Nikhil Panwar

PINCurve-S: Physics-Informed Neural Curves with Spatial Attention for Efficient Low-Light Image Enhancement

Anubhav Jain, Nikhil Panwar, Vivek Kumar, Ali Reza Alaei et autres

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Deep learning approaches for crop disease classification: adapting to environmental challenges and hazy conditions

Ashutosh Kumar, Nikhil Panwar, Prity Kumari, Partha Pratim Roy

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HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection

Shubham Gupta, Nikhil Panwar, Partha Pratim Roy

While Deep Learning (DL) enhances automated electrocardiogram (ECG) analysis, clinical deployment is hindered by class imbalance and the generalization gap. This paper presents HeartBeatAI, a deep learning framework combining domain generalization, multi-scale feature aggregation, and clinical explainability for robust 12-lead ECG classification. …

HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection

Shubham Gupta, Nikhil Panwar, Partha Pratim Roy

While Deep Learning (DL) enhances automated electrocardiogram (ECG) analysis, clinical deployment is hindered by class imbalance and the generalization gap. This paper presents HeartBeatAI, a deep learning framework combining domain generalization, multi-scale feature aggregation, and clinical explainability for robust 12-lead ECG classification. …

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IoT Based Hydroponic Crop Growth Management Farming Technique Using Hybrid Methods of Deep Learning

Tushar Kumar, Neha Tyagi, Nikhil Panwar, Hazik Ibni Syed

Empirical Evaluation of Situational Awareness and Its Cognitive Correlates Using EEG

Vishal Pandey, Nikhil Panwar, Byung‐Gyu Kim, Partha Pratim Roy

Situational Awareness (SA) is critical for performance in dynamic environments, yet its neurophysiological basis and continuous assessment remain poorly understood. This study presents an integrated behavioral, electrophysiological, and computational investigation of SA and its cognitive correlates using EEG. Thirty participants performed a …

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Electroencephalography-Based Cognitive Workload Estimation Using Deep Learning With Analysis of Assistance Effects

Nikhil Panwar, Vishal Pandey, Partha Pratim Roy, Rajkumar Saini

Cognitive workload plays a critical role in human performance during complex problem solving and decision making, particularly in machine-assisted environments where excessive workload can degrade efficiency and increase fatigue. This study presents a computational approach for analyzing and classifying cognitive workload from …

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Forecasting Stock Market Prices with Long Short-Term Memory (LSTM) Networks

Prerna Prerna, Dhruv Bansal, Nikhil Panwar

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Combining Spatio-Temporal Networks and Graph Attention Architectures for EEG-Based Workload Classification

Nikhil Panwar, Vishal Pandey, Partha Pratim Roy

Estimation of cognitive workload from EEG signals is a key challenge in advancing neuroergonomic systems and brain-computer interfaces (BCIs). A hybrid approach is presented, combining EEGNet and Graph Attention Networks (GATs) to effectively capture the intricate spatial and temporal dynamics within EEG …

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WL-GraphTrax: A Graph-Transformer Framework for EEG-Based Cognitive Workload Classification

Vishal Pandey, Nikhil Panwar, Partha Pratim Roy, Sushil Chandra

Accurately measuring cognitive workload is critical in the development of adaptive human-machine systems, particularly in high-stakes environments such as driving. This study introduces a novel EEG-based framework for cognitive workload classification using graph neural networks. EEG data were collected during a controlled …

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Enhanced Cross-Task EEG Classification: Domain Adaptation with EEGNet

Vishal Pandey, Nikhil Panwar, Atharva Kumbhar, Partha Pratim Roy et autres

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EEG-CogNet: A deep learning framework for cognitive state assessment using EEG brain connectivity

Nikhil Panwar, Vishal Pandey, Partha Pratim Roy

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