Bøger i Intelligent Data-Centric Systems: Sensor Collected Intelligence serien

Implementation of Smart Healthcare Systems using AI, IoT, and Blockchain provides imperative research on the development of data fusion and analytics for healthcare and their implementation into current issues in a real-time environment. While highlighting IoT, bio-inspired computing, big data, and evolutionary programming, the book explores various concepts and theories of data fusion, IoT, and Big Data Analytics. It also investigates the challenges and methodologies required to integrate data from multiple heterogeneous sources, analytical platforms in healthcare sectors. This book is unique in the way that it provides useful insights into the implementation of a smart and intelligent healthcare system in a post-Covid-19 world using enabling technologies like Artificial Intelligence, Internet of Things, and blockchain in providing transparent, faster, secure and privacy preserved healthcare ecosystem for the masses.

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.

Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering merges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected by intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial neural networks, support vector machine, boosted regression tree, simulated annealing, ant colony algorithm, decision tree, immune algorithm, and imperialist competitive algorithm. This book is a fundamental information source because it is the first book to present the foundational reference material in this new research field. Furthermore, it gives a critical overview of the latest cross-domain research findings and technological developments on the recent advances in computer-aided intelligent environmental data engineering. Captures the application of data science and artificial intelligence for a broader spectrum of environmental engineering problems Presents methods and procedures as well as case studies where state-of-the-art technologies are applied in actual environmental scenarios Offers a compilation of essential and critical reviews on the application of data science and artificial intelligence to the entire spectrum of environmental engineering

Autonomous and Connected Heavy Vehicle Technology presents the fundamentals, definitions, technologies, standards and future developments of autonomous and connected heavy vehicles. This book provides insights into various issues pertaining to heavy vehicle technology and helps users develop solutions towards autonomous, connected, cognitive solutions through the convergence of Big Data, IoT, cloud computing and cognition analysis. Various physical, cyber-physical and computational key points related to connected vehicles are covered, along with concepts such as edge computing, dynamic resource optimization, engineering process, methodology and future directions. The book also contains a wide range of case studies that help to identify research problems and an analysis of the issues and synthesis solutions. This essential resource for graduate-level students from different engineering disciplines such as automotive and mechanical engineering, computer science, data science and business analytics combines both basic concepts and advanced level content from technical experts.

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.

Approx.24 pages

Nature-Inspired Computing Paradigms in Systems: Reliability, Availability, Maintainability, Safety and Cost (RAMS+C) and Prognostics and Health Management (PHM) covers several areas that include bioinspired techniques and optimization approaches for system dependability. The book addresses the issue of integration and interaction of the bioinspired techniques in system dependability computing so that intelligent decisions, design, and architectures can be supported. It brings together these emerging areas under the umbrella of bio- and nature-inspired computational intelligence. The primary audience of this book includes experts and developers who want to deepen their understanding of bioinspired computing in basic theory, algorithms, and applications. The book is also intended to be used as a textbook for masters and doctoral students who want to enhance their knowledge and understanding of the role of bioinspired techniques in system dependability.Provides the latest reviewCovers various nature-inspired techniques applied to RAMS+C and PHM problemsIncludes techniques applied to new applications

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems.Includes several privacy preservation techniques for medical data.Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis.Offers case studies and applications relating to machine learning, big data, and health care analysis.

E-health applications such as tele-medicine, tele-radiology, tele-ophthalmology, and tele-diagnosis are very promising and have immense potential to improve global healthcare. They can improve access, equity, and quality through the connection of healthcare facilities and healthcare professionals, diminishing geographical and physical barriers. One critical issue, however, is related to the security of data transmission and access to the technologies of medical information. Currently, medical-related identity theft costs billions of dollars each year and altered medical information can put a person''s health at risk through misdiagnosis, delayed treatment or incorrect prescriptions. Yet, the use of hand-held devices for storing, accessing, and transmitting medical information is outpacing the privacy and security protections on those devices. Researchers are starting to develop some imperceptible marks to ensure the tamper-proofing, cost effective, and guaranteed originality of the medical records. However, the robustness, security and efficient image archiving and retrieval of medical data information against these cyberattacks is a challenging area for researchers in the field of e-health applications. Intelligent Data Security Solutions for e-Health Applications focuses on cutting-edge academic and industry-related research in this field, with particular emphasis on interdisciplinary approaches and novel techniques to provide security solutions for smart applications. The book provides an overview of cutting-edge security techniques and ideas to help graduate students, researchers, as well as IT professionals who want to understand the opportunities and challenges of using emerging techniques and algorithms for designing and developing more secure systems and methods for e-health applications. Investigates new security and privacy requirements related to eHealth technologies and large sets of applicationsReviews how the abundance of digital information on system behavior is now being captured, processed, and used to improve and strengthen security and privacyProvides an overview of innovative security techniques which are being developed to ensure the guaranteed authenticity of transmitted, shared or stored data/information

