May, 2016

Bioengineering and Computer Science

Kahramanmaraş Sütçü İmam Üniversitesi

Jun, 2006

Computer Science


Professional Experience
Dec, 2017 - Jan, 2022
Rapporteur of Department

Bardarash Technical Institute \ IT Department


The details

Sep, 2017 - Dec, 2017
Curriculum Development Unit

Bardarash Technical Institute


The Details

Jan, 2011 - Feb, 2014
Rapporteur of Department

Bardarash Technical Institute


The Details

Digital image processing:

Digital image processing is the use of a digital computer to process digital images through an algorithm.

Machine Learning:

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Image Noise:

Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. Image noise can also originate in film grain and in the unavoidable shot noise of an ideal photon detector.

Publication Journal
Sep, 2021
Pre-Cancer Diagnosis via TP53 Gene Mutations Applied Ensemble Algorithms

techniumscience (Issue: 4 pp.9-16 (2021) (Volume: 2)

According to the current study, individuals with cancer who have a gene mutation have a bad prognosis. Young women with breast cancer had a poorer prognosis than older women, although it is unknown if the p53 gene mutation contributed to this. Due in part to the devastation of cancer, the appropriate technology may help cancer sufferers in regaining their lives. Researchers seek mutations in cancer-causing gene sequences to identify the precancerous stage. While genetic testing may be used to forecast some kinds of cancer, there is presently no effective technique for identifying all cancers caused by gene mutations. It is one of the most often discovered genetic anomalies in human cancer is a malfunction in the action of the protein P53. As a consequence, the Universal Mutation Database is used to identify gene mutations (UMDCell-line2010). The issue is that, although many basic databases (for example, Excel format databases) exist that include datasets of TP53 gene mutations associated with disease (cancer), this huge database is incapable of detecting cancer. Thus, the purpose the objective of this study is to create an approach for data mining that utilizes a neural network to ascertain the pre-cancerous state. To begin, bioinformatics techniques such as BLAST, CLUSTALW, and NCBI were used to determine whether or not there were any malignant mutations; second, the proposed method was carried out in two stages: to begin, bioinformatics techniques such as BLAST, CLUSTALW, and NCBI were used to determine whether or not there were any malignant mutations; and third, the proposed method was carried out in two stages: to begin, bioinformatics techniques such as To begin, bioinformatics tools such as BLAST and CLUSTAL Vote Algorithms were utilized to classify pre-cancer by malignant mutations in the disease's early stages. The writers teach and evaluate their subjects using a variety of situations, including cross-validation and percentages. This page contains a review of the algorithms discussed before.

Feb, 2020
Medical Image Classification Using Different Machine Learning Algorithms

AL-Rafidain Journal of Computer Sciences and Mathematics (Issue: 1) (Volume: 14)

The different types of white blood cells equips us an important data for diagnosing and identifying of many diseases. The automation of this task can save time and avoid errors in the identification process. In this paper, we explore whether using shape features of nucleus is sufficient to classify white blood cells or not. According to this, an automatic system is implemented that is able to identify and analyze White Blood Cells (WBCs) into five categories (Basophil, Eosinophil, Lymphocyte, Monocyte, and Neutrophil). Four steps are required for such a system; the first step represents the segmentation of the cell images and the second step involves the scanning of each segmented image to prepare its dataset. Extracting the shapes and textures from scanned image are performed in the third step. Finally, different machine learning algorithms such as (K* classifier, Additive Regression, Bagging, Input Mapped Classifier, or Decision Table) is separately applied to the extracted (shapes and textures) to obtain the results. Each algorithm results are compared to select the best one according to different criteria’s.

Dec, 2016
A New Approach of Image Denoising Based on Discrete Wavelet Transform


Image denoising is a process that used to enhance the image quality after degraded by the noise. There are several methods have been proposed for image denoising. In this paper, a new method of image denoising has been proposed. The proposed method is based on using wavelet transform. Wavelet transform is best method for analysis the image due to the ability to split the image into sub-bands and work on each sub-band frequencies separately. Also, the robust median estimator has been used to estimate the noise ratio at the noisy image. According to experimental results, the proposed method presents best values for MSE and PSNR for denoised images. Also, by using different types of wavelet transform filters is make the proposed approach can obtained best results for image denoising process.

Mar, 2016 - Mar, 2016
World Symposium on Computer Applications & Research (WSCAR)

Egypt, Cairo As Presenter

World Symposium on Computer Applications & Research (WSCAR) 2016


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