S. S. Iyengar – författare

Parallel algorithms Made Easy The complexity of today's applications coupled with the widespread use of parallel computing has made the design and analysis of parallel algorithms topics of growing interest. This volume fills a need in the field for an introductory treatment of parallel algorithms-appropriate even at the undergraduate level, where no other textbooks on the subject exist. It features a systematic approach to the latest design techniques, providing analysis and implementation details for each parallel algorithm described in the book. Introduction to Parallel Algorithms covers foundations of parallel computing; parallel algorithms for trees and graphs; parallel algorithms for sorting, searching, and merging; and numerical algorithms. This remarkable book:* Presents basic concepts in clear and simple terms* Incorporates numerous examples to enhance students' understanding* Shows how to develop parallel algorithms for all classical problems in computer science, mathematics, and engineering* Employs extensive illustrations of new design techniques* Discusses parallel algorithms in the context of PRAM model* Includes end-of-chapter exercises and detailed references on parallel computing. This book enables universities to offer parallel algorithm courses at the senior undergraduate level in computer science and engineering. It is also an invaluable text/reference for graduate students, scientists, and engineers in computer science, mathematics, and engineering.

This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.

In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries.

This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners.

This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners.

This book explores the transformative role of artificial intelligence (AI) in the field of digital forensics, offering a comprehensive overview of how AI technologies are reshaping crime investigation, cybersecurity, and evidence analysis. The book bridges traditional forensic methodologies with cutting-edge AI techniques, such as machine learning, deep learning, natural language processing, and graph analytics, to improve the accuracy, speed, and scalability of forensic investigations. Structured around real-world applications and case studies, the book covers critical areas including AI-enhanced malware detection, user behavior profiling, deep fake identification, social media profile cloning detection, canine forensics and hybrid approaches to analyzing digital artifacts. It also delves into the integration of AI with forensic tools for automating incident response, identifying anomalies in massive log files, and supporting decision-making in complex cybercrime scenarios. A key feature of the book is its interdisciplinary approach, blending insights from computer science, cybersecurity, law, and ethics. It addresses not only the technical mechanisms behind AI-driven forensics but also the legal, ethical, and societal implications, particularly regarding privacy, accountability, and the admissibility of AI-generated evidence in courts. Geared toward researchers, practitioners, students, and policymakers, Artificial Intelligence Driven Forensics serves as both a foundational text and an advanced guide to understanding the future of forensic science in the age of AI.

This book provides a thorough treatment of privacy and security issues for researchers in the fields of smart grids, engineering, and computer science. It presents comprehensive insight to understanding the big picture of privacy and security challenges in both physical and information aspects of smart grids. The authors utilize an advanced interdisciplinary approach to address the existing security and privacy issues and propose legitimate countermeasures for each of them in the standpoint of both computing and electrical engineering. The proposed methods are theoretically proofed by mathematical tools and illustrated by real-world examples.

This book provides a thorough treatment of privacy and security issues for researchers in the fields of smart grids, engineering, and computer science.

This book paves the way for researchers working on the sustainable interdependent networks spread over the fields of computer science, electrical engineering, and smart infrastructures. It provides the readers with a comprehensive insight to understand an in-depth big picture of smart cities as a thorough example of interdependent large-scale networks in both theory and application aspects. The contributors specify the importance and position of the interdependent networks in the context of developing the sustainable smart cities and provide a comprehensive investigation of recently developed optimization methods for large-scale networks. There has been an emerging concern regarding the optimal operation of power and transportation networks. In the second volume of Sustainable Interdependent Networks book, we focus on the interdependencies of these two networks, optimization methods to deal with the computational complexity of them, and their role in future smart cities. We further investigate other networks, such as communication networks, that indirectly affect the operation of power and transportation networks. Our reliance on these networks as global platforms for sustainable development has led to the need for developing novel means to deal with arising issues. The considerable scale of such networks, due to the large number of buses in smart power grids and the increasing number of electric vehicles in transportation networks, brings a large variety of computational complexity and optimization challenges. Although the independent optimization of these networks lead to locally optimum operation points, there is an exigent need to move towards obtaining the globally-optimum operation point of such networks while satisfying the constraints of each network properly.The book is suitable for senior undergraduate students, graduate students interested in research in multidisciplinary areas related to future sustainable networks, and the researchers working in the related areas. It also covers the application of interdependent networks which makes it a perfect source of study for audience out of academia to obtain a general insight of interdependent networks.

This volume contains the selected proceedings of the International Conference on Information Security, Privacy, and Digital Forensics (ICISPD 2023). The content highlights novel contributions and recent developments in areas such as cyber-attacks and defenses, computer forensics, cybersecurity database forensics, cyber threat intelligence, data analytics for security, anonymity, penetration testing, incident response, Internet of Things security, malware and botnets, social media security, humanitarian forensics, software and media piracy, crime analysis, and hardware security, among others. This volume will serve as a valuable resource for researchers in both industry and academia who are working in the fields of security, privacy, and digital forensics from both technological and social perspectives.

This volume comprises the select proceedings of the International Conference on Information Security, Privacy, and Digital Forensics (ICISPD 2022). The content discusses novel contributions and latest developments in cyber-attacks and defenses, computer forensics and cybersecurity database forensics, cyber threat intelligence, data analytics for security, anonymity, penetration testing, incident response, Internet of Things security, malware and botnets, social media security, humanitarian forensics, software and media piracy, crime analysis, hardware security, among others. This volume will be a useful guide for researchers across industry and academia working in the field of security, privacy, and digital forensics from both technological and social perspectives.