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The book underscores AI's transformative impact on reshaping physical, digital, and biological boundaries, converging with technologies like robotics, IoT, 3D printing, genetic engineering, and quantum computing—termed Web 3.0 Industrial Revolution. This global revolution integrates advanced production techniques beyond connected machines, extending into gene sequencing, nanotechnology, renewable energies, and quantum computing. The book's main goals include providing a collaborative platform for academia and industry researchers to share contributions and shape the future through knowledge exchange. Recognizing recent progress driven by increased computing power, it highlights the positive impact of digital technology—AI, IoT, AR/VR, Additive Manufacturing, CPS, cloud computing, and robotics—on industrial efficiency and quality. Revolutionary AI Fusion: AI revolutionizes by blending physical, digital, and biological boundaries through cutting-edge technologies like robotics, IoT, 3D printing, genetic engineering, and quantum computing. Global Manufacturing Cooperation: AI creates a collaborative landscape where virtual and physical systems flexibly cooperate on a global scale. AI's Diverse Impact: Beyond smart machines, AI drives breakthroughs in gene sequencing, nanotechnology, renewable energies, and quantum computing, distinguishing it from prior industrial revolutions. Progress and Digital Interface: Recent progress, powered by computing advancements, boosts industrial efficiency. The digital technology interface (AI, IoT, AR/VR, 3D Printing, CPS, CC, Robotics) significantly impacts industrial performance. In conclusion, AI spearheads a transformative revolution, redefining the boundaries of the physical, digital, and biological realms. The fusion of AI with Web 3.0 Industrial Revolution, integrating advanced production techniques and global manufacturing cooperation, surpassing past industrial shifts. The book aims to be a collaborative platform for academia and industry researchers, fostering knowledge exchange to shape the future. In AI-driven manufacturing within Web 3.0, a paradigm shift envisions maximum output with minimal resource use. Coupled with 'Digital Reality,' it transforms business practices, consumer behaviour, and employment dynamics, redistributing wealth toward innovation and technology.
Multimedia and its rich semantics are profligate in today’s digital environment. Databases and content management systems serve as essential tools to ensure that the endless supply of multimedia content are indexed and remain accessible to end users. Methods and Innovations for Multimedia Database Content Management highlights original research on new theories, algorithms, technologies, system design, and implementation in multimedia data engineering and management with an emphasis on automatic indexing, tagging, high-order ranking, and rule mining. This book is an ideal resource for university researchers, scientists, industry professionals, software engineers and graduate students.
This book focuses on different applications of multimedia with supervised and unsupervised data engineering in the modern world. It includes AI-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, manufacturing, data science, automation in electronics industries, and many more relevant fields. Multimedia Data Processing and Computing provides a complete introduction to machine learning concepts, as well as practical guidance on how to use machine learning tools and techniques in real-world data engineering situations. It is divided into three sections. In this book on multimedia data engineering and machine learning, the reader will learn how to prepare inputs, interpret outputs, appraise discoveries, and employ algorithmic strategies that are at the heart of successful data mining. The chapters focus on the use of various machine learning algorithms, neural net- work algorithms, evolutionary techniques, fuzzy logic techniques, and deep learning techniques through projects, so that the reader can easily understand not only the concept of different algorithms but also the real-world implementation of the algorithms using IoT devices. The authors bring together concepts, ideas, paradigms, tools, methodologies, and strategies that span both supervised and unsupervised engineering, with a particular emphasis on multimedia data engineering. The authors also emphasize the need for developing a foundation of machine learning expertise in order to deal with a variety of real-world case studies in a variety of sectors such as biological communication systems, healthcare, security, finance, and economics, among others. Finally, the book also presents real-world case studies from machine learning ecosystems to demonstrate the necessary machine learning skills to become a successful practitioner. The primary users for the book include undergraduate and postgraduate students, researchers, academicians, specialists, and practitioners in computer science and engineering.
This book provides comprehensive coverage of the major aspects in designing, implementing, and deploying wireless sensor networks by discussing present research on WSNs and their applications in various disciplines. It familiarizes readers with the current state of WSNs and how such networks can be improved to achieve effectiveness and efficiency. It starts with a detailed introduction of wireless sensor networks and their applications and proceeds with layered architecture of WSNs. It also addresses prominent issues such as mobility, heterogeneity, fault-tolerance, intermittent connectivity, and cross layer optimization along with a number of existing solutions to stimulate future research.
Managing Information Technology Resources in Organizations in the Next Millennium contains more than 200 unique perspectives on numerous timely issues of managing information technology in organizations around the world. This book, featuring the latest research and applied IT practices, is a valuable source in support of teaching and research agendas.
​Engineering and infrastructure assets maintain the lifeline of economies. It is, therefore, critical to manage these assets in such a way that they provide a consistent level of service throughout their lifecycle. Management of asset lifecycle, however, is information intensive and utilises a plethora of information systems. The role of theses systems in asset management is much more profound. It extends beyond the organizational boundaries and addresses business relationships with external stakeholders to deliver enhanced level of business outcomes. In doing so information systems are not only required to translate business strategic considerations into action, but are also expected to produce learnings and feedback that informs business strategy and aids in strategic reorientation.
The concept of concurrent engineering (CE) was first developed in the 1980s. Now often referred to as transdiciplinary engineering, it is based on the idea that different phases of a product life cycle should be conducted concurrently and initiated as early as possible within the Product Creation Process (PCP). The main goal of CE is to increase the efficiency and effectiveness of the PCP and reduce errors in later phases, as well as incorporating considerations – including environmental implications – for the full lifecycle of the product. It has become a substantive methodology in many industries, and has also been adopted in the development of new services and service support. This book presents the proceedings of the 25th ISPE Inc. International Conference on Transdisciplinary Engineering, held in Modena, Italy, in July 2018. This international conference attracts researchers, industry experts, students, and government representatives interested in recent transdisciplinary engineering research, advancements and applications. The book contains 120 peer-reviewed papers, selected from 259 submissions from all continents of the world, ranging from the theoretical and conceptual to papers addressing industrial best practice, and is divided into 11 sections reflecting the themes addressed in the conference program and addressing topics as diverse as industry 4.0 and smart manufacturing; human-centered design; modeling, simulation and virtual design; and knowledge and data management among others. With an overview of the latest research results, product creation processes and related methodologies, this book will be of interest to researchers, design practitioners and educators alike.
As today's organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages.Pre
The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases