Journal title Wasit Journal for Pure sciences Indexing Online ISSN Print ISSN 2790-5241 2790-5233 Frequency 4 issues per year DOI prefix 10.31185 Editor in Chief Managing Editor Prof. Dr. Ali Khalaf Hussain , College of Education for Pure Science, Wasit University, Iraq Email: alhachamia@uowasit.edu.iq Assist. Prof. Dr. Abdul Hadi M.Alaidi, College of Computer Science and IT, Wasit University, Iraq Email: alaidi@uowasit.edu.iq Organized by Wasit University - College of Computer Science and IT Citation Analysis <a href="https://www.scopus.com/results/results.uri?s=REF%28%22wasit+journal+for+pure+science%22+OR+%22Wasit+J.+Pure+Sci.%22+OR+%22+Wasit+J+Pure+sci%22+OR+%22Wasit+J.+Pure+Sci.%22+OR+%22Wasit+Journal+of+Pure+sciences%22+OR+%22+Wasit+J+Pure+Sci%22+OR+%22+Wasit+J+Pure+Sci%22+OR+%22+Wasit+J.+Pure+Sci.%22+OR+%22+Wasit+J.+Pure+Sci.%22%29&limit=10&origin=searchhistory&sort=plf-f&src=s&sot=b&sdt=b&sessionSearchId=2cd33205ae7d7106b9940c3e5d8db675">Scopus | Google Scholar Acceptance Rate: 40% Review Speed: 90 days Before submission, You have to make sure that your paper is prepared using the WJPS paper Template, or Latex Template has been carefully proofread and polished, and conformed to the author's guidelines. Online Submissions Do you already have a username/password for WJPS? GO TO LOGIN Need a username/password? GO TO REGISTRATION Registration and login are required to submit items online and check current submissions' status.
Real-Time Lightweight Emotional Face Recognition Framework Based on CNN Algorithms
In this study, emotional face recognition uses the CNN algorithm. CNN is employed because of its ability to process local tissue and high-resolution connections. This design is planned to authenticate emotion scores for real-time and user-friendly implementations. The preliminary work begins in the neighboring part of the study. Software is utilized to preprocess the data in this study. A CNN model is created using a framework. The process consists of three phases: preprocessing, classifying, and evaluating data. The system was tested on several datasets. The lightweight CNN's performance for training and testing images showed approximately 88.67%. The performance of the system was also compared with existing methods. The research highlights the CNN model's ability to provide state-of-the-art performance in real-time applications, with potential implications for medical and safety applications. However, limitations such as data imbalance and a lack of multimodal approaches were identified for future exploration.
Face recognition, Lightweight face detection, Pattern Recognition, Real-Time Systems
Copyright (c) 2024 Suryanti Awang, MOHAMMED AHMED TALAB https://creativecommons.org/licenses/by/4.0
Fractional Diffusion Equations Solved via Log–Laplace Residual Power Series
In this paper, fractional diffusion equations of Caputo-Hadamard presented as interesting classes of fractional differential equations have not studied before. For the first time, the solution to a diffusion equation of this type was calculated. Therefore, it is considered a new addition to the diffusion equation with the presence of this form of fractional derivatives, with its initial conditions. Additionally, this paper presents a suitable Laplace logarithmic formula for the given fractional equation, derived from the generalized Laplace formula and its established conditions. This also relies on the formula for the fractional derivative as a transformation. The first time this type of fractional derivative was established, it helped in finding an approximate formula for the solution. The residual power series method used is effective in solving many fractional equations and was even more effective when used with the Laplace logarithmic formula. The Log(t+1) –Laplace residual power series method combines the two concepts, formulating and evaluating the method, shown that from any changing of the values of the parameters involving in formulation of derivative as well as the values of fractional order which found in tables and figures for illustrative examples which illustrates the presented method.
Copyright (c) 2025 Ali Zeki, Sameer Qasim Hasan https://creativecommons.org/licenses/by/4.0
Marine Predator Optimized BiLSTM Framework for Real-Time Intrusion Detection in IoT Environments
The rapid expansion and interconnectivity of Internet of Things (IoT) environments have intensified the risk of advanced cyber-attacks, necessitating the development of intelligent and resource-efficient Intrusion Detection Systems (IDS). This study introduces a novel hybrid IDS framework that integrates the Marine Predators Algorithm (MPA) with a Bidirectional Long Short-Term Memory (BiLSTM) network to enhance both detection accuracy and computational efficiency. Within the proposed model, MPA is employed for feature selection and hyperparameter optimization, thereby refining the learning dynamics of the BiLSTM architecture. Experimental evaluations conducted on two benchmark IoT datasets, CICIoT2023 and TON_IoT, demonstrate that the MPA-BiLSTM model consistently achieves detection accuracies exceeding 99.5%, while maintaining low inference latency and minimal memory consumption—characteristics that ensure suitability for real-time edge deployment. Additionally, SHAP-based explainability mechanisms are integrated to provide interpretability of feature importance, thereby fostering transparency and trust in the decision-making process. The results confirm that the synergistic integration of MPA with BiLSTM significantly improves both predictive performance and operational efficiency, establishing the proposed framework as a scalable and practical solution for IoT security.
