90 citations · 107 across the 11 of their papers we have counts for
11 papers
NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation
Rong Fu, Yiqing Lyu, Chunlei Meng +9
Automatic generation of radiology reports seeks to reduce clinician workload while improving documentation consistency. Existing methods that adopt encoder-decoder or retrieval-aug…
Soybean Disease Detection via Interpretable Hybrid CNN-GNN: Integrating MobileNetV2 and GraphSAGE with Cross-Modal Attention
Md Abrar Jahin, Soudeep Shahriar, M. F. Mridha +2
Soybean leaf disease detection is critical for agricultural productivity but faces challenges due to visually similar symptoms and limited interpretability in conventional methods.…
MobilePlantViT: A Mobile-friendly Hybrid ViT for Generalized Plant Disease Image Classification
Moshiur Rahman Tonmoy, Md. Mithun Hossain, Nilanjan Dey +1
Plant diseases significantly threaten global food security by reducing crop yields and undermining agricultural sustainability. AI-driven automated classification has emerged as a…
A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution
Md. Ariful Islam, Md Abrar Jahin, M. F. Mridha +1
Explainable Artificial Intelligence (XAI) methods are increasingly used in safety-critical domains, yet there is no unified framework to jointly evaluate fidelity, interpretability…
Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking
Shahriar Soudeep, Md Abrar Jahin, M. F. Mridha
The detection and tracking of small, occluded objects such as pedestrians, cyclists, and motorbikes pose significant challenges for traffic surveillance systems because of their er…
Human-in-the-Loop Feature Selection Using Interpretable Kolmogorov-Arnold Network-based Double Deep Q-Network
Md Abrar Jahin, M. F. Mridha, Nilanjan Dey +1
Feature selection is critical for improving the performance and interpretability of machine learning models, particularly in high-dimensional spaces where complex feature interacti…
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination
Md Abrar Jahin, Md. Akmol Masud, M. F. Mridha +2
In high-energy physics, particle jet tagging plays a pivotal role in distinguishing quark from gluon jets using data from collider experiments. While graph-based deep learning meth…
Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics
Md Abrar Jahin, Md. Akmol Masud, Md Wahiduzzaman Suva +2
The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particl…
KACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network for Heart Disease Detection
Md Abrar Jahin, Md. Akmol Masud, M. F. Mridha +2
Heart failure is a leading cause of global mortality, necessitating improved diagnostic strategies. Classical machine learning models struggle with challenges such as high-dimensio…
TriQXNet: Forecasting Dst Index from Solar Wind Data Using an Interpretable Parallel Classical-Quantum Framework with Uncertainty Quantification
Md Abrar Jahin, M. F. Mridha, Zeyar Aung +2
Geomagnetic storms, caused by solar wind energy transfer to Earth's magnetic field, can disrupt critical infrastructure like GPS, satellite communications, and power grids. The dis…
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities
Md Farhan Ishmam, Md Sakib Hossain Shovon, M. F. Mridha +1
The multimodal task of Visual Question Answering (VQA) encompassing elements of Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on…