Semantic Models in IoT and eHealth Applications explores the key role of semantic web modeling in eHealth technologies, including remote monitoring, mobile health, cloud data and biomedical ontologies. The book explores different challenges and issues through the lens of various case studies of healthcare systems currently adopting these technologies. Chapters introduce the concepts of semantic interoperability within a healthcare model setting and explore how semantic representation is key to classifying, analyzing and understanding the massive amounts of biomedical data being generated by connected medical devices. Continuous health monitoring is a strong solution which can provide eHealth services to a community through the use of IoT-based devices that collect sensor data for efficient health diagnosis, monitoring and treatment. All of this collected data needs to be represented in the form of ontologies which are considered the cornerstone of the Semantic Web for knowledge sharing, information integration and information extraction. - Presents comprehensive coverage of advances in the application of semantic web in the field of eHealth - Explores different challenges and issues through various case studies of healthcare systems that are adopting semantic web technologies - Covers applications across a range of eHealth technologies, including remote monitoring and mobile health

Semantic Models in IoT and eHealth Applications explores the key role of semantic web modeling in eHealth technologies, including remote monitoring, mobile health, cloud data and biomedical ontologies. The book explores different challenges and issues through the lens of various case studies of healthcare systems currently adopting these technologies. Chapters introduce the concepts of semantic interoperability within a healthcare model setting and explore how semantic representation is key to classifying, analyzing and understanding the massive amounts of biomedical data being generated by connected medical devices. Continuous health monitoring is a strong solution which can provide eHealth services to a community through the use of IoT-based devices that collect sensor data for efficient health diagnosis, monitoring and treatment. All of this collected data needs to be represented in the form of ontologies which are considered the cornerstone of the Semantic Web for knowledge sharing, information integration and information extraction. Presents comprehensive coverage of advances in the application of semantic web in the field of eHealth Explores different challenges and issues through various case studies of healthcare systems that are adopting semantic web technologies Covers applications across a range of eHealth technologies, including remote monitoring and mobile health

The data that must be processed in healthcare includes text, numbers, statistics, and images, and healthcare systems are continuously acquiring novel data from cutting-edge technologies like wearable devices. Semantic intelligence technologies, such as artificial intelligence, machine learning, and the internet of things, together with the hybrid methodologies which combine these approaches, are central to the development of the intelligent, knowledge-based systems now used in healthcare. This book, Roles and Challenges of Semantic Intelligence in Healthcare Cognitive Computing explores those emerging fields of science and technology in which cognitive computing techniques offer the effective solutions poised to impact healthcare in the foreseeable future, minimizing errors and improving the effectiveness of personalized care models. The book assesses the current landscape, and identifies the roles and challenges of integrating cognitive computing techniques into the widespread adoption of innovative smart healthcare solutions. Each chapter is the result of collaboration by experts from various domains, and provides a detailed overview of the potential offered by new technologies in the field. A wide spectrum of topics and emerging trends are covered, reflecting the multidisciplinary nature of healthcare and cognitive computing and including digital twins, eXplainable AI, AI-based decision-support systems in intensive care, and culinary healthcare, as well as the semantic internet of things (SIoT), natural language processing, and deep learning and graph models. The book presents new ideas which will facilitate collaboration among the different disciplines involved, and will be of interest to all those working in this rapidly evolving field.

This book constitutes the refereed proceedings of the 5th Iberoamerican Conference and 4th Indo-American Conference on Knowledge Graphs and Semantic Web, KGSWC 2023, held jointly in Zaragoza, Spain, during November 13–15, 2023. The 18 full and 2 short papers presented were carefully reviewed and selected from 50 submissions. They focus on the following topics: knowledge representation; natural language processing/text mining; and machine/deep learning research.