Copyright (c) 2025 Huda Kadhum Ayoob, Haneen Hasan Hadi Rubaye, Sarah Hayder Hashim https://creativecommons.org/licenses/by/4.0
Experimental Analysis of Random Forest and LSTM Models for Optimizing CPU Efficiency in Cloud Computing
Cloud computing provides scalable and on-demand computational resources; nevertheless, effective utilization of the CPU is a critical issue because insufficient provisioning can result in higher latency and increased energy consumption. This research aims to measure, assess, and optimize CPU performance in cloud systems through the use of two machine learning models, namely, Random Forest (RF) and Long Short-Term Memory (LSTM), and subsequently using the Multi-Objective Grey Wolf Optimizer (MOGWO) to optimize the hyperparameters of the models to predict time series. The models were evaluated on the Google 2019 cluster trace using common metrics (Accuracy, Precision, Recall, and F1). The experimental findings demonstrate that optimization using MOGWO is an effective method for enhancing the performance of the models. Specifically, the accuracy of the Random Forest model increased from 65.15% to 89.70%, while the LSTM model improved from 95.02% to 98.60%, with consistent gains in precision, recall, and F1-score. These results verify the usefulness of the optimized models in forecasting the patterns of CPU usage and optimizing the use of CPU in cloud computing systems.
Keywords: Cloud computing, CPU efficiency, Random Forest, LSTM, machine learning, resource optimization, performance prediction, time-series analysis.
Copyright (c) 2025 Sajjad Adnan Ajeel, Sundos A. Hameed https://creativecommons.org/licenses/by/4.0
Characterization of Group of Invertible Elements of Six Index Zero Completely Primary Finite Rings of Characteristic p
The study of finite extension of Galois rings in the recent past have given rise to commutative completely primary finite rings that have attracted much attention as they have yielded important results towards classification of finite rings into well-known structures. In this paper, we give a construction of a class of completely primary finite ring<!-- [if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="_x0000_i1031" type="#_x0000_t75" alt="" style='width:16pt; height:16pt;mso-width-percent:0;mso-height-percent:0;mso-width-percent:0; mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image001.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1031" DrawAspect="Content" ObjectID="_1819869786"> </o:OLEObject> </xml><![endif]-->of characteristic<!-- [if gte vml 1]><v:shape id="_x0000_i1030" type="#_x0000_t75" alt="" style='width:16pt;height:13pt;mso-width-percent:0; mso-height-percent:0;mso-width-percent:0;mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image003.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1030" DrawAspect="Content" ObjectID="_1819869787"> </o:OLEObject> </xml><![endif]-->whose subsets of zero divisors <!-- [if gte vml 1]><v:shape id="_x0000_i1029" type="#_x0000_t75" alt="" style='width:31pt;height:20pt; mso-width-percent:0;mso-height-percent:0;mso-width-percent:0; mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image005.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1029" DrawAspect="Content" ObjectID="_1819869788"> </o:OLEObject> </xml><![endif]--> satisfy the condition <!-- [if gte vml 1]><v:shape id="_x0000_i1028" type="#_x0000_t75" alt="" style='width:150pt;height:23pt; mso-width-percent:0;mso-height-percent:0;mso-width-percent:0; mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image007.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1028" DrawAspect="Content" ObjectID="_1819869789"> </o:OLEObject> </xml><![endif]-->. The ring <!-- [if gte vml 1]><v:shape id="_x0000_i1027" type="#_x0000_t75" alt="" style='width:12pt;height:13pt;mso-width-percent:0; mso-height-percent:0;mso-width-percent:0;mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image009.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1027" DrawAspect="Content" ObjectID="_1819869790"> </o:OLEObject> </xml><![endif]-->is constructed over its subring <!-- [if gte vml 1]><v:shape id="_x0000_i1026" type="#_x0000_t75" alt="" style='width:79pt;height:22pt; mso-width-percent:0;mso-height-percent:0;mso-width-percent:0; mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image011.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1026" DrawAspect="Content" ObjectID="_1819869791"> </o:OLEObject> </xml><![endif]--> as an idealization of the <!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" alt="" style='width:15pt;height:18pt;mso-width-percent:0;mso-height-percent:0; mso-width-percent:0;mso-height-percent:0' o:ole=""> <v:imagedata src="file:////Users/hadi/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image013.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1819869792"> </o:OLEObject> </xml><![endif]-->- modules. A thorough determination and classification of the structure of the group of invertible elements using fundamental theorem of finitely generated abelian groups is given.
Completely Primary Finite Rings, Group of Invertible Elements, Six Index Zero
Copyright (c) 2025 Hezron Were, Mr. Nicholas Waweru, Dr. Edward Njuguna, Dr. John Matuya https://creativecommons.org/licenses/by/4.0
Comparison Between Different Probability Distributions for Modeling Exchange Rate Volatility Behavior in Oil Countries for the Period 1990 – 2022
this study examines the distributional properties of exchange rate volatility for major oil-exporting developing economies from 1990-2022. Three probability distributions [ Normal, Fr'echet, and Log Normal] are compared using moment method and maximum likelihood approaches.The Bayesian information criterion (BIC) and the Akaike information criterion (AIC) are used to assess goodness-of-fit. The results indicate that the Normal distribution tends to provide the greatest fit for many countries [Iraq, Kuwait, Libya, Iran, Algeria, Oman, and Bahrain]. The Log Normal distribution is found to be optimal for[ Saudi Araia, Qatar, and Egypt] , while the Fr'echet distribution fits best only for United Arab Emirates. This analysis provides insights into the underlying distributional characteristics of exchange rate returns in these countries, which can inform appropriate model selection for volatility forecasting and risk management applications . The research adds to existing literature by conducting a entire distributional comparison across a broader set of oil- exporting developing economies over an extended time.
