UHD Journal of Science and Technology

An Image Analysis for Designing an Optimal Stirrer in Metal Matrix Composites Manufacturing

Farooq Muhammad, Muzhir Shaban Al-Ani, Hamsa D. Majeed — Sat, 11 Jan 2025 00:00:00 +0000

The global market for aluminum-based composites, widely used in manufacturing and construction, is expected to grow significantly. However, enhancing the cost-to-performance ratio is essential to improving their commercial viability. Efficient mixing plays a critical role in many industrial and chemical applications. Stir casting is the leading method for producing aluminum alloy matrix composites, but achieving a uniform particle distribution remains a significant challenge. In this study, the optimal stirrer design was identified using image processing techniques to analyze the distribution of ceramic grains. The stirrer that achieved the most uniform grain distribution was selected, eliminating the need for destructive testing. The mechanical properties of the final products validated the accuracy of the image analysis results.

Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features

Hawkar K. Hama, Hamsa D. Majeed, Goran Saman Nariman — Mon, 13 Jan 2025 00:00:00 +0000

Nephrolithiasis is a scientific term that refers to kidney stones and means the formation of crystal concretions in the kidney. It is considered a widespread situation that affects millions of people worldwide. Those stones can cause serious discomfort to infected people, especially when they traverse the urinary system, although, the big stones may need a surgical intervention. Various systems are already in use to address kidney stones, including ultrasound imaging for detection, extracorporeal shock wave lithotripsy (ESWL) for non-invasive stone fragmentation, and ureteroscopy for surgical removal, showcasing the advances in medical technology for managing this condition. This study presents an approach for detecting stones in the affected kidney. A public dataset has been employed in this work, containing (2370) images of healthy and affected kidneys. The dataset was utilized to train the proposed approach for the aim of stone detection. To achieve high detection accuracy, we implemented two key phases before classification. The preprocessing phase enhances image quality by reducing noise using a median filter and improving contrast through contrast stretching and tone enhancement. The segmentation phase follows, accurately identifying the kidney’s edges and regions of interest for effective feature extraction. The Local Binary Pattern (LBP) technique, combined with the support vector machine (SVM) algorithm serves as the primary components of the proposed model. The feature extraction comes into action through the LBP technique as a preparation step for the SVM classifier to complete the stone detection process. The approach introduced in this paper has the potential to enhance detection accuracy and efficiency. Furthermore, it could be used as an early detection tool to identify potential cases, thereby helping to prevent complications and adverse outcomes. This method aims to improve on the traditional manual process employed by radiologists, which could be described as time and effort consumption rather than the exposure of the interpretations. The obtained results were compared with the most relevant approaches in the field of kidney stone detection, demonstrating the model’s effectiveness in achieving the desired goal with a diagnostic accuracy of 96.37% for kidney stones.

Exploring Post-Quantum Cryptography: Evaluating Algorithm Resilience against Global Quantum Threats

Tara Nawzad Ahmad Al Attar, Mohammed Anwar Mohammed, Rebaz Nawzad Mohammed — Sat, 25 Jan 2025 00:00:00 +0000

Cryptographic algorithms perform a vital part in protecting information in general and safeguarding digital platforms. Nevertheless, improvements in quantum computing pose important concerns to traditional cryptographic approaches, demanding the development of quantum-resistant explanations. This study offers an inclusive investigation of post-quantum cryptographic algorithms, assessing their flexibility, competence, and practicality in justifying quantum risks. Through an equivalent approach, the research identifies optimistic applicants for upcoming cryptographic standards. Moreover, the study highlights the international essential for embracing these algorithms to ensure secure communication and data protection in the quantum era. These conclusions aim to notify the progress of strong cryptographic systems that address the appearing objections of quantum technologies.

