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Nabeel Salih Ali
ITRDC Center, University of Kufa. ECE, Faculty of Engineering, University of Kufa.

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Multi-objective attendance and management information system using computer application in industry strip Ahmed Hazim Alhilali; Nabeel Salih Ali; Mohammed Falih Kadhim; Basheer Al-Sadawi; Haider Alsharqi
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp371-381

Abstract

Information technology has played a vital factor in a competitive advantage for various business firms recently. Hence, catching up Investment of IT in most of the traditional industries for competitiveness purposes due to the relationship between organizational performance and IT use. In this study, attendanceand management information system (AMIS) was presented in the industry field based on multi-modules. Modules are management information, time- scheduling and attendance, and employee self- service module. The system used VB.NET environment for programming perspective as well MS SQL server for database storage. The system is secure and robust recordkeeping, keep employee up to date via self- service access, easy to use (simplified) and powerful time and attendance, active work schedules to make an employee happy and keep workers in the loop, as well, review and exporting different reports for staff manager, supervisor, and employee such as absence, vacation, time-off, employee wages, and etc.
A cluster-based feature selection method for image texture classification Abbas F. H. Alharan; Hayder K. Fatlawi; Nabeel Salih Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1433-1442

Abstract

Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.
Co-Authors Abbas F. H. Alharan Ahmed Hazim Alhilali Basheer Al-Sadawi Haider Alsharqi Hayder K. Fatlawi Mohammed Falih Kadhim