Normal Distribution , Fréchet Distribution ,Log Normal Distribution, Oil-Exporting Countries, Exchange Rate Volatility.
Copyright (c) 2025 Awatif Al-Dubaicy https://creativecommons.org/licenses/by/4.0
Synthesis, Characterization and Study of Liquid Crystal Behavior of Linear-Shaped Mesogens Containing Cyano and Alkyl as a Terminal Groups
A novel series of liner-shaped liquid crystalline compounds containing thiazolidine-2,4-dione moieties with cyano and alkyl as terminal groups were synthesized and characterized. The molecular design incorporated a thiazolidine-2,4-dione unit linked to flexible alkyl chains via Knoevenagel condensation, yielding mesogens with varying terminal lengths. Structural confirmation was achieved through FT-IR, ¹H-NMR, and mass spectrometry. Mesomorphic properties were investigated using polarized optical microscopy (POM) and differential scanning calorimetry (DSC). All compounds [V] 4,5,6,8 exhibit liquid crystal properties, while, compound [V]3 do not show mesomorphic behavior. Odd-even effects were observed in transition temperatures, highlighting the influence of alkyl chain length on mesophase stability.
liquid crystal, heterocyclic, Nematic phase, Smectic phase
Copyright (c) 2025 Hassan Jabori, Nisreen H. Karam https://creativecommons.org/licenses/by/4.0
Indirect Spectrophotometric Determination of Nitrate Ion in Groundwater Using an Organic Reagent
Low river water levels in summer and low rainfall in winter have led to desertification, leading to widespread use of groundwater in many areas of Iraq, especially in the southern and central regions. Groundwater is considered an alternative solution to address desertification, but in reality, many health problems have emerged due to the ions present in groundwater, which cause numerous diseases. Among these ions studied in this research is the nitrate ion (NO3⁻). This spectroscopic method was proposed to determine the concentration of nitrate ions in groundwater. Eriochrome Black T (EBT) colorimetric detector was used as a spectroscopic detector after complexing the detector with silver nitrate to determine the nitrate ion in groundwater. Several parameters and optimal conditions for this reaction were studied, and the assessment of nitrate levels was found to be consistent with the linear equation of the Beer-Lambert law in the range of 0.5–5.0 mg/L at a wavelength of (530) nm. When plotting the calibration curve, good linearity was recorded (R2 = 0.9979). The limits of detection (LOD) and quantification (LOQ) of the method were determined to be 0.269 and 0.816 mg/L, respectively, based on the standard deviation values. The precision measured in terms of relative standard deviation (RSD) over the specified time period at five concentration levels was less than 6.7%. The recovery rate was satisfactory, ranging from 96 to 103, Sandell's sensitivity equal to (0.0263) μg.cm-2. The rationale for using spectrophotometric methods lies in their simplicity of operation, low cost, and applicability in laboratories with limited resources, as opposed to more advanced techniques such as HPLC or LC-MS. Based on these results, the method was approved for the determination of nitrate ions.
Groundwater; Eriochrome Black T (EBT); Spectrophotometer; Silver Nitrate.
Copyright (c) 2025 Ryadh mohammed https://creativecommons.org/licenses/by/4.0
Total Italian Domination: Structural Results on Middle Graphs of Standard Families
If we have graph , an Italian dominating function (IDF) is defined as a mapping satisfying the condition, if for any vertex must be have neighbor there is no fewer than 1 vertex to be or no fewer than 2 vertices where The weight of a (IDF) is the sum of all these functions for all vertices . And the minimum weight selected from overall (IDF) of and indicated by A total Italian dominating function (TIDF) is an (IDF) if the collection of non-zero elements, each of which is contiguous to at least one non-zero element. A minimum weight of any (TIDF) is referred to as a total Italian domination number indicated by . We commenced the study of (TIDF) of graph and the graph can be issued by add a new vertex between every two old vertices in and draw the edges between every two added vertices provided that there exist common vertex binds them, we called middle graph of . For selected standard graph families. The accurate numerical values of these graph-related parameters were established. We determined these parameters' exact values.
Italian domination, total Italian domination, Middle graph
Copyright (c) 2025 Maryam ALTABATABAII https://creativecommons.org/licenses/by/4.0
Computational Approaches to Studying Reaction Mechanisms and Transition States in Quantum Chemistry
The Computational chemistry plays a crucial role in understanding reaction mechanisms and transition states at the atomic level. Quantum mechanical methods, particularly Density Functional Theory (DFT), provide insights into electronic structures, energy landscapes, and reaction kinetics. This study explores computational techniques such as Transition State Theory (TST), the Nudged Elastic Band (NEB) method, and post-Hartree-Fock approaches to identify reaction pathways and energy barriers. Molecular Dynamics (MD) simulations further examine reaction dynamics over time under various conditions. This analysis of model reactions highlights key transition states and activation energies, demonstrating that DFT-based methods balance computational efficiency and accuracy. NEB simulations effectively mapped minimum energy paths, while TST calculations provided reliable reaction rate constants. The integration of time-dependent DFT (TD-DFT) enabled the study of excited-state reaction dynamics, crucial for photochemical processes. These findings confirm that quantum mechanical simulations can accurately describe chemical transformations, aiding in the design of novel catalysts, materials, and pharmaceuticals.