The Clinical Neurological Manifestations of Patients Diagnosed with Carpal Tunnel Syndrome

Omar Hussein Shareef, Shorsh Ahmed Mohammed, Hemn Mohammed Gharib — Thu, 20 Feb 2025 00:00:00 +0000

Background: Carpal tunnel syndrome (CTS) is a condition, in which the median nerve becomes pressed or squeezed at the wrist. This causes pain and numbness in the fingers. Therefore, a neurological study is crucial to assess the condition.Objectives: The objective of this study was to assess the neurological manifestations of CTS and their association with demographic and clinical features from October 2022 to March 2023. Materials and Methods: A quantitative study was carried out over the period of 5 months by prospectively selecting and enrolling 100 CTS patients with a confirmed diagnosis. The CTS assessment questionnaire was modified and patients consented to the study before the data collection. Results: Adults aged 35–44 were the dominant group and the disease was found in females 10 times more than males. The least assigned symptoms were tingling and numbness in the little finger (4%) and neck pain 22%. All the patients with CTS presented with severe levels of CTS. Statistically significant associations were found between occupations, duration of the disease, affected side, other chronic diseases, and the prevalence of the symptoms at P ≤ 0.05. Self-management to sub-side pain and numbness had crucial impact on reducing the symptoms (P ≤ 0.05). Conclusion: The prevalence of the neurological symptoms varied depending on the sociodemographic and clinical features. Self-management had a significant positive impact on reducing some of the neurological symptoms, such as pain in the wrist at night and tingling and numbness in the morning.

An Effective Computer-aided diagnosis Technique for Alzheimer’s Disease Classification using U-net-based Deep Learning

Tue, 25 Feb 2025 00:00:00 +0000

The diagnosis of Alzheimer’s disease (AD), a common neurodegenerative disease that impairs thinking and memory abilities in older adults and ultimately results in cognitive impairment and dementia, is made possible in large part by computer-aided diagnosis (CAD). The idea has been to use either machine learning models or deep learning models to develop classification techniques for this disease. CAD techniques and mechanisms have emerged to help and facilitate early detection of this disease as a fundamental step in its treatment plan. As part of our approach, we proposed a model that included the following two pre-processing steps: Contrast Limited Adaptive Histogram Equalization (CLAHE) was utilized to enhance image contrast, especially in low-contrast areas. Normalization was then incorporated to ensure reliable training and faster convergence. A Gray-level co-occurrence matrix technique was used to extract seven texture features from the images following pre-processing: contrast, homogeneity, energy, correlation, variance, dissimilarity, and entropy. After that, these characteristics were added to the model output before the last classification layer. The best hybrid framework out of the five models we examined in this paper was utilized to build a convolutional neural network that can be used to identify AD characteristics from magnetic resonance images. As discussed in Section IV of this article, the U-Net model was selected because of its superior performance. The experimental results demonstrate that this technique showed great accuracy in segmentation and classification for each of the five AD Neuroimaging Initiative categories when a specific diagnosis was made. These results are as follows: Overall, the five classes’ final average scores for the four measures were as follows: 94.46% for Accuracy, 94.32% for Precision, 94.49% for Recall, and 94.41% for F1-score.

A review: Multi-Objective Algorithm for Community Detection in Complex Social Networks

Mariwan Wahid Ahmed, Hama Ali — Thu, 27 Feb 2025 00:00:00 +0000

Recently, research on multi-objective optimization algorithms for community detection in complex networks has grown considerably. Community detection based on multi-objective algorithms (MOAs) in complex social networks is a fundamental scheduler, and it supports knowing the dynamics of a society, finding influential groups, and improving information dissemination. The traditional methodologies often cannot cope with the features that real-world network usually present, related to optimizing various and sometimes conflicting objectives. This paper provides an overview of some recent works on MOAs for community detection in complex social networks. This paper will explore the balance of the reached objectives, such as modularity, community size, and edge density. Which are analyzed by 15 different approaches in order to choose from works published during the period 2019–2024. These strengths and limitations of various MOAs are reviewed with a comparative analysis to provide insights into both the effectiveness and computational efficiency of these methods. The present trends and future research are discussed that underline the need for the development of solutions to be more adaptive and scalable in coping with the gradually increasing complexity of social networks.

