Ahmet Çınar’s scientific contributions

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Publications (7)


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July 2024

Hakar J Mohamed Salih

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Shaimaa Q. Sabri

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Ahmet Çınar


Figure 2. Use case diagram
Figure 3. Home page Login: Using their user passport number and password, users can access their personalized account from the homepage by logging in as illustrated in figure (4).
Figure 4. Login page
Figure 5. Registration form
Figure 6. Admin Page Services: press the "Services" button. a new page with all of the office's services listed on it, and the administrator can edit or remove any of the services.

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(online) Design Web-based Portal to Management a Pilgrim Office in Kurdistan Region
  • Article
  • Full-text available

June 2024

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108 Reads

Indonesian Journal of Computer Science

In the era of globalization, the region of Kurdistan, is participation in several ongoing projects where IT-enable systems are intended to be implemented in various sectors as part of the national IT policy. However, because of the establish system complex structure and the outdated system poor design, implementing IT enable systems locally is a significant issue. Particularly in the section dedicated to Hajj services, where files are still manually stored overcrowding happens ate registration offices since pilgrims cannot get any fresh instruction or announcements at the time they must got to the office or call them and insufficient security and safety measure for pilgrims' data. The purpose of developing this file filing system was to facilitate and ease the management of current files by staff. As part of the research process, issues with Hajj file management were identified and information was gathered via literature research, interview and observation. Laravel (PHP), Bootstrap and HTML5 are programming language used in system development, along MySQL, Xampp. System Usability Scale (SUS) approach was used to test the system, and 10 participations sign up for it. The highest overall satisfaction rate, at roughly 80.1%.

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FIGURE 1. -Leukemia detection phases 3. IMAGE SEGMENTATIO: Three important steps are associated with sectioning the picture agreed: 1. Division of cellular element 2. Symmetry of stone and cytoplasm 3. Season of interfere (BC)
FIGURE 4.-Leukemia detection model
Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

March 2024

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24 Reads

This article presents a preliminary report that uses minuscule images of blood tests to develop a diagnosis of leukemia. Examining through images is crucial since illnesses can be recognized and examined at an earlier stage using the images. The framework will be centered on leukemia and white blood cell illness. In fact, even the hematologist has trouble organizing the leukemic cells, and manually arranging the platelets takes a long time and is quite loose. In this way, early detection of leukemia recurrence allows the patient to receive the appropriate treatment. In order to address this problem, the framework will make use of the capabilities in small images and examine surface, geometry, shading, and quantifiable investigation modifications. These features' variations will be utilized as the classifier input. has transformed the use of images K proposes that (NN) and agglomeration. Examining a wide range of failure measures and increasing the intricacy of every system, the findings are examined. Utilizing feedforward (NN), image division is accomplished with less noise and a very sluggish conjunction rate. K-means agglomeration and (ANN) are intentionally used in this analysis to create a collection of processes that will work together to produce a much better presentation in (IS). An analysis has been conducted to determine the best rule for (IS).


Fig.1. Schematic diagram of the proposed methodology
A Comparative study of Chest Radiographs and Detection of The Covid 19 Virus Using Machine Learning Algorithm

March 2024

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32 Reads

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7 Citations

Mesopotamian Journal of Computer Science

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that is causing coronavirus disease 2019 is being deemed a pandemic because of its quick spread around the globe. Because chest X-ray pictures have shown to be beneficial in monitoring a variety of lung disorders, they have recently been utilized to monitor COVID-19 disease. It takes time to manually analyze a lot of chest X-ray pictures. Several previous studies have suggested machine-learning (ML)-based techniques for COVID-19 detection from chest X-ray pictures as a solution to this issue. Though little effort has been made to use traditional machine learning (ML) methods, the majority of these investigations use deep learning (DL) based techniques. Conventional ML-based algorithms will be favored for implementation if they can yield identical outcomes as DL-based methods. In this effort, we constructed four classic ML-based models for COVID-19 identification, driven by the need to close the gap in the literature. The accuracy rates for the various classification models were as follows, according to the results: 93.4% for Support Vector Machine (SVM), 93.3% for Random Forest (RF), 90.5% for K-Nearest Neighbors (KNN), and 87.9% for Decision Tree (DT). The results of the study showed that machine learning-based algorithms can produce great results for COVID-19 identification by being refined and improved using several well-known data preparation approaches.


Figure 3. After Applying the Ramp filter.
Assessment of The Effects of The Number of Projections And Use of Selected Filters on A Reconstructed Artificial Phantom

March 2024

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19 Reads

Appropriate selection of features may lead to the specificity of classification methods and identify the most critical features from all sparse or dense impact data using a filter based on the recognition selection method characterized. Filtration is used to reduce sample complexity, improve the clarity of viscous samples, and reduce background signals, resulting in increased signal-to-noise ratios in analytical tests. Depending on the filtration method applied, particles are separated based on properties such as size. This study assessed the impact of filter selection and the variation in the number of projections on the final reconstructed artificial phantom images. Utilizing image reconstruction techniques, it delves into the application of mathematical transforms, including Radon and Fourier, to improve image quality and resolution, particularly in medical imaging modalities such as CT and MRI. The research predominantly focuses on the application of the Filtered Back Projection (FBP) algorithm to reconstruct images from changing numbers of projections. The results underscore the main role of filter choice in removing noise, with the Ramp filter presenting the most promising results. The investigation concludes that reducing the number of projections results in a decline in image contrast and an increase in image noise.


Figure 3. After Applying the Ramp filter.
Assessment of The Effects of The Number of Projections And Use of Selected Filters on A Reconstructed Artificial Phantom

March 2024

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25 Reads

JOURNAL OF EDUCATION AND SCIENCE

Appropriate selection of features may lead to the specificity of classification methods and identify the most critical features from all sparse or dense impact data using a filter based on the recognition selection method characterized. Filtration is used to reduce sample complexity, improve the clarity of viscous samples, and reduce background signals, resulting in increased signal-to-noise ratios in analytical tests. Depending on the filtration method applied, particles are separated based on properties such as size. This study assessed the impact of filter selection and the variation in the number of projections on the final reconstructed artificial phantom images. Utilizing image reconstruction techniques, it delves into the application of mathematical transforms, including Radon and Fourier, to improve image quality and resolution, particularly in medical imaging modalities such as CT and MRI. The research predominantly focuses on the application of the Filtered Back Projection (FBP) algorithm to reconstruct images from changing numbers of projections. The results underscore the main role of filter choice in removing noise, with the Ramp filter presenting the most promising results. The investigation concludes that reducing the number of projections results in a decline in image contrast and an increase in image noise.

Citations (1)


... Conventional machine learning algorithms are favored for implementation in most of the healthcare systems including seasonal and non-seasonal diseases. Shaimaa et al., in 40 showed that different ML models achieve better accuracy rates in healthcare solutions, for example, SVM 93.4%, RF 93.3%, KNN 90.5%, and DT 87.9%. These higher accuracy rates reveal the effectiveness of implementing ML-based techniques. ...

Reference:

AI based predictive acceptability model for effective vaccine delivery in healthcare systems
A Comparative study of Chest Radiographs and Detection of The Covid 19 Virus Using Machine Learning Algorithm

Mesopotamian Journal of Computer Science