It was established in 2009-2010 under the name of the College of Computer Technology, then its name was changed to the College of Information Technology in 2012, as a result of the development of scientific curricula. It is one of the advanced colleges in the university in terms of infrastructure and laboratories. The college grants a bachelor's degree (computer science) according to the departments mentioned above. The college follows the course system. Postgraduate studies (Master - PhD) in the software department.
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The University of Babylon is one of the largest universities in Iraq. It is situated on the banks of the Euphrates River in the province of Babil in central Iraq. The institution is made up of 21 colleges spread across three main locations in Hilla. The central university campus is in the medical colleges complex in the center of Hilla -Al-Iskan.
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Pre-Thesis Seminar by Master’s Student Buraq Ghassan in the Department of Software Duhaa Fadill Abbas As part of the scientific activities organized by the Department of Software at the College of Information Technology – University of Babylon, Master’s student Buraq Ghassan Hamza Abdulnasser delivered her pre-thesis seminar to present and discuss the framework of her thesis titled: “Using Augmented Random Search for Multiple Sequence Alignment.” The seminar was held under the supervision of Asst. Prof. Dr. Muhannad Mohammed Jasim, with the attendance of the department’s scientific committee, several faculty members, and postgraduate students. The presentation addressed one of the core challenges in computational biology: Multiple Sequence Alignment (MSA), which plays a crucial role in evolutionary inference, protein structure prediction, and functional annotation. The study highlighted the limitations of conventional MSA techniques that often rely on inference or dynamic programming, methods that can become computationally intensive and less efficient when applied to large or complex datasets. In response to these challenges, the thesis proposes the adoption of the Augmented Random Search (ARS) algorithm as an alternative optimization strategy. ARS is a gradient-free reinforcement learning algorithm, and the research treats MSA as a black-box optimization problem. The algorithm iteratively refines sequence alignments by applying random perturbations to a linear policy, guided by alignment quality metrics such as the Sum-of-Pairs (SP) Score. The findings demonstrate the effectiveness of ARS in exploring the solution space and generating high-quality alignments. This suggests the algorithm’s potential as a scalable and viable approach for addressing complex MSA tasks.
by: Duhaa Fadill Abbas
Date: 10/07/2025
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