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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Marwan B. Mohammed
Al-Nahrain University

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Inheritance issues’ features extraction using Arabic text analyzer (IFAA) Abeer K. Al-Mashhadany; Marwan B. Mohammed; Mawlood Alrawi Alrawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp611-624

Abstract

Inheritance issue is part of our life. Daily, many persons may die. Person is gone, while his money stays for others. Islamic law took care of the issue of inheritance. Al-Quran has verses dedicated in inheritance issue. Al-Quran gives every person its rights, so; the share for each heir is determined. Islamic law jurists are asked frequently to solve inheritance issues. This work; inheritance issues’ features extraction using Arabic text analyzer (IFAA) hopes to analyze inheritance issue. It receives the issue as Arabic unstructured characterized text. It applies Arabic analyzer system to extract all features. Many commercial applications are constructed to solve inheritance issue; they receive the features manually, while this work is an attempt to computerize features' extracting. This work needs a good experience in analyzing Arabic text. So, this research attempts developing Arabic analyzer system dedicated in inheritance issues, which has the ability to analyze inheritance issue and extract its features. It will be shown that Arabic analyzer system is useful in converting Arabic text into data that are understandable by programming languages, and those data could be used to perform arithmetic calculations, and achieve high accuracy reaches to 100%.
Evaluating face recognition with different texture descriptions and convolution neural network Wafaa Mohammed Saeed Hamzah Al-Hameed; Marwan B. Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp332-340

Abstract

Extracting the remarkable attributes of the image objects is an issue of ongoing research special in the face recognition problem. This paper presents two directions. The first is a comparison between the local binary patterns (LBP) and its modified center symmetric LBP drawn from localized facial expressions and due to the efficiency, K-nearest neighbor (KNN) and the support vector machine (SVM) techniques play significant roles in this research used to implement the proposed system efficiently. The second direction proposes an efficient architecture by depending on deep learning convolution neural network (CNN) to implement face recognition. Such a design consists of two parts: a convolutional learning feature model and a classification model. The first one learns the important feature,while the second part produces a score class for each sample input. Many experiments are implemented on the known dataset once for the number of nearest neighbors (K value), and then decrease the number of expression samples for each individual the other time. The cross-validation method is used to provide a true picture of the accuracy of the face recognition system. In all experiment results, the center symmetric LBP with KNN outperforms the classic LBP. While significant progress in the results accuracy recognition ratio of the CNN model compared with other methods used.
Co-Authors Abeer K. AL-Mashhadany Mawlood Alrawi Alrawi Wafaa Mohammed Saeed Hamzah Al-Hameed