F
Fazla Rabbi Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
3 h-index
10 pubs
44 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Fazla Rabbi's research centers on the application of machine learning and deep learning techniques to diverse fields, including healthcare, environmental science, and cybersecurity. His work in healthcare involves developing frameworks for disease detection and risk prediction, such as Parkinson's disease from EEG signals using hybrid deep learning and cardiovascular risk prediction via stacking classifiers. He also investigates the use of NLP and CNN models for disaster detection from social media data and explores machine learning for predicting corrosion levels in the oil and gas industry. Rabbi's recent publications also address the impacts of climate change on crop distribution using explainable AI and genre classification for Bangla music lyrics. He has a h-index of 3 with 35 citations across 10 publications. Collaborators include Alexandr M. Sokolov, Alok Madamanchi, and Saroj Raut, all from Arkansas State University.
Metrics
- h-index: 3
- Publications: 10
- Citations: 44
Selected Publications
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Unraveling the climate induced drought impacts on crop pattern distribution using explainable machine learning algorithms (2025)
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A comparative analysis of machine learning techniques for detecting probing attack with SHAP algorithm (2025)
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Performance evaluation of NLP and CNN models for disaster detection using social media data (2024)
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Cardiovascular Risk Prediction Through Stacking Classifier (2024)
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A Machine Learning-Based Corrosion Level Prediction in the Oil and Gas Industry (2024)
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Using process mining algorithms for process improvement in healthcare (2024)
Collaboration Network
Top Collaborators
Niamat Ullah Ibne Hossain
The University of Texas at El Paso (US)
5 shared publications
- Performance evaluation of NLP and CNN models for disaster detection using social media data
- A comparative analysis of machine learning techniques for detecting probing attack with SHAP algorithm
- A Machine Learning-Based Corrosion Level Prediction in the Oil and Gas Industry
- Cardiovascular Risk Prediction Through Stacking Classifier
- Unraveling the climate induced drought impacts on crop pattern distribution using explainable machine learning algorithms
Alok Madamanchi
Arkansas State University
1 shared publication
In database
- A Machine Learning-Based Corrosion Level Prediction in the Oil and Gas Industry
Alexandr M. Sokolov
Arkansas State University
1 shared publication
In database
- A Machine Learning-Based Corrosion Level Prediction in the Oil and Gas Industry
Saroj Raut
Arkansas State University
1 shared publication
In database
- Cardiovascular Risk Prediction Through Stacking Classifier
Md. Azharul Islam
University at Buffalo, State University of New York (US)
1 shared publication
- Performance evaluation of NLP and CNN models for disaster detection using social media data
Ferdaus Anam Jibon
International University of Business Agriculture and Technology (BD)
1 shared publication
- Parkinson's disease detection from EEG signal employing autoencoder and RBFNN-based hybrid deep learning framework utilizing power spectral density
Alif Tasbir
University of Information Technology and Sciences (BD)
1 shared publication
- Parkinson's disease detection from EEG signal employing autoencoder and RBFNN-based hybrid deep learning framework utilizing power spectral density
Md. Alamin Talukder
International University of Business Agriculture and Technology (BD)
1 shared publication
- Parkinson's disease detection from EEG signal employing autoencoder and RBFNN-based hybrid deep learning framework utilizing power spectral density
Md. Ashraf Uddin
1 shared publication
- Parkinson's disease detection from EEG signal employing autoencoder and RBFNN-based hybrid deep learning framework utilizing power spectral density
Md Salah Uddin
University of Information Technology and Sciences (BD)
1 shared publication
- Parkinson's disease detection from EEG signal employing autoencoder and RBFNN-based hybrid deep learning framework utilizing power spectral density
Fars K. Alanazi
King Saud University (SA)
1 shared publication
- Parkinson's disease detection from EEG signal employing autoencoder and RBFNN-based hybrid deep learning framework utilizing power spectral density
Mohsin Kazi
King Saud University (SA)
1 shared publication
- Parkinson's disease detection from EEG signal employing autoencoder and RBFNN-based hybrid deep learning framework utilizing power spectral density
Saikat Das
Utah Valley University (US)
1 shared publication
- A comparative analysis of machine learning techniques for detecting probing attack with SHAP algorithm
Md Umaid Hasan
Shahjalal University of Science and Technology (BD)
1 shared publication
- Genre Classification from Bangla Music Lyrics: Dataset and Classifiers
M. Shahidur Rahman
Shahjalal University of Science and Technology (BD)
1 shared publication
- Genre Classification from Bangla Music Lyrics: Dataset and Classifiers
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