This new volume provides a collection of chapters on diverse topics in machine learning algorithms and security analytics, AI and machine learning, and network security applications. It presents a variety of design algorithms that allow computers to employ machine learning to display behavior learned from past experiences rather than human interaction for solutions to security issues and other challenges in data management.
The book discusses a variety of algorithms, including Convolutional Neural Network (CNN), Random Forest Algorithm, K-Nearest Neighbor (KNN), Apriori algorithm, MapReduce algorithm, Genetic Algorithm used in IoT applications, and more.
The volume presents a survey of speculative parallelism techniques, overheads due to mis-speculation of parallel threads, performance reviews, and finally efficient power consumption. It discusses measuring perceived quality of software ecosystems based on transactions in customer management tools and offers a study of the background modeling and background subtraction along with various other literature studies that justify the role of moving object detection in computer vision. The book also discusses the major challenging issues that occur in real-time environments, outlines the key developments of UAV networks for disaster management applications, and addresses open research issues and challenges based on UAV for disaster management. It also covers the concepts of learning with NASA datasets.
Scientists, researchers, faculty, and students involved in research in the area of AI, machine learning, and network security will find valuable information in this volume.
About the Author: Karan Singh, PhD, is Assistant Professor in the School of Computer and Systems Sciences at Jawaharlal Nehru University, New Delhi, India. His areas of interest include multicast communications, information security, cryptography, security issues in wireless sensor networks, intelligent vehicular networks, Internet of Things, and cyber-physical systems. He has published many journal articles and conference papers. He has also attended and many international conferences and workshops and has delivered invited talks and lectures. He is a senior member of IEEE and is an IEEE MGM awardee.
Latha Banda, PhD, is Associate Professor of Computer Sciences and Engineering at ABES Engineering College, Ghaziabad, U.P., India. She was previously an Associate Professor at Sharda University and Lingaya's University, India. Her areas of interest include artificial intelligence, recommender systems, machine learning, network security, and soft computing. She has published several journal articles and conference papers and has attended and participated at international conferences and workshops. She is a member of the Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Institution of Electronics and Telecommunication Engineers.
Manisha Manjul, PhD, is working with the Department of Computer Engineering at G. B. Pant DESU Okhla-I Campus, New Delhi, India. She has published several papers in Scopus-indexed journals as well as several conference papers. She organized workshops, conferences, and faculty development programs. Her primary research interests are in computer network, network security, multicast communication, and object-oriented programming. She is a life member of the Computer Society of India.
