Fernando Ortiz Rodriguez – författare

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.

This edited book is a collection of quality research articles reporting research advances in the area of deep learning, IoT and urban computing. It describes new insights based on deep learning and IoT for urban computing and is useful for architects, engineers, policymakers, facility managers, academicians, and researchers who are interested in expanding their knowledge of the applications of deep learning trends involving urban computing.

This edited book is a collection of quality research articles reporting research advances in the area of deep learning, IoT and urban computing. It describes new insights based on deep learning and IoT for urban computing and is useful for architects, engineers, policymakers, facility managers, academicians, and researchers who are interested in expanding their knowledge of the applications of deep learning trends involving urban computing.

Recent combinations of semantic technology and artificial intelligence (AI) present new techniques to build intelligent systems that identify more precise results. Semantic AI in Knowledge Graphs locates itself at the forefront of this novel development, uncovering the role of machine learning to extend the knowledge graphs by graph mapping or corpus-based ontology learning.

Securing efficient results via the combination of symbolic AI and statistical AI such as entity extraction based on machine learning, text mining methods, semantic knowledge graphs, and related reasoning power, this book is the first of its kind to explore semantic AI and knowledge graphs. A range of topics are covered, from neuro-symbolic AI, explainable AI and deep learning to knowledge discovery and mining, and knowledge representation and reasoning.

A trailblazing exploration of semantic AI in knowledge graphs, this book is a significant contribution to both researchers in the field of AI and data mining as well as beginner academicians.

Recent combinations of semantic technology and artificial intelligence (AI) present new techniques to build intelligent systems that identify more precise results. Semantic AI in Knowledge Graphs locates itself at the forefront of this novel development, uncovering the role of machine learning to extend the knowledge graphs by graph mapping or corpus-based ontology learning.

Securing efficient results via the combination of symbolic AI and statistical AI such as entity extraction based on machine learning, text mining methods, semantic knowledge graphs, and related reasoning power, this book is the first of its kind to explore semantic AI and knowledge graphs. A range of topics are covered, from neuro-symbolic AI, explainable AI and deep learning to knowledge discovery and mining, and knowledge representation and reasoning.

A trailblazing exploration of semantic AI in knowledge graphs, this book is a significant contribution to both researchers in the field of AI and data mining as well as beginner academicians.

This book explores the transformative impact of drone technology and unmanned aerial systems (UAS) across diverse industries, from precision agriculture and logistics to disaster response and forensic investigations. It highlights how the integration of Artificial Intelligence (AI) into UAVs is addressing contemporary challenges, optimizing operations, and shaping the future of aerial systems. It offers technical methodologies, case studies, and a detailed roadmap for integrated drone forensics, making it an essential resource for understanding the dynamic sector of Intelligent Aerial Systems.

This reference book serves as a comprehensive guide for professionals, researchers, and scholars interested in the rapidly advancing world of drone technology.

This book explores the transformative impact of drone technology and unmanned aerial systems (UAS) across diverse industries, from precision agriculture and logistics to disaster response and forensic investigations. It highlights how the integration of Artificial Intelligence (AI) into UAVs is addressing contemporary challenges, optimizing operations, and shaping the future of aerial systems. It offers technical methodologies, case studies, and a detailed roadmap for integrated drone forensics, making it an essential resource for understanding the dynamic sector of Intelligent Aerial Systems.

This reference book serves as a comprehensive guide for professionals, researchers, and scholars interested in the rapidly advancing world of drone technology.

This book constitutes the thoroughly refereed proceedings of the Second Iberoamerican Conference, KGSWC 2020, held in Mérida, Mexico, in November 2020. Due to the COVID-19 pandemic the conference was held online. 

The 15 papers presented were carefully reviewed and selected from 45 submissions. The papers cover research and practices in several fields of AI, such as knowledge representation and reasoning, natural language processing/text mining, machine/deep learning, semantic web, and knowledge graphs.