Science Journal of University of Zakho

DEEP NEURAL NETWORK-BASED APPROACH FOR COMPUTING SINGULAR VALUES OF MATRICES

Diyari A. Hassan — 2025-01-05

Matrix factorization techniques, such as Singular Value Decomposition (SVD), Eigenvalue Decomposition (EVD), and QR decomposition, have long been pivotal in computational mathematics, particularly for applications in signal processing, machine learning, and data analysis. With the growing size and complexity of data, traditional methods of matrix factorization face challenges in efficiency and scalability. This paper investigates the implementation of Convolutional Neural Networks (CNNs) for computing the singular values of both real and complex matrices. By leveraging the hierarchical feature extraction capabilities of CNNs, this approach aims to enhance the accuracy, efficiency, and scalability of SVD calculations. The proposed CNN-based SVD method is evaluated against the conventional SVD algorithm, demonstrating superior performance in terms of computational time and accuracy.

DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS

Mohammed Hayder Abbas — 2025-01-05

Due to the large population of motorway users in the country of Iraq, various approaches have been adopted to manage queues such as implementation of traffic lights, avoidance of illegal parking, amongst others. However, defaulters are recorded daily, hence the need to develop a mean of identifying these defaulters and bring them to book. This article discusses the development of an approach of recognizing Iraqi licence plates such that defaulters of queue management systems are identified. Multiple agencies worldwide have quickly and widely adopted the recognition of a vehicle license plate technology to expand their ability in investigative and security matters. License plate helps detect the vehicle's information automatically rather than a long time consuming manually gathering for the information. In this article, transfer learning is employed to train two distinct YOLOv8 models for enhanced automatic number plate recognition (ANPR). This approach leverages the strengths of YOLOv8 in handling complex patterns and variations in license plate designs, showcasing significant promise for real-world applications in vehicle identification and law enforcement.

A SPECTROSCOPIC IN-VITRO METHOD FOR DETERMINING THE SUN PRODUCTION FACTOR (SPF)AND PHYSICAL PROPERTIES OF MARKETED SUNSCREEN FORMULATIONS

Ahmed Hassan kurda — 2025-01-05

Applying sunscreen or lotion protects the skin from harmful ultraviolet rays, reducing the risk of sun exposure-related diseases such as skin cancer. The increasing awareness of these dangers has driven a demand for testing sunscreen formulations. This research utilizes ultraviolet-visible spectroscopy to measure the Sun Protection Factor (SPF) and the UVA/UVB ratio for several different sunscreens from various brands. The SPF values of the tested products range from 15 to 100. The spectroscopy method is simple, fast, and accurate in determining SPF values in a controlled environment. Notably, some sunscreen formulations in the Kurdistan market display SPF values 62.5% below the label amount, indicating that many people in Kurdistan may be using sunscreens with less protection than they believe. Besides evaluating SPF, the research also examines the tested sunscreens' optical band gap and refractive index.

KNOWLEDGE, ATTITUDES, AND PRACTICES RELATED TO THE DISPOSAL OF EXPIRED AND UNUSED MEDICATIONS IN HOUSEHOLD IN DUHOK CITY, KURDISTAN REGION, IRAQ

