Roles And Challenges Of Semantic Intelligence In Healthcare Cognitive Computing
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Roles And Challenges Of Semantic Intelligence In Healthcare Cognitive Computing
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Author : A. Carbonaro
language : en
Publisher: IOS Press
Release Date : 2024-01-26
Roles And Challenges Of Semantic Intelligence In Healthcare Cognitive Computing written by A. Carbonaro and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-26 with Computers categories.
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.
Empirical Ontology Design Patterns
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Author : V.A. Carriero
language : en
Publisher: IOS Press
Release Date : 2024-01-26
Empirical Ontology Design Patterns written by V.A. Carriero and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-26 with Computers categories.
In recent years, knowledge graphs (KGs) and ontologies have been widely adopted for modeling many kinds of domain. They are frequently released openly, something which benefits those who are starting new projects, because it offers them a wide choice of ontology reuse and the possibility to link to existing data. Understanding the content of an ontology or a knowledge graph is far from straightforward, however, and existing methods address this issue only partially, while exploring and comparing multiple ontologies can be a tedious manual task. This book, Empirical Ontology Design Patterns, starts from the premise that identifying the Ontology Design Patterns (ODPs) used in an ontology or a knowledge graph will go some way to addressing this problem. Its main focus is to provide tools which will effectively support the task of automatically identifying ODPs in existing ontologies and knowledge graphs. The book analyses the role of ODPs in ontology engineering, placing this analysis in the wider context of existing approaches to ontology reuse and implementation. It introduces a novel method for extracting empirical ontology design patterns (EODPs) from ontologies, and another for extracting EODPs from knowledge graphs whose schemas are implicit. Both methods are applied to ontologies and knowledge graphs frequently adopted and reused, such as Wikidata. The book also offers an ontology which can be used as a basis for annotating ODPs in ontologies and knowledge graphs, whether manually or automatically. The book will be of interest to all those whose work involves the use or reuse of ontologies and knowledge graphs.
Intelligent Systems In Big Data Semantic Web And Machine Learning
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Author : Noreddine Gherabi
language : en
Publisher: Springer Nature
Release Date : 2021-05-28
Intelligent Systems In Big Data Semantic Web And Machine Learning written by Noreddine Gherabi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-28 with Computers categories.
This book describes important methodologies, tools and techniques from the fields of artificial intelligence, basically those which are based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use of data on the Semantic Web, with many new topics. The contributions are concerned with machine learning, big data, data processing in medicine, similarity processing in ontologies, semantic image analysis, as well as many applications including the use of machine leaning techniques for cloud security, artificial intelligence techniques for detecting COVID-19, the Internet of things, etc. The book is meant to be a very important and useful source of information for researchers and doctoral students in data analysis, Semantic Web, big data, machine learning, computer engineering and related disciplines, as well as for postgraduate students who want to integrate the doctoral cycle.
Ai In The Social And Business World A Comprehensive Approach
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Author : Parul Dubey
language : en
Publisher: Bentham Science Publishers
Release Date : 2024-10-15
Ai In The Social And Business World A Comprehensive Approach written by Parul Dubey and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-15 with Computers categories.
AI in the Social and Business World: A Comprehensive Approach offers an in-depth exploration of the transformative impact of Artificial Intelligence (AI) across a wide range of sectors. This edited collection features 13 chapters, each penned by field experts, providing a comprehensive understanding of AI's theoretical foundations, practical applications, and societal implications. Each chapter offers strategic insights, case studies, and discussions on ethical considerations and future trends. Beginning with an overview of AI's historical evolution, the book navigates through its diverse applications in healthcare, social welfare, business intelligence, and more. Chapters systematically explore AI's role in enhancing healthcare delivery, optimizing business operations, and fostering social inclusion through innovative technologies like AI-based sign recognition and IoT in agriculture. With strategic insights, case studies, and discussions on ethical considerations and future trends, this book is a valuable resource for researchers, practitioners, and anyone interested in understanding AI's multifaceted influence. It is designed to foster informed discussions and strategic decisions in navigating the evolving landscape of AI in today's dynamic world. This book is an essential resource for researchers, practitioners, and anyone interested in understanding AI’s multifaceted influence across the social and business landscapes.
