Looking beyond the communications technology horizon and projecting future competency-specific employment demand, this book presents an evaluation of desirable information systems enhancements by integrating two disparate-domain computer ontologies. It provides readers a fresh solutions approach based on dynamic modeling and methodological contributions to philosophical and assistive communications system development in healthcare, addressing the need for both demand intelligence and practical work environment support. The pace of change in redefining occupation-specific employee resourcing needs is unrelenting and continues to accelerate. And the exponential growth in the demand for healthcare service delivery is correspondingly daunting. As such, the public and private sectors are faced with the challenge of sustaining credible relevant demand intelligence and recruitment practices, while integration, expansion and enrichment of ostensibly unconnected ontologies represent key R&D issues.

This book gathers the proceedings of the International Conference on Information, Communication and Cybersecurity, held on November 10–11, 2021, in Khouribga, Morocco. The conference was jointly coorganized by The National School of Applied Sciences of Sultan Moulay Slimane University, Morocco, and Charles Darwin University, Australia. This book provides an opportunity to account for state-of-the-art works, future trends impacting information technology, communications, and cybersecurity, focusing on elucidating the challenges, opportunities, and inter-dependencies that are just around the corner. This book is helpful for students and researchers as well as practitioners. ICI2C 2021 was devoted to advances in smart information technologies, communication, and cybersecurity. It was considered a meeting point for researchers and practitioners to implement advanced information technologies into various industries. There were 159 paper submissions from 24 countries. Each submission was reviewed by at least three chairs or PC members. We accepted 54 regular papers (34\%). Unfortunately, due to limitations of conference topics and edited volumes, the Program Committee was forced to reject some interesting papers, which did not satisfy these topics or publisher requirements. We would like to thank all authors and reviewers for their work and valuable contributions. The friendly and welcoming attitude of conference supporters and contributors made this event a success!

This book constitutes selected papers from the 20th International Conference on Transport Systems Telematics, TST 2020, held in Kraków, Poland, in October 2020. The 34 full papers presented in this volume were carefully reviewed and selected from 97 submissions. They were organized in topical sections named: telematics in road transport - general view; telematics in road transport - details in applications.- telematics in rail and marine transport; general about telematics.

Decision support systems (DSS) are widely touted for their effectiveness in aiding decision making, particularly across a wide and diverse range of industries including healthcare, business, and engineering applications. The concepts, principles, and theories of enhanced decision making are essential points of research as well as the exact methods, tools, and technologies being implemented in these industries. From both a standpoint of DSS interfaces, namely the design and development of these technologies, along with the implementations, including experiences and utilization of these tools, one can get a better sense of how exactly DSS has changed the face of decision making and management in multi-industry applications. Furthermore, the evaluation of the impact of these technologies is essential in moving forward in the future. The Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering explores how decision support systems have been developed and implemented across diverse industries through perspectives on the technology, the utilizations of these tools, and from a decision management standpoint. The chapters will cover not only the interfaces, implementations, and functionality of these tools, but also the overall impacts they have had on the specific industries mentioned. This book also evaluates the effectiveness along with benefits and challenges of using DSS as well as the outlook for the future. This book is ideal for decision makers, IT consultants and specialists, software developers, design professionals, academicians, policymakers, researchers, professionals, and students interested in how DSS is being used in different industries.

When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

Roadmap to Successful Digital Health Ecosystems: A Global Perspective presents evidence-based solutions found on adopting open platforms, standard information models, technology neutral data repositories, and computable clinical data and knowledge (ontologies, terminologies, content models, process models, and guidelines), resulting in improved patient, organizational, and global health outcomes. The book helps engaging countries and stakeholders take action and commit to a digital health strategy, create a global environment and processes that will facilitate and induce collaboration, develop processes for monitoring and evaluating national digital health strategies, and enable learnings to be shared in support of WHO's global strategy for digital health. The book explains different perspectives and local environments for digital health implementation, including data/information and technology governance, secondary data use, need for effective data interpretation, costly adverse events, models of care, HR management, workforce planning, system connectivity, data sharing and linking, small and big data, change management, and future vision. All proposed solutions are based on real-world scientific, social, and political evidence. - Provides a roadmap, based on examples already in place, to develop and implement digital health systems on a large-scale that are easily reproducible in different environments - Addresses World Health Organization (WHO)-identified research gaps associated with the feasibility and effectiveness of various digital health interventions - Helps readers improve future decision-making within a digital environment by detailing insights into the complexities of the health system - Presents evidence from real-world case studies from multiple countries to discuss new skills that suit new paradigms

The book is covering knowledge and results in theory, methodology, and applications of artificial intelligence and machine learning in academia and industry. Nowadays, artificial intelligence has been used in every company where intelligence elements are embedded inside sensors, devices, machines, computers and networks. The chapters in this book integrated approach toward global exchange of information on technological advances, scientific innovations, and the effectiveness of various regulatory programs toward AI application in medicine, biology, chemistry, financial, games, law, and engineering. Readers can find AI application in industrial workplace safety, manufacturing systems, medical imaging, biomedical engineering application, different computational paradigm, COVID-19, liver tracking, drug delivery system, and cost-effectiveness analysis. Real examples from academia and industry give beyond state of the art for application of AI and ML in different areas. These chapters are extended papers from the First Serbian International Conference on Applied Artificial Intelligence (SICAAI), which was held in Kragujevac, Serbia, on May 19–20, 2022.

This book constitutes the proceedings of the 7th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2012, held in Kiel, Germany, in March 2012. The 12 regular and 8 short papers, presented together with two invited talks in full paper-length, were carefully reviewed and selected from 53 submissions. The contributions cover foundational aspects of information and knowledge systems. These include the application of ideas, theories or methods from specific disciplines to information and knowledge systems, such as discrete mathematics, logic and algebra, model theory, informaiton theory, complexity theory, algorithmics and computation, statistics, and optimization.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

This handbook brings together diverse domains and technical competences of Model Based Systems Engineering (MBSE) into a single, comprehensive publication. It is intended for researchers, practitioners, and students/educators who require a wide-ranging and authoritative reference on MBSE with a multidisciplinary, global perspective. It is also meant for those who want to develop a sound understanding of the practice of systems engineering and MBSE, and/or who wish to teach both introductory and advanced graduate courses in systems engineering. It is specifically focused on individuals who want to understand what MBSE is, the deficiencies in current practice that MBSE overcomes, where and how it has been successfully applied, its benefits and payoffs, and how it is being deployed in different industries and across multiple applications. MBSE engineering practitioners and educators with expertise in different domains have contributed chapters that address various uses of MBSE and related technologies such as simulation and digital twin in the systems lifecycle. The introductory chapter reviews the current state of practice, discusses the genesis of MBSE and makes the business case. Subsequent chapters present the role of ontologies and meta-models in capturing system interdependencies, reasoning about system behavior with design and operational constraints; the use of formal modeling in system (model) verification and validation; ontology-enabled integration of systems and system-of-systems; digital twin-enabled model-based testing; system model design synthesis; model-based tradespace exploration; design for reuse; human-system integration; and role of simulation and Internet-of-Things (IoT) within MBSE.