Najmaldin Ezaldin Hassan — 2025-01-05

Unused, unwanted, and expired household medications can accumulate and be improperly disposed of, posing a major national and international public health and environmental concern. This problem is primarily caused by a lack of awareness and understanding of appropriate medicine disposal techniques. This study aimed to assess the knowledge, attitudes, and practices regarding the disposal of unused, unwanted, or expired household medicines among the general public in Duhok city, in the Kurdistan Region of Iraq. Between March 6 and June 6, 2023, 250 participants were interviewed in person using a pre-validated structured questionnaire for this cross-sectional study. The accuracy of the returned questionnaires was verified multiple times. Descriptive statistics were calculated for the sample population's characteristics, and the findings were presented in tables as percentages. Out of the 250 respondents included in the study, 115 (46%) were females, and 165 (66%) were married. A total of 205 (82%) of the respondents had formal education. The most commonly used disposal practice for unused medicines was throwing them in household garbage, as reported by 137 (54.8%). Interestingly, 127 (61.95%) of the participants who disposed of medicines in the garbage had formal education. About 68 (27.2%) of the participants kept unused or unwanted medicines at home for future use or until expiration, followed by giving them to friends and relatives (23, or 18.07%), while a few (12, or 9.27%) burned medicines at home. Significantly, 206 (82.4%) of the respondents did not check the expiry date before taking medicine. A majority of respondents, 155 (62%), held the government responsible for creating awareness about proper medicine disposal. Almost all participants (220, or 88%) felt that improper disposal of unused and expired medicines can affect the environment and health. This study revealed that the most common ways to dispose of unused, unwanted, and expired medication were tossing them in the trash and storing them for later use. There is an urgent need to address the fact that most study participants disposed of medicine in unsafe ways. Nonetheless, the majority of participants agreed that improper disposal of medications poses a threat to public health and the environment. Therefore, both national and local stakeholders urgently need to implement tailored interventions to address the situation.

DIETARY SILICA NANOPARTICLE AMELIORATES THE GROWTH PERFORMANCE AND MUSCLE COMPOSITION OF STINGING CATFISH, HETEROPNEUSTES FOSSILIS

Partha Sarathi Das — 2025-01-05

Silica nanoparticles (Si-NPs) are increasingly prevalent in various industrial applications, potentially impacting aquaculture. The study investigated the effects of dietary Si-NPs on growth performance and its repercussions on muscle compositions in Stinging catfish, Heteropneustes fossilis (H. fossilis). In this study, four isonitrogenous diets containing Si-NPs (0, 1, 2, and 3 mg/kg) were fed to juvenile H. fossilis for 60 days. Several growth indices (weight gain, WG; percent weight gain, %WG; length gain, LG; and specific growth rate, SGR), feed utility parameters (feed conversion ratio, FCR; feed conversion efficiency, FCE; and protein efficiency ratio, PER) and survival rate was assessed at the end of the feeding trial. This study showed significant effects of Si-NPs (2 mg/kg) in the growth and muscle composition of H. fossilis. However, Si-NPs did not significantly affect the feed utility of H. fossilis. The findings of this study recommended that Si-NPs can be effectively supplemented into the diets of H. fossilis for better production.

ACCELERATED FRACTAL ENCODING VIA DOMAIN CLASSIFICATION

Ziyad T. Najim — 2025-01-06

The story of fractal image compression is that the image to be compressed is partitioned into many blocks (range pool). Domain pool is created from range pool. Then, each range block is compared with all domain blocks, searching for the best match. This comparison process is highly time-consuming. Many ideas were proposed trying to reduce search time. Here in this paper, a fractal image encoding algorithm based on domain pool classification and domain pool reduction is proposed to speed up the searching process. It works on classifying domain blocks—according to some criteria—into many classes. In this way, range blocks are compared only with the domain blocks that belong to the same class as the range blocks. Experiments showed a considerable reduction in encoding time when compared with the standard Fisher's fractal image compression algorithm while maintaining image quality.

HISTOLOGICAL, HISTOCHEMICAL, AND IMMUNOHISTOCHEMICAL CHARACTERIZATION OF THE EFFERENT DUCTULES OF THE DOVE

Fatma El-Zahraa Ahmed Mustafa — 2025-01-08

This research examines the structural features of the efferent ductules (EDs) in healthy doves (Spilopelia senegalensis) collected from local hunters in Assiut, Egypt. After slaughter, the efferent ductules were promptly dissected and preserved in Bouin's fluid. Utilizing a combination of histological, histochemical, and immunohistochemical techniques, we explored the architecture of EDs, focusing on the stroma and parenchyma. The lining epithelium was made up of a simple columnar to the pseudostratified columnar epithelium, ciliated, secretory, and basal cells. However, this study is the first to identify telocytes within the peritubular region of the dove’s efferent ductules, highlighting their potential role in tissue communication and regeneration. Additionally, androgen receptor expression was detected in the epithelial cells and spermatozoa within the lumen, suggesting a significant role in the male reproductive system.

