arXiv subjects

Muhammad Yaqoob

Publications and source records attributed to Muhammad Yaqoob.

4 recordsLinked to original sources

arXiv2026

Early Comparative Evaluation of Transformer Models for Multilingual Software Vulnerability Detection

Software vulnerability detection is increasingly important as modern applications combine multiple programming languages. This paper presents an early comparative evaluation of BERT, RoBERTa, and CodeBERT for binary vulnerability detection across HTML, Python, JavaScript, and PHP using the CVEFixes dataset and language-wise three-fold stratified cross-validation. The results show clear performance differences across languages, indicating that multilingual vulnerability detection requires more language-aware and robust transformer-based modelling strategies.

cs.SE↗

arXiv2026

Multimodal Brain Tumour Classification Using Feature Fusion

Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement. However, most deep learning models rely on MRI/CT images alone, failing to replicate the clinicians multimodal reasoning. We explore a two-branch multimodal network combining raw MRI scans with 91 extracted radiomic features (intensity, texture, shape, and boundary descriptors) to classify brain tumors into glioma, meningioma, pituitary, and no-tumor. A pre-trained CNN backbone encodes the image stream, whereas a dedicated MLP encodes the radiomic stream. Both streams are fused via concatenation, gated, or bidirectional cross-modal attention strategies. Across nine experimental runs on a balanced 7,200 image dataset, all multimodal configurations outperform unimodal baselines with gated fusion achieving the best accuracy of 96.13%.

eess.IV↗

arXiv2026

A systematic literature Review for Transformer-based Software Vulnerability detection

Context: Software vulnerabilities pose significant security threats to software systems, especially as software is increasingly used across many areas of daily life, including health, government, and finance. Recently, transformer-based models have demonstrated promising results in automatic software vulnerability identification due to their robust contextual modelling and representation learning capabilities. Objectives: While numerous systematic literature reviews (SLRs) have examined machine learning and deep learning methods for identifying vulnerabilities, a more transformer-centric analysis remains to be explored. This SLR critically analysed 80 studies published between 2021 and 2025 that utilised transformer models to identify software vulnerabilities. Methods: Using Kitchenhams SLR guidelines, we methodically evaluate current research from various perspectives, encompassing study trends, datasets and sources, programming languages, transformer frameworks, detection detail levels, assessment metrics, reference models, types of vulnerabilities, and experimental configurations. Results: We classify transformer models into encoder, decoder, and combined architectures and analyse both pre-trained and fine-tuned versions utilized on source code, logs, and smart contracts. The results emphasise prevailing research trends, frequently utilised benchmarks, and main baselines. It also uncovers crucial technical issues like data imbalance, interpretability, scalability, and generalization across programming languages. Conclusion: By integrating current evidence and recognising unaddressed research areas, this SLR provides a consolidated resource for researchers and professionals seeking to develop more reliable, precise, and interpretable transformer-based vulnerability identification systems.

cs.SE↗

arXiv2025

Personalized and Constructive Feedback for Computer Science Students Using the Large Language Model (LLM)

The evolving pedagogy paradigms are leading toward educational transformations. One fundamental aspect of effective learning is relevant, immediate, and constructive feedback to students. Providing constructive feedback to large cohorts in academia is an ongoing challenge. Therefore, academics are moving towards automated assessment to provide immediate feedback. However, current approaches are often limited in scope, offering simplistic responses that do not provide students with personalized feedback to guide them toward improvements. This paper addresses this limitation by investigating the performance of Large Language Models (LLMs) in processing students assessments with predefined rubrics and marking criteria to generate personalized feedback for in-depth learning. We aim to leverage the power of existing LLMs for Marking Assessments, Tracking, and Evaluation (LLM-MATE) with personalized feedback to enhance students learning. To evaluate the performance of LLM-MATE, we consider the Software Architecture (SA) module as a case study. The LLM-MATE approach can help module leaders overcome assessment challenges with large cohorts. Also, it helps students improve their learning by obtaining personalized feedback in a timely manner. Additionally, the proposed approach will facilitate the establishment of ground truth for automating the generation of students assessment feedback using the ChatGPT API, thereby reducing the overhead associated with large cohort assessments.

cs.CY↗