Open Computer Science Volume 3 Issue 3

Open Computer Science

Volume 3 Issue 3

Contents
Journal Overview

Pruning algorithms of neural networks — a comparative study

M. Augasta, T. Kathirvalavakumar September 25, 2013 Page range: 105-115

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Abstract

The neural network with optimal architecture speeds up the learning process and generalizes the problem well for further knowledge extraction. As a result researchers have developed various techniques for pruning the neural networks. This paper provides a survey of existing pruning techniques that optimize the architecture of neural networks and discusses their advantages and limitations. Also the paper evaluates the effectiveness of various pruning techniques by comparing the performance of some traditional and recent pruning algorithms based on sensitivity analysis, mutual information and significance on four real datasets namely Iris, Wisconsin breast cancer, Hepatitis Domain and Pima Indian Diabetes.

Efficient computer aided diagnosis of abnormal parts detection in magnetic resonance images using hybrid abnormality detection algorithm

C. Lakshmi Devasena, M. Hemalatha September 25, 2013 Page range: 117-128

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Abstract

In Medical Diagnosis, Magnetic Resonance Image (MRI) plays a momentous role. MRI is based on the physical and chemical principles of Nuclear Magnetic Resonance (NMR), a technique used to gain information about the nature of molecules. Retrieving a high quality MR Image for a medical diagnosis is critical. So denoising of Magnetic Resonance (MR) images and making them easy for human understanding form is a challenge. This research work presents an efficient Hybrid Abnormal Detection Algorithm (HADA) to detect the abnormalities in any part of the human body by MRIs. The proposed technique includes five stages: Noise Reduction, Smoothing, Feature Extraction, Feature Reduction and Classification. The proposed algorithm has been implemented and Classification accuracy of 98.80% has been achieved. The result shows that the proposed technique is robust and effective compared to other recent works. The system developed using the proposed algorithm will be a good computer aided diagnosis and decision making system in healthcare.

Face identification under uncontrolled environment with LGFSV face representation technique

Kavita Singh, Mukesh Zaveri, Mukesh Raghuwanshi September 25, 2013 Page range: 129-148

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Abstract

This paper presents a new log Gabor-FPLBP-SVD (LGFSV) face representation technique that extracts singular values from log Gabor_FPLBP response image to form the LGFSV feature for face representation. The proposed LGFSV is invariant to changes in pose, illumination and facial expression. The novelty of this paper comes from (i) the design of minimal number of log Gabor filters to cover all directional shape features from face image which is further applied with Four phase Local Binary Pattern to enhance texture features in all direction; (ii) the extraction of singular value from each local matrix of log Gabor_FPLBP response image to form feature for face identification using nearest neighbour classifier; and (iii) extensive performance evaluation. In particular, the performance of the proposed LGFSV for face identification under pose variation and change in illumination and expression is evaluated on standard face databases such as ORL; Head Pose Image Database, Georgia Tech Face Database, CMU-PIE, GTAV and RLCI face databases. Experimental results with LGFSV show a significant improvement over individual face representation techniques.

About this journal

Open Computer Science publishes original and significant research results in the following fields:

algorithms and complexity theory
artificial intelligence
bioinformatics
networking and security systems
programming languages
system and software engineering
theoretical foundations of computer science

The journal considers submissions of different types of articles, including "Research Articles" as well as “Review Articles”, which present the existing literature and achievements on the specific topic from new perspectives. We accept both theoretical and empirical contributions.

Why read

Open Computer Science is a premier source of a high quality research. Thanks to Open Access all articles are freely available to the academic community worldwide.

Why submit

Authors benefit from:

fair and constructive peer-review
no space constraints
quick publication of accepted papers*
Open Access for all articles**
promotion of published papers to readers and citers
long-term preservation of the content***

* The journal uses continuous publication model − accepted manuscripts are published quickly after the acceptance.
** Thanks to Open Access, all articles are freely available to the academic community worldwide without any restrictions, there are more liberal policies on copyrights (authors retain copyright) and on self-archiving (no embargo periods).
*** The journal is archived in Portico.

Detailed information on Editorial Policy, Publication Ethics, Instructions for Authors etc. can be found below and in the Supplementary Information section.