msc.it@uokufa.edu.iq

Using EEG features for Authentication network

In recent years, more focus has been paid to brain biometrics research with electroencephalographic data (EEG). This study introduces a real time control system of authentication and disabled electric wheel chair based on using electroencephalography (EEG). The scientific community has recently made huge efforts to perceive the distinctive characteristics of …

Read More »

The Faculty of Computer Science and Mathematics held a Master Defence

The Department of Computer Science at The Faculty of Computer Science and Mathematics held a master defence. The thesis entitled “age-invariant face recognition based on machine learning techniques” was defended by student Montadhar Hussein Ibrahim. In this project, facial features change during the aging process. Two approaches were proposed: first, …

Read More »

The Faculty of Computer Science and Mathematics held a Symposium on Scientific Publication.

The Faculty of Computer Science and Mathematics held a symposium on scientific publication and the process of reaching high impact journals. The symposium discusses the journal indexing metrics and how to avoid publishing in predatory and hijacked journals. This activity had been attended by the faculty academic staff.

Read More »

The Faculty of Computer Science and Mathematics held a Symposium on Artificial Intelligence and its Application.

The Faculty of Computer Science and Mathematics held the first meeting of the Google’s developer Club to discuss the recent application of Artificial Intelligence. The dean of the faculty provides a talk to support the students to improve their knowledge in this important topic. Some of the recent advances in …

Read More »

Master Student ,Waheed Fadel Waheed, defend his master dissertation entitled ”Detection of Malicious Android Software Using Machine Learning Techniques”

In this work, a study on the use of a type of genetic algorithm for Android operating purposes. This application involves training the algorithm first on a sample set of samples from safe, fashionable, and samples from malicious applications, so that the developed model can then detect the type of …

Read More »

A Ph.D. thesis in the Faculty discusses the detection of electric power theft using deep learning Technique.

The Faculty of Computer Science and Mathematics, Computer Science Department, discussed the doctoral thesis titled ” Energy Theft Detection Based on CNN Approach ” by the student Maali Hashim Alameedi. The thesis presented a study on the use of deep learning techniques in analyzing and distinguishing abnormal consumer behavior. The …

Read More »