Computational Chemistry, Reaction mechanisms, Transition states, Density Functional Theory (DFT).
Copyright (c) 2025 Sarah Badri Jasim, Khalidah H. M. Al Furaiji, Asam Hussein Ali https://creativecommons.org/licenses/by/4.0
The Impact of Rheumatoid Arthritis on Cardiovascular Health
Rheumatoid arthritis is a chronic disease caused by immune system dysfunction, characterized by an inflammatory response that affects joints and may extend to other organs like the heart. The exact cause of the disease is unknown, but it is believed to be linked to genetic, environmental, and some types of bacterial infections. It affects both sexes and people of all ages, with a higher prevalence among women. Symptoms often begin in the joints of the hands and feet and then extend to the knees and arms. This study assessed the risk of heart disease among rheumatoid arthritis patients in Wasit Governorate. The sample included 90 participants aged between 30-60 years, divided into two groups: a patient group (60 individuals diagnosed with rheumatoid arthritis) and a control group (30 healthy individuals). The results showed that the percentage of women with rheumatoid arthritis was significantly higher than that of men (73.3% versus 26.7%). The majority of rheumatoid arthritis patients were obese (70.0%) or overweight (21.7%), with a small percentage of those with normal weight (8.3%). Hematological parameters showed a significant increase in the erythrocyte sedimentation rate (ESR) in patients of both sexes compared to healthy controls. There were also differences in basic hematological parameters, such as red blood cell count, white blood cell count, hemoglobin, and platelets. RA patients had higher levels of triglycerides (TG), total cholesterol (TC), very-low-density lipoprotein (VLDL), and low-density lipoprotein (LDL), while significantly lower levels of high-density lipoprotein (HDL) were observed compared to the healthy control group.
Copyright (c) 2025 Amal Azeez, Mohammed R.S.AL-Attabi https://creativecommons.org/licenses/by/4.0
t^p/p-Laplace transformation for Finite Time Stability for Composition and Riemann- Katugampola Fractional Integro-Differential Systems
In this research, the finite-time stability of Multi-Composition Caputo-Katugampola fractional Integro- Differential Nonlinear system with many values ​​of fractional derivatives is studied with some sufficient and necessary conditions as Lipchitz conditions for nonlinear functions involving Riemann– Katugampola fractional integral and the formulas derived from them with their respective bounded value as well as finding the solution under these conditions that contains Mittag Leffler functions which appeared through the use of the generalized Laplace formula which suitable with Caputo-Katugampola fractional derivative. Therefore, important conditions appeared that contain the parameters that played a good role in finding and computing the stability as in the attached tables of illustrative examples that explain the necessary time requirements for it.
Copyright (c) 2025 Mohammed Salah, Sameer Qasim Hasan https://creativecommons.org/licenses/by/4.0
Screening of C677T polymorphism in the methylenetetrahydrofolate reductase gene in Anbar, Iraqi patients with β-thalassemia major
The synthesis of proteins, DNA, and RNA as well as the metabolism of methionine and folate, depend on the enzyme MTHFR, a crucial regulator of the one-carbon cycle. This enzyme is necessary for the remethylation of homocysteine to methionine and transforms 5,10-methylenetetrahydrofolate into its active form, 5-methyltetrahydrofolate. Determining the degree of polymorphism in the gene for the enzyme methylenetetrahydrofolate reductase (MTHFR) in patients with beta-thalassemia major and comparing it to the control group, as well as sequencing, analyzing, and detecting mutations in the DNA nucleotides of the gene for the enzyme MTHFR, were the objectives of the study. 90 samples were taken from patients at the Al-Ramadi Teaching Hospital for Women and Children, both male and female. The study included 60 samples of individuals with β-thalassaemia major. The patients were between the ages of 3 and 52. The control sample consisted of 30 samples of each sex, ages ranging from 16 to 65. For every sample in the investigation, the MTHFR C677T gene band was amplified using the polymerase chain reaction (PCR). When compared to the 100 bp marker, all samples had a band size of 198 bp. The CHROMAS was used to examine the sequence data. The BLAST SEARCH tool was used to record the data, which had the same gene, C677T, between 9800 and 9930, and showed no alterations from the reference gene (NG_013351). These findings show that the participants in this research were wild type for this location, meaning they did not carry the C677T mutation
Copyright (c) 2025 Dalal Talak Ali Abtan, Hadeel Abdelelah Abdel Razaaq https://creativecommons.org/licenses/by/4.0
AI in Medical Imaging & Diagnostics: review
ABSTRACT: The application of artificial intelligence (AI) has transformed medical diagnostics for the better with the adoption and evolution of radiology, histopathology, clinical laboratory testing, robotic surgery, and expert systems. The implementation of deep learning algorithms using AI, such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Generative Adversarial Networks (GANs), have outperformed humans in the analysis of medical images, tumor categorization, and biomarker detection. AI powered biopsy systems, which are a type of robotic assisted diagnostics, have improved the accuracy and reduced the invasiveness of the procedure, which in return, enables for better detection of diseases at earlier stages. The use of pipetting robots and automated blood analyzers for AI powered automation of clinical lab diagnostics improves the speed and volume of diagnostic assays while reducing the rate of human error. Additionally, AI powered expert systems, like OncoKB for the analysis of genetic mutations and deep mind’s AlphaFold for predicting protein structures, are advancing precision oncology and personalized medicine by offering non-biased treatment suggestions. The issues that impede the further integration of AI in diagnostics include data diversity, model generalization, obtaining legal certification, and the absence of XAI which would heighten clinician acceptance. To overcome these barriers, there needs to be standard validation frameworks, federated learning for privacy-centric AI training, and multimodal AI that incorporates medical images, genomic data, and EHRs. Emerging directions in self-supervised learning (SSL), active AI robotics, and human-machine interaction will likely improve the precision of diagnoses, automate clinical processes, and expand healthcare access around the world. This review outlines the role of AI in transforming medical diagnostics while analyzing existing obstacles, emerging possibilities, and recommendations for AI in clinical decision support systems.
