The manufacturing industry is facing significant challenges as it grapples with the need to adapt to changing customer expectations and embrace emerging technologies. Manufacturers globally were making some progress in digital transformation before the pandemic hit, but the disruptions caused by the global crisis have highlighted the urgent need for innovation and resilience. There is a growing gap between traditional manufacturing practices and the demands of the digital age, necessitating a shift towards technology-driven approaches. Emerging Technologies and Digital Transformation in the Manufacturing Industry offers a comprehensive solution to the challenges faced by manufacturers. Edited by esteemed academic scholars, this book brings together a collection of high-quality chapters that delve into the manufacturing process from a technology-driven perspective. By exploring the use of Internet-connected machinery, artificial intelligence, sensors, and other emerging technologies, the book presents practical approaches that can lead to increased efficiency, productivity, and overall better performance. The book addresses key topics such as machine learning, blockchain technologies, big data, and the Industrial Internet of Things (IIoT), among others. It also delves into transformative concepts like digital twins, extended reality, and the impact of 5G and edge computing. With its wide-ranging coverage and authoritative contributors, this book serves as a vital resource for graduate and undergraduate students, researchers, and IT employees working in manufacturing plants. By providing insights and practical guidance, it equips readers with the knowledge needed to navigate the complexities of digital transformation in manufacturing and seize the opportunities presented by emerging technologies. Emerging Technologies and Digital Transformation in the Manufacturing Industry offers a timely and relevant solution to the pressing challenges faced by the manufacturing industry. By bridging the gap between traditional manufacturing and intelligent manufacturing, the book helps manufacturers adapt and thrive in the digital age. With its academic rigor and practical applicability, this book is poised to make a significant impact on the manufacturing IT community and academic scholars worldwide. Whether you are a student, researcher, or IT professional, this book provides the necessary insights and tools to navigate the digital transformation journey and embrace emerging technologies for a more efficient and resilient manufacturing future.

A key focus in recent years has been on sustainable development and promoting environmentally conscious practices. In today's rapidly evolving technological world, it is important to consider how technology can be applied to solve problems across disciplines and fields in these areas. Further study is needed in order to understand how technology can be applied to sustainability and the best practices, considerations, and challenges that follow. Futuristic Trends for Sustainable Development and Sustainable Ecosystems discusses recent advances and innovative research in the area of information and communication technology for sustainable development and covers practices in several artificial intelligence fields such as knowledge representation and reasoning, natural language processing, machine learning, and the semantic web. Covering topics such as blockchain, deep learning, and renewable energy, this reference work is ideal for computer scientists, industry professionals, researchers, academicians, scholars, instructors, and students.

Provides a proficient guide on the relationship between AI and healthcare and how AI is changing all aspects of the health care industry Covers how deep learning will help in the diagnosis and the prediction of disease spread Presents a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing Highlights various challenges in applying the deep learning in health care Promotes research in deep learning application in understanding the biomedical process

Existing research papers do not have complete information in depth about the Semantic AI in Knowledge Graphs. This book has all the basic information required to gain in-depth knowledge of this field. Covers neuro-symbolic AI, explainable AI and deep learning to knowledge discover and mining, and knowledge representation and reasoning.

Provides a proficient guide on the relationship between AI and healthcare and how AI is changing all aspects of the health care industry Covers how deep learning will help in the diagnosis and the prediction of disease spread Presents a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing Highlights various challenges in applying the deep learning in health care Promotes research in deep learning application in understanding the biomedical process

This book constitutes the refereed proceedings of the 5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: Machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.

Since the development of the semantic web, knowledge graphs (KGs) have been used by search engines, knowledge-engines and question-answering services as well as social networks. A knowledge graph, also known as a semantic network, represents and illustrates a network of real-world entities such as objects, events, situations, or concepts and the relationships between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term "knowledge graph". Knowledge graphs structure the information of entities, their properties and the relation between them. Personal knowledge graphs (PKG) encode the same information at an individual level and therefore vary widely. PKGs require the processing of each person's individual information and is constructed in an automated fashion. Once a PKG is constructed, it will be integrated in broader purpose KGs. A PKG is a representation of all relevant common-sense knowledge and personal data for a user and can support the development of innovative applications such as a digitalized personalized coach. It empowers stakeholders to make more effective decisions. This book explores in a structured manner the global advanced research around PKGs to support the development of innovative digitalized personalized applications such as personal banking, personalized book-keeping, daily health-related activities monitoring and goal management tracking. The authors present methodologies, tools and applications including innovative topics tailored for PKGs such as named entity recognition and linking, construction approaches, modelling of personalization and context-awareness, evaluation approaches, relation extraction techniques, query answering in user specific knowledge graphs, knowledge representation and reasoning (KRR), visualization tools, integration tools and techniques, and fact summarization. The book provides systematic coverage of this complex topic for researchers, scientists and engineers in both industry and academia working in data science, ICTs, knowledge engineering, semantic web, reasoning, information retrieval, and machine and deep learning with a focus on knowledge graphs. Advanced students with an interest in the field will also find this to be a useful resource.

This book highlights recent research on soft computing, pattern recognition and biologically inspired computing. It presents 24 selected papers from the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) and 5 papers from the 11th World Congress on Nature and Biologically Inspired Computing (NaBIC 2019), held at Vardhaman College of Engineering, Hyderabad, India, on December 13-15, 2019. SoCPaR-NaBIC is a premier conference and brings together researchers, engineers and practitioners whose work involves soft computing and bio-inspired computing, as well as their industrial and real-world applications. Including contributions by authors from 15 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.

This book constitutes selected and revised papers presented at the First International Conference on Electronic Governance with Emerging Technologies, EGETC 2022, held in Tampico, Mexico, in September 2022. The 15 full papers and 2 short papers presented were thoroughly reviewed and selected from the 54 submissions. This volume focuses on the recent developmentsin the domain of eGovernment and governance of digital organizations also aims to shed light on the emerging research trends and their applications.

This book constitutes the proceedings of the 4th Iberoamerican Conference and third Indo-American Conference on Knowledge Graphs and Semantic Web, KGSWC 2022, which took place in Madrid, Spain, in November 2022. The 22 full and 3 short research papers presented in this volume were carefully reviewed and selected from 63 submissions. The papers cover topics related to software and its engineering, software creation and management, Emerging technologies, Analysis and design of emerging devices and systems, Emerging tools and methodologies and others.

This book constitutes the thoroughly refereed proceedings of the Third Iberoamerican Conference, KGSWC 2021, held in Kingsville, Texas, USA, in November 2021.* The 22 full and 2 short papers presented were carefully reviewed and selected from 85 submissions. The papers cover topics related to software and its engineering, information systems, software creation and management, World Wide Web, web data description languages, and others. *Due to the Covid-19 pandemic the conference was held virtually.

A key focus in recent years has been on sustainable development and promoting environmentally conscious practices. In today's rapidly evolving technological world, it is important to consider how technology can be applied to solve problems across disciplines and fields in these areas. Further study is needed in order to understand how technology can be applied to sustainability and the best practices, considerations, and challenges that follow. Futuristic Trends for Sustainable Development and Sustainable Ecosystems discusses recent advances and innovative research in the area of information and communication technology for sustainable development and covers practices in several artificial intelligence fields such as knowledge representation and reasoning, natural language processing, machine learning, and the semantic web. Covering topics such as blockchain, deep learning, and renewable energy, this reference work is ideal for computer scientists, industry professionals, researchers, academicians, scholars, instructors, and students.