Md Rizwanul Kabir
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Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Md Rizwanul Kabir's research focuses on the application of deep learning and machine learning techniques to analyze complex data, particularly in the domains of medical imaging and financial forecasting. His work includes developing hybrid deep learning models for financial time series prediction and ensemble-based approaches for detecting COVID-19 from chest X-ray images. Kabir has also investigated the use of deep learning for emotion analysis from EEG signals and for sentiment analysis using MFCCs. Further research explores transformer-based generative adversarial networks for speed-of-sound reconstruction in pulse-echo mode, alongside GPU-based acceleration for real-time speed-of-sound imaging. His collaborations include work with Mariofanna Milanova, Md. Samin Morshed, and John R. Talburt at the University of Arkansas at Little Rock, with whom he shares multiple publications. Kabir's scholarly output includes 9 publications and 110 citations, with an h-index of 4.
Metrics
- h-index: 4
- Publications: 9
- Citations: 110
Selected Publications
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Entity Resolution Using Transformers for Synthetic Datasets (2025)
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Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach (2025)
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LSTM–Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting (2025)
Collaboration Network
Top Collaborators
- LSTM–Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting
- Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach
- Entity Resolution Using Transformers for Synthetic Datasets
- Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach
- Entity Resolution Using Transformers for Synthetic Datasets
- Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach
- Entity Resolution Using Transformers for Synthetic Datasets
- LSTM–Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting
- LSTM–Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting
- Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach
- Entity Resolution Using Transformers for Synthetic Datasets
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