AIRCC's International Journal of Computer Science and Information Technology

R. A. Soltan ¹, M. Z. Rashad ², B. El-Desouky ³

Affiliations
1 Dep. of Statistic and Computer Science, Mansoura University, EG
2 Dep. of Comp. Scienecs, Mansoura University, EG
3 Dep. of Mathematics, Mansoura University, EG

Abstract

Nowadays medical application especially diagnosis of some heart diseases has been rapidly increased because its importance and effectiveness to detect diseases and classify patients. In this research, we present the design of an expert system that aims to provide the patient with background for suitable diagnosis and treatment (Especially Angina Pectoris and Myocardial infarction). The proposed methodology is composed of four stages. The first stage is receiving the symptoms from the patient. The second stage is requesting from the patient to make some analysis and investigation to help the system to make a correct decision in the diagnosis. The third stage is doing diagnosis of patient according to information from patient (symptoms, analysis and investigation). The four stage is determining the name of appropriate medication or what should be done until the patient recovers (step therapy), so this system is able to give appropriate diagnosis and treatment for two heart diseases namely; angina pectoris and infarction. There are several programs used for diagnosis and system analysis, such as CLIPS and PROLOG. A medical expert system in this search made by Visual Prolog 7.3 is proposed.

Keywords

Experts System, Diagnosis, Medical, Coronary Artery Diseases, Experts System, Diagnosis, Medical, Clips, Prolog, Coronary Artery Diseases.

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B. S. El-Desouky ¹, M. El-Kady ², M. Z. Rashad ³, Mahmoud M. Eid ¹

Affiliations
1 Mathematics Department, Mansoura University, EG
2 Mathematics Department, Helwan University, EG
3 Computer Science Department, Mansoura University, EG

Abstract

Personal identification using 2D ear images still has many problems such as occlusion mostly caused by hair, earrings, and clothes. To avoid this problem, we propose to divide the ear image into non-overlapping equal divisions and identify persons through these non-occluded parts separately and then combine outputs of the classification of these parts in abstract, rank, and measurement level fusion. Experimental results show that the increasing of recognition rate through combining small parts of non-occluded divisions of ear image.

Keywords

Ear Recognition, LDA (Linear Discriminante Analysis), DCT (Discrete Cosine Transform), Combining Classifiers.

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M. Z. Rashad ¹

Affiliations
1 Mansoura University, EG

Abstract

Real Time strategy games offer an environment where game AI is known to conduct actuality. One feature of realistic behavior in game AI is the ability to recognize the strategy of the opponent player. This is known as opponent modeling. In this paper, a classification Rough-Neuro hybrid model of the RTS opponent player behavior process is proposed. As a mean to achieve better game performance, reduction of the agent decision space and better high-level winning of real-time strategy games. The Rough-Neuro methodology allows the classification model to some extent simulate opponent behavior in playing RTS games. The methodology incorporates a two-stage hybrid mechanism. Rough sets for reduction of relevant attributes and artificial neural networks for classification opponent behavior during game playing. The proposed hybrid approach has been tested on an open source 3D RTS game called Glest. From our results we can deduce that the tactic may be successfully used for foretelling the demeanor of contender in the Glest game.

Keywords

Real-Time Strategy Games, Rough Sets, Attribute Reduction, Opponent Modeling, Neural Network.

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M. E. ElAraby ¹, M. M. Sakre ¹, M. Z. Rashad ², O. Nomir ²

Affiliations
1 Dept. of Computer Science, High Institute for Computers and Information Systems, Al Shorouk Academy, EG
2 Dept. of Computer Science, Mansoura University, EG

Abstract

Crawler is one of the main components in the search engines which use URLs to fetch web pages to build a repository of web pages starting with entering URL. Each web page is parsed to extract the URLs included in it and store the extracted URLs in the URLs Queue to fetch by the crawlers in sequential. The process of crawling takes long time to collect more web pages, and it has become necessary to utilize the unused computing resources and cost/time savings in organizations. This paper deals with the crawler of search engine using grid computing. This paper presents the grid computing that has been implemented by Alchemi. Alchemi is an open source project developed at the University of Melbourne, provides middleware for creating an enterprise grid computing environment. The crawling processes are passed to Alchemi manager which distribute the processes over a number of computers as executors. The search engine crawler with the grid computing is implemented, tested and the results are analyzed. There is an increase in performance and less time over the single computer.

Keywords

Crawler, URL, Grid Computing, Alchemi, Manager, Executor, Performance, and Web Pages.

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M. Z. Rashad ¹, M. Y. Shams ¹, O. Nomir ¹, R. M. El-Awady ¹

Affiliations
1 Dept. of Computer Science, Mansoura University, EG

Abstract

Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical approaches for feature extraction, and Combined Learning Vector Quantization Classifier as Neural Network approach for classification, in order to build a hybrid model depends on both features. The localization and segmentation techniques are presented using both Canny edge detection and Hough Circular Transform in order to isolate an iris from the whole eye image and for noise detection .Feature vectors results from LBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptable performance, and the result is based on majority voting among several LVQ classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different extensions and size are presented. Since LBP is working on a grayscale level so colored iris images should be transformed into a grayscale level. The proposed system gives a high recognition rate 99.87 % on different iris datasets compared with other methods.

Keywords

Iris Recognition System (IRS), Local Binary Pattern (LBP), Histogram properties, Learning Vector Quantization (LVQ), and Combined Classifier.

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M. Z. Rashad ¹, B. S. el-Desouky ², Manal S. Khawasik ¹

Affiliations
1 Dept. of Computer Science, Mansoura University, EG
2 Dept. of the Mathematics, Mansoura University, EG

Abstract

This paper introduces an approach of plant classification which is based on the characterization of texture properties. We used the combined classifier learning vector quantization. We randomly took out 30 blocks of each texture as a training set and another 30 blocks as a testing set. We found that the combined classifier method gave a high performance which is a superior than other tested methods. The experimental results indicated that our algorithm is applicable and its average correct recognition rate was 98.7%.

Keywords

Plant Classification, Learning Vector Quantization(LVQ), Radial Base Function(RBF), Texture Classification, Neural Networks, Texture Classification.

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M. Z. Rashad ¹, Hazem M. El-Bakry ², Islam R. Isma'il ²

Affiliations
1 Department of Computer Science, Mansoura University, EG
2 Department of Information Systems, Mansoura University, EG

Abstract

Concatenative speech synthesis systems generate speech by concatenating small prerecorded speech units which are stored in the speech unit inventory. The most commonly used type of these units is the diphone which is a unit that starts at the middle of one phone and extends to the middle of the following one. Diphones have the advantage of modeling coarticulation by including the transition to the next phone inside the diphone itself. In this paper, a diphone speech synthesis system for the Arabic language using MARY TTS has been developed and evaluated by two types of tests which are the Diagnostic Rhyme Test (DRT) that measures the intelligibility of the synthesized speech and the Categorical Estimation (CE) test that measures the overall quality of the synthesized speech. The results of these tests are illustrated in the experiments and results section.

Keywords

Speech Synthesis, Concatenative Synthesis, Diphone Inventory, Natural Language Processing, Markup Language, Digital Signal Processing.

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