of biological data, while in virtual reality and robotics, they can be used to create realistic and responsive simulations and automatons. Additionally, the book also highlights the rapidly advancing theoretical foundations of fuzzy sets, mathematical logic, and non-classical logic. These are important theoretical frameworks for the development of intelligent systems, as they provide the foundation for the representation and manipulation of uncertainty, complexity, and imprecision. These theoretical foundations are essential in the development of intelligent systems that can make decisions and perform tasks in uncertain, complex, and dynamic environments.
This book presents a broad variety of different contemporary IT methods and applications in Intelligent Systems is displayed. Every book chapter represents a detailed, specific, far reaching and original re-search in a respective scientific and practical field. However, all of the chapters share the common point of strong similarity in a sense of being innovative, applicable and mutually compatible with each other. In other words, the methods from the different chapters can be viewed as bricks for building the next generation “thinking machines” as well as for other futuristic logical applications that are rapidly changing our world nowadays.
The book explores the concepts and challenges in developing novel approaches using the Internet of Things, intelligent systems, machine intelligence systems, and data analytics in various industrial sectors such as manufacturing, smart agriculture, smart cities, food processing, environment, defense, stock market and healthcare. Further, it discusses the latest improvements in the industrial sectors using machine intelligence learning and intelligent systems techniques, especially robotics. Features: • Highlights case studies and solutions to industrial problems using machine learning and intelligent systems. • Covers applications in smart agriculture, smart healthcare, intelligent machines for disaster management, and smart manufacturing. • Provides the latest methodologies using machine intelligence systems in the early forecasting of weather. • Examines the research challenges and identifies the gaps in data collection and data analysis, especially imagery, signal, and speech. • Provides applications of digitization and smart processing using the Internet of Things and effective intelligent agent systems in manufacturing. • Discusses a systematic and exhaustive analysis of intelligent software effort estimation models. It will serve as an ideal reference text for graduate students, post-graduate students, IT Professionals, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.
This book presents recent advances in the field of intelligent systems. Composed of fourteen selected chapters, it covers a wide range of research that varies from applications in industrial data science to those in applied science. Today the word INNOVATION is more and more connected with the words INTELLIGENT and SECURITY, as such the book discusses the theory and applications of hot topics such as big data, education applications of robots with different levels of autonomy, knowledge-based modeling and control of complex dynamical systems, sign-based synthesis of behavior, security issues with intelligent systems, innovative intelligent control design, neuromorphic computation, data-driven classification, intelligent modeling and measurement innovations, multisensor data association, personal education assistants, a modern production architecture, study of peer review and scientometrics, intelligent research on bug report data, and clustering non-Gaussian data. The broad and varied research discussed represents the mainstream of contemporary intelligent innovations that are slowly but surely changing the world.