Jaya Sil

This book offers a single-resource reference on different analysis strategies of the ball mill acoustics. One of the major problems in the mining industry is the raw material wastage. The biggest challenge faced by the researchers is to install sensors to acquire meaningful data during the crushing operation due to huge dust and the presence of grinding media. The book aims to propose an intelligent system to predict the particle size distribution of the raw material in the closed-loop mill operation and stop the mill automatically by receiving the desired particle size ranges. The simulated results are validated with the experimental results, obtained from an instrumented lab-based ball mill. The acoustics of the running ball mill is considered as the key parameter which is a state of art in this field. This book will be useful for Researchers who work in the field of Digital signal processing.

This book offers a single-resource reference on different analysis strategies of the ball mill acoustics. One of the major problems in the mining industry is the raw material wastage. The biggest challenge faced by the researchers is to install sensors to acquire meaningful data during the crushing operation due to huge dust and the presence of grinding media. The book aims to propose an intelligent system to predict the particle size distribution of the raw material in the closed-loop mill operation and stop the mill automatically by receiving the desired particle size ranges. The simulated results are validated with the experimental results, obtained from an instrumented lab-based ball mill. The acoustics of the running ball mill is considered as the key parameter which is a state of art in this field. This book will be useful for Researchers who work in the field of Digital signal processing.

This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion. The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.

This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion. The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.

This book introduces characteristic features of the protein structure prediction (PSP) problem. It focuses on systematic selection and improvement of the most appropriate metaheuristic algorithm to solve the problem based on a fitness landscape analysis, rather than on the nature of the problem, which was the focus of methodologies in the past. Protein structure prediction is concerned with the question of how to determine the three-dimensional structure of a protein from its primary sequence. Recently a number of successful metaheuristic algorithms have been developed to determine the native structure, which plays an important role in medicine, drug design, and disease prediction. This interdisciplinary book consolidates the concepts most relevant to protein structure prediction (PSP) through global non-convex optimization. It is intended for graduate students from fields such as computer science, engineering, bioinformatics and as a reference for researchers and practitioners.

This book introduces characteristic features of the protein structure prediction (PSP) problem. It focuses on systematic selection and improvement of the most appropriate metaheuristic algorithm to solve the problem based on a fitness landscape analysis, rather than on the nature of the problem, which was the focus of methodologies in the past. Protein structure prediction is concerned with the question of how to determine the three-dimensional structure of a protein from its primary sequence. Recently a number of successful metaheuristic algorithms have been developed to determine the native structure, which plays an important role in medicine, drug design, and disease prediction. This interdisciplinary book consolidates the concepts most relevant to protein structure prediction (PSP) through global non-convex optimization. It is intended for graduate students from fields such as computer science, engineering, bioinformatics and as a reference for researchers and practitioners.