SMARANDACHE ANTI ZERO DIVISORS

Rebin M. Hassan — 2025-01-12

In this paper, we study and discuss the concept of Smarandache anti zero divisor (SAZD) element of the ring and the group ring , where is a cyclic group of order generated by . Moreover, we introduce and discuss the concept of SAZD ideal of the ring . Some results related to the given concepts are proved in detail. Accordingly, a Computer Algebra System (GAP) is used to verify the results of this study

WEB VULNERABILITIES DETECTION USING A HYBRID MODEL OF CNN, GRU AND ATTENTION MECHANISM

Sarbast H. Ali — 2025-01-12

The frequency of cyber-attacks has been rising in recent years due to the fact that startup developers have failed to overlook security issues in the core web services. This stated serious concerns about the security of the web. Therefore, this paper proposes a hybrid model built on the base of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU) and an attention mechanism to detect vulnerabilities in application code. Particularly, the model can help detect attacks based on Structured Query Language Injection (SQLi), Cross-Site Scripting (XSS), and command injection. When using the dataset SXCM1, our model achieved 99.77%, 99.66% and 99.63% for training, validation and testing, respectively. The results obtained on data from the DPU-WVD dataset are even better because it was 99.97%, 99.98% and 99.99% for training, validation and testing, respectively. These results significantly outperform the state-of-the-art models and can strongly identify vulnerabilities in web applications. Through training, on both the SXCM1 and DPU-WVD datasets, the model achieved an accuracy rate of 99.99%. The results show that this combination model is highly effective at recognizing three vulnerability categories and surpasses cutting-edge models that usually specialize in just one type of vulnerability detection.

USING INFRARED THERMOGRAPHIC TECHNIQUE AS AN ALTERNATIVE TO CONVENTIONAL RECTAL THERMOMETER IN SHEEP

Nizar J. Hussein — 2025-01-14

This research was established to assess the precision of the infrared (IR) camera and thermometer in measuring different body regions of sheep. This study was undertaken in the animal farm at the College of Agricultural Engineering Sciences, University of Duhok, Kurdistan Region of Iraq. The temperature was measured from the rectum using a digital thermometer. At the same time, the peripheral temperatures were measured from the ears, muzzle (MUZ), inner thigh (IT), inguinal region (ING), rectum, vulva, and five areas of the eyes, including nasal conjunctiva (NC), nasal limbus (NL), center cornea (CC), temporal limbus (TL), and temporal conjunctiva (TC) using IR thermometer and IR camera. The temperatures obtained from the IR thermometer were higher than those obtained from the digital thermometer. only NC temperature was found to be significantly (P<0.05) higher than other eye regions, and rectal and vulva temperatures. IR thermal camera showed no significant differences in the eyes' NL, CC, TL, and TC regions compared to rectum and vulva temperatures using the digital thermometer. The muzzle temperature was significantly (P<0.05) lower than all other body parts. In addition, the inner thigh, inguinal region, and ear temperatures were significantly (P<0.05) higher than rectum and vulva temperatures. The rectum and vulva temperatures obtained from the IR thermometer were significantly higher (P<0.05) than thermal camera and digital thermometer temperatures. In conclusion, thermal cameras as a non-invasive and accurate method can be an alternative temperature measuring method.

