3 citations · 5 across the 2 of their papers we have counts for
6 papers
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…
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…