Výsledky vyhledávání - "DEY, Nilanjan"

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A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications

Autor: Islam, Md. Ariful, Mridha, M. F., Jahin, Md Abrar, Dey, Nilanjan

The rapid advancement of deep learning has resulted in substantial advancements in AI-driven applications; however, the "black box" characteristic of these models frequently constrains their interpretability, transparency, and reliability. Explainabl

Externí odkaz: http://arxiv.org/abs/2412.03884

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DGNN-YOLO: Interpretable Dynamic Graph Neural Networks with YOLO11 for Small Object Detection and Tracking in Traffic Surveillance

Autor: Soudeep, Shahriar, Mridha, M. F., Jahin, Md Abrar, Dey, Nilanjan

Accurate detection and tracking of small objects, such as pedestrians, cyclists, and motorbikes, is critical for traffic surveillance systems, which are crucial for improving road safety and decision-making in intelligent transportation systems. Howe

Externí odkaz: http://arxiv.org/abs/2411.17251

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Human-in-the-Loop Feature Selection Using Interpretable Kolmogorov-Arnold Network-based Double Deep Q-Network

Autor: Jahin, Md Abrar, Mridha, M. F., Dey, Nilanjan

Feature selection is critical for improving the performance and interpretability of machine learning models, particularly in high-dimensional spaces where complex feature interactions can reduce accuracy and increase computational demands. Existing a

Externí odkaz: http://arxiv.org/abs/2411.03740

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Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics

Autor: Jahin, Md Abrar, Masud, Md. Akmol, Suva, Md Wahiduzzaman, Mridha, M. F., Dey, Nilanjan

The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particle physics. Quantum machine learning, with its capability to leverage t

Externí odkaz: http://arxiv.org/abs/2411.01641

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Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination

Autor: Jahin, Md Abrar, Masud, Md. Akmol, Mridha, M. F., Dey, Nilanjan

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 methods have advanced this task beyond traditional feature-engineered appr

Externí odkaz: http://arxiv.org/abs/2411.01642

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KACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network for Heart Disease Detection

Autor: Jahin, Md Abrar, Masud, Md. Akmol, Mridha, M. F., Aung, Zeyar, Dey, Nilanjan

Heart failure remains a major global health challenge, contributing significantly to the 17.8 million annual deaths from cardiovascular disease, highlighting the need for improved diagnostic tools. Current heart disease prediction models based on cla

Externí odkaz: http://arxiv.org/abs/2410.07446

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TriQXNet: Forecasting Dst Index from Solar Wind Data Using an Interpretable Parallel Classical-Quantum Framework with Uncertainty Quantification

Autor: Jahin, Md Abrar, Mridha, M. F., Aung, Zeyar, Dey, Nilanjan, Sherratt, R. Simon

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 disturbance storm-time (Dst) index measures storm intensity. Despite adva

Externí odkaz: http://arxiv.org/abs/2407.06658

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