UHD Journal of Science and Technology https://journals.uhd.edu.iq/index.php/uhdjst <p><em>UHD Journal of Science and Technology</em>&nbsp;(UHDJST) is a semi-annual academic journal<strong>&nbsp;</strong>published by the University of Human Development, Sulaimani, Kurdistan Region, Iraq. UHDJST publishes original research in all areas of Science, Engineering, and Technology. UHDJST is a Peer-Reviewed Open Access journal with CC BY-NC-ND 4.0&nbsp;license. UHDJST provides immediate, worldwide, barrier-free access to the full text of research articles without requiring a subscription to the journal, and has no article processing charge (APC). UHDJST Section Policy includes three types of publications; Articles, Review Articles, and Letters. UHDJST is a member of ROAD, e-ISSN: 2521-4217, p-ISSN: 2521-4209 and a member of Crossref, DOI:&nbsp;<strong><span style="font-weight: 400;">10.21928/issn.2521-4217</span></strong></p> University of Human Development - Iraq en-US UHD Journal of Science and Technology 2521-4209 The Luminosity Function of Galaxies in Some Nearby Clusters https://journals.uhd.edu.iq/index.php/uhdjst/article/view/817 <p>In the present work, the galaxy luminosity function (LF) has been studied for a sample of seven clusters in the redshift range (0.0 ≲ z ≲ 0.1), within Abell radius (1.5 h−1 Mpc), in the five SDSS passbands ugriz. In each case, the absolute magnitude distribution is found and then fitted with a Schechter function. The fitting is done, using the χ2 – minimization method to find the best values of Schechter parameters Ф* (normalization constant), M* (characteristic absolute magnitude), and α (faint-end slope). No remarkable changes are found in the values of M* and α, for any cluster, in any passband. Furthermore, the LF does not seem to vary with such cluster parameters as richness, velocity dispersion, and Bautz–Morgan morphology. Finally, it is found that M* becomes brighter toward redder bands, whereas almost no variation is seen in the value of α with passband, being around (−1.00).</p> Mariwan Ahmed Rasheed Khalid K. Mohammad Copyright (c) 2021 Mariwan Ahmed Rasheed, Khalid K. Mohammad http://creativecommons.org/licenses/by-nc-nd/4.0 2021-07-03 2021-07-03 5 2 1 10 10.21928/uhdjst.v5n2y2021.pp1-10 Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm https://journals.uhd.edu.iq/index.php/uhdjst/article/view/811 <p>As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.</p> Yadgar Sirwan Abdulrahman Copyright (c) 2021 Yadgar Sirwan Abdulrahman 2021-07-16 2021-07-16 5 2 11 19 10.21928/uhdjst.v5n2y2021.pp11-19 Comparison of Different Ensemble Methods in Credit Card Default Prediction https://journals.uhd.edu.iq/index.php/uhdjst/article/view/806 <p>Credit card defaults pause a business-critical threat in banking systems thus prompt detection of defaulters is a crucial and challenging research problem. Machine learning algorithms must deal with a heavily skewed dataset since the ratio of defaulters to non-defaulters is very small. The purpose of this research is to apply different ensemble methods and compare their performance in detecting the probability of defaults customer’s credit card default payments in Taiwan from the UCI Machine learning repository. This is done on both the original skewed dataset and then on balanced dataset several studies have showed the superiority of neural networks as compared to traditional machine learning algorithms, the results of our study show that ensemble methods consistently outperform Neural Networks and other machine learning algorithms in terms of F1 score and area under receiver operating characteristic curve regardless of balancing the dataset or ignoring the imbalance</p> Azhi Abdalmohammed Faraj Didam Ahmed Mahmud Bilal Najmaddin Rashid Copyright (c) 2021 Azhi Abdalmohammed Faraj, Didam Ahmed Mahmud, Bilal Najmaddin Rashid http://creativecommons.org/licenses/by-nc-nd/4.0 2021-07-19 2021-07-19 5 2 20 25 10.21928/uhdjst.v5n2y2021.pp20-25 Face Recognition Use Local Image Dataset and Correlation Technique https://journals.uhd.edu.iq/index.php/uhdjst/article/view/839 <p>Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.</p> Dana Faiq Abd Copyright (c) 2021 Dana Faiq Abd http://creativecommons.org/licenses/by-nc-nd/4.0 2021-08-05 2021-08-05 5 2 26 31 10.21928/uhdjst.v5n2y2021.pp26-31