Feb 16, 2026
Automatic Fake News Identification through Sequential Text Modeling with LSTM
IEEE QPAIN 2026 — Quantum Photonics, AI & Networking
Conference PaperPaper ID #6528
Portfolio · 2026
Computer vision for medicine,
intelligence for cities.
I’m Abu Al Shahriar Rifat, a researcher building deep-learning systems that span medical imaging, digital healthcare and intelligent transportation — with a deep focus on explainable AI.
CurrentlyHohai University · 2025
Medical Imaging✦Explainable AI✦Cancer Research✦Road Damage Detection✦Intelligent Transportation✦Biomedical Signals
01 — Practice
Two worlds, one method.
Healthcare AI
Brain tumour, ovarian cancer and sleep apnea models — built with attention to clinical interpretability.
Smart Mobility
UNet and Mask R-CNN pipelines for cracks, potholes and rutting; vehicle detection at scale.
Explainable AI
Grad-CAM, LIME and SHAP — turning black boxes into evidence clinicians and engineers can trust.
Full-Stack Delivery
Django, Flask and React when research needs to leave the notebook and meet real users.
02 — Selected Writing
Recent publications.
Jan 3, 2026
Distinguishing Human and AI-Generated Text: A BiLSTM-Based Deep Learning Approach
IEEE QPAIN 2026
Conference PaperPaper ID #3650
Nov 25, 2025
Leveraging Deep Learning for Ovarian Cancer Classification Using Image Data
International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
Conference PaperPaper ID #10749
“Models that doctors can trust are models that doctors will use.”
— guiding principle of my research