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The book studies emerging and sustaining technologies for applications of Industry 5.0 to develop technological solutions to address numerous real-life challenges to solve sustainable development-related issues. It identifies limitations, pitfalls, and open research questions in industry 5.0, discusses real-time problems, and challenges with equivalent solutions with a focus on sustainable growth to develop, humanization and environmentally friendly intelligent system applications. It analyses applications enabled by Industry 5.0 such as healthcare, supply chain, smart framing, remote sensing, production in manufacturing, and cloud manufacturing. It also includes the difficulties and problems posed by the organization between robots and humans on the assembly line to maintain sustainability. Addresses key challenges in implementing intelligent systems in IoT-based applications, including issues ranging from cost and energy efficiency to availability and quality of service Explores the technologies to allow human-machine association and its impact on consumption and sustainability Provides sustainable solutions to emerging industrial problems, especially in healthcare, manufacturing, remote sensing, environmental engineering Examines need for data pre-processing, classification & prediction, Cluster Analysis, Mining Multimedia, Text, and Web Data, Advanced machine learning techniques for scientific programming in Industry Presents success stories in the form of case studies of IIoT, IIoRT, Big Data, Intelligent Systems, Deep Learning in Industry 5.0 era The text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology, especially for professionals and researchers interested in the technological side of sustainable development.

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.

Dieses Buch ist eine Open-Access-Publikation unter einer CC BY 4.0 Lizenz.Unter dem Titel „Digitalisierung souverän gestalten“ wirft der zweite Themenband des Instituts für Innovation und Technik (iit) einen schlaglichtartigen Blick auf die Transformation von digitalen Wertschöpfungsnetzwerken und deren Potenziale. In insgesamt elf Beiträgen beleuchten Expert:innen aus verschiedenen Disziplinen Ansätze hybrider und humanzentrierter künstlicher Intelligenz (KI), praxisnahe Konzepte für eine zielgerichtete Kompetenzentwicklung in Betrieben sowie digitale Innovationen im Werkzeug- und Formenbau. Neben zahlreichen Use Cases werden dabei auch Antworten auf juristische Fragen zur Regulierung und Zertifizierung von KI gegeben.

Artificial Intelligence in Mechanical and Industrial Engineering offers a unified platform for the dissemination of basic and applied knowledge on the integration of artificial intelligence within the realm of mechanical and industrial engineering. The book covers the tools and information needed to build successful careers and a source of knowledge for those working with AI within these domains.The book offers a systematic approach to explicate fundamentals as well as recent advances. It incorporates various case studies for major topics as well as numerous examples. It will also include real-time intelligent automation and associated supporting methodologies and techniques, and cover decision-support systems, as well as applications of Chaos Theory and Fractals.The book will give scientists, researchers, instructors, students, and practitioners the tools and information needed to build successful careers and to be an impetus to advancements in next-generation mechanical and industrial engineering domains.

Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers. Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints. The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data.

This book offers the latest research advances in the field of Industry 4.0, focusing on enabling technologies for its deployment in a comprehensive way. This book offers successful implementation of technologies such as artificial intelligence, augmented and virtual reality, autonomous and collaborative robots, cloud computing, and up-to-date guidelines. It investigates how the technologies and principles surrounding Industry 4.0 (e.g., interoperability, decentralized decisions, information transparency, etc.) serve as support for organizational routines and workers (and vice versa). Included are applications of technologies for different sectors and environments as well as for the supply chain management. It also offers a domestic and international mix of case studies that spotlight successes and failures. Features Provides a historical review of Industry 4.0 and its roots Discusses the applications of technologies in different sectors and environments (e.g., public vs. private) Presents key enabling technologies for successful implementation in any industrial and service environment Offers case studies of successes and failures to illustrate how to put theory into practice Investigates how technologies serve as support for organizational routines and workers

In today's developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.