Bøger i Intelligent Data-Centric Systems serien

Internet of Multimedia Things (IoMT): Techniques and Applications disseminates research efforts in the security and resilience of intelligent data-centric critical systems to support advanced research in this area. Sections cover the background of IoMT Architectures and Technologies, describe the problems that arise in IoMT Computing and protocols, and illustrate the application of IoMT on Industrial applications. The book will be beneficial for engineers, developers, solution designers, architects, system engineers and specialists from professional environments interested in the IoMT to seek appropriate solutions to their specific problems. Addresses recent developments, along with relevant prospects on opportunities and challenges in IoMT Presents the concept and vision of the IoMT, whose potentialities are discussed with speci?c use-cases Discusses the distinct architectural design and characteristics of IoMT as compared to the existing multimedia systems

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

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

Computer science is an area in which cognitive science is experiencing a renaissance given its multidisciplinary nature. This combination with technical sciences has developed into a new field of study, Cognitive Informatics. Cognitive Information systems study how the human brain processes information and how decisions are made and studies how these processes can be computationally mimicked or enhanced. Cognitive Information Systems in Management Sciences summarizes the body of work in this area and takes an analytical approach to interpreting the data and provides an approach that can be used for practical implementation in the fields of computing, economics, and engineering. Using numerous illustrative examples and following both theoretical and practical results Dr. Lidia Ogiela discusses; Concepts and principles of Cognitive information SystemsThe relationship between intelligent computer data analysis and how to utilize computational intelligent approaches to enhance information retrievalA unified structured approach that can be used to develop information flow in cognitive management systemsReal world implantation use cases in management science, computer science and engineering Discusses the basic concepts and principles of Cognitive information systems and provides, ''real-world'' implementation examplesExplains the relationship between intelligent computer data analysis and how to utilize computational intelligent approaches to enhance information retrievalProvides a unified structured approach that can be used to develop information flow in cognitive management systems

Security and Resilience in Intelligent Data-Centric Systems and Communication Networks presents current, state-of-the-art work on novel research in theoretical and practical resilience and security aspects of intelligent data-centric critical systems and networks. The book analyzes concepts and technologies that are successfully used in the implementation of intelligent data-centric critical systems and communication networks, also touching on future developments. In addition, readers will find in-demand information for domain experts and developers who want to understand and realize the aspects (opportunities and challenges) of using emerging technologies for designing and developing more secure and resilient intelligent data-centric critical systems and communication networks. Topics covered include airports, seaports, rail transport systems, plants for the provision of water and energy, and business transactional systems. The book is well suited for researchers and PhD interested in the use of security and resilient computing technologies. Includes tools and techniques to prevent and avoid both accidental and malicious behaviorsExplains the state-of-the-art technological solutions for main issues hindering the development of monitoring and reaction solutionsDescribes new methods and technologies, advanced prototypes, systems, tools and techniques of future direction