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All Journal Bulletin of Electrical Engineering and Informatics
Parul Dubey
Symbiosis International (Deemed University)
Electrical & Electronics Engineering

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Deep spatiotemporal signal learning with transformers for multi-day wildfire forecasting Parul Dubey; Gaurav Vishnu Londhe; Vinay Keswani; Akshita Chanchlani; Murtuza Murtuza; Pushkar Dubey
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10936

Abstract

Wildfire forecasting is a critical challenge in environmental signal processing and disaster response planning. The ability to interpret multimodal spatiotemporal signals is essential for early warning systems and resource deployment. This study addresses these limitations by proposing a unified prediction-to-action framework. We utilized four open-access datasets—wildland fire emissions database (WFED), fire information for resource management system (FIRMS), Sentinel Hub, and a custom moderate resolution imaging spectroradiometer+shuttle radar topography mission (ERA5+MODIS+SRTM) fusion—covering fire occurrences, vegetation indices, meteorological parameters, and topographic features. These heterogeneous signals were preprocessed, aligned, and transformed into structured tensors for model training and evaluation. We use a transformer-based system to understand long-term patterns in space and time, enhanced by a belief–desire–intention (BDI) reasoning module that connects our predictions to flexible wildfire response plans. The novelty lies in the integration of signal-aware attention mechanisms with symbolic decision modeling. Model performance was evaluated using F1-score, intersection over union (IoU), mean absolute error (MAE), and directional accuracy. The suggested framework did better than the basic convolutional neural network (CNN) models, reaching an F1-score of 0.74, a directional accuracy of 84.3%, and lowering the MAE to 7.6 km², while also providing clear and relevant action suggestions.
Efficient transformer architecture for sarcasm detection: a study on compression and performance Parul Dubey; Amit Mishra; Aruna Singh; Murtuza Murtuza; Akshita Chanchlani; Pushkar Dubey
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11102

Abstract

This sarcasm detection is a crucial subtask in natural language processing (NLP) particularly for sentiment analysis and conversational AI. Its complexity lies in interpreting context, tone, and intent beyond literal meanings. Traditional models often struggle to capture such nuances, especially in informal and diverse language settings. Moreover, existing approaches lack computational efficiency and fail to adapt well across different domains. This study evaluates three benchmark datasets—News Headlines, Mustard, and Reddit (SARC)—representing structured, scripted, and conversational sarcasm, respectively. Each dataset poses unique linguistic and contextual challenges. The proposed methodology integrates transformer-based models (RoBERTa and DistilBERT) with context summarization using BART and metadata embedding. A comparative analysis is conducted on both linguistic accuracy and computational efficiency. The novelty lies in aligning sarcasm detection performance with architectural optimization for real-time deployment. Evaluation is conducted using accuracy, F1-score, Jaccard coefficient, precision, and recall. Results show that RoBERTa delivers peak performance, while DistilBERT achieves a 1.74× speedup with competitive results, making it suitable for scalable and efficient sarcasm detection.
Co-Authors Akshita Chanchlani Amit Mishra Aruna Singh Gaurav Vishnu Londhe Murtuza Murtuza Pushkar Dubey Pushkar Dubey Vinay Keswani