Jimson Mathew – författare

Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aims to systematically collect quality research spanning AI, ML, and deep learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights on the applicability of the theoretical similitudes, otherwise a rarity in many such books.

Features:

This book serves as a valuable reference book for students, researchers, and practitioners who wish to study and get acquainted with the application of cutting-edge AI, ML, and DL techniques to network management and cyber security.

Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aims to systematically collect quality research spanning AI, ML, and deep learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights on the applicability of the theoretical similitudes, otherwise a rarity in many such books.

Features:

This book serves as a valuable reference book for students, researchers, and practitioners who wish to study and get acquainted with the application of cutting-edge AI, ML, and DL techniques to network management and cyber security.

This book covers various aspects of optimization in design and testing of Network-on-Chip (NoC) based multicore systems.  It gives a complete account of the state-of-the-art and emerging techniques for near optimal mapping and test scheduling for NoC-based multicores. The authors describe the use of the Integer Line Programming (ILP) technique for smaller benchmarks and a Particle Swarm Optimization (PSO) to get a near optimal mapping and test schedule for bigger benchmarks. The PSO-based approach is also augmented with several innovative techniques to get the best possible solution. The tradeoff between performance (communication or test time) of the system and thermal-safety is also discussed, based on designer specifications.  

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

This book covers the important aspects involved in making cognitive radio devices portable, mobile and green, while also extending their service life. At the same time, it presents a variety of established theories and practices concerning cognitive radio from academia and industry.

Cognitive radio can be utilized as a backbone communication medium for wireless devices. To effectively achieve its commercial application, various aspects of quality of service and energy management need to be addressed. The topics covered in the book include energy management and quality of service provisioning at Layer 2 of the protocol stack from the perspectives of medium access control, spectrum selection, and self-coexistence for cognitive radio networks.

This book highlights selected papers presented at the 10th International Symposium on Embedded Computing and System Design (ISED) 2021. This symposium provides a platform for researchers to share the latest scientific achievements of embedded computing and system design. The book is divided into three broad sections. The first section discusses topics like VLSI and testing, circuits and systems with a focus on emerging technologies. The second section discusses topics like embedded hardware and software systems and novel applications. The final section discusses the state-of-the-art technologies involving IoT, artificial intelligence, green and edge computing that demonstrates the issues currently.

This book comprises select proceedings of the 7th International Conference on Data Science and Engineering (ICDSE 2021). The contents of this book focus on responsible data science. This book tries to integrate research across diverse topics related to data science, such as fairness, trust, ethics, confidentiality, transparency, and accuracy. The chapters in this book represent research from different perspectives that offer novel theoretical implications that span multiple disciplines. The book will serve as a reference resource for researchers and practitioners in academia and industry.