Ahmed Abdullah

This manuscript presents GHaLIB, a state-of-the-art solution for the PolyHope-M shared task at RANLP 2025. The study explores the intersection of sarcasm and hope in textual sentiment using advanced natural language processing techniques. It introduces novel models and benchmarks that distinguish between ironic negativity and genuine optimism. GHaLIB contributes a valuable resource for understanding nuanced emotional tones in text, with implications for mental health analysis, social media monitoring, and empathetic AI systems.

This project addresses the classification of degenerative lumbar spine conditions using GANs to impute missing MRI data. Over 70 models were tested, achieving state-of-the-art results with a minority class accuracy of 92.24% and an AUC-ROC of 64%. The approach effectively handles incomplete MRI datasets, leveraging GANs for data imputation and robust classification. The study demonstrates significant improvements in classifying underrepresented conditions. It highlights the potential of advanced generative and classification techniques in medical imaging.

This study examines the rise in research paper retractions and declining research quality in Pakistan through surveys and interviews with over 300 participants. Key issues include plagiarism, data fabrication, lack of ethics training, institutional barriers, and the pressure to “publish or perish.” Results reveal that international aid supports less than 60% of researchers, and 70% of students lack awareness of research ethics. To address these issues, the study develops a cosine similarity tool for citation analysis, reducing errors in academic writing. It recommends mandatory ethics training, institutional support, and automated tools to promote ethical research and improve Pakistan’s academic reputation.