ASSESSMENT OF BACTERICIDAL ROLE OF EPIDERMAL MUCUS OF MAJOR CARPS AGAINST PATHOGENIC MICROBIAL STRAINS

Farkhanda Asad — 2025-01-15

This research evaluated the antimicrobial potential of mucus from major carps, specifically Cirrhinus mrigala and Labeo rohita, against Klebsiella pneumoniae and Escherichia coli. Fish weighing 300-350 grams were stoked and acclimated for 15 days, fed with commercially available diet at 4% of their body weight. Fish were treated with KMnO₄solution to avoid any infection before mucus collection and collected mucus was stored at 4°C. Mucus extracts were screened for antibacterial potential using the agar well diffusion method, measuring antibacterial effects by the zone of inhibition (ZOI) in mm. L. rohita secreted more mucus than C. mrigala. The mucus appearance of L. rohita was highly viscous, while less viscous in C. mrigala. In L. rohita mucus, maximum antimicrobial efficiency was observed. Results showed greater efficacy in limiting the growth of E. coli with zone of inhibition (16mm). Mucus is a key defense against disease. Fish skin mucus can serve as an alternative to antibiotics for use in aquaculture and potentially for human application. As a natural product, it may help reduce problems associated with antibiotic resistance.

DEVELOPING A COST-EFFECTIVE MICROPREDATION PROTOCOL FOR TWO CARNATION (DIANTHUS CARYOPHYLLUS L.) VARIETIES UNDER IN VITRO CONDITIONS

Lolav Rajab Al-Mizory — 2025-01-19

A successful attempt was made through this investigation to develop a cost-effective protocol for two varieties of carnation (Dianthus caryophyllus L.) ornamental plants named Clove Pink and Scarlet. Healthy and vigorous cultures were established by inoculating axillary buds on MS medium after a successful disinfestation process by sterilizing in 2.5% NaOCl for 10 minutes. The microshoots were multiplied by testing different benzyladenine (BA) and kinetin concentrations. Luckily, the best multiplication parameters were achieved with MS medium free of growth regulators, which will reduce the propagation costs. The higher number of shoots (11.60 shoots/ explant), leaves (59.80 leaves/ explant), and longest shoots (10.90 cm) when grown on MS medium without Plant growth regulators (PGRs), Additionally, the clove pink variety on MS medium with 1.5 mg l^-1 BA and Scarlet variety grown on MS-free of PGRs respectively. A 100% percent rooting was achieved for both varieties when 1.5 mg l^-1 Naphthalene acetic acid (NAA) was added to the culture media. The same NAA concentration for scarlet variety microshoots resulted in the highest number of roots (26.2 roots/explant) and the longest roots (9.0 cm). Successful acclimatization was observed by transferring the well-rooted plantlets to greenhouse conditions while putting them in peat moss

GLOBAL STABILITY AND HOPF BIFURCATION OF A DELAYED PREDATOR-PREY MODEL INCORPORATING ALLEE EFFECT AND FEAR EFFECT

Didar Akram Abdulqadr — 2025-01-22

This paper aims to discover the impact of the fear of predators in prey, Allee effect for predator reproduction and time delay corresponding to the gestation period on the dynamics of a predator- prey model. Existence, non-negativity, and boundedness of the model solutions are guaranteed. The criteria for asymptotically stability of all the biologically feasible steady state points are determined. It is also determined a critical value for time delay, where the model under goes Hopf -bifurcation near coexistence steady state point. Finally, with the help of the MATLAB program, to confirm the analytical results and discover the impact of fear, the Allee effect, and time delay, the model was solved numerically.it is observed that fear affect negatively on both prey and predator species and the time delay may system may induce a transition of the dynamics of system from the a stability situation to the state where the populations oscillate periodically or vice versa

ASSESSING DROUGHT IMPACTS IN ERBIL, IRAQI KURDISTAN: A STUDY OF LAND SURFACE TEMPERATURE AND VEGETATION HEALTH INDEX USING LANDSAT TIME-SERIES

