UHD Journal of Science and Technology
https://journals.uhd.edu.iq/index.php/uhdjst
<p><em>UHD Journal of Science and Technology</em> (UHDJST) is a semi-annual academic journal<strong> </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 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: <strong><span style="font-weight: 400;">10.21928/issn.2521-4217</span></strong></p>University of Human Development - Iraqen-USUHD Journal of Science and Technology2521-4209Investigating the Influence of Environmental Factors on Corrosion in Pipelines Using Geospatial Modeling
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1231
<p>This study integrated geographic information system (GIS) and remote sensing technology to identify areas around pipelines that are more susceptible to corrosion having the Kurdistan pipeline as a case study. Geospatial data are used to target factors such as rainfall, temperature, rivers, and minerals which increase the corrosion rate. Spatial data such as, the direction of slope, rainfall, proximity to rivers, and minerals, were collected and analyzed; maps were created for every individual factor to visualize their distribution. By overlaying these maps, regions that are at higher risk of corrosion were identified, which can be prioritized for further investigation or preventive measures. This paper’s findings are significant for oil and gas industries, including pipeline operators and designers as corrosion can lead to devastating consequences. The novelty of this study is to identify areas along the pipeline at higher risk of corrosion through the application of geospatial information systems and remote sensing. This methodology holds immense potential for industries looking to proactively prevent corrosion through the implementation of preventative maintenance, monitoring programs, and the application of protective coatings and inhibitors. The results of this research demonstrate that environmental data, GIS, and remote sensing can predict corrosion in oil pipelines, offering valuable insights for better managing corrosion risk.</p>Jafar A. AliLoghman KhodakaramiZulfa J. KhudadadJehan M. RustamAya B. ShawkatSrwa S. AliBala A. Faqe
Copyright (c) 2024 Jafar A. Ali, Loghman Khodakarami, Zulfa J. Khudadad, Jehan M. Rustam, Aya B. Shawkat, Srwa S. Ali, Bala A. Faqe
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2024-01-042024-01-048111210.21928/uhdjst.v8n1y2024.pp1-12Kurdish Sorani Dialect Morphology Generation Using a Concatenative Strategy
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1250
<p>In natural language processing, morphological generation refers to the creation of the appropriate inflected forms of words based on a predetermined set of morphological rules. However, it might be difficult to generate morphology in languages with intricate morphological systems, like the Kurdish Sorani dialect. The concatenative morphology-based unique technique to morphological generation in Kurdish Sorani is proposed in this research. The suggested strategy tries to get over the drawbacks of current approaches and enhance the precision and effectiveness of morphological generation in Kurdish Sorani. The suggested technique generates all conceivable subjective and objective pronouns in both positive and negative forms, together with the various verb tenses for Kurdish morphology. The study presents a detailed examination of Kurdish Sorani’s morphology and points out the difficulties in coming up with the right verbforms. The authors suggest a concatenative morphology-based morphological generating system that comprises of a morphological analyzer and a morphological generator.</p>Kardo Othman AzizRamyar Abdulrahman TeimoorTofiq Ahmed TofiqDilman Salih Abdulla
Copyright (c) 2024 Kardo O. Aziz, Ramyar A. Teimoor, Tofiq A. Tofiq, Dilman S. Abdulla
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2024-01-102024-01-1081131910.21928/uhdjst.v8n1y2024.pp13-19Performance Evaluation using Spanning Tree Protocol, Rapid Spanning Tree Protocol, Per-VLAN Spanning Tree, and Multiple Spanning Tree
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1254
<p>This paper examines the concepts and practical applications of the spanning tree protocol (STP). It also covers per-VLAN spanning tree (PVST), multiple spanning tree (MST), and rapid STP (RSTP). Moreover, practical scenarios are presented to help the reader understand the concepts and implementations of these protocols. This study analyzes protocols using seven metrics. All protocols have been evaluated using these metrics in both small and big topology scenarios to obtain the best results. In addition, all metrics are mentioned in the introduction chapter, and the way used to apply tests on the metrics is described in the methodology chapter. Based on the experiments, different STPs performance are compared, including