Internet of Things (IoT) is a new platform of various physical objects or "things” equipped with sensors, electronics, smart devices, software, and network connections. IoT represents a new revolution of the Internet network which is driven by the recent advances of technologies such as sensor networks (wearable and implantable), mobile devices, networking, and cloud computing technologies. IoT permits these the smart devices to collect, store and analyze the collected data with limited storage and processing capacities. Swarm Intelligence for Resource Management in the Internet of Things presents a new approach in Artificial Intelligence that can be used for resources management in IoT, which is considered a critical issue for this network. The authors demonstrate these resource management applications using swarm intelligence techniques. Currently, IoT can be used in many important applications which include healthcare, smart cities, smart homes, smart hospitals, environment monitoring, and video surveillance. IoT devices cannot perform complex on-site data processing due to their limited battery and processing. However, the major processing unit of an application can be transmitted to other nodes, which are more powerful in terms of storage and processing. By applying swarm intelligence algorithms for IoT devices, we can provide major advantages for energy saving in IoT devices. Swarm Intelligence for Resource Management in the Internet of Things shows the reader how to overcome the problems and challenges of creating and implementing swarm intelligence algorithms for each applicationExamines the development and application of swarm intelligence systems in artificial intelligence as applied to the Internet of ThingsDiscusses intelligent techniques for the implementation of swarm intelligence in IoTPrepared for researchers and specialists who are interested in the use and integration of IoT and cloud computing technologies

Smart Delivery Systems: Solving Complex Vehicle Routing Problems examines both exact and approximate methods for delivering optimal solutions to rich vehicle routing problems, showing both the advantages and disadvantages of each approach. It shows how to apply machine learning and advanced data analysis techniques to improve routing systems, familiarizing readers with the concepts and technologies used in successfully implemented delivery systems. The book explains both the latest theoretical and practical advances in intelligent delivery and scheduling systems and presents practical applications for designing new algorithms for real-life scenarios.Emphasizes both sequential and parallel algorithmsUniquely combines methods and algorithms, real-life applications, and parallel computingIncludes recommendations on how to choose between different methods for solving applicationsProvides learning aids, end of chapter references, bibliography, worked examples and exercises

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases.Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collectionContains an analysis of medical databases to provide diagnostic expert systemsAddresses the integration of intelligent data analysis techniques within biomedical information systems

Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people''s behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Contains contributions from noted scholars in computer science and electrical engineering from around the globeProvides a broad overview of recent developments in sensor collected intelligenceEdited by a team comprised of leading thinkers in big data analytics

Pervasive Computing: Next Generation Platforms for Intelligent Data Collection presents current advances and state-of-the-art work on methods, techniques, and algorithms designed to support pervasive collection of data under ubiquitous networks of devices able to intelligently collaborate towards common goals. Using numerous illustrative examples and following both theoretical and practical results the authors discuss: a coherent and realistic image of today's architectures, techniques, protocols, components, orchestration, choreography, and developments related to pervasive computing components for intelligently collecting data, resource, and data management issues; the importance of data security and privacy in the era of big data; the benefits of pervasive computing and the development process for scientific and commercial applications and platforms to support them in this field. Pervasive computing has developed technology that allows sensing, computing, and wireless communication to be embedded in everyday objects, from cell phones to running shoes, enabling a range of context-aware applications. Pervasive computing is supported by technology able to acquire and make use of the ubiquitous data sensed or produced by many sensors blended into our environment, designed to make available a wide range of new context-aware applications and systems. While such applications and systems are useful, the time has come to develop the next generation of pervasive computing systems. Future systems will be data oriented and need to support quality data, in terms of accuracy, latency and availability. Pervasive Computing is intended as a platform for the dissemination of research efforts and presentation of advances in the pervasive computing area, and constitutes a flagship driver towards presenting and supporting advanced research in this area.