Volume 2, Number 1, April 2004

IJCIS

Computing and Information Sciences is a peer reviewed journal that is committed to timely publication of original research, surveying and tutorial contributions on the analysis and development of computing and information science. The journal is designed mainly to serve researchers and developers, dealing with information and computing. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. The journal is published 2-3 times per year with distribution to librarians, universities, research centers, researchers in computing, mathematics, and information science. The journal maintains strict refereeing procedures through its editorial policies in order to publish papers of only the highest quality. The refereeing is done by anonymous Reviewers. Often, reviews take four months to six months to obtain, occasionally longer, and it takes an additional several months for the publication process.

Paper 1: Structural and Syntactic Fault Correction Algorithms in Rule-Based Systems

Structural and Syntactic Fault Correction Algorithms in Rule-Based Systems

Nabil Arman, Dana Richards and David Rine

Abstract: The development of efficient algorithms to correct faults in rule-based systems is very crucial in extending the verification and validation of rule sets and in the development of rule-based systems. While it is important to detect various kinds of faults in rule sets, it is also equally important to provide a user/expert with a set of heuristics that can aid in correcting these faults. In this paper, a set of correction algorithms/heuristics for inconsistency, contradiction, circularity, redundancy, and unreachability faults are presented.

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Paper 2: A Multi-Agent Approach for Texture Based Classification and Retrieval

A Multi-Agent Approach for Texture Based Classification and Retrieval

Ramachandran Baskaran, Mallaya Deivamani and Arputharaj Kannan

Abstract:Texture Analysis has been used in a range of studies for recognizing synthetic and natural textures. We propose a simple, novel and yet effective method for classifying and retrieving images based on texture descriptor. In our system, the information needed for classifying the different types of textures are extracted from the Gabor features, Co-occurrence matrices and Law’s Method. For Feature pre-selection the contextual merit algorithm is used along with the decision tree. For Multi-class classification, SVM with Binary Decision Tree is used. Canberra distance metrics is used for similarity computation. A Multi-Agent system consists of a group of distributed Texture agents that organize their knowledge, goals and plans. In addition, it supports relevance feedback. Our MATBCR model results have been compared with other Texture Based Retrieval System and better prediction accuracy has been observed.

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Paper 3: Hearing Aids System for Impaired Peoples

Hearing Aids System for Impaired Peoples

Othman O. Khalifa,  M. H. Makhtar, and M.  S. Baharom

Abstract: Traditional analog hearing aids are similar to a simple radio. They can be tuned and adjusted for volume, bass and treble. But hearing loss is not just a technical loss of volume. Rather, hearing deficiency can increase sensitivity and reduce tolerance to certain sounds while diminishing sensitivity to others. For instance, digital technology can tell the difference between speech and background noise, allowing one in while filtering out the other. Approximately 10% of the world's population suffers from some type of hearing loss, yet only a small percentage of this statistic use a hearing aid. The stigma associated with wearing a hearing aid, customer dissatisfaction with hearing aid performance, and the cost associated with a high performance solution are all causes of low market penetration. Through the use of digital signal processing, digital hearing aid now offers what the analog hearing aid cannot offer. It proposes the possibility of performing signal-to noise enhancement, flexible gain-processing, digital feedback reduction, etc. In this paper, the simulation of simple digital hearing aid was developed using MATLAB programming language. The implementation of this configurable digital hearing aid (DHA) system includes the noise reduction filter, frequency shaper function, and amplitude compression function. This digital hearing aid system is design to adapt for mild and moderate hearing loss patient since different gain can be set to map different levels of hearing loss.

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Paper 4: A Fragile Watermarking Algorithm for Content Authentication

A Fragile Watermarking Algorithm for Content Authentication 

Raja S. Al Omari and Ahmed Al-Jaber

Abstract: In many multimedia applications, there is a need to authenticate a source that has been subjected to potential tampering attacks. This application is called Content Authentication. Watermarking is among the emerging fields that are used in Content Authentication. Fragile Watermarking Algorithms are usually used in building Content Authentication Systems. This Paper Proposes a Secure Fragile Watermarking Algorithm. This algorithm is an extension of an existing data hiding scheme which is proposed for binary images by Tseng et al. in [11]. The proposed algorithm shows a very high fidelity and fragility. Those two properties enabled the applicability of this algorithm for Content Authentication. A Signature is extracted from each block of the image and is inserted in that block. Extraction of this signature and appropriate parameters for computation of this signature are studied in this paper. The technique by which this signature is extracted is a modified version of a signature extraction function proposed in [5]. A detailed study for the applicability of this algorithm for Content Authentication is done. Experimental results show a very high ability for tamper detection. Many tamper attacks are applied and analyzed.

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Paper 5: Evolutionary Algorithms for Multi-Criterion Optimization: A Survey

Evolutionary Algorithms for Multi-Criterion Optimization: A Survey

Ashish Ghosh and Sathidananda Dehuri

Abstract: In this paper, we review some of the most popular evolutionary algorithms and a systematic comparison among them. Then we show its importance in single and multi-objective optimization problems. Thereafter focuses some of the multi-objective evolutionary algorithms, which are currently being used by many researchers, and merits & demerits of multi-objective evolutionary algorithms (MOEAs). Finally, the future trends in this area and some possible paths of further research are addressed.

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Paper 6: Artificial Neural Networks (ANN) Approach to PPG Signal Classification

Artificial Neural Networks (ANN) Approach to PGG Signal Classification 

Mohamed Soltane, Mahamod Ismail, Zainol Abidin Abdul Rashid

Abstract: Ageing and disease states associated with an increase in cardiovascular events alter the physical characteristics of blood vessel walls and impair the pulsatile function of arteries. A variety of techniques are employed to evaluate the mechanical properties of arteries. All techniques have theoretical, technical and practical limitations that impact on their widespread application in the clinical setting and use as measurement tools to improve cardiovascular risk stratification [2]. This paper presents Artificial Neural Network (ANN) approaches that classify a PPG signal into two distinct classes. Multistage based on time-series data mining framework for building classification models in the presence temporal high dimensional data, was suggested. First we reduce the dimensionality by smoothing the input signal and we assume that the smoothing accuracy serve features by exploring the highly parallelised nature of multilayer feed-forward networks (MFN). The classification results showed that multilayer perceptron neural network employing back propagation-training algorithm was effective to distinct between the two classes, based on the good selection of the training data set samples. The correct classification rate was 100% for the training data sets and 94.7% for testing data sets. We used for testing the algorithm 170 samples, in which 56 samples are pathologies and 114 are healthier. The paper also discusses the future research directions.

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Contacts

Editor-in-Chief
Prof. Jihad Mohamad Alja'am 
Email: journal.editor.ijcis@gmail.com

The Journal Secretary
Eng. Dana Bandok
Ontario, Canada 
Email: sec.ijcis@gmail.com 

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