Journal of Education for Pure Science-University of Thi-Qar

In this paper, we study neutrosophic hesitant fuzzy ideal. We introduce the notions of neutrosophic
hesitant fuzzy primary ideal, neutrosophic hesitant fuzzy semiprimary ideal of a ring and discussion of
some important theorems and results.

The homotopypermutation approach is used in this article to solve the fractional-order biological population model (FOBPM). The fractional derivative is defined using the Atangana-Baleanu operator (ABO). The suggested technique provides of several solutions for FOBPM. The HPM technique is regarded as one of the finest analytical processes for solving fractional-order, nonlinear PDEs, notably the FOBPM, Since it requires fewer computations and has a greater rate of convergence than other analytical approaches.

This paper investigates the use of the fractional variational iteration method (FVIM) to obtain approximate analytical solutions to two dimensional Burger’s Equations with the Atangana-Baleanu fractional operator (ABFO). This study provides insight on the fractional variational iteration method's accuracy and reliability while approximating fractional differential equation solutions.

Beta thalassemia is a genetic disorder inherited by autosomal recessive inheritance, is present all over the world . Lifelong transfusions are necessary for people with beta-thalassemia, which can result in an iron buildup in the skin, liver, and kidneys that reduces the production of vitamin D. This study aims to evaluate vitamin D levels, blood groups, and demographic characteristics in beta-thalassemia patients. The currentstudy was conducted at the thalassemia center and hereditary blood disease in Thi-Qar Provincewith eighty-eight blood samples collected from individuals. It was divided into three groups according to age range, the first group (2-6), the second group (7-11),and the third group (12-15).In the current study, there was a significant decrease in Vit-D3 levels among thalassemia patients compared to the control group (p ≤ 0.05) . As for age between the patients group and the control group,the highest proportion of patients in the third age group. As for gender,the results of the study found a non-significant difference at the (P ≤ 0.05 ) level between the patients group and the control group. The mostcommon blood group in thalassemia
45was O followed by blood groups A, B,and AB. It also shows that the majority of the population was positive Rh

Image processing is a challenging task in the field of computer vision. Fourth-order equations are a powerful tool for removing noise, as they can avoid the block effect typically seen in second-order equations. In addition, these equations show high efficiency in high-frequency regions. This paper suggests combining the direction of isophote and fourth-order partial differential equations to reduce the problem of edges loss and preserve important details of the image. The direction of the isophote can regulate the direction of diffusion. Thus, we have a proposed model that can remove the noise in the area while preserving the important edges and details of the image. We have proven the efficiency and superiority of the proposed model by applying it to a set of images and solving it numerically using the finite difference method (FDM).

In this work, we introduce a different interpretation of the notion of a partial fuzzy metric, which we refer to as a partial fuzzy co-metric. We define a partial fuzzy co-metric from a t-conorm and compare it with partial fuzzy metric, in contrast to the conventional approach to the theory of partial fuzzy metric spaces, which is based on the use of a t-norm. Here, we limit the scope of our analysis to Sedghi's definition of partial fuzzy metrics. Additionally, we proposed and compared the ideas of strong partial fuzzy co-metric spaces and strong partial fuzzy metric spaces. We also presented a few examples of these novel ideas.

Makeup-Invariant Face Recognition is a critical area of research that aims to identify and classify manipulated or forged face images in the field of face recognition systems. Advances in deep learning techniques, such as pre-trained neural networks, have shown promising results in this domain. In this paper, a novel approach for detecting manipulated faces is proposed, combining the strengths of the Grasshopper Optimization (GOA) Algorithm, and Random Forest classifier. The proposed method utilizes the powerful feature extraction capabilities of pre-trained deep neural networks to capture intricate details and patterns in face images. Subsequently, the GOA algorithm is employed to select optimal features. Additionally, the Random Forest classifier is utilized for effective classification of face images and identifying different individuals based on the selected features. By integrating
JournalofEducationfor Pure Science-UniversityofThi-QarVol.14, No.3(2024)Website:jceps.utq.edu.iqEmail:[email protected] three algorithms, the proposed approach demonstrates significant improvements in detecting manipulated faces, achieving higher detection rates and lower false positive rates compared to existing state of the art methods. Simulation results on data from 330 different individuals with manipulated face images show an average accuracy of 97.23%, which represents an enhancement over the compared methods

Heart disease is a kind of cardiovascular disease (CVD) which globally considered death\\'s number one cause. Data Science plays an important role in processing large data amounts in the healthcare domain. There are several problems that could hinder appropriate cardiac monitoring, including limited of medical dataset, lack of depth analysis, and feature selection. In this paper, we exploit the method of Fast Correlation-Based Feature Selection (FCBF) for filtering extra features for developing heart disease classification quality. Next, we implement the classification according to various algorithms of classification like Decision tree, Naïve Bayes, Logistic Regression, K-Nearest Neighbour (KNN), Random Forest, Support Vector Machine (SVM), and a Multilayer Perception which is optimized with the modified whale optimization algorithm evaluation version named Modified Whale Optimization Algorithm (MWOA). The proposed model optimized with FCBF as well as MWOA obtain an 83.26% accuracy score by Logistic Regression

This paper describes a video summary approach for an actual video file that provides a rapid way to assess its information. This is a difficult task to identify relevant or informative sections. When the original video demands knowledge of its content, webuild a video summary viaextracting in-depth characteristics of each original video.Video summarization deals with the generation of a condensed version of the original video by including meaningful frames or segments while eliminating redundant informationprovides a concise and accurate representation of the original video clips by providing the most representative summary with increased interest through the application of a matching function between the current and prior frames. The primary goal of this work is to give a strategy for recording video clips using security cameras and converting them into a short video to provide a clear analysis of the video by reducing excess and extracting the key video contents into the real video. In a lengthy video clip, viewers may not have enough time to see the full thing. The viewer may be interested in seeing a specific topic under the

