Bachelor's degree

Oct 1, 2007 - Aug 28, 2011

Bachelor's degree in computer science from the University of Kirkuk in 2011

Master of Science in Computer Science

Apr 13, 2014 - Nov 16, 2017

I hold a Master’s degree in Computer Science with a specialization in Digital Image Processing using Neural Network techniques

Digital Image Processing - Computer Vision - Machine Learning - Artificial Intelligence [Lecturer]

Nov 29, 2018 - Sep 1, 2024

I worked as a lecturer at Imam Ja'far al-Sadiq University – Kirkuk campus, teaching undergraduate courses in the Department of Computer Engineering Technologies.

Algorithms of Experimental Medical Data Analysis

Jul 16, 2020

Journal 2020 International Conference on Computer Science and Software Engineering (CSASE)

publisher IEEE

DOI 10.1109/CSASE48920.2020.9142094

The paper is devoted to the development of a computational technique for assessing the performance of tissue regeneration in an experiment using mesh nickel-titanium implants with shape memory. Observational data obtained from electron microscopy and classical histological examination are processed and analyzed by the use of proprietary algorithms and their modifications. This can significantly facilitate the procedure of data analysis and increase the accuracy of the estimates by 5–10%. As a computational method for examining the dynamics of the studied process and determining the internal geometrical characteristics of empirical images of objects concerned, the suggested technique includes algorithms of shearlet transform, wavelet transform and construction of elastic maps for efficient visualization of spatial data. The significant part of the recommended method is the computing means of visual data preprocessing for increasing the brightness and contrast of the examined images based on the Retinex technology. This part has a significant impact on the quality of applying the tools of the computer-based evaluation presented in this work.

A ROBUST STEGANALYSIS METHOD FOR DETECTING THE STEGANOGRAPHY IN IMAGES

Jul 3, 2017

Journal International Journal of Intelligent Computing and Infmmation Science

publisher Ain Shams University, Faculty of Computer and Information Science

Issue 3

Volume 17

Recently, steganography and steganalysis have been received an increasing attention due the nature of our modern societies which depends on exchanging information on a large scale. Steganography is the art of communication through sharing secret messages by embedding them into useless cover messages. The cover message can be an image, audio, or video file. On the other side, the steganalysis techniques are concerned with discovering the existence of steganography. This paper presents a specific image steganalysis technique with main objective is to detect the existence of steganography made by the least significant bit (LSB) technique in a certain image. The proposed approach extracts the gray level co-occurrence matrix (GLCM) as salient features which capable to distinguish a stego image from a non-stego one using a Back-Propagation (BP) classifier at the classification phase. Experimental results on standard datasets that consists of 297 images are encouraging. The proposed method is robust and high accuracy level has been achieved.