STP, RSTP, PVST, and MST. In summary, findings show that STP is easy to use and performs well overall, but it consistently has high latency issues. RSTP is suitable for small networks and has quick convergence, but it cannot handle as much load as STP. PVST performed the best in the experiments, as it demonstrated high scalability and the ability to handle a lot of pressure, although it requires strong hardware. However, MST did not perform as well as expected, as it struggled with delay problems and high jitter. In conclusion, it is recommended to use RSTP for simple networks that require fast convergence with dependable delay and capacity, or STP for networks that require good scaling and bandwidth. PVST is an excellent option for those who can afford high-performance hardware, while MST is suitable for simple networks or those with outdated hardware.</p>Dana Faiq AbdRawyer Asaad RashidDanyar Awat OthmanHero Muhammed Abdulqader
Copyright (c) 2024 Dana Faiq Abd, Rawyer Asaad Rashid, Danyar Awat Othman, Hero Muhammed Abdulqader
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2024-01-202024-01-2081203010.21928/uhdjst.v8n1y2024.pp20-30Assessment of Health-care Professional’s Knowledge Regarding the Comorbidities of Vitamin D Deficiency and its Relationship with Uterine Fibroids
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1237
<p>Vitamin D deficiency is a widespread global health issue, notably prevalent in the Middle East and more severe in women. Vitamin D deficiency increases the incidence of uterine fibroids in black and white women, the most frequent benign gynecologic malignancies. This study examined health-care providers’ understanding of the relationship between vitamin D insufficiency and uterine fibroid at Sulaimani Hospitals in Kurdistan, Iraq. A quantitative design, cross-sectional-descriptive study (non-probability purposive sample) of 113 female nurses and gynecologists. Data were collected using a checklist through Google Forms. The results showed that the majority of the participants were nurses (88.5%), and the remaining (11.5%) were gynecologist. Two-thirds of them work in maternity teaching hospital while others work in smart hospitals and Faroq medical cities. The results revealed that the level of knowledge was significantly associated with the position of participants and level of education (P < 0.05). As well, the study demonstrated that the majority of the participants were not trained regarding uterine fibroid and vitamin D deficiency. The study concluded that nearly a quarter of the health-care professionals had a medium level of knowledge, while nearly a quarter (24.8%) of them had a high level of knowledge. The study recommended to the Ministry of Health giving opportunities to health-care professionals, especially nurses, to participate in training courses, workshops, and conferences regarding the relationship between vitamin D deficiency and uterine fibroid.</p>Bayan Omar SharifSyamnd Mirza AbdullahSarwar Arif StarHadeel Abdulelah IbrahimHawar Mardan MuhammadZhino Raouf AliHussein Mustafa Hamasalih
Copyright (c) 2024 Bayan Omar Sharif, Syamnd Mirza Abdullah, Sarwar Arif Star, Hadeel Abdulelah Ibrahim, Hawar Mardan Muhammad, Zhino Raouf Ali, Hussein Mustafa Hamasalih
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2024-01-242024-01-2481314110.21928/uhdjst.v8n1y2024.pp31-41A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1256
<p>Due to the ever-increasing progress of software projects and their widespread impact on all industries, models must be designed and implemented to analyze and estimate costs and time. Until now, most of the software cost estimation (SCE) has been based on the analyst’s experiences and similar projects and these models are often inaccurate and inappropriate. The project will not be finished in the specified time and will include additional costs. Algorithmic models such as COCOMO are not very accurate in SCE. They are linear and the appropriate value for effort factors is not considered. On the other hand, artificial intelligence models have made significant progress in the cost estimation modeling of software projects in the past three decades. These models determine the correct value for effort factors through iteration and training, providing a more accurate estimate compared to algorithmic models. This paper employs a hybrid model incorporating the Tabu Search (TS) algorithm and the Invasive Weed Optimization (IWO) algorithm for SCE. IWO algorithm solutions are improved using the TS algorithm. The NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets are used for the evaluation. The proposed model has been able to reduce the MMRE rate compared to the IWO algorithm and the TS algorithm. The proposed model on the NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets obtained values of MMRE of 15.43, 17.05, 28.75, 58.43, and 22.46, respectively.</p>Hoshmen Murad MohamedyusfHawar Othman SharifMazen Ismaeel Ghareb
