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This proceedings book constitutes the refereed proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics (AISI 2023), which took place in Port Said University, Port Said, Egypt, during September 20–22, 2023, Egypt, and is an international interdisciplinary conference that presents a spectrum of scientific research on all aspects of informatics and intelligent systems, technologies, and applications.
This book will constitute the proceedings of the International Health Informatics Conference (IHIC 2022). This volume focus on artificial intelligence, machine learning, and deep learning approach with their automated intelligent cognitive knowledge as an assisting tool to the existing healthcare tools. The topics covered in this volume are data mining, patient electronic health records, healthcare portals, telemedicine, automatic identification and data collector systems, RFID and localization techniques, usability and ubiquity in e-Health, artificial intelligence for healthcare decision-making, etc. This volume will prove a valuable resource for those in academia and industry.
This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well. The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems. The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included. This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.
This book features selected papers presented at the 5th International Conference on Recent Innovations in Computing (ICRIC 2022), held on May 13–14, 2022, at the Central University of Jammu, India, and organized by the university’s Department of Computer Science and Information Technology. The conference was hosted in association with ELTE, Hungary; Knowledge University, Erbil; Cyber Security Research Lab and many other national & international partners. The book is divided into two volumes, and it includes the latest research in the areas of software engineering, cloud computing, computer networks and Internet technologies, artificial intelligence, information security, database and distributed computing, and digital India.
This book constitutes the refereed post-conference proceedings of the 10th International Conference on Mobile Communication and Healthcare, MobiHealth 2021, held in November 2021. Due to Covid-19 pandemic the conference was held virtually.
This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to create opportunities for healthcare communities to build effective FL solutions around the presented themes, and to support work in related areas that will benefit from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in the IoMT domain. The emphasis of this book is on understanding the contributions of IoMT to healthcare analytics, and its aim is to provide insights including evolution, research directions, challenges, and the way to empower healthcare services through federated learning. The book also intends to cover the ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. The book is mainly intended for undergraduates, post-graduates, researchers, and healthcare professionals who wish to learn FL-based solutions right from scratch, and build practical FL solutions in different IoMT verticals.
With the rapid penetration of technology in varied application domains, the existing cities are getting connected more seamlessly. Cities becomes smart by inducing ICT in the classical city infrastructure for its management. According to McKenzie Report, about 68% of the world population will migrate towards urban settlements in near future. This migration is largely because of the improved Quality of Life (QoL) and livelihood in urban settlements. In the light of urbanization, climate change, democratic flaws, and rising urban welfare expenditures, smart cities have emerged as an important approach for society’s future development. Smart cities have achieved enhanced QoL by giving smart information to people regarding healthcare, transportation, smart parking, smart traffic structure, smart home, smart agronomy, community security etc. Typically, in smart cities data is sensed by the sensor devices and provided to end users for further use. The sensitive data is transferred with the help of internet creating higher chances for the adversaries to breach the data. Considering the privacy and security as the area of prime focus, this book covers the most prominent security vulnerabilities associated with varied application areas like healthcare, manufacturing, transportation, education and agriculture etc. Furthermore, the massive amount of data being generated through ubiquitous sensors placed across the smart cities needs to be handled in an effective, efficient, secured and privacy preserved manner. Since a typical smart city ecosystem is data driven, it is imperative to manage this data in an optimal manner. Enabling technologies like Internet of Things (IoT), Natural Language Processing (NLP), Blockchain Technology, Deep Learning, Machine Learning, Computer vision, Big Data Analytics, Next Generation Networks and Software Defined Networks (SDN) provide exemplary benefits if they are integrated in the classical city ecosystem in an effective manner. The application of Artificial Intelligence (AI) is expanding across many domains in the smart city, such as infrastructure, transportation, environmental protection, power and energy, privacy and security, governance, data management, healthcare, and more. AI has the potential to improve human health, prosperity, and happiness by reducing our reliance on manual labor and accelerating our progress in the sciences and technologies. NLP is an extensive domain of AI and is used in collaboration with machine learning and deep learning algorithms for clinical informatics and data processing. In modern smart cities, blockchain provides a complete framework that controls the city operations and ensures that they are managed as effectively as possible. Besides having an impact on our daily lives, it also facilitates many areas of city management.
This book focuses on both theoretical and practical aspects of the “Device-Edge-Cloud continuum”, a development approach aimed at the seamless provision of next-generation cyber-physical services through the dynamic orchestration of heterogeneous computing resources, located at different distances to the user and featured by different peculiarities (high responsiveness, high computing power, etc.). The book specifically explores recent advances in paradigms, architectures, models, and applications for the “Device-Edge-Cloud continuum”, which raises many 'in-the-small' and 'in-the-large' issues involving device programming, system architectures and methods for the development of IoT ecosystem. In this direction, the contributions presented in the book propose original solutions and aim at relevant domains spanning from healthcare to industry, agriculture and transportation.