The agricultural sector's importance is highlighted by the upcoming global population growth predicted by the food and agriculture organization. This growth will lead to increased food demand. However, traditional farming methods require a lot of time and effort and result in diminishing returns. Recent technological advancements, especially in the Internet of Things (IoT), are changing this landscape by introducing smart agriculture. Smart agriculture aims to improve decision-making and crop management using IoT's connectivity and data analysis capabilities. Artificial intelligence (AI) is becoming crucial in this context, offering speed, accuracy, and cost-effectiveness. Machine learning, a type of AI, is beneficial in detecting yields and forecasting weather.This survey highlights the evolving field of intelligent agriculture, covering topics like crop monitoring,and anomaly detection, and explains the main challenges in farming practices

This article deal with the proposal of suggest and study of the properties of pre-test shrinkage estimators of Reliability Function for the Burr XII distribution using Progressive Type II censored sample. Since some difficultiesto derive equations of risk function for proposed shrinkage estimators of reliability function under Precautionary Loss Function (PLF), we to study properties by using Monte-Carlo simulation. The numerical and Monte-Carlo simulationsshow that the performance of the proposed estimators is better than classical estimators in terms of relative risk.

Recently, methods for classifying brain tumors have used Deep Learning (DL), in particular,Convolutional Neural Networks (CNNs), to obtain promising results. Unlike conventional Machine Learning (ML)algorithms, CNNs can automatically extract features from images; they do not, however, need the extraction regardinghand-crafted features. Many obstacles reduce the accuracy ofbrain tumor classification systems; to address suchobstacles, we propose a DL-based feature extraction method for brain tumor classification. A total of two pretrained CNNs, VggNet-16 and ResNet-18, are applied as deep feature extractors in the scheme to accomplish deep feature extraction. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique has beenfirst used for enhancingthe images. Second, each separately pretrained CNN, VggNet-16 and ResNet-18, is used forextractingthe deep features from Magnetic Resonance Imaging (MRI). Lastly, a Multiclass Support Vector Machines (Multiclass -SVM) classifier is used to complete a classification task. Tests conducted on the aforementioned datasets: the effectiveness of the suggested approach is demonstrated by the CE-MRI Figshare, the REMBRANDT,and the Brain Tumor Classification, where the pre-trained CNN models and SVM classifier enhance performance. Specifically, the pretrained CNN with SVM approach produces accuracy ranging from 95.28% to 98.62% across all data-sets.

The current study was conducted on aborted and pregnant women suspected of being infected with cytomegalovirus in The-Qar Governorate. (90) Serum samples of pregnant and aborted women were examined using the Enzyme-linked Immunosorbent assay(ELISA). through the examination, (50) sample of women infected with CMV virus were found, with rate of 55.55%, where it was found that (43) sample of them carried the IgG antibody, with a rate of 48.88%, and(5) sample with the IgM immunoglobulin antibody, with a rate of (5.5%), and (2) sample of Igm+IgG, with a rate of (2.2% .)Through the study of blood parameters that was conducted on (40) blood samples of women infected with the virus, compared to (10) blood samples of uninfected women there was a decrease in the blood hemoglobin rate (Hb)(11.54%) and compressed blood volume by (0.35) compared to the control group, three were no significant difference(p>0.05) for the total number of white blood cells. When conducting a differential of white blood cells for infected women it was found that there was an increase in the percentageof lymphocytes by (40.50), While no significant differences were observed (p>0.05) in the percentage of monocytes,neutrophils eosinophils, and basophils. As for the phagocytosis factor, an increase in the phagocytosis factor was recorded for infected women compared to the control group, as the phagocytosis factor for infected women reached 12-60% and for the control group 47.10%.

The aerosols play a main role in precipitation formation in the atmosphere. Organic Carbon (OC) is a must-imported aerosol because it is considered a cloud condensation nuclear CCN. This work dealt with the relationship between Organic Carbon (OC)and many variables that affect precipitation formation in South Iraq (Basra and AL-Muthana) including Cloud Top Temb Cloud Top Temperature CTT, Total PrecipitableWater TPW, and Precipitation. The cases selected are 14 –15 Nov. 2018 and 26 –29 Jan. 2019 to test the relationship betweenvariables. Data obtained from retrospective analysis for research and applications, version 2 (MERRA-2). Including OC, total Precipitablewater (TPW), and precipitation amount of3 hours. The times are fixed according to thecases. The cloud top temperature (CTT) was obtained by the infrared satellite images provided by the European Space Agency, using a group of satellites. The result indicates a strong effect of OC on the cloud depth and the TPW when finally increases the precipitation amount.

With the tremendous and rapid evolution taking place in the field of technology and considering the rise in data volume that is dealt with daily, managing this data, whether in terms of security or terms of storage especially if the data is huge, is considered a necessary issue. Therefore, in this research, we established a framework that provides both security and storage/repository management. Where the security issue in the suggested framework is supported by the use of lightweight hash functions and public-key encryption algorithms represented by SPONGENT and elliptic curve cryptography (ECC). Additionally, the fast random number generator is also used to support the security algorithms included in the framework, while managing the repository storage is controlled by the use of a hybrid Blockchain to manage storage for this type of big data. The process of storing this data in databases or any of the traditional centralized methods exposes the data to loss or penetration. After analyzing the proposed framework, itsuccessfully addressed the prevention of malicious within the field of proposed research attacks. Moreover, the performance analysis of the framework proposed was quite effective with the lightweight SPONGENT and ECC results, while the creation of blocks in the storage phase was not more than 0.18 ns. Thus, we obtained an effective framework in terms of security, performance, and terms of data repository management and control.