Copyright (c) 2024 Hoshmen Murad Mohamedyusf, Hawar Othman Sharif, Mazen Ismaeel Ghareb
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2024-02-132024-02-1381425410.21928/uhdjst.v8n1y2024.pp42-54Comparative Analysis of Word Embeddings for Multiclass Cyberbullying Detection
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1252
<p>Cyberbullying has emerged as a pervasive concern in modern society, particularly within social media platforms. This phenomenon encompasses employing digital communication to instill fear, threaten, harass, or harm individuals. Given the prevalence of social media in our lives, there is an escalating need for effective methods to detect and combat cyberbullying. This paper aims to explore the utilization of word embeddings and to discern the comparative effectiveness of trainable word embeddings, pre-trained word embeddings, and fine-tuned language models in multiclass cyberbullying detection. Distinguishing from previous binary classification methods, our research delves into nuanced multiclass detection. The exploration of word embeddings holds significant promise due to its ability to transform words into dense numerical vectors within a high-dimensional space. This transformation captures intricate semantic and syntactic relationships inherent in language, enabling machine learning (ML) algorithms to discern patterns that might signify cyberbullying. In contrast to previous research, this work delves beyond primary binary classification and centers on the nuanced realm of multiclass cyberbullying detection. The research employs diverse techniques, including convolutional neural networks and bidirectional long short-term memory, alongside well-known pre-trained models such as word2vec and bidirectional encoder representations from transformers (BERT). Moreover, traditional ML algorithms such as K-nearest neighbors, Random Forest, and Naïve Bayes are integrated to evaluate their performance vis-à-vis deep learning models. The findings underscore the promise of a fine-tuned BERT model on our dataset, yielding the most promising results in multiclass cyberbullying detection, and achieving the best-recorded accuracy of 85% on the dataset.</p>Azhi FarajSemih Utku
Copyright (c) 2024 Azhi Faraj, Semih Utku
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2024-02-202024-02-2081556310.21928/uhdjst.v8n1y2024.pp55-63Iraqi Kurd or Arab Male Authenticity Detection Based on Facial Feature
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1266
<p>As an inherent human characteristic, ethnicity plays a fundamental and critical role in biometric identification. On the other hand, the human face is the core of man’s identity, and facts such as age and race are often extrapolated automatically from the face. The objective is to utilize computer technologies to identify and categorize ethnic groups based on facial features. Convolutional neural networks (CNN), which can automatically identify underlying patterns from data, excel at learning image features and have shown state-of-the-art performance in several visual recognition challenges, such as ethnicity detection. Although the automated classification of traits such as age, gender, and ethnicity is a well-researched topic, Iraqi ethnic groupings have not yet been addressed. This study seeks to tackle the challenge of predicting the ethnicity of Iraqi male individuals based on their facial traits for the two largest ethnic groups, the Arabs, and the Kurds. Male Iraqi Kurds and Arabs were each represented by 260 image samples. The dataset underwent a diverse array of preprocessing and data enhancement techniques, including image resizing, isolation, gamma correction, and contrast stretching. Moreover, to augment the dataset and expand its diversity, various techniques such as brightness adjustment, rotation, horizontal flip, and grayscale augmentations were systematically applied, effectively increasing the overall number of images, and enriching the dataset for improved model performance. Face images of Kurds and Arabs were classified using the Faster region-based CNN (RCNN) approach of deep learning. Due to insufficient data in the dataset, we propose employing transfer learning to extract features using several pre-trained models. Specifically, we examined EfficientNetB4, ResNet-50, SqueezeNet, VGG16, and MobileNetV2, resulting in accuracies of 96.73%, 94.91%, 93.39%, 92.48%, and 90.32%, accompanied by corresponding precision values of 0.86, 0.81, 0.80, 0.70, and 0.69, respectively. It is essential to emphasize that the following inference speeds – VGG16 (4.5 ms), ResNet-50 (4.6 ms), SqueezeNet (3.8 ms), MobileNetV2 (3.7 ms), and EfficientNet-B4 (16 ms) – represent the computing times needed for each