Ragheb K. Mohammad — 2025-02-02

Research conducted in the Erbil Province, Kurdistan Region, Iraq from 1998 to 2017 aims to determine the frequency and intensity of drought, highlighting its diverse effects on society, encompassing the economy, agriculture, environment, and local community. Indicators, for instance, the Land Surface Temperature (LST) obtained from satellite data (Landsat) and the Vegetation Health Index (VHI), were employed to evaluate the severity of droughts. The outcomes indicated that Erbil encountered severe droughts in 1999, 2000, and 2008, which resulted in a significant decrease in crop outcomes. Additionally, 2008 was marked by escalating drought conditions, as measured by VHI values exceeding 40; as a result, the percentages reached (86.5%, 67.6%, and 53.7%), respectively. Significant relationships were revealed, with a confidence level of 0.9, between VHI and various factors such as precipitation, LST, and crop yield, with corresponding degrees of (-0.612, 0.615, 0.613, and 0.635). The study also disclosed that alterations and decreases in precipitation occur as the growing season progresses, whereas the following years (2000, 2008, and 2012) saw a pronounced decline in yield, failing to meet the lower limit of water demands for crops. Moreover, the most affected locations in Erbil Province were found to be in the central and southwestern parts of the province

WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS IDENTIFIES BIOLOGICAL PATHWAYS AND BIOMARKER GENES ASSOCIATED WITH CHICKENS' ADAPTATION TO BOTH LOW AND HIGH ALTITUDES

Pshtiwan S.A. Bebane — 2025-02-02

The main aim of the study was to identify modules, hub genes, and possible pathways linked with hypoxia adaptation in six types of tissues and organs (heart, kidney, liver, lung, muscle, and spleen) at altitudes ranging from 2,300 to 3,500 metersOn a transcription dataset from hypoxia-sensitive tissues, we performed weighted gene co-expression network analysis on 13,940 selected genes, and 10 transcriptional modules in total were detected (Turquoise 196 genes, Purple 27 genes, Blue 196, Brown 182, Yellow 108, Green 79 genes, Red 69 genes, Black 50 genes, Pink 44 genes, and Magenta 37 genes). Furthermore, we discovered that the majority of variable genes were screened by sub-setting 1000 genes; samples belonging to the same tissue clearly clustered together, and the expression in the liver and lung was more associated than in the heart and spleen. Functional enrichment analysis of all genes in 12 selected modules revealed that 9 KEGG pathways were considerably enriched, 13 Gene ontology terms were significantly enriched in the biological process and cellular component pathways, and 15 gene ontology terms were significantly enriched in the molecular function pathway. Through weighted gene co-expression network analysis, the results of this study expand our knowledge of the molecular pathway of catalytic and metabolic activity as a biomarker pathway

THE EFFECT OF FEATURE SELECTION METHODS ON MACHINE LEARNING MODEL PERFORMANCE: A COMPARATIVE STUDY FOR BREAST CANCER PREDICTION

Diman Siddiq Hassan — 2025-02-13

Developing countries often face a high incidence of breast cancer, making early detection vital for effective treatment. The risk of developing breast cancer can be evaluated using machine learning methods and regular diagnostic data. In cancer datasets, there is a wealth of patient information, but not all of it is valuable for predicting cancer. This highlights the significance of feature selection methods in uncovering the relevant data. In this field, many studies have attempted to predict the different types of breast tumours, since it is important to diagnose breast cancer medication accurately. This paper aims to perform a comparison such that to show the effect of different feature selection methods on the accuracy of various existing machine learning algorithms. The study focuses on seven machine learning algorithms: K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Trees (DT), Support Vector Machines (SVM), Logistic Regression (LR), Neural Network (NN), and Random Forest (RF). The feature selection techniques examined include F-test Feature Selection, Mutual Information (MI), and Spearman Correlation Coefficient. The dataset used for the experiments is the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, which is publicly available from the UCI Repository. The findings reveal that when feature selection is implemented, the LR and NN algorithms demonstrate superior accuracy and perform exceptionally well across other metrics compared to the other models