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Author

Hamza Shahbaz

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Open access Sep 2026

Predicting rule-based dairy milk intake recommendations using machine learning: A robustness, calibration, and explainability study

Personalized dairy guidance is clinically complex because nutritional needs, intolerance, allergy, bone health, and cardiometabolic factors can point toward different decisions. Machine learning can help study how structured variables map to recommendations, but performance on synthetic labels must not be confused with...

Hamza Shahbaz, Fazal Khaliq, Abdurrahman Sarnikli · 0 citations
Open access Sep 2026

NeuroDEEP-CNN: A Multi-Task Attention Framework for Joint Classification, Localization, and Segmentation of Brain Tumors on MRI

NeuroDEEP-CNN closely approaches the strongest of five widely used transfer-learning baselines on a related three-class benchmark, while covering thirteen times the number of classes and simultaneously providing validated tumor detection and segmentation outputs suitable for radiologist-facing deployment.

Hamza Shahbaz · 0 citations
Open access Sep 2026

An Intelligent Research Paper Assistant: Integrated Retrieval, Explainable Domain Classification, and Research-Gap Ranking

This study presents an end-to-end Intelligent Research Paper Assistant built over 499,999 scholarly abstracts spanning 10 domains and 25 subdomains, integrating six modules: title suggestion, abstract retrieval, methodology generation, dual-branch domain classification, post-hoc explainability, and research-gap ranking...

Hamza Shahbaz · 0 citations

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