Victor Hugo Costa de Albuquerque – författare

Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation.

Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and treatment of the COVID-19 virus Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including both positive and negative research findings Provides insights into innovative data-oriented modeling and predictive techniques from COVID-19 researchers Includes real-world feedback and user experiences from physicians and medical staff from around the world on the effectiveness of applied Data Science solutions

Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data discusses the insight of data processing applications in various domains through soft computing techniques and enormous advancements in the field.

The book focuses on the cross-disciplinary mechanisms and ground-breaking research ideas on novel techniques and data processing approaches in handling structured and unstructured healthcare data. It also gives insight into various information-processing models and many memories associated with it while processing the information for forecasting future trends and decision making.

This book is an excellent resource for researchers and professionals who work in the Healthcare Industry, Data Science, and Machine learning.

Focuses on data-centric operations in the Healthcare industry Provides the latest trends in healthcare data analytics and practical implementation outcomes of the proposed models Addresses real-time challenges and case studies in the Healthcare industry

5G IoT and Edge Computing for Smart Healthcare addresses the importance of a 5G IoT and Edge-Cognitive-Computing-based system for the successful implementation and realization of a smart-healthcare system. The book provides insights on 5G technologies, along with intelligent processing algorithms/processors that have been adopted for processing the medical data that would assist in addressing the challenges in computer-aided diagnosis and clinical risk analysis on a real-time basis. Each chapter is self-sufficient, solving real-time problems through novel approaches that help the audience acquire the right knowledge.

With the progressive development of medical and communication - computer technologies, the healthcare system has seen a tremendous opportunity to support the demand of today's new requirements.

Focuses on the advancement of 5G in terms of its security and privacy aspects, which is very important in health care systems Address advancements in signal processing and, more specifically, the cognitive computing algorithm to make the system more real-time Gives insights into various information-processing models and the architecture of layers to realize a 5G based smart health care system

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible. Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and the treatment of the COVID-19 virus Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including the wide variety of impacts the virus is having on societies and medical practice Presents insights into innovative, data-oriented modeling and predictive techniques from COVID-19 researchers around the world, including geoprocessing and tracking, lab data analysis, and theoretical views on a variety of technical applications Includes real-world feedback and user experiences from physicians and medical staff from around the world for medical treatment perspectives, public safety policies and impacts, sociological and psychological perspectives, the effects of COVID-19 in agriculture, economies, and education, and insights on future pandemics

Quaternion-Sparse Image Processing: Advances in Multispectral Processing brings together the technologies, research, and managerial applications of quaternion-sparse based complex algebra in image processing. The book covers the entire range of complicated tasks performed on color images, including denoising, reconstruction, classification, hallucination, feature extraction, dimension reduction, and regularization. It provides easy understanding and smooth adaptability of basic and advanced concepts for graduate students, researchers, doctors, academics, and practitioners.Uncovers the innovative features of complex algebra, specifically the quaternion-sparse concept in image processing and how it can help in improving the computational efficiency of image processingDeals with the most common quaternion convolution neural network, quaternion wavelet, and sparse representation-based techniques in multispectral image processingFocuses on how evolution in algebraic concepts, i.e., quaternion and sparse, help in improving accuracy and efficiency of various color image restoration, reconstruction, and recognitionIllustrates how important features are extracted and complete information is stored in extracted features to help and process tasks in an easy and computationally efficient way

Fundamentals of Fog Computing and the Internet of Things for Smart Healthcare explores the intersection of two transformative technologies, fog computing and the IoT, shedding light on how they are revolutionizing healthcare. The book serves as an essential guide for researchers and graduate students, explaining the underlying concepts, operational benefits, and potential challenges of these technologies. By delving into how fog computing enhances real-time data processing, the book's authors provide invaluable insights into the practical applications of these advancements in the healthcare industry.Additionally, together with its companion book, Advances in Fog computing and the Internet of Things for Smart Healthcare, the series provides a comprehensive understanding of how these technologies are enabling more efficient, personalized, and accessible healthcare services. By facilitating smart applications and services across various industries, fog computing optimizes performance, latency, privacy, and overall system efficiency, ultimately contributing to the development of more effective and responsive IoT ecosystems.Discusses the fundamentals of fog computing, IoT technology, and their applications in healthcare, offering both technical and non-technical readers a clear understanding of the conceptsOffers in-depth insights into the technical architecture, networking protocols, security considerations, and data management strategies, thus catering to IT professionals and researchersExplores how fog computing and IoT can be integrated to optimize resource allocation, patient care, and operational efficiency

Advances in Fog Computing and the Internet of Things for Smart Healthcare delves into the transformative impact of fog computing and IoT on modern healthcare systems. This comprehensive guide educates researchers and graduate students on the fundamental concepts of these technologies, illustrating their practical applications in healthcare. Real-world use cases showcase the technologies' ability to enhance patient monitoring and personalized medicine. The book also addresses significant challenges such as privacy, security, data management, and regulatory compliance, providing strategies for overcoming them.Alongside its companion book, Fundamentals of Fog Computing and the Internet of Things for Smart Healthcare, this volume empowers various industries to leverage IoT technologies while optimizing performance and system efficiency. It highlights the crucial role of fog computing in processing data closer to the source, ensuring faster and more efficient data processing and contributing to the creation of effective IoT ecosystems. This synergy between fog computing and IoT facilitates smarter applications and services, advancing the landscape of healthcare with more efficient, personalized, and accessible services.Provides a comprehensive introduction to fog computing, IoT technology, and their applications in healthcare, offering both technical and non-technical readers a clear understanding of the conceptsShowcases real-world case studies and examples of fog computing and IoT implementations in healthcare settings, illustrating practical benefits and challengesProvides strategic insights into how fog computing and IoT can be integrated to optimize resource allocation, patient care, and operational efficiencyExplores the ethical, privacy, and regulatory challenges associated with implementing these technologies in healthcare, guiding readers through the potential pitfallsDiscusses emerging trends and potential future developments at the intersection of fog computing, IoT, and healthcare, equipping readers with forward-looking perspectives

Intelligent Control in Smart Energy Systems provides a clear and practical guide to the implementation of Artificial Intelligent (AI) techniques in modern, sustainable energy systems. Building from essential theory to advanced application processes, this book outlines the essential challenge of control for renewable and distributed energy sources and applies a range of AI solutions from deep learning, data-driven decision-making, advanced control, and optimization algorithms for smart grids. Case studies and latest results cover control across design, development, and operation of smart energy systems, touching on control in generation, distribution, and supply.Catering to newly interdisciplinary researchers as well as those with a basic understanding of AI technologies, this book supports higher-stage undergraduates, graduate students, researchers, and system engineers in implementing best practice control techniques for a sustainable energy grid.Incorporates a stepped approach, from basic theoretical principles to advanced application examplesIncludes case studies in a variety of scenarios, from a global perspective of experts, for maximum versatilityAdds to the cutting-edge series Advances in Intelligent Energy Systems, which provides the latest research and best practice for utilizing AI to enable sustainable energy systems