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

43

Publications

31

Academic Staff

1683

Students

149

Graduates

Faculty of Information Technology News

2021-08-30 71 0

With the God' well, a new data cente rhas been installed within the faculty premises. The new data center is donated by Almadar Aljadid and Huawei company in Libya.  The data center is the first of its kind in Tripoli University and across all technical colleges in Libya. It will support the practical side within the IT students. The current storage capacity of the data center is 10TB, with 8 servers installed. The faculty would like to thank both Almadar aljadid and the Huawei companies for their contribution. Thanks is also extended to those who where behind this acheivement within the faculty. 

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Who works at the Faculty of Information Technology

Faculty of Information Technology has more than 31 academic staff members

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Dr. Trial Lecturer Lecturer Lecturer

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Publications

Some of publications in Faculty of Information Technology

Agent Based Computing Technique for Epidemiological Disease Modelling

Agent-Based Models (ABM) have become popular as tools for epidemiological simulations due to their ability to model real life phenomena at individual entity levels. ABM is a relatively new area for modelling as compared to the classical modelling methods. Many different fields use agentbased models including ecology, demography, geography, political science and epidemiology. Recently, an abundance of literature has presented applications of agent-based modeling in the biological systems. In this paper, the authors present an agent-based model attempts to simulate an epidemiological disease known as Cutaneous Leishmaniasis (CL). The model is developed to investigate the ability of ABM in modelling a disease that keeps speeding in Libya. The methodology used for describing and designing CL model is derived from nature of the disease mechanism. The ABM model involves three types of agents: Human, Rodent and Sand-fly. Each agent has its own properties, in addition to other global parameters which affect the human infection processes. The main parameter used for monitoring the model's performance is the number of people infected. The model experiments are designed to investigate ABM’s performance in modeling CL disease. Simulation results show that human infection rate is increasing or decreasing dependent on number of sand-fly vectors, number of host rodents, and human population awareness level arabic 7 English 62
Rudwan A. Husain, Hala Shaar, Marwa Solla, Hassan A. H. Ebrahem(3-2019)
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An Extensive Study on Online and Mobile Ad Fraud

The advertising ecosystem faces major threats from ad fraud caused by artificial display requests or clicks, created by malicious codes, bot-nets, and click-firms. Currently, there is a multibillion-dollar online advertisement market which generates the primary revenue for some of the internet's most successful websites. Unfortunately, the complexities of the advertisement ecosystem attract a considerable amount of cybercrime activity, which profits at the expense of advertisers. Web ad fraud has been extensively studied whereas fraud in mobile ads has received very little attention. Most of these studies have been carried out to identify fraudulent online and mobile ads clicks. However, the identification of individual fraudulent displays in mobile ads has yet to be explored. Additionally, other fraudulent activity aspects such as hacking ad-campaign accounts have rarely been addressed. The purpose of this study is to provide a comprehensive review of state-of-the-art ad fraud in web content as well as mobile apps. In this context, we will introduce a deeper understanding of vulnerabilities of online/mobile advertising ecosystems, the ad fraud’s well-known attacks, their effective detection methods and prevention mechanisms. arabic 8 English 40
Hala Shaari, Nuredin Ahmed(12-2020)
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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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