Faculty of Information Technology

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About Faculty of Information Technology

Faculty of Information Technology

The Faculty of Information Technology is one of the most recent faculties at the University of Tripoli, as it was established in pursuant to the former General People's Committee for Higher Education Decision No. 535 of 2007 regarding the creation of Information Technology Faculties in the main universities in Libya.

 

Upon its establishment, the Faculty consisted of three departments: Computer Networks Department, Computer Science Department and Software Engineering Department. It now includes five departments: Mobile Computing Department, Computer Network Department, Internet Technologies Department, Information Systems Department and Software Engineering Department.

 

The Faculty’s study system follows the open semester system by two (Fall and Spring) terms per year. The Faculty began to actually accept students and teach with the beginning of the Fall semester 2008. It grants a specialized (university) degree in information technology in any of the aforementioned disciplines. Obtaining the degree requires the successful completion of at least 135 credit hours. Arabic is the language of study in the college, and English may be also used alongside it. It takes eight semesters to graduate from the Faculty if Information Technology.

 

The Faculty aspires to open postgraduate programs in the departments of computer networks and software engineering in the near future.

Facts about Faculty of Information Technology

We are proud of what we offer to the world and the community

69

Publications

38

Academic Staff

1710

Students

159

Graduates

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Who works at the Faculty of Information Technology

Faculty of Information Technology has more than 38 academic staff members

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Mr. Ahmed Ali Sadegh Elhoni

Publications

Some of publications in Faculty of Information Technology

A Comparative Study of VoIP over IEEE 802.11(b, g) and WiMax (UGS, ertPS) Wireless Network Technologies

This paper describes a comparative study of the performance of VoIP over wireless networks using OPNET tool. The simulation study is completed by running VoIP application in different network scenarios with IEEE 802.16 (UGS, ertPS) and IEEE 802.11 (b, g) with best effort service and interactive service The result clearly illustrated that the WiMax type ertPS has the best performance among all tested cases
Azeddien M. Sllame, Hana Soso, Mona Aown, Lamya Abdelmajeed(9-2016)
Publisher's website

Medical 3D Integral Images Visualization in True Space

3D Integral Imaging (also referred to as 3D Holoscopic imaging) methodology uses the principle of “Fly’s eye” and hence allows natural viewing of objects (i.e. fatigue free viewing); 3D-holoscopic technology produces images that are true optical models. This technology is based on physical principles with duplication of light fields. In this paper, a new method of visualization medical 3D integral images is proposed. Digital Imaging and Communications in Medicine data images (DICOM) taken from CT, MRI, PET and US images that produced by 3D-Doctor software to generate medical 3D integral images visualization of anatomy without glass in natural light. The method is mainly based on multiprocessor ray tracing system as renderer. The medical 3D content is captured in real time with the content viewed by multiple viewers independently of their position, without the needs of 3D eyewear. Experimental results show validation of the new algorithm and demonstrated that medical 3D integral images content can be displayed on commercially available multi-view auto-stereoscopic display. Medical 3D integral images content is parsed into multiprocessor ray tracing system, consequently, short time of medical 3D integral images movie of such pelvis scene is generated and displayed on PC screen, LCD and Holovizio display.
Dr. Mahmoud Geat Mahmoud Eljadid, Prof. Amar Aggoun(5-2016)
Publisher's website

OPTIMIZATION SEGMENTATION AND CLASSIFICATION FROM MRI OF BRAIN TUMOR AND ITS LOCATION CALCULATION USING MACHINE LEARNING AND DEEP LEARNING APPROACH

The manual detection and classification finding correct location and identifying type of tumor becomes a rigorous and hectic task for the radiologists. Medical diagnosis via image processing and machine learning is considered one of the most important issues of artificial intelligence systems. Deep learning has been used successfully in supervised classification tasks in order to learn complex patterns. The main contributions of this paper are as create a more generalized method for brain tumor classification using deep learning a variety of neural networks were constructed based on the preprocessing of image data., analyze the application of tumorless brain images on brain tumor classification and empirically evaluate neural networks on the given datasets with per image accuracy and per patient accuracy. And also presents an efficient image segmentation using machine learning algorithm with some optimization techniques to detect brain tumors. arabic 19 English 128
Mohamed Abdeldaiem Abdelhadi Mahboub(3-2019)
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