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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics
Amit Mishra
Dr. Vishwanath Karad MIT World Peace University
Economics, Econometrics & Finance Electrical & Electronics Engineering

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Synaptic shield: fusion of ResNext–50 and long short-term memory for enhanaced deepfake detection Amit Mishra; Prajwal Chinchmalatpure; Govinda B. Sambare; Viomesh Kumar Singh; Atul Gulabrao Pawar; Rahul Prakash Mirajkar; Priyanka K. Takalkar; Kuldeep Vayadande
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp224-235

Abstract

Recent developments in deepfakes have created much anxiety about the authenticity of any digital content and thus, calls for implementing detection mechanisms that will work accordingly. This paper uses Synaptic Shield, a innovative deep learning (DL) framework which is customized to detect alterations by deepfakes with high precision levels. It employs both convolution neural networks (CNNs) as well as modules for time feature extractions to test spatial and motion indicators from video data. High-level preprocessing pipelines in combination with confidence scoring mechanism help make Synaptic Shield adaptive toward manipulation techniques such as FaceSwap and DeepFake. The accuracy of our model surpasses other deepfake detection models with a high accuracy of 98.3%. The above results are based on exhaustive experimentation on standard datasets like FaceForensics++, DeepFake detection challenge (DFDC), and Celeb DeepFake (Celeb-DF). Synaptic Shield is shown to be the best with outstanding results that maintain a higher confidence score equivalent to its precision and reliability. Scalability in having the capacity to accommodate various manipulation techniques and levels of video quality indicates robustness in offering an effective method toward ensuring integrity in digital media. The work is an important move forward in addressing the problems created by DeepFake technologies.
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.
Adaptive multimodal transformer for wildfire spread prediction using feature-weighted attention Parul Dubey; Gunjan Keswani; Amit Mishra; Manjushree Nayak; Md Rashid Mahmood; Pushkar Dubey
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11326

Abstract

Hazard modelling technology has evolved quickly in recent years to predict wildfires, which are an important class of natural disaster for accurate forecasting and proactive control. The combination of remote sensing and machine learning has been playing an important role in this area. Yet traditional models fail to accommodate the sophisticated spatiotemporal dynamics in fire-affected areas and tend to be agnostic toward individual predictors. This study utilizes a multimodal dataset comprising normalized difference vegetation index (NDVI), wind vectors, surface temperature, humidity, land cover, and elevation from sources such as moderate resolution imaging spectroradiometer (MODIS), Sentinel-2, ERA5, and shuttle radar topography mission (SRTM). These inputs were normalized within spatial grids, and temporally-aligned for day-ahead prediction. We introduce an adaptive multimodal transformer (AMT) with a feature weighting module (FWM) to adaptively emphasize informative features. The transformer architecture allows for long-range spatial learning, and the FWM strengthens contextual sensitivity. The novelty of this approach lies in its interpretable feature reweighting mechanism for dynamic environmental conditions. Model performance was evaluated using F1-score, intersection over union (IoU), mean absolute error (MAE), and mean average precision (MAP). Results show that the proposed model outperforms the MA-Net baseline, achieving a 5% improvement in F1-score and a 14.7% reduction in MAE, demonstrating superior accuracy, generalization, and interpretability.
Co-Authors Akshita Chanchlani Aruna Singh Atul Gulabrao Pawar Govinda B. Sambare Gunjan Keswani Kuldeep Vayadande Manjushree Nayak Md Rashid Mahmood Murtuza Murtuza Parul Dubey Parul Dubey Prajwal Chinchmalatpure Priyanka K. Takalkar Pushkar Dubey Pushkar Dubey Rahul Prakash Mirajkar Viomesh Kumar Singh