Artificial Intelligence (AI), Medical Diagnostics, Deep Learning, Computer-Aided Diagnosis (CAD), Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Explainable AI (XAI), Generative Adversarial Networks (GANs), Robotic-Assisted Biopsy, Precision Oncology, Automated Blood Analyzers, Digital Pathology.
Copyright (c) 2025 Saif Hameed Abbood Al-Waeli1, Haza Nuzly bin Abdul Hameed https://creativecommons.org/licenses/by/4.0
The relationship between the presence of Prevotella intermedia and Porphyromonas gingivalis and increased severity of gingivitis
ABSTRACT: The study aimed to analyze the relationship between the presence of P.intermedia and P.gingivalis and the increased severity of gingivitis. The results are consistent with previous studies that demonstrated the multiplicity of periodontal pathogens and their varying prevalence according to geographical, genetic, and environmental criteria, confirming that the use of molecular analysis techniques such as polymerase chain reaction provides high accuracy in detecting disease-causing bacteria and contributes to a deeper understanding of how the presence of these microbes is associated with the clinical development of gingivitis and periodontitis.
Keywords: P. gingivalis, P. intermedia, Real-Time Quantitative Polymerase Chain Reaction
Copyright (c) 2025 Hodaa Qassim, Rana Salih Al-Taweel https://creativecommons.org/licenses/by/4.0
Numerical Simulation of a Nonlinear Predator-Prey System with Robin Boundary Conditions
This study presents a numerical simulation of a nonlinear predator-prey system described by a reaction-diffusion model within a convex, open domain, subject to Robin boundary conditions. The Finite Element Method (FEM) is employed to discretize and solve the system, offering a practical approach to capture the spatiotemporal dynamics of predator-prey interactions. Furthermore, an error estimation is derived to evaluate the accuracy of the numerical approximation in comparison to the exact continuous solution. The results confirm the reliability and efficiency of the proposed numerical scheme, particularly in handling the complexities introduced by the Robin boundary conditions.
Copyright (c) 2025 Ghassan Al-Juaifri, Huda Al-Maliky https://creativecommons.org/licenses/by/4.0
Assessment of Testosterone Levels in Tobacco Smokers, Dependence Over the Long Term with Some Haemato-Biochemical Parameters
Smoking is considered a pervasive global concern, negatively affecting health and resulting in several diseases, causing millions of fatalities annually. The purpose of this research was to determine how blood smokers' testosterone levels, blood parameters, renal function, cholesterol, triglycerides, and liver enzymes were affected by heavy and chronic tobacco use. Participants (n =60) were obtained from a population cohort of men in Al-Ramadi city aged 20-63 years , Smokers (n=30) have been addicted to smoking for 10 years or more with number of 15-40 cigarette/ day . The subjects answered a detailed survey on their health, therapy or drug intake, and other symptoms or disease. The sex hormone, RBC Count, Hb, HCT, MCV, MCH, MCHC, WBC Count, Platelets, renal function, lipid profiles, ALP,AST and ALT were measured. Our findings have shown a substantial statistical difference in blood total testosterone levels (p < 0.001) among chronic smokers and non-smokers, with lower levels observed in the former group. 1.653±0.097 and 5.980±0.195 respectively , Cigarette smoking adversely affects Haematological parameters ( count of red blood cell , hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular haemoglobin concentration, total leukocyte count) demonstrated a statistically significant increase (p < 0.05). No significance variations are observed on mean corpuscular haemoglobin and platelets (p > 0.05). The findings indicated a highly substantial increase in the levels of urea and creatinine (p<0.001), Furthermore lipid and liver enzymes it was also revealed that it a highly significant elevated (p< 0.001). Therefore, the current investigation indicates detrimental consequences of prolonged smoking on testosterone level in serum , hematological variables , moreover smoking are a risk factor for kidney disorders indicated by urea and creatinine , dyslipidemia was found including change in total cholesterol and triglyceride finally liver enzymes abnormalities were recorded in our study including ALP,AST and ALT in the addicted smokers
Tobacco smoking, Testosterone, Blood parameters, Renal function, Cholesterol, liver function
Copyright (c) 2025 Nbaa Mutea Abid Al Alh , Asmaa Wajeh Jumaa, Nagam Khudhair https://creativecommons.org/licenses/by/4.0
Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease
Proposed here is a speech-based diagnostic framework for detecting Parkinson's disease that utilizes a Long Short-Term Memory neural network and the Grasshopper Optimization Algorithm. The framework aims to improve the detection of PD while ensuring accurate and efficient classification of speech-based signals. Incorporated within the system is a dimensionality reduction technique called Linear Discriminant Analysis. This approach allows the system to optimally utilize the features extracted from the given speech signal while ensuring that all the conditions necessary for successful classification are met. The LSTM neural network serves as the core deep learning model for this framework. It is trained on the speech signals produced by both healthy individuals and those with PD, allowing it to detect the class of a given speech signal based on the conditions necessary for successful classification. We assess the suggested technique on two speech datasets that are publicly available: NeuroVoz, a Castilian-Spanish body of work, and EWA-DB, a database in the Slovak language. The classification accuracy of our model on NeuroVoz is 99.45%, while on EWA-DB it is 99.71%. Moreover, these results exceed those from several contemporary approaches that were also tested on these two datasets. We interpret our results as strong evidence that uniting deep learning with nature-inspired optimization does yield an effective tool for providing, in a non-invasive way, a diagnosis of early-stage Parkinson's across diverse linguistic contexts.
