Marcello Trovati - Böcker

Advances in Multimodal Large Language Models for Healthcare: Methods and Applications provides valuable insights on Large Language Models in healthcare applications for researchers, academics, and practitioners. The book explains key concepts, including artificial intelligence, machine learning, deep learning, and the evolution of neural networks and transformer models. It then covers generative AI and LLMs for a wide spectrum of healthcare applications, including mental health, clinical decision support, interactive system design, and sensitive analysis. Readers will find this to be a valuable deep dive into the emergent intersection of LLMs and health care, with guidance into applications, technical and programming methods, and more.Although LLMs have shown some promising results in the healthcare sector, numerous challenges need to be addressed before they can be used in patient care. The two key issues with the adoption of LLMs regarding healthcare settings are reliability, transparency, interpretation of results and bias (data and algorithm) management. Unless properly and adequately validated, there may be incorrect medical information provided by the LLM-based systems, which can lead to misdiagnosis or hazardous treatment errors. At this point, LLMs have not only been used for decision making or documentation, they have also proven to be useful in patient engagement through QA systems, medical chatbots, and virtual healthcare.Covers the basics of Artificial Intelligence, Machine Learning, and Deep LearningHighlights the evolution of Transformer Based Deep Neural Network ModelsPresents key concepts and methods in Generative AI and Large Language Models (LLMs)Discusses the application of LLMs in clinical decision-makingDelves into the utility of LLMs for virtual assistance and human-machine interactionDescribes transformer-based pretrained models for healthcare applications

This book explores current research and development in the area of digital healthcare using recent technologies such as data science and artificial intelligence.

This book includes the scientific outcome of the 17th Annual International Symposium on Intelligent Distributed Systems (IDC2024) which was hosted by the University of Brighton, United Kingdom, 18-19 September 2024. This book includes all sixteen full papers that were accepted for presentation after a rigorous peer review process by experts in their respective fields. The papers are presented here grouped in five areas of focus, namely distributed systems, cyber security, predictive modelling, blockchain applications, and large language models. They provide a wide range of areas of application that demonstrate the dynamics and constantly evolving nature of distributed systems research. The authors work in two continents, six countries, and a total of fourteen different institutions and organizations. The book spans different levels of progress of research, completed projects at institutional level, outcomes of PhD student work, and results that relate to large transnational projects funded by the European Union and other national and international funders. The authors hope that the readers of this book of proceedings will find the contents informative and an opportunity to engage with new outcomes of innovative research. The editors are:Nikolaos Polatidis, Elias Pimenidis, Marcelo Trovati, Mehmet Aydin.

This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.

This practical and didactic text/reference discusses the leading edge of secure cloud computing, exploring the essential concepts and principles, tools, techniques and deployment models in this field. Enlightening perspectives are presented by an international collection of pre-eminent authorities in cloud security assurance from both academia and industry.Topics and features:·         Describes the important general concepts and principles of security assurance in cloud-based environments·         Presents applications and approaches to cloud security that illustrate the current state of the art·         Reviews pertinent issues in relation to challenges that prevent organizations moving to cloud architectures·         Provides relevant theoretical frameworks and the latest empirical research findings·         Discusses real-world vulnerabilities of cloud-based software in order to address the challenges of securing distributed software·         Highlights the practicalities of cloud security, and how applications can assure and comply with legislation·         Includes review questions at the end of each chapterThis Guide to Security Assurance for Cloud Computing will be of great benefit to a broad audience covering enterprise architects, business analysts and leaders, IT infrastructure managers, cloud security engineers and consultants, and application developers involved in system design and implementation. The work is also suitable as a textbook for university instructors, with the outline for a possible course structure suggested in the preface.The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Shao Ying Zhu serves as a Senior Lecturer in Computing, Dr. Richard Hill as a Professor and Head of the Computing and Mathematics Department, and Dr. Marcello Trovati as a Senior Lecturer in Mathematics. The other publications of the editors include the Springer titles Big-Data Analytics and Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.

This interdisciplinary reference and guide provides an introduction to modeling methodologies and models which form the starting point for deriving efficient and effective solution techniques, and presents a series of case studies that demonstrate how heuristic and analytical approaches may be used to solve large and complex problems. Topics and features: introduces the key modeling methods and tools, including heuristic and mathematical programming-based models, and queueing theory and simulation techniques; demonstrates the use of heuristic methods to not only solve complex decision-making problems, but also to derive a simpler solution technique; presents case studies on a broad range of applications that make use of techniques from genetic algorithms and fuzzy logic, tabu search, and queueing theory; reviews examples incorporating system dynamics modeling, cellular automata and agent-based simulations, and the use of big data; supplies expanded descriptions and examples in the appendices.

This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures;

This practical and didactic text/reference discusses the leading edge of secure cloud computing, exploring the essential concepts and principles, tools, techniques and deployment models in this field. Enlightening perspectives are presented by an international collection of pre-eminent authorities in cloud security assurance from both academia and industry.Topics and features:·         Describes the important general concepts and principles of security assurance in cloud-based environments·         Presents applications and approaches to cloud security that illustrate the current state of the art·         Reviews pertinent issues in relation to challenges that prevent organizations moving to cloud architectures·         Provides relevant theoretical frameworks and the latest empirical research findings·         Discusses real-world vulnerabilities of cloud-based software in order to address the challenges of securing distributed software·         Highlights the practicalities of cloud security, and how applications can assure and comply with legislation·         Includes review questions at the end of each chapterThis Guide to Security Assurance for Cloud Computing will be of great benefit to a broad audience covering enterprise architects, business analysts and leaders, IT infrastructure managers, cloud security engineers and consultants, and application developers involved in system design and implementation. The work is also suitable as a textbook for university instructors, with the outline for a possible course structure suggested in the preface.The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Shao Ying Zhu serves as a Senior Lecturer in Computing, Dr. Richard Hill as a Professor and Head of the Computing and Mathematics Department, and Dr. Marcello Trovati as a Senior Lecturer in Mathematics. The other publications of the editors include the Springer titles Big-Data Analytics and Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.

This interdisciplinary reference and guide provides an introduction to modeling methodologies and models which form the starting point for deriving efficient and effective solution techniques, and presents a series of case studies that demonstrate how heuristic and analytical approaches may be used to solve large and complex problems.