Sep, 2021
Master of Engineering

Software Engineering

Firat university

Jul, 2010

computer engineering

technical college of Mosul

Deep learning:

EEG signals

Medical Image processing:

to classify the medical images like Chest X-ray and lung etc.

Deep learning:

text classification

Aug, 2021 - Aug, 2021
Feature Extraction Approach Based on Statistical Methods and Wavelet Packet Decomposition for Emotion Recognition using EEG Signals

France, 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) As Presenter

Classification of human emotions via EEG signals is a hot topic today. In this study, a method for feature extraction from EEG signals is presented. It is applied for the first time on the GAMEEMO dataset, using a combination of the wavelet packet decomposition (WPD) method with the statistical feature method (SF). The GAMEEMO dataset was classified using the discrete model (2 classes) and the dimensional model (4 classes). This study passed through three sequential main steps: In the first step, four methods were used to extract the features from EEG signals, which are the SF, the combination of the SF with the Welsh power spectral density using (PSD), the combination of the SF with the fast Fourier transform (FFT), and the combination of the SF with the WPD method. In a second step, the Decision Tree Classifier (DT), and the recurrent neural network (RNN) with the long short-term memory (LSTM) algorithm …

Dec, 2020 - Dec, 2020
Fake News Detection Using Machine Learning and Deep Learning Algorithms

Iraq, 2020 International Conference on Advanced Science and Engineering (ICOASE) As Presenter

Classification of fake news on social media has gained a lot of attention in the last decade due to the ease of adding fake content through social media sites. In addition, people prefer to get news on social media instead of on traditional televisions. These trends have led to an increased interest in fake news and its identification by researchers. This study focused on classifying fake news on social media with textual content (text classification). In this classification, four traditional methods were applied to extract features from texts (term frequency-inverse document frequency, count vector, character level vector, and N-Gram level vector), employing 10 different machine learning and deep learning classifiers to categorize the fake news dataset. The results obtained showed that fake news with textual content can indeed be classified, especially using a convolutional neural network. This study obtained an accuracy …

Jun, 2020 - Jun, 2020
A Review of Image Segmentation Using MATLAB Environment

Lebanon, 2020 8th International Symposium on Digital Forensics and Security (ISDFS) As Presenter

Image segmentation is of great importance in understanding and analysing objects within images. The process involves dividing vague images into meaningful and useful ones by segmenting them and subsequently evaluating them based on colour density. This process is used in the medical, cultural and industrial fields, among others. There are many functions used in image segmentation, including edge and threshold functions. This paper will review these techniques, provide examples, and illustrate the types of applicable images.