Grasshopper Optimization Algorithm (GOA)
Copyright (c) 2025 Saif Wali Ali Alsudani, Ghassan Khudair Saud https://creativecommons.org/licenses/by/4.0
Evaluation of ADH4 gene expression and hematological parameters as biomarkers for the pathogenesis of ITP in the pediatric
This study investigates the role of the ADH4 gene expression and hematological biomarkers in the pathogenesis of Immune Thrombocytopenic Purpura (ITP) in pediatric patients. The study involved 100 children, 50 with ITP and 50 healthy controls, to evaluate blood parameters and ADH4 gene expression. Results showed the mean white blood cell (WBC) count was significantly lower in ITP patients (8.11 ± 0.73) compared to controls (11.94 ± 0.69), Similarly, lymphocyte counts were significantly reduced in ITP patients (3.29 ± 0.43) versus controls (5.65 ± 0.42), The platelet count was markedly lower in ITP patients (26.18 ± 2.77) compared to controls (277.40 ± 10.74), On the contrary, The results show there was a non-significant difference between ITP patients and healthy individuals according to both neutrophil count and hemoglobin (Hb) levels (P < 0.05), while, ADH4 gene expression was significantly higher in ITP patients shown The mean 13.29, 1.00 between the ITP patients and Control respectively, suggesting its involvement in immune dysregulation. These findings highlight the potential of the ADH4 gene and some hematological parameter as a biomarker and therapeutic target in ITP, emphasizing the need for personalized treatment approaches
Copyright (c) 2025 Hayder Dawood saleem, Muthana Ibrahim Maleek https://creativecommons.org/licenses/by/4.0
A Study of a New Class of Meromorphic Univalent Functions Through Subordination Applications
In this research, I wanted to create a new beautiful class defined by subordination and the integral operator and the combination between them and I named it so that I inferred results from the geometric properties of this nice class in terms of studying the radius of convexity and estimating the coefficient and other new results. I also presented the previous information related to the distinguished researchers who before me studied the subordination and the integral operator that I used in my beautiful research.
Meromorphic Functions, Integral Operator, Coefficient Bounds, Radii of Starlikeness, Convolution
Copyright (c) 2025 Hazha Zirar Hussain https://creativecommons.org/licenses/by/4.0
Evaluation of Decision Tree and Support Vector Machine Classifiers in Comparison for Flood Prediction
Protection from floods is one of the most significant activities aimed at risk reduction. Flood prediction and disaster preparedness have relevance for reducing the association between floods and people and infrastructure. The goal of this study was to find out how well the Support Vector Machine (SVM) and Decision Tree classifiers work at predicting flooding based on different environmental and infrastructure factors. Data collection had been done through gathering 140 samples with a total of 21 variables where analysis had been done in order to identify significant contributory factors: topography, urbanization, and climate change. The results show that an SVM model was capable of achieving an accuracy of 91%, while the Decision Tree classifier did much better at an accuracy of 95%. The decision tree model was also more precise in flood prediction (1.00) and recall for non-flood cases (1.00), while for both models, the recall for flood cases was the same (0.88). This indicates that both the models had some false negatives for floods. The current study focuses more on machine learning applications and disaster readiness in flood risk assessment for better and more effective mitigation.