This book is a guide to the combination of the Internet of Things (IoT) and the Semantic Web, covering a variety of tools, technologies and applications that serve the myriad needs of the researchers in this field. It provides a multi dimensional view of the concepts, tools, techniques and issues that are involved in the development of semantics for the Web of Things. The various aspects studied in this book include Multi-Model Multi-Platform (SHM3P) databases for the IoT, clustering techniques for discovery services for the semantic IoT, dynamic security testing methods for the Semantic Web of Things, Semantic Web-enabled IoT integration for a smart city, IoT security issues, the role of the Semantic Web of Things in Industry 4.0, the integration of the Semantic Web and the IoT for e-health, smart healthcare systems to monitor patients, Semantic Web-based ontologies for the water domain, science fiction and searching for a job.

With the advancements of semantic web, ontology has become the crucial mechanism for representing concepts in various domains. For research and dispersal of customized healthcare services, a major challenge is to efficiently retrieve and analyze individual patient data from a large volume of heterogeneous data over a long time span. This requirement demands effective ontology-based information retrieval approaches for clinical information systems so that the pertinent information can be mined from large amount of distributed data. This unique and groundbreaking book highlights the key advances in ontology-based information retrieval techniques being applied in the healthcare domain and covers the following areas: Semantic data integration in e-health care systems Keyword-based medical information retrieval Ontology-based query retrieval support for e-health implementation Ontologies as a database management system technology for medical information retrieval Information integration using contextual knowledge and ontology merging Collaborative ontology-based information indexing and retrieval in health informatics An ontology-based text mining framework for vulnerability assessment in health and social care An ontology-based multi-agent system for matchmaking patient healthcare monitoring A multi-agent system for querying heterogeneous data sources with ontologies for reducing cost of customized healthcare systems A methodology for ontology based multi agent systems development Ontology based systems for clinical systems: validity, ethics and regulation

Artificial intelligence (AI) has shown promise as an effective tool in disaster preparedness and response, providing a unique perspective on some of the most urgent health challenges. Rapid advances in AI technology can potentially revolutionize the way how we respond to emergencies and disasters that affect the world's health, including early warning systems, resource allocation, and real-time decision-making. This Research Topic aims to explore the latest developments in AI and its applications in global health and disaster response, providing a comprehensive overview of the potential and challenges of AI in improving health outcomes in crises. This Research Topic will bring together leading researchers, practitioners, and policymakers in global health and disaster response to share their experiences and insights on how AI can be leveraged to improve response efforts and enhance healthcare delivery.

With the increase in the development of the advanced cellular communication system, it is assumed that several sectors, such as the health industry, education, transport industry, business model, and so on, will rapidly grow. However, the requirements of the above-mentioned sectors are different and difficult to fulfill. Hence, 5G will be integral to several networks and will also need a unique management system for its successful rollout around the globe. 5G-Based Smart Hospitals and Healthcare Systems: Evaluation, Integration, and Deployment provides an overview of the role of advanced technologies in transforming the healthcare industry. It emphasizes the technical requirements of smart hospitals and the technologies associated with them along with explaining how technologies such as IoT, machine learning, and AI can be integrated with smart hospitals and 5G networks. The book evaluates several concerns such as privacy of data, infrastructure costs, and regular upgradability of technologies. Since the storage of information is a major concern with the implantation of 5G-based hospitals, this book will specifically address those issues along with examining the potential pitfalls of 5G-based hospitals and the factors that cause their failures. This book specifically targets professionals, academicians, engineers, researchers, management firms, technical institutes, R&D establishments, and individuals researching in the fields of 5G, healthcare, medical sensors, IoT, big data, and related fields. The main objectives of this book are to accumulate state-of-the-art IoT, 5G, AI, and machine learning-based approaches for resolving healthcare problems.

Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing. - Provides the state-of-the-art in IoT Data Stream Processing, Semantic Data Enrichment, Reasoning and Knowledge - Covers extraction (Anomaly Detection) - Illustrates new, scalable and reliable processing techniques based on IoT stream technologies - Offers applications to new, real-time anomaly detection scenarios in the health domain