Reconnoitering The Landscape Of Edge Intelligence In Healthcare
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Author : Suneeta Satpathy
language : en
Publisher: CRC Press
Release Date : 2024-04-23
Reconnoitering The Landscape Of Edge Intelligence In Healthcare written by Suneeta Satpathy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-23 with Computers categories.
The revolution in healthcare as well as demand for efficient real-time healthcare services are driving the progression of edge computing, AI-mediated techniques, deep learning, and IoT applications for healthcare industries and cloud computing. Edge computing helps to meet the demand for newer and more sophisticated healthcare systems that are more personalized and that match the speed of modern life. With applications of edge computing, automated intelligence and intuitions are incorporated into existing healthcare analysis tools for identifying, forecasting, and preventing high-risk diseases. Reconnoitering the Landscape of Edge Intelligence in Healthcare provides comprehensive research on edge intelligence technology with the emphasis on application in the healthcare industry. It covers all the various areas of edge intelligence for data analysis in healthcare, looking at the emerging technologies such as AI-based techniques, machine learning, IoT, cloud computing, and deep learning with illustrations of the design, implementation, and management of smart and intelligent healthcare systems. Chapters showcase the advantages and highlights of the adoption of the intelligent edge models toward smart healthcare infrastructure. The book also addresses the increased need for a high level of medical data security while transferring real-time data to cloud-based architecture, a matter of prime concern for both patient and doctor. Topics include edge intelligence for wearable sensor technologies and their applications for health monitoring, the various edge computing techniques for disease prediction, e-health services and e-security solutions through IoT devices that aim to improve the quality of care for transgender patients, smart technology in ambient assisted living, the role of edge intelligence in limiting virus spread during pandemics, neuroscience in decoding and analysis of visual perception from the neural patterns and visual image reconstruction, and more. The technology addressed include energy aware cross-layer routing protocol (ECRP), OMKELM-IDS technique, graphical user interface (GUI), IOST (an ultra-fast, decentralized blockchain platform), etc. This volume will be helpful to engineering students, research scholars, and manufacturing industry professionals in the fields of engineering applications initiatives on AI, machine learning, and deep learning techniques for edge computing.
Services For Connecting And Integrating Big Numbers Of Linked Datasets
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Author : M. Mountantonakis
language : en
Publisher: IOS Press
Release Date : 2021-02-19
Services For Connecting And Integrating Big Numbers Of Linked Datasets written by M. Mountantonakis and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-19 with Computers categories.
Linked Data is a method of publishing structured data to facilitate sharing, linking, searching and re-use. Many such datasets have already been published, but although their number and size continues to increase, the main objectives of linking and integration have not yet been fully realized, and even seemingly simple tasks, like finding all the available information for an entity, are still challenging. This book, Services for Connecting and Integrating Big Numbers of Linked Datasets, is the 50th volume in the series ‘Studies on the Semantic Web’. The book analyzes the research work done in the area of linked data integration, and focuses on methods that can be used at large scale. It then proposes indexes and algorithms for tackling some of the challenges, such as, methods for performing cross-dataset identity reasoning, finding all the available information for an entity, methods for ordering content-based dataset discovery, and others. The author demonstrates how content-based dataset discovery can be reduced to solving optimization problems, and techniques are proposed for solving these efficiently while taking the contents of the datasets into consideration. To order them in real time, the proposed indexes and algorithms have been implemented in a suite of services called LODsyndesis, in turn enabling the implementation of other high level services, such as techniques for knowledge graph embeddings, and services for data enrichment which can be exploited for machine-learning tasks, and which also improve the prediction of machine-learning problems.