: Flood Prediction, Machine Learning, Support Vector Machine, Decision Tree, Disaster Preparedness
Copyright (c) 2025 Suha Abdullah https://creativecommons.org/licenses/by/4.0
Morphological Description of the Pancreas in Indian Roller Coracias benghalensis (Linnaeus, 1758)
The current study aimed to determine the morphological description of the pancreas in the Indian Roller Coracias benghalensis (Linnaeus, 1758). In this study 15-10 adult live birds were used, which were anesthetized with chloroform and then dissected and the pancreatic gland was removed, then the samples were fixed with fixative solutions. The morphological examination revealed that the Indian roller bird's pancreas is rounded and long, and is a pale pink color. It is found on the lower right side of the body cavity and it has three lobes: the ventral lobe, the dorsal lobe, and the splenic lobe. The ventral lobe was the longest and largest cylindrical lobe and extends within the mesenteric tissue of the duodenal loop. While the dorsal lobe appears flat and lay parallel to the descending arm of the duodenum, the splenic lobe appears in the form of a small volume pear-shaped lobe extending from the center of the dorsal part of the ventral lobe towards the spleen
Bird, Coracias benghalensis, Indian Roller, Morphological description, Pancreas.
Copyright (c) 2025 Haneen Saad Khamas, Intidhar Mohammed Mnati https://creativecommons.org/licenses/by/4.0
Eight-Figure Pattern for Enhancing the Searching Process of Grey Wolf Optimization (Eight-GWO)
Evolutionary algorithms suffer significantly from a stack at the local optima. This paper proposes a new strategy that detects when the search gets stuck in a local optimum and then switches to a more dynamic approach to escape. The proposed model is based on simulating eight pattern movements and embedded with a Grey Wolf Optimizer algorithm (GWO). It is called the Eight-Figure Grey Wolf Optimizer (Eight-GWO). The proposed model combines two phases: regular search when searching progresses over time while the second phase, searching by eight patterns when the algorithm reaches stuck. The Eight-pattern updates the gray position based on the sin and cos function. The proposed Eight-GWO on the 24 functions of the CEC2005 benchmark suite and compared its results with both the standard GWO and Particle Swarm Optimization (PSO). The experiments result show the proposed Eight-GWO gets better results than GWO and PSO where it achieved the best results on 80% of the test functions. The proposed Eight-GWO runs 23% faster than the original GWO and 44% faster than PSO.
Copyright (c) 2025 Lec. Ali Hakem Alsaeedi, Suha Muhammed Hadi, Yarub Alazzawi, Emad Badry Badry https://creativecommons.org/licenses/by/4.0
Medical Image Segmentation Based on Hybrid Deep Learning Techniques
ABSTRACT: Medical image segmentation is an integral component of computer-aided treatment planning and diagnosis, enabling accurate analysis of abnormalities and anatomical structures. In this paper, a novel U-Net and transformer encoder-based hybrid deep learning architecture is developed to advance precision and speed in medical image segmentation. The U-Net architecture, for its remarkable capability to capture local details, is integrated in synergy with a transformer encoder, whose power is in capturing data's long-range dependencies. The synergy between such capabilities makes the resultant hybrid architecture robust for segmentation in diverse medical imaging modalities. The approach is rigorously compared on publicly available datasets, including brain MRI, Chest X-Rays, and ISIC, and is shown to have better Dice coefficients than current state-of-the-art. The synergy between U-Net and transformer components, in addition to better segmentation, is shown to result in computational savings, thus becoming efficient for clinical application. The result demonstrates the capability of such a hybrid architecture to advance diagnostic precision and contribute to enhanced treatment planning in clinical settings. The work is in alignment with growing literature on using advanced deep learning to overcome challenging medical image analysis problems, ultimately leading to enhanced clinical outcomes through accurate and reliable tools for segmentation.
Copyright (c) 2025 Mohammed Abdulameer Aljanabi , Noor Abd Alrazak Shnain https://creativecommons.org/licenses/by/4.0
Intelligent Cyber-Attack Detection in IoT Networks Using IDAOA-Based Wrapper Feature Selection
ABSTRACT: In the realm of cybersecurity, the increasing sophistication of cyber-attacks demands the creation of sophisticated intrusion detection systems (IDS) designed to accurately detect and counteract threats in real-time. This study presents an innovative framework that integrates the Improved Dynamic Arithmetic Optimization Algorithm (IDAOA) with a Bagging technique to enhance the performance of intelligent cyber intrusion detection systems. The IDAOA serves as a wrapper-based feature selection method, optimizing the identification of the most impactful features while balancing local exploration and global exploitation. The Bagging technique further strengthens the classification phase by combining predictions from multiple classifiers, effectively addressing issues of class imbalance and improving overall system robustness. Evaluation of the proposed system using the NSL-KDD dataset demonstrates its superior performance, achieving an accuracy of 99.45%, significantly outperforming state-of-the-art approaches. These findings underscore the potential of intelligent optimization and ensemble learning techniques in advancing cybersecurity for IoT networks.
Copyright (c) 2025 Mohammed Abdullah, Ryna Svyd https://creativecommons.org/licenses/by/4.0
Quantum-Inspired Temporal Synchronization in Dynamic Mesh Networks: A Non-Local Approach to Latency Optimization
This paper presents a novel method for achieving temporal synchronization in Network-on-Chip (NoC) architectures, using optimization techniques derived from quantum mechanics. We provide a non-local temporal coordination framework to optimize network latency in dynamic mesh networks using quantum principles such as entanglement and superposition. A specialized router design using quantum-inspired control units incorporates the Quantum-Inspired Temporal Coordination Algorithm (QTCA) and Non-Local State Synchronization Protocol (NSSP), which are essential components of the proposed architecture. The experimental results indicate that the 16x16 mesh network significantly outperforms conventional route strategies. Latency is diminished by 31.2%, the network saturation threshold is enhanced by 37.8%, and packet loss is decreased by 76.3%. Notwithstanding a minor 8.2% increase in logic overhead and a 5.7% rise in power usage, the framework sustains robust phase coherence (0.92 local, 0.87 non-local). The results demonstrate that next-generation NoC designs might gain from temporal synchronization influenced by quantum computing, particularly in addressing performance and scalability challenges in complex multi-core systems.
