AI-Enabled IoT for Smart Health Care Systems, Tawseef Ayoub Shaikh
Автор: Karimipour Hadis, Derakhshan Farnaz Название: Ai-Enabled Threat Detection and Security Analysis for Industrial Iot ISBN: 3030766128 ISBN-13(EAN): 9783030766122 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial Intelligence for Threat Detection and Analysis in Industrial IoT: Applications and Challenges.- Complementing IIoT Services through AI: Feasibility and Suitability.- Data Security and Privacy in Industrial IoT.- Blockchain Applications in the Industrial Internet of Things.- Application of Deep Learning on IoT-enabled Smart Grid Monitoring.- Cyber Security of Smart Manufacturing Execution Systems: A Bibliometric Analysis.- The Role of Machine Learning in IIoT Through FPGAs.- Deep Representation Learning for Cyber-Attack Detection in Industrial IoT.- Classification and Intelligent Mining of Anomalies in Industrial IoT.- A Snapshot Ensemble Deep Neural Network Model for Attack Detection in Industrial Internet of Things.- Privacy Preserving Federated Learning Solution for Security of Industrial Cyber Physical Systems.- A Multi-Stage Machine Learning Model for Security Analysis in Industrial Control System.- A Recurrent Attention Model for Cyber Attack Classification.
Описание: This contributed volume provides the state-of-the-art development on security and privacy for cyber-physical systems (CPS) and industrial Internet of Things (IIoT). More specifically, this book discusses the security challenges in CPS and IIoT systems as well as how Artificial Intelligence (AI) and Machine Learning (ML) can be used to address these challenges. Furthermore, this book proposes various defence strategies, including intelligent cyber-attack and anomaly detection algorithms for different IIoT applications. Each chapter corresponds to an important snapshot including an overview of the opportunities and challenges of realizing the AI in IIoT environments, issues related to data security, privacy and application of blockchain technology in the IIoT environment. This book also examines more advanced and specific topics in AI-based solutions developed for efficient anomaly detection in IIoT environments. Different AI/ML techniques including deep representation learning, Snapshot Ensemble Deep Neural Network (SEDNN), federated learning and multi-stage learning are discussed and analysed as well. Researchers and professionals working in computer security with an emphasis on the scientific foundations and engineering techniques for securing IIoT systems and their underlying computing and communicating systems will find this book useful as a reference. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, cyber security, and information systems. It also applies to advanced-level students studying electrical engineering and system engineering, who would benefit from the case studies.
Описание: In practical health care cases, semi-structured and unstructured decision-making issues involve multiple criteria (or goals) that may conflict with each other. Thus, the use of MCDM is a promising source of practical solutions for such problems.
Автор: Bhattacharyya Siddhartha, Das Gautam, de Sourav Название: Intelligence Enabled Research: DoSIER 2021 ISBN: 981190488X ISBN-13(EAN): 9789811904882 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book gathers extended versions of papers presented at DoSIER 2021 (the 2021 Third Doctoral Symposium on Intelligence Enabled Research, held at Cooch Behar Government Engineering College, West Bengal, India, during November 12–13, 2021). The papers address the rapidly expanding research area of computational intelligence, which, no longer limited to specific computational fields, has since made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design, to name but a few. Presenting chapters written by experts active in these areas, the book offers a valuable reference guide for researchers and industrial practitioners alike and inspires future studies.
Автор: Dilip Singh Sisodia, Ram Bilas Pachori, Lalit Garg Название: Advancement of Artificial Intelligence in Healthcare Engineering ISBN: 179982120X ISBN-13(EAN): 9781799821205 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 39640.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides comprehensive research on the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of healthcare engineering solutions. The book features a range of topics such as genetic algorithms, mobile robotics, and neuroinformatics.
Автор: Prasant Kumar Pattnaik Название: Smart Healthcare Analytics in IoT Enabled Environment ISBN: 3030375501 ISBN-13(EAN): 9783030375508 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book addresses various aspects of how smart healthcare can be used to detect and analyze diseases, the underlying methodologies, and related security concerns.
Описание: In recent years, new applications on computer-aided technologies for telemedicine have emerged. Furthermore, this volume includes comprehensive reviews describing procedures and techniques, which are crucial to support researchers in the field who want to replicate these methodologies in solving their related research problems.
Описание: This book addresses one of the most overlooked practical, methodological, and moral questions in the journey to secure and handle the massive amount of data being generated from smart devices interactions: the integration of Blockchain with 5G-enabled IoT.
Описание: The book covers a variety of topics in Information and Communications Technology (ICT) and their impact on innovation and business. The authors discuss various innovations, business and industrial motivations, and impact on humans and the interplay between those factors in terms of finance, demand, and competition. Topics discussed include the convergence of Machine to Machine (M2M), Internet of Things (IoT), Social, and Big Data. They also discuss AI and its integration into technologies from machine learning, predictive analytics, security software, to intelligent agents, and many more. Contributions come from academics and professionals around the world.
Covers the most recent practices in ICT related topics pertaining to technological growth, innovation, and business;Presents a survey on the most recent technological areas revolutionizing how humans communicate and interact;Features four sections: IoT, Wireless Ad Hoc & Sensor Networks, Fog Computing, and Big Data Analytics.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Описание: Reviews current and future trends in Semantic Web research with the aim of making existing and potential applications more accessible to a broader community of academics, practitioners, and industry professionals. Covering topics including recommendation systems, semantic search, and ontologies, this reference is a valuable contribution to the existing literature in this discipline.
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