Dr. Ghada Khoriba

The increasing adoption of programming education necessitates efficient and accurate methods for evaluating students’ coding assignments. Traditional manual grading is time-consuming, often inconsistent, and prone to subjective biases. This paper explores the application of large language models (LLMs) for the automated evaluation of programming assignments. LLMs can use advanced natural language

In an attempt to bridge the semantic gap between language understanding and visuals, Visual Question Answering (VQA) offers a challenging intersection of computer vision and natural language processing. Large Language Models (LLMs) have shown remarkable ability in natural language understanding; however, their use in VQA, particularly for Arabic, is still largely unexplored. This study aims to

Visual Question Answering (VQA) systems have achieved remarkable advancements by combining text-based question answering with image analysis. This integration has resulted in the creating of machines that can comprehend and address questions related to visual content. Despite these technological developments, a notable lack of VQA solutions specifically designed for the Arabic language remains

Large Language Models (LLMs) have emerged as powerful tools for extracting course topics from textbooks in today's fast-paced educational landscape. Additionally, harnessing the potential of Knowledge Graphs to visualize the mutuality among topics enhances the informativeness of the extracted content. This paper presents a comprehensive comparative study that explores and assesses the

This paper comprehensively evaluates the efficacy of different large language models (LLMs) in addressing mathematical challenges expressed in natural languages, mainly focusing on low-resource languages like Arabic. The main challenge of this problem is that despite the considerable size and impressive problem-solving capabilities of these models, they still require enhancements to achieve

Uni-Buddy is an advanced AI system developed to simplify university life at Nile University. It efficiently handles questions in everyday language, accesses real-time university databases, and simultaneously provides accurate responses for multiple users. Its goals include assisting with course registration, academic advising, financial inquiries, campus navigation, and research support. The

This paper presents a novel approach to Arabic Word Sense Disambiguation (WSD) leveraging transformer-based models to tackle the complexities of the Arabic language. Utilizing the SALMA dataset, we applied several techniques, including Sentence Transformers with Siamese networks and the SetFit framework optimized for few-shot learning. Our experiments, structured around a robust evaluation

Image segmentation is considered one of the essen-tial tasks for extracting useful information from an image. Given the brain tumor and its consumption of medical resources, the development of a deep learning method for MRI to segment the brain tumor of patients’ MRI is illustrated here. Brain tumor segmentation technique is crucial in detecting and treating MRI brain tumors. Furthermore, it

The automatic generation of textual descriptions from images, known as image captioning, holds significant importance in various applications. Image captioning applications include accessibility for the visually impaired, social media enhancement, automatic image description for search engines, assistive technology for education, and many more. While extensive research has been conducted in