Mesh Networks, Network-on-Chip, Quantum-Inspired Computing, Time Synchronization, Non-Local Correlation
Copyright (c) 2025 Yousri Sabah https://creativecommons.org/licenses/by/4.0
A diagnostic study of Cryptosporidiosis in the bile of some ruminant’s species in Wasit Province, Iraq
The current study aimed to determine the prevalence of Cryptosporidium spp. in gallbladder fluid samples collected from cattle and sheep in Wasit Governorate, Iraq. Using the modified Ziehl-Neelsen stain technique,the presence of oocysts of the parasite was confirmed microscopically, showing spherical forms that ranged in color from pink to bright red. A total of 300 samples (150 from cattle and 150 from sheep) were examined between July 1, 2024, and October 30, 2024. The results revealed an infection rate of 30% in cattle and 8% in sheep. Additionally, younger animals, particularly calves under one year of age, exhibited higher infection rates due to weaker immune systems and overcrowded rearing conditions. The results also showed no significant difference in infection rates between rural and urban areas for both cattle and sheep, indicating common environmental risk factors such as water contamination and poor waste management. Statistical analysis revealed that infection rates varied with age, with younger animals being more susceptible. These findings emphasize the need to raise health awareness among farmers,improve livestock management practices, and implement advanced diagnostic techniques to reduce the spread of Cryptosporidium spp. This study contributes to the understanding of the epidemiology of cryptosporidiosis in livestock and highlights its potential as a zoonotic disease.
Cryptosporidium, Cattle, Cow, Sheep, Microscopic examination
Copyright (c) 2025 Yasmin Ghani https://creativecommons.org/licenses/by/4.0
Efficiency of spraying with spirulina and variety on the growth of bean plant under drip irrigation system
The utilization of bio-stimulants in agricultural settings has emerged as a potentially fruitful technique for enhancing the development and production of crops. Microalgae extracts are a form of bio-stimulant that has recently garnered much attention. These extracts are well-known for their high metabolic activity, bioactive chemicals, and capacity to boost plant growth and development. The experiment aimed to determine the efficiency of Spirulina platensis extracts by foliar spraying on bean plants. The experiments were carried out in the autumn of 2023 in Diyala Governorate to study the efficiency of spraying with spirulina extract and its physiological effect on bean plant growth. The experiment was carried out in RCBD design with three replications, using four concentrations of spirulina (0, 0.25, 0.50, and 1.0) % and two varieties of bean plants ( Barcelona and luz de otono). The results showed a superiority concentration of 1.0% in all studied traits. Which record in 73.28 cm,51,43 SPAD,1.41,42.03 g/ plant,3.90%,0.156%, and 1.41% for the high plant, chlorophyll index, leaf area index, dry matter, N%, P%, and K% respectively, Barcelona variety superiority in all characteristics except leaf area index (LAI). The results also showed that the binary interaction between spray and variety significantly affected the studied factors.
Copyright (c) 2025 Intesar Kareem Abdulhassan https://creativecommons.org/licenses/by/4.0
Article Review: Advancements in Veterinary Histopathology: Linking Tissue Pathology to Clinical Outcomes
stressors. Histopathologists in veterinary pathology are therefore engaged in examining tissue alterations resulting from the direct or indirect influence of diseases on cells, tissues, organs, and/or body systems. Such microscopic alterations are collectively considered as 'lesions,' and the terminology used to describe these changes is unique to histopathology. Although mainly involved in Histopathology comprises the study of morphological changes occurring in tissues and organs during various pathological conditions or following exposure to chemicals, drugs, and other environmental diagnostic work, the veterinary histopathologist often plays a substantial role in understanding the pathogenesis of an individual case or collection of cases and may contribute to fundamental studies of the etiology and pathogenesis of a wide range of diseases affecting different species of animals. In review, where the aim is to understand why and how a disease develops, the broad term "anatomical pathology" is used.
Copyright (c) 2025 Samer Fadhail https://creativecommons.org/licenses/by/4.0
Antimicrobial Activities of Chitosan and Nanochitosan Against Aerobic Infection in Wound
In this study aerobic bacteria were isolated from wound and burn samples taken from diabetic foot patient at Al-Salam Teaching Hospital. chitosan, which comes from insects and arthropods, were employed as an antibacterial agent.it binds to negatively charged bacterial cell wall, producing damage and alterations in permeability. The concentration0f 1% was discovered to be the most efficient against aerobic bacterial infections. The degree of deacetylation, which directly effects the positive charge density, has significant effect on the antibacterial activity of chitosan and its derivatives.
pseudomonas aeroginosa; chitosan; nanochitosan ,Antibacterial activity; The degree of deacetylation
Copyright (c) 2025 wafaa mohson https://creativecommons.org/licenses/by/4.0