backbone. Moreover, to achieve a harmonious trade-off between precision and the time required for inference, we chose ResNet-50 as the foundational framework for our model aimed at classifying ethnicity. The study also acknowledges limitations such as the availability and diversity of the dataset. Nevertheless, despite these limitations, it provides valuable perspectives on the automated prediction of Iraqi male ethnicity through facial features, presenting potential applications in various domains. The findings contribute to the broader conversation surrounding biometric identification and ethnic categorization, underscoring the importance of ongoing research and heightened awareness of the inherent limitations associated with such studies.</p>Bnar Abdulsalam AbdulrahmanNama Ezzaalddin Mustafa
Copyright (c) 2024 Bnar Abdulsalam Abdulrahman, Nama Ezzaalddin Mustafa
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2024-03-032024-03-0381647710.21928/uhdjst.v8n1y2024.pp64-77Blood storage impacts on the hematological indices of healthy subjects and patients with iron-deficiency anemia and beta-thalassemia – A comparative study
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1263
<p style="font-weight: 400;">Background: There are scientific evidence confirmed specific changes in blood cell counts, reducing the efficacy and feasibly the safeness of blood transmission when storing blood at 4°C for 5 weeks or more.<br />Objectives: The study aimed to investigate the effects of stored blood obtained from healthy subjects and patients with anemia due to iron deficiency and beta-thalassemia, on hematological indices.<br />Materials and Methods: A total of 37 participants, consisting of 14 healthy subjects, 13 patients with iron-deficiency anemia, and 10 patients with beta-thalassemia minor, were recruited from Hiwa Hospital between November 2021 and July 2022. Blood samples were obtained from the participants and stored at 4°C for 5 weeks. Hematological indices, including red cell distribution, platelet distribution width, and mean platelet volume, were determined using a hematology analyzer at weekly intervals.<br />Results: Blood storage caused significantly increased mean values of hematological indices among healthy subjects as well as among patients with iron-deficiency anemia and beta-thalassemia, although the pattern of changes was differed.<br />Conclusions: The storage of whole blood significantly increased hematological indices, showing variations in both healthy subjects and patients with iron-deficiency anemia and beta-thalassemia. The pattern of raise in these hematological indices is specific to iron-deficiency anemia and thalassemia when compared with healthy subjects.</p>Mudhafar Mohamed M. Saeed
Copyright (c) 2024 Mudhafar Mohamed M. Saeed
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2024-03-102024-03-1081788310.21928/uhdjst.v8n1y2024.pp78-83Enhancing COVID-19 Detection Accuracy: Optimal Gene Combinations, Kit Performance, and Reliable Detection Intervals
https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1259
<p>A significant challenge and threat to public health have been created by the COVID-19 pandemic for the entire global population. The study aimed to compare the SARS-CoV-2 RNA detection capabilities of available primers and probes to identify the most reliable, efficient, and affordable method. From 200 previously detected samples of SARS CoV-2, 94 samples were selected randomly and used for the optimization of our primers and probes. We compared our results with two kits that have been approved by the health authority. In addition, we evaluated the detectability of each gene. The study compared the diagnostic performance of different gene combinations for COVID-19 detection using kits A and B and a novel approach combining RdRp, N, and E genes. Results showed that the combined approach exhibited superior discriminatory power, particularly with the inclusion of the E gene, boasting area under the curve (AUC) values of 83.3%, 79.1%, and 93.7% for the respective genes. Kit B, with Orf1ab and N genes, outperformed Kit A (RdRp and S genes), with AUC values of 81.2% and 90.6% versus 80.2% and 75%, respectively. The chart representation highlighted gene detection frequencies across various cycle threshold (Ct) ranges, demonstrating robust identification within the 20.1–30 Ct range across all kits and genes, emphasizing the reliability of detection within specific intervals. Combining RdRp, N, and E genes showed the highest accuracy for COVID-19 diagnosis, particularly with the E gene. Detection was most reliable within the 20.1–30 Ct range across all gene combinations and kits.</p>Dara A. TahirSehand Arif
Copyright (c) 2024 Dara A. Tahir, Sehand Arif
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2024-03-112024-03-1181849210.21928/uhdjst.v8n1y2024.pp84-92