Cognitive Computing Systems
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Author : Vishal Jain
language : en
Publisher: CRC Press
Release Date : 2021-05-10
Cognitive Computing Systems written by Vishal Jain and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-10 with Computers categories.
This new volume, Cognitive Computing Systems: Applications and Technological Advancements, explores the emerging area of artificial intelligence that encompasses machine self-learning, human-computer interaction, natural language processing, data mining and more. It introduces cognitive computing systems, highlights their key applications, discusses the technologies used in cognitive systems, and explains underlying models and architectures. Focusing on scientific work for real-world applications, each chapter presents the use of cognitive computing and machine learning in specific application areas. These include the use of speech recognition technology, application of neural networks in construction management, elevating competency in education, comprehensive health monitoring systems, predicting type 2 diabetes, applications for smart agricultural technology, human resource management, and more. With chapters from knowledgeable researchers in the area of artificial intelligence, cognitive computing, and allied areas, this book will be an asset for researchers, faculty, advances students, and industry professionals in many fields.
Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges
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Author : I. Tiddi
language : en
Publisher: IOS Press
Release Date : 2020-05-06
Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges written by I. Tiddi and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-06 with Computers categories.
The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
Artificial Intelligence Techniques For Analysing Sensitive Data In Medical Cyber Physical Systems
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Author : Massimo Ficco
language : en
Publisher: Springer Nature
Release Date : 2025-01-22
Artificial Intelligence Techniques For Analysing Sensitive Data In Medical Cyber Physical Systems written by Massimo Ficco and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Computers categories.
This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs. The advances in Internet of things and artificial intelligence (AI) have witnessed great progress on healthcare technologies in several application domains. In particular, the interconnection between the physical spaces, characterized by physical devices able to collect users’ health information, with the cyberspace, also known as the virtual space, has fostered the development of intelligent Medical Cyber-Physical Systems (MCPSs) with the capability to deliver real-time healthcare services. On the other hand, the potential innovation that these technologies bring to improve patient care, by remotely analysing health parameters using medical devices, advanced smart sensors, and AI, is hampered by security and privacy challenges related to the managed sensitive data. Starting from the state of the art on AI and ML for medical applications and digital health, an accurate analysis of privacy and security risks associated with the use of the MCPSs is presented. Then, Digital Twins are introduced as a significant technique to enhance decision-making through learning and reasoning of collected on-field real-time data. Moreover, decentralized healthcare data management approaches based on federated learning, tiny machine learning, and blockchain technologies have been introduced to shift control and responsibility of healthcare data management from individual centralized entities to a more distributed structure, preserving privacy and security. Finally, the application of AI-based security monitoring approaches in healthcare is discussed. In this book, both theoretical and practical approaches are used to allow readers to understand complex topics and concepts easily also through real-life scenarios.
Clinical Practice And Unmet Challenges In Ai Enhanced Healthcare Systems
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Author : Liu, Haipeng
language : en
Publisher: IGI Global
Release Date : 2024-08-05
Clinical Practice And Unmet Challenges In Ai Enhanced Healthcare Systems written by Liu, Haipeng and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-05 with Medical categories.
As the demand for advanced technologies to revolutionize patient care intensifies, the medical industry faces a pressing need to confront challenges hindering the assimilation of AI-enhanced healthcare systems. Issues such as data interoperability, ethical considerations, and the translation of AI advancements into practical clinical applications pose formidable hurdles that demand immediate attention. It is within this context of challenges and opportunities that the book, Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems promises to pave the way for a transformative era in healthcare. The book serves as a comprehensive guide for academic scholars, researchers, and healthcare professionals navigating the dynamic landscape of data-driven, AI-enhanced healthcare. By showcasing the latest advancements, the book empowers its readers to not only comprehend the existing frontiers in data sciences and healthcare technologies but also to actively contribute to overcoming obstacles. Through detailed case studies and practical guidance, the publication equips its audience with the skills necessary to implement AI in various clinical settings.