Advances in Computing Applications

Advances in Computing ApplicationsReviews
Author: A. Chakrabarti, N. Sharma, V. Balas
Pub Date: 2017
ISBN: 978-981-10-2629-4
Pages: 285
Language: English
Format: PDF
Size: 11 Mb

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This edited volume presents the latest high-quality technical contributions and research results in the areas of computing, informatics, and information management. The book deals with state-of art topics, discussing challenges and possible solutions, and explores future research directions. The main goal of this volume is not only to summarize new research findings but also place these in the context of past work. This volume is designed for professional audience, composed of researchers, practitioners, scientists and engineers in both the academia and the industry.

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Table of Contents

1 Wait Event Tuning in Database Engine
2 Machine Learning Using K-Nearest Neighbor for Library Resources Classification in Agent-Based Library Recommender System
3 An Efficient Dynamic Scheduling of Tasks for Multicore Real-Time Systems
4 Model-Based Approach for Shadow Detection of Static Images
5 Light Fidelity (Li-Fi): In Mobile Communication and Ubiquitous Computing Applications
6 Performance Analysis of Denoising Filters for MR Images
7 A Detailed View on SecureString 3.0
8 Performance Comparison for EMD Based Classification of Unstructured Acoustic Environments Using GMM and k-NN Classifiers
9 Performance of Multimodal Biometric System Based on Level and Method of Fusion
10 DSK-Based Authentication Technique for Secure Smart Grid Wireless Communication
11 A Smart Security Framework for High-Risk Locations Using Wireless Authentication by Smartphone
12 High Performance Computation Analysis for Medical Images Using High Computational Method
13 Terrorist Scanner Radar and Multiple Object Detection System
14 Inexact Implementation of Wavelet Transform and Its Performance Evaluation Through Bit Width Reduction
15 A Vulnerability Analysis Mechanism Utilizing Avalanche Attack Model for Dependency-Based Systems
16 Performance of Statistical and Neural Network Method for Prediction of Survival of Oral Cancer Patients