Small Dam Design and Construction for Sustainable Water Resources Management: A Comprehensive Review

Abdalmajeed Mohammed Rahman, Nawbahar Faraj Mustafa — Thu, 27 Mar 2025 00:00:00 +0000

Small dams are crucial in water resource management, particularly in regions with water scarcity and climate unpredictability. Despite their cost-effectiveness, the construction of small dams often lacks engineering standards, which raises concerns about their long-term stability and safety. This study reviews the design, construction, stability, and protection of small dams, emphasizing the importance of proper site selection, geological and hydrological studies, and advanced methodologies, such as Geographic Information Systems and multi-criteria decision-making approaches in dam evaluation. Furthermore, the study highlights the significance of detailed planning, material selection, and quality construction to ensure dam longevity. It also discusses the role of modern tools, such as HEC-HMS, HEC-RAS, and GeoStudio in assessing flood risks, seepage, and stability. Inadequate design, particularly in the face of extreme weather events, can lead to dam failures, emphasizing the need for comprehensive planning and rigorous assessments. Through an analysis of various studies and case examples, this paper aims to provide insights into sustainable small dam construction and water resources management practices that ensure their effectiveness and resilience in addressing water scarcity challenges.

Awareness of Menstrual Abnormalities among Female Nursing Students at the University of Sulaimani

Peshwaz Abdulrahman Ahmad, Amani Fadhil Abbas, Nazera Salam Mena Qadir — Sun, 13 Apr 2025 00:00:00 +0000

Background: The menstrual cycle, which occurs on a monthly basis from menarche to menopause and facilitates fertilization and conception, is a normal function in the female reproductive system. A 28-day cycle is the typical length. Any variations from the typical menstrual cycle in terms of frequency, irregularity of onset, duration of flow, or volume of blood are referred to as menstrual abnormalities. Aim: The current study set out to evaluate nursing students’ awareness regarding menstrual abnormalities. Materials and Methods: In a descriptive study of the quantitative method, the sample of 100 female students was conducted at the University of Sulaimani/Nursing College from January 15 to May 30, 2024. A questionnaire format was created according to the aim of the study and delivered by a team of six experts, consisting of three parts. Part one: sociodemographic characteristics of students. Part two: Menstrual patterns of students. Part three. Awareness of students regarding menstrual abnormalities. Data were collected by direct interviews with the students. Statistical Package for the Social Science version 22 was used for analyzing the data. The frequency, percentage, and Chi-square test were used. Results: Results of the present study indicated that the highest percentage of participants were in the age group (20–24); they mostly dwelled in dormitory. Financial state for the majority was sufficient and the vast majority were unmarried. The majority of participants experienced painful menstruation which affected their academic performance. Moreover, only one-fifth of participants had a high awareness regarding menstrual abnormalities. Finally, the study showed that there was a significant association between the group age of students and their awareness regarding menstrual abnormalities. Conclusion and Recommendations: The research concludes that the majority of participants demonstrated low awareness of menstrual abnormalities. Information, education, and awareness programs need to be strengthened to spread awareness regarding menstrual abnormalities.

Deep Learning Approaches for Retinal Disease Identification in Fundus Imaging: A Comprehensive Overview

