Maanak Gupta – författare

Mathematical Modeling for Intelligent Systems: Theory, Methods, and Simulation aims to provide a reference for the applications of mathematical modeling using intelligent techniques in various unique industry problems in the era of Industry 4.0. Providing a thorough introduction to the field of soft-computing techniques, this book covers every major technique in artificial intelligence in a clear and practical style. It also highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms. This book demonstrates concepts through simulation examples and practical experimental results.

Key Features:

• Offers a well-balanced mathematical analysis of modeling physical systems

• Summarizes basic principles in differential geometry and convex analysis as needed

• Covers a wide range of industrial and social applications and bridges the gap between core theory and costly experiments through simulations and modeling

• Focuses on manifold ranging from stability of fluid flows, nanofluids, drug delivery, and security of image data to pandemic modeling, etc.

This book is primarily aimed at advanced undergraduates and postgraduate students studying computer science, mathematics, and statistics. Researchers and professionals will also find this book useful.

Mathematical Modeling for Intelligent Systems: Theory, Methods, and Simulation aims to provide a reference for the applications of mathematical modeling using intelligent techniques in various unique industry problems in the era of Industry 4.0. Providing a thorough introduction to the field of soft-computing techniques, this book covers every major technique in artificial intelligence in a clear and practical style. It also highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms. This book demonstrates concepts through simulation examples and practical experimental results.

Key Features:

• Offers a well-balanced mathematical analysis of modeling physical systems

• Summarizes basic principles in differential geometry and convex analysis as needed

• Covers a wide range of industrial and social applications and bridges the gap between core theory and costly experiments through simulations and modeling

• Focuses on manifold ranging from stability of fluid flows, nanofluids, drug delivery, and security of image data to pandemic modeling, etc.

This book is primarily aimed at advanced undergraduates and postgraduate students studying computer science, mathematics, and statistics. Researchers and professionals will also find this book useful.

Data has revolutionized the digital ecosystem. Readily available large datasets foster AI and machine learning automated solutions. The data generated from diverse and varied sources including IoT, social platforms, healthcare, system logs, bio-informatics, etc. contribute to and define the ethos of Big Data which is volume, velocity and variety. Data lakes formed by the amalgamation of data from these sources requires powerful, scalable and resilient storage and processing platforms to reveal the true value hidden inside this data mine. Data formats and its collection from various sources not only introduce unprecedented challenges to different domains including IoT, manufacturing, smart cars, power grids etc., but also highlight the security and privacy issues in this age of big data. Security and privacy in big data is facing many challenges, such as generative adversary networks, efficient encryption and decryption algorithms, encrypted information retrieval, attribute-based encryption, attacks on availability, and reliability. Providing security and privacy for big data storage, transmission, and processing have been attracting much attention in all big data related areas.

The book provides timely and comprehensive information for researchers and industry partners in communications and networking domains to review the latest results in security and privacy related work of Big Data. It will serve computer science and cybersecurity communities including researchers, academicians, students, and practitioners who have interest in big data trust privacy and security aspects. It is a comprehensive work on the most recent developments in security of datasets from varied sources including IoT, cyber physical domains, big data architectures, studies for trustworthy computing, and approaches for distributed systems and big data security solutions etc.

Data has revolutionized the digital ecosystem. Readily available large datasets foster AI and machine learning automated solutions. The data generated from diverse and varied sources including IoT, social platforms, healthcare, system logs, bio-informatics, etc. contribute to and define the ethos of Big Data which is volume, velocity and variety. Data lakes formed by the amalgamation of data from these sources requires powerful, scalable and resilient storage and processing platforms to reveal the true value hidden inside this data mine. Data formats and its collection from various sources not only introduce unprecedented challenges to different domains including IoT, manufacturing, smart cars, power grids etc., but also highlight the security and privacy issues in this age of big data. Security and privacy in big data is facing many challenges, such as generative adversary networks, efficient encryption and decryption algorithms, encrypted information retrieval, attribute-based encryption, attacks on availability, and reliability. Providing security and privacy for big data storage, transmission, and processing have been attracting much attention in all big data related areas.

The book provides timely and comprehensive information for researchers and industry partners in communications and networking domains to review the latest results in security and privacy related work of Big Data. It will serve computer science and cybersecurity communities including researchers, academicians, students, and practitioners who have interest in big data trust privacy and security aspects. It is a comprehensive work on the most recent developments in security of datasets from varied sources including IoT, cyber physical domains, big data architectures, studies for trustworthy computing, and approaches for distributed systems and big data security solutions etc.

The main aim of the book is to familiarize readers with the concepts of convergence of different connected and smart domains that are assisted by Cloud Computing, core technologies behind Cloud Computing, driving factors towards Cloud Computing, and security challenges and proposed solutions in Cloud Computing. The book covers not only the cloud, but also other pertinent topics such as Machine Learning, Deep Learning, IoT and Fog/Edge Computing. The last section of the book mainly focuses on the security aspects of connected technologies.

The highpoints of the book is that it reviews the relation and combination of the mentioned topics, which together creates a better understanding about almost every aspect of Cloud Computing & related technologies.

The main aim of the book is to familiarize readers with the concepts of convergence of different connected and smart domains that are assisted by Cloud Computing, core technologies behind Cloud Computing, driving factors towards Cloud Computing, and security challenges and proposed solutions in Cloud Computing. The book covers not only the cloud, but also other pertinent topics such as Machine Learning, Deep Learning, IoT and Fog/Edge Computing. The last section of the book mainly focuses on the security aspects of connected technologies.

The highpoints of the book is that it reviews the relation and combination of the mentioned topics, which together creates a better understanding about almost every aspect of Cloud Computing & related technologies.

This book presents cybersecurity aspects of ubiquitous and growing IoT and Cyber Physical Systems. It also introduces a range of conceptual, theoretical, and foundational access control solutions. This was developed by the authors to provide an overall broader perspective and grounded approach to solve access control problems in IoT and CPS.

The authors discuss different architectures, frameworks, access control models, implementation scenarios, and a broad set of use-cases in different IoT and CPS domains. This provides readers an intuitive and easy to read set of chapters. The authors also discuss IoT and CPS access control solutions provided by key industry players including Amazon Web Services (AWS) and Google Cloud Platform (GCP). It provides extensions of the authors proposed fine grained solutions with these widely used cloud and edge supported platforms.

This book is designed to serve the computer science and the cybersecurity community including researchers,academicians and students.  Practitioners who have a wider interest in IoT, CPS, privacy and security aspects will also find this book useful. Thanks to the holistic planning and thoughtful organization of this book, the readers are expected to gain in-depth knowledge of the state-of-the-art access control architectures and security models for resilient IoT and CPS.