collaborators

6 papers

cs.LG2026

Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset

Shahnawaz Qureshi, Raja Khurram Shahzad, Muhammad Fozan +4

Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. To address this pro…

cs.CL2026

Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges

Husnain Amjad, Raja Khurram Shahzad, Aamir Shahzad +1

Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems. As Large…

cs.CL2026

Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning

Azher Ali, Ibtsam Haider, Raja Khurram Shahzad +2

Recent LLMs demonstrate strong mathematical reasoning capabilities, but existing gains rely heavily on English-centric training resources and benchmarks. As a result, reasoning per…

cs.AI2026

From Affect Prediction to Affect Forecasting: Evidence for Distinct Information Sources in Longitudinal Text

Sadia Noor, Seemab Latif, Raja Khurram Shahzad +1

Modeling dimensional affect in longitudinal text requires distinguishing current affect estimation from future affective change forecasting. Existing approaches often treat each te…

cs.CV2026

A Multi-Domain Feature Fusion Framework for Generalizable Deepfake Detection Across Different Generators

Amna Amjid, Sana Qadir, Mehwish Fatima +1

Deepfakes are artificially generated images, audio, or videos that threaten privacy, security, and information integrity. Detecting such content is crucial for countering disinform…

cs.CR2026

A Hybrid Approach For Malware Classification Using Secondary Features Fusion

Raja Khurram Shahzad, Muhammad Mustaqeem, Haroon Elahi

The number of malware (either variant or novel) is rapidly increasing, making malware detection and mitigation a complex problem. One approach to improving malware mitigation is au…