Ismael Abdulkareem Ali, Sozan Abdullah Mahmood — Sat, 19 Apr 2025 00:00:00 +0000

Vision impairment is becoming a major health concern, especially in elderly people. While in the medical field, manually detecting ocular pathology has significant difficulty. Therefore, deep learning diagnostic techniques are widely used for identifying eye diseases and play a crucial role in diagnosing vision-related problems. Examination of fundoscopy allows for analyzing eyes for diagnosis of eye diseases, including Diabetic retinopathy (DR), Cataracts, Glaucoma, Age‑related macular degeneration, Pathologic Myopia, and more. In this paper, we propose a concise review of introducing most of the DL, hybrid, and ensemble models utilized for the purpose of identification and classification of eye diseases. Various datasets, feature extraction techniques, and metrics for performance evaluation are discussed. The chosen papers come from conferences and scholarly publications published from 2019 to 2024. We evaluate the performance of chosen researches using different datasets, the most common ones include ocular disease intelligent recognition, Indian DR image dataset, EyePACS, methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology, methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology-version 2, DIARETDB, Structured analysis of the retina, high-resolution fundus, digital retinal images for vessel extraction, online retinal fundus image dataset for Gl analysis and research, retinal fundus multi-disease image dataset and Kaggle datasets. The detection studies that have been reviewed show that the accuracy of these approaches varies between 73% and 99%, the sensitivity ranges from 69% to 99% and precision is between 89% and 99%. The results show that great accuracy is consistently achieved with DL algorithms compared to traditional Machine learning approaches. However, there are still some challenges and limitations remaining including excessive resource consumption and over-fitting due to dataset size and diversity issues. This review offers useful insight for researchers and healthcare professionals to comprehend AI technologies properly for the detection, classification, and diagnosis of retinal diseases. We succinctly summarize the methodologies of all the chosen studies and focus on the elements that define the aim of the studies.

Optimization of Lattice-Based Cryptographic Key Generation using Genetic Algorithms for Post-Quantum Security

Tara Nawzad Ahmad Al Attar, Rebaz Nawzad Mohammed — Sun, 20 Apr 2025 00:00:00 +0000

The progress of quantum computing has posed serious threats to classical cryptographic systems, necessitating much research into developing post-quantum cryptography (PQC). Of the schemes available in PQC, the strongest candidates appear to be lattice-based cryptography (LBC), which encompasses an ample security basis and good computation efficiency. However, practically implementing LBC is faced with key-generation and optimization difficulties, mainly because of its enormous key sizes and computational overhead. The research proposes a novel concept whereby genetic algorithms (GAs) are blended with LBC to increase the merits of key generation while guaranteeing security. Through the evolutionary capacity of GAs, the proposed method optimizes lattice-based keys through selection, crossover, and mutation to ensure high entropy and computationally feasible with experimental results indicating that the GA-based method can cut down memory requirements and computational complexity, making it favorable for resource-constrained environments such as the Internet of Things and embedded systems. The method thus suggested accelerates encryption speed and simultaneously strengthens the security of the optimized key structures. This study emphasizes evolutionary algorithms’ potential to facilitate PQC advancement and provides a scalable and efficient framework for cryptographic systems.

Knowledge, Attitude, and Practice towards Menstrual Hygiene Management among Adolescent School Girls in Sulaymaniyah City/Iraq

Sara Rebwar Mohammed-Amin, Gona Othman Faris — Sun, 04 May 2025 00:00:00 +0000

Background: Menstrual hygiene management (MHM) remains a significant public health challenge for adolescent girls in many communities. Despite its importance for reproductive health and overall well-being, many girls lack adequate information, resources, and supportive environments to manage menstruation effectively. MHM being crucial for adolescent girls’ health, gaps in knowledge and practices persist among schoolgirls across different educational levels. Objectives: The objective of the study was to assess knowledge, attitude, and practices (KAP) regarding MHM among adolescent schoolgirls across different grades as well as to find out the association between sociodemographic characteristics with KAP scores. Methods: A quantitative descriptive analytic (Cross-sectional study) was conducted with 432 adolescent girls across different grade levels, The data collection started from November 20, 2024, to February 10, 2025, using an interviewed questionnaire. Results: Among participants, 45.8% had poor knowledge, 31.5% had negative attitudes, and 42.6% demonstrated poor practices toward MHM. KAP scores were significantly associated with age, class level, parental education, and maternal occupation (P < 0.05). Conclusion: The findings indicate a need for comprehensive menstrual health education programs targeting adolescent girls, particularly in lower grades, to improve MHM and address the gaps in KAP.

