arXiv subjects

Md. Toufiqur Rahman

Publications and source records attributed to Md. Toufiqur Rahman.

3 recordsLinked to original sources

arXiv2026

Echoes of Unrest: A Multimodal NLP Framework for Early Warning of Fake News and Violence-Driven Mob Activity

Rapid growth in social media has transformed global communication by enabling fast information exchange, but it has also accelerated the spread of misinformation. Fake news, manipulated content, and provocative narratives are increasingly linked to social unrest, political instability, and mob violence. Incidents in South Asia and elsewhere demonstrate how false information disseminated via platforms such as Facebook and WhatsApp can trigger real-world harm, often spreading faster than fact-checking efforts can respond. To address this challenge, this chapter presents a multilingual, multimodal Natural Language Processing (NLP) framework for early detection of misinformation and violence-prone dynamics. A fused dataset of 138,256 Bangla and English samples was created by combining multiple benchmark datasets. The framework integrates XLM-RoBERTa for multilingual text representation, CLIP for visual embedding, and a multi-head attention mechanism for multimodal fusion, enhanced with auxiliary features such as sarcasm and geospatial metadata. Experiments on a stratified 30% subset achieved 98% test accuracy with strong precision and recall. The outcomes show the efficacy of multimodal approaches in early misinformation detection and highlight the added value of geospatial signals for anticipating real-world escalation.

cs.CL↗

arXiv2024

EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN

Amyotrophic Lateral Sclerosis (ALS) and Myopathy present considerable challenges in the realm of neuromuscular disorder diagnostics. In this study, we employ advanced deep-learning techniques to address the detection of ALS and Myopathy, two debilitating conditions. Our methodology begins with the extraction of informative features from raw electromyography (EMG) signals, leveraging the Log-spectrum, and Delta Log spectrum, which capture the frequency contents, and spectral and temporal characteristics of the signals. Subsequently, we applied a deep-learning model, SpectroEMG-Net, combined with Convolutional Neural Networks (CNNs) and Attention for the classification of three classes. The robustness of our approach is rigorously evaluated, demonstrating its remarkable performance in distinguishing among the classes: Myopathy, Normal, and ALS, with an outstanding overall accuracy of 92\%. This study marks a contribution to addressing the diagnostic challenges posed by neuromuscular disorders through a data-driven, multi-class classification approach, providing valuable insights into the potential for early and accurate detection.

eess.SP↗

arXiv2023

A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography

The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI, mm-wave radar, neural network

eess.SP↗