Satchidananda Dehuri

Alok Kumar Jagadev

Sandeep Kumar Satapathy

Shruti Mishra

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

B. S.P. Mishra

Euiwhan Kim

Gi-Name Wang

This volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role in big data analysis by adopting Parallel, Grid, and Cloud computing environments.

Parallel, Cloud, and Grid Computing

Techniques and Environments for Big Data Analysis

Sung-Bae Cho

Knowledge Mining Using Intelligent Agents explores the concept of knowledge discovery processes and enhances decision-making capability through the use of intelligent agents like ants, termites and honey bees. In order to provide readers with an integrated set of concepts and techniques for understanding knowledge discovery and its practical utility, this book blends two distinct disciplines — data mining and knowledge discovery process, and intelligent agents-based computing (swarm intelligence and computational intelligence). For the more advanced reader, researchers, and decision/policy-makers are given an insight into emerging technologies and their possible hybridization, which can be used for activities like dredging, capturing, distributions and the utilization of knowledge in their domain of interest (i.e. business, policy-making, etc.). By studying the behavior of swarm intelligence, this book aims to integrate the computational intelligence paradigm and intelligent distributed agents architecture to optimize various engineering problems and efficiently represent knowledge from the large gamut of data. Contents: Theoretical Foundations of Knowledge Mining and Intelligent Agent (S Dehuri & S-B Cho)The Use of Evolutionary Computation in Knowledge Discovery: The Example of Intrusion Detection Systems (S X Wu & W Banzhaf)Evolution of Neural Network and Polynomial Network (B B Misra et al.)Design of Alloy Steels Using Multi-Objective Optimization (M Chen et a.)An Extended Bayesian/HAPSO Intelligent Method in Intrusion Detection System (S Dehuri & S Tripathy)Mining Knowledge from Network Intrusion Data Using Data Mining Techniques (M Panda & M R Patra)Particle Swarm Optimization for Multi-Objective Optimal Operational Planning of Energy Plants (Y Fukuyama et al.)Soft Computing for Feature Selection (A K Jagadev et al.)Optimized Polynomial Fuzzy Swarm Net for Classification (B B Misra et al.)Software Testing Using Genetic Algorithms (M Ray & D P Mohapatra) Readership: Researchers and professionals in the knowledge discovery industry. Keywords:Intelligent Agent;Knowledge Mining;Data Mining;Knowledge Discovery;Computational Intelligence;Swarm Intelligence;Evolutionary ComputationKey Features:Addresses the various issues/problems of knowledge discovery, data mining tasks and the various design challenges by the use of different intelligent agents technologiesCovers new and unique intelligent agents techniques (computational intelligence + swarm intelligence) for knowledge discovery in databases and data mining to solve the tasks of different phases of knowledge discoveryHighlights data pre-processing for knowledge mining and post-processing of knowledge that is ignored by most of the authorsConsists of a collection of well-organized chapters written by prospective authors who are actively engaged in this active area of research

Knowledge Mining Using Intelligent Agents

Himansu Das

Bhabani Shankar Prasad Mishra

This book discusses harnessing the real power of cloud computing in optimization problems, presenting state-of-the-art computing paradigms, advances in applications, and challenges concerning both the theories and applications of cloud computing in optimization with a focus on diverse fields like the Internet of Things, fog-assisted cloud computing, and big data. In real life, many problems – ranging from social science to engineering sciences – can be identified as complex optimization problems. Very often these are intractable, and as a result researchers from industry as well as the academic community are concentrating their efforts on developing methods of addressing them. Further, the cloud computing paradigm plays a vital role in many areas of interest, like resource allocation, scheduling, energy management, virtualization, and security, and these areas are intertwined with many optimization problems. Using illustrations and figures, this book offers students and researchers a clear overview of the concepts and practices of cloud computing and its use in numerous complex optimization problems.

Satchidananda Dehuri

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