Enhancing Oat Yield and Yield components by Salicylic Acid in Different agro-ecosystem

Arol M. Anwar — Tue, 06 May 2025 00:00:00 +0000

Climate elements including temperature, humidity, and precipitation all are effects on crop growth and development especially in the arid and semi-arid areas of Northern parts of Iraq. Recently, the stresses of global climate change appear as an effective challenge, so, this study was carried out at two different locations in the Northern part of Iraq; Koya/Erbil and Altun Kopru/Kirkuk to study the effects of salicylic acid (SA) (0, 100, 200, and 300 ppm) twice as foliar spraying on five oat varieties. Results indicate that the Koya district environment significantly improved most studied characteristics, except for panicle number, compared to Altun Kopru. Except the harvest index (HI) which was non-significant, 100 and 200 ppm of SA improved significantly all studied characteristics compared to the control and 300 ppm of SA. Kangaroo, ICARDA Short, and ICARDA Tall varieties were the tallest plants (85.969, 83.469, and 82.833 cm) respectively. Each of Possum and ICARDA Short varieties were superior in panicle number, seed yield, biological yield, and straw yield. Kangaroo variety has the lowest harvest index (42.695%)significantly compared to all other varieties, whereas ICARDA Short variety was the lowest in panicle grain weight(0.898 g/panicle-1).

Hybrid E-Recommendation System for Multi-Shop Environment

Hawraz Abdalla Abubakr, Kamaran Faraj — Sun, 01 Jun 2025 00:00:00 +0000

In the Kurdistan Regional Government, most computer shops and markets conduct their marketing offline and do not have electronic systems. Nevertheless, customers live in a digital age; they often face challenges in finding products among these markets and shops. The most common question that customers ask is which shop they should purchase from. Therefore, data from five laptop stores and ratings for markets were collected to build an integrated recommender system to help customers find products and select the best store. Our proposed system is a hybrid e-recommendation system that combines machine learning techniques to provide personalized shop and product recommendations. Methods include data collection from multiple laptop shops and dataset preparation. The system uses techniques such as hybrid/blended methods using singular value decomposition and K-nearest neighbors for collaborative filtering (CF) to recommend shops and products based on customer ratings, alongside term frequency-inverse document frequency vectorization and cosine similarity for content-based filtering. The CF’s performance was evaluated using metrics like RMSE = 0.14 and MAE = 0.11, which demonstrated positive results for product and market recommendation. Overall, this study offers solutions through HE-RS to address key challenges such as market fragmentation, cold-start problems, and data scarcity.

Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data

Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood — Mon, 02 Jun 2025 00:00:00 +0000

Cancer classification through genetic evaluation has become a hot topic among researchers. It holds the promise of delivering systematic, precise, and scientifically backed diagnoses for different types of cancer. Lately, several studies have delved into cancer classification by leveraging data mining techniques, machine learning algorithms, and statistical methods to thoroughly analyze high-dimensional datasets. Detecting cancer early by examining gene expression data is vital for providing effective patient care. Each sample in the Gene dataset usually includes a range of features, each representing a specific gene. In this paper, we propose a unique approach that utilizes DistilBERT, a distilled version of the Bidirectional Encoder Representations from Transformers, for cancer classification and prediction. In addition, our model integrates a self-attention mechanism in the transformer layers to enhance the model’s focus on key features and employs an embedding layer for dimensionality reduction, improving the processing of gene statistics, preventing overfitting, and boosting generalization. We utilized datasets from important resources: The gene expression omnibus, which provided microarray records of lung and ovarian cancers, and the cancer genome atlas (TCGA), which offered RNA-Seq facts encompassing multiple most cancer types (breast invasive carcinoma, kidney renal clear cell carcinoma, colon adenocarcinoma, lung adenocarcinoma, and prostate adenocarcinoma). Our approach established excessive accuracy across all datasets, showcasing big upgrades in overall model performance compared to present strategies within the subject. The results underscore the ability to leverage transformer-primarily based architectures for strong cancer-type prediction and classification. Our approach achieved and improved exceptional accuracy compared to previous studies, with DS1: 97.56% for lung cancer, DS2: 100% for ovarian cancer, and DS3: 99.504% for the TCGA dataset.

Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets

Tue, 03 Jun 2025 00:00:00 +0000

The global rise in cardiovascular disease (CVD) cases underscores the critical need for accurate and early diagnostic solutions. This study introduces a robust machine learning (ML) framework for predicting CVD risk by integrating two large, feature-identical datasets containing clinical and biological indicators along with patient history. Seven classification algorithms – logistic regression, random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GNB), gradient boosting (GB), K-nearest neighbors, and decision tree (DT) – were employed. A stratified sampling strategy was used to ensure balanced class distribution, and model performance was further validated using k-fold cross-validation to enhance robustness and generalizability. The datasets, sourced from the UCI repository, were pre-processed and evaluated using metrics such as accuracy, precision, F1-score, log loss, and error rate, with performance further assessed using confusion matrices. Results revealed that ensemble models, particularly RF and DT, achieved optimal performance with 100% accuracy, while stratification significantly improved the outcomes of SVM, GNB, and GB. The integration of datasets, stratified sampling, and k-fold validation effectively enhanced model reliability while minimizing overfitting. These findings highlight the potential of ML to support early CVD diagnosis and lay the groundwork for future research on hybrid models and real-world clinical applications.

Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman

Qeethara Al-Shayea, Huthaifa Aljawazneh — Thu, 12 Jun 2025 00:00:00 +0000

Traffic congestion is a significant issue in urban road networks, particularly in Amman, where peak hours cause major delays for commuters. Developing an advanced traffic management system is essential to helping residents save time, reduce congestion, and alleviate traffic jams. To address this challenge, we have implemented a simulation model powered by machine learning techniques to effectively and accurately manage traffic flow on Amman's streets. This innovative system leverages real-world data from the Jordanian capital to dynamically optimize traffic control. By automating traffic management processes, the model aims to reduce congestion while easing the workload of traffic personnel. This approach promises to enhance urban mobility and contribute to building a smarter and more efficient traffic management infrastructure in Amman, ensuring a better quality of life for its residents. After implementing the metering strategy, the traffic flow became more balanced, with less congestion and smoother transitions between intersections. The metering points effectively regulated the entry of vehicles into the circles, preventing congestion buildup and improving overall traffic efficiency.

Surgery Versus Flexible Endoscopic Rubber Band Ligation for Grade 2 and 3 Internal Hemorrhoids

Diyaree Nihad Ismael — Sun, 15 Jun 2025 00:00:00 +0000

Surgery has traditionally been the primary treatment for symptomatic internal hemorrhoids. However, office-based interventions such as rubber band ligation (RBL) are increasingly used for Grades 1–3 hemorrhoids. Flexible endoscopic RBL offers a minimally invasive alternative, whereas surgery remains standard for Grade 4. To compare the effectiveness of flexible endoscopic RBL versus surgical hemorrhoidectomy in managing symptomatic Grades 1–3 internal hemorrhoids, focusing on bleeding control, pain, recovery time, and recurrence. A comparative study of 55 patients treated with flexible endoscopic RBL (using Olympus kits) and 55 matched patients undergoing conventional excisional hemorrhoidectomy (open technique). Patients choose their treatment after counseling. Outcomes were assessed over 1 year, with follow-up at 1 week, 3, 6, and 12 months. Pain was measured using a Visual Analog Scale (≥4 defined significant pain). Statistical analysis used a statistical package for the social sciences v26 (t-tests for continuous variables, Chi-square for categorical; P < 0.05 significant). Both groups showed comparable efficacy: Bleeding control (95% vs. 93%), mucosal prolapse resolution (96% vs. 97%), and 1-year recurrence (30% vs. 29%). RBL had superior post-procedural outcomes: Lower pain (10% vs. 90%), fewer work absences (5% vs. 95%), and no bed-boundness (0% vs. 100%; all P < 0.05). Flexible endoscopic RBL is as effective as surgery for Grades 1–3 hemorrhoids but significantly reduces pain, recovery time, and work absenteeism. RBL should be considered a first-line option for eligible patients.