Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Fahad A. Alghamdi
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Hybrid feature selection method based on particle swarm optimization and adaptive local search method Malek Alzaqebah; Sana Jawarneh; Rami Mustafa A. Mohammad; Mutasem K. Alsmadi; Ibrahim Al-marashdeh; Eman A. E. Ahmed; Nashat Alrefai; Fahad A. Alghamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2414-2422

Abstract

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.
Learning trends in customer churn with rule-based and kernel methods Nahier Aldhafferi; Abdullah Alqahtani; Fatema Sabeen Shaikh; Sunday Olusanya Olatunji; Abdullah Almurayh; Fahad A. Alghamdi; Ghalib H. Alshammri; Amani K. Samha; Mutasem Khalil Alsmadi; Hayat Alfagham; Abderrazak Ben Salah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5364-5374

Abstract

In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.
Comparison of specific segmentation methods used for copy move detection Eman Abdulazeem Ahmed; Malek Alzaqebah; Sana Jawarneh; Jehad Saad Alqurni; Fahad A. Alghamdi; Hayat Alfagham; Lubna Mahmoud Abdel Jawad; Usama A. Badawi; Mutasem K. Alsmadi; Ibrahim Almarashdeh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2363-2374

Abstract

In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.
Co-Authors Abderrazak Ben Salah Abdullah Almurayh Abdullah Alqahtani Amani K. Samha Eman A. E. Ahmed Eman Abdulazeem Ahmed Fatema Sabeen Shaikh Ghalib H. Alshammri Hayat Alfagham Ibrahim Al-Marashdeh Ibrahim Almarashdeh Jehad Saad Alqurni Lubna Mahmoud Abdel Jawad Malek Alzaqebah Mutasem K. Alsmadi Mutasem Khalil Alsmadi Nahier Aldhafferi Nashat Alrefai Rami Mustafa A. Mohammad Sana Jawarneh Sunday Olusanya Olatunji Usama A. Badawi