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AI-Driven Smart Healthcare
Reimagine the future of healthcare with a deep dive into hyperscale computing and distributed networks In AI-Driven Smart Healthcare: Powered by Hyperscale Computing and Next Generation Networks, a team of distinguished researchers delivers an insightful and practical discussion of the healthcare applications of artificial intelligence and fog-enabled next-generation networks. The book provides practical insights and methodologies for the design, development, and deployment of these technologies throughout the healthcare industry. Readers will explore key areas of recent advancement, including the Internet of Things, fog computing, artificial intelligence, machine learning, serverless comput...
New Directions of Modern Cryptography
Modern cryptography has evolved dramatically since the 1970s. With the rise of new network architectures and services, the field encompasses much more than traditional communication where each side is of a single user. It also covers emerging communication where at least one side is of multiple users. New Directions of Modern Cryptography presents
Security and Privacy in Communication Networks
This book constitutes the refereed conference proceedings of the 12th International Conference on Security and Privacy in Communications Networks, SecureComm 2016, held in Guangzhou, China, in October 2016. The 32 revised full papers and 18 poster papers were carefully reviewed and selected from 137 submissions. The papers are organized thematically starting with mobile and network security, followed by applied cryptography, web security and privacy, system security, hardware security. The volume also includes papers from the ATCS workshop and the poster session.
Advanced Machine Learning for Cyber-Attack Detection in IoT Networks
Advanced Machine Learning for Cyber-Attack Detection in IoT Networks analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, along with their applications in detecting and preventing cyberattacks in future IoT systems. Chapters investigate the key challenges and vulnerabilities found in IoT security, how to handle challenges in data collection and pre-processing specific to IoT environments, as well as what metrics to consider for evaluating the performance of machine learning models. Other sections look at the training, validation, and evaluation of supervised learning models and present case studies and examples that demonstrate the application of supervised learning in IoT security. - Presents a comprehensive overview of research on IoT security threats and potential attacks - Investigates machine learning techniques, their mathematical foundations, and their application in cybersecurity - Presents metrics for evaluating the performance of machine learning models as well as benchmark datasets and evaluation frameworks for assessing IoT systems
Dynamics of Disasters
Based on the “Sixth International Conference on Dynamics of Disasters” (Piraeus, Greece, July 2023), this volume includes contributions from experts who share their latest discoveries on disasters either caused by natural phenomena or human activities. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response and recovery and uses for mathematical models to describe disasters and their impacts. This volume includes additional invited manuscripts from other experts and leaders in the field. Topics covered include economics, optimization, machine learning, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies and will engage readers from a wide variety of fields and backgrounds.
Mathematical Reviews
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