36 citations · 67 across the 13 of their papers we have counts for
15 papers
Computer Vision and Knowledge-Grounded Language Models for Intelligent Beekeeper Assistance
Asaduz Zaman, Mohaimenul Azam Khan Raiaan, Malika Nisal Ratnayake
Managed and wild bees sustain roughly a third of the global food products, yet colonies face mounting stress from climate-change including extreme weather patterns, altered parasit…
EVADE: Evidence-Verified Agentic Diagnosis with Escape
Mohaimenul Azam Khan Raiaan, Nur Mohammad Fahad
Medical vision-language models (VLMs) can achieve high accuracy but remain unreliable: they are systematically overconfident, benefit little from test-time reasoning, and lack the…
A physics-informed, behavior-aware digital twin for robust multimodal forecasting of core body temperature in precision livestock farming
Riasad Alvi, Mohaimenul Azam Khan Raiaan, Sadia Sultana Chowa +6
Precision livestock farming requires accurate and timely heat stress prediction to ensure animal welfare and optimize farm management. This study presents a physics-informed digita…
Learning to Weigh Waste: A Physics-Informed Multimodal Fusion Framework and Large-Scale Dataset for Commercial and Industrial Applications
Md. Adnanul Islam, Wasimul Karim, Md Mahbub Alam +7
Accurate weight estimation of commercial and industrial waste is important for efficient operations, yet image-based estimation remains difficult because similar-looking objects ma…
A Source-Free Approach for Domain Adaptation via Multiview Image Transformation and Latent Space Consistency
Debopom Sutradhar, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan +2
Domain adaptation (DA) addresses the challenge of transferring knowledge from a source domain to a target domain where image data distributions may differ. Existing DA methods ofte…
ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization
Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Md Noman Hossain +4
Gliomas are among the most aggressive cancers, with complex diagnostic processes. Existing glioma segmentation methods often struggle with high variability in imaging data and inad…
Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation
Musarrat Zeba, Abdullah Al Mamun, Kishoar Jahan Tithee +8
In healthcare, it is essential for any Large Language Model (LLM)-generated output to be reliable and accurate, particularly in cases involving decision-making and patient safety.…
WeCKD: Weakly-supervised Chained Distillation Network for Efficient Multimodal Medical Imaging
Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam +3
Knowledge distillation (KD) has traditionally relied on a static teacher-student framework, where a large, well-trained teacher transfers knowledge to a single student model. Howev…
Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances
Wasimul Karim, Riasad Alvi, Sayeem Been Zaman +7
Generative artificial intelligence (GenAI) is transforming bioinformatics by advancing genomics, proteomics, transcriptomics, structural biology, and drug discovery. Following the…
Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models
Subhey Sadi Rahman, Md. Adnanul Islam, Md. Mahbub Alam +5
Large Language Models (LLMs) are trained on vast and diverse internet corpora that often include inaccurate or misleading content. Consequently, LLMs can generate misinformation, m…
PPORLD-EDNetLDCT: A Proximal Policy Optimization-Based Reinforcement Learning Framework for Adaptive Low-Dose CT Denoising
Debopom Sutradhar, Ripon Kumar Debnath, Mohaimenul Azam Khan Raiaan +3
Low-dose computed tomography (LDCT) is critical for minimizing radiation exposure, but it often leads to increased noise and reduced image quality. Traditional denoising methods, s…
CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation
Debopom Sutradhar, Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan +3
Accurate land cover classification from satellite imagery is crucial in environmental monitoring and sustainable resource management. However, it remains challenging due to the com…
A fine-grained attention and geometric correspondence model for musculoskeletal risk classification in athletes using multimodal visual and skeletal features
Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Tamanna Shermin +3
Musculoskeletal disorders pose significant risks to athletes, and early risk assessment is essential for prevention. However, most existing methods are designed for controlled sett…
From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users
Sadia Sultana Chowa, Riasad Alvi, Subhey Sadi Rahman +5
The pursuit of human-level artificial intelligence (AI) has significantly advanced the development of autonomous agents and Large Language Models (LLMs). LLMs are now widely utiliz…
HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging
Arefin Ittesafun Abian, Ripon Kumar Debnath, Md. Abdur Rahman +5
Accurate liver and tumor segmentation on abdominal CT images is critical for reliable diagnosis and treatment planning, but remains challenging due to complex anatomical structures…