Moklesur Rahman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Moklesur Rahman has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Fabio Badilini
Division of Cardiology, University of California , San Francisco, CA
3 shared publications - Massimo Walter Rivolta
Università degli Studi di Milano
3 shared publications - Roberto Sassi
Department of Computer Science, University of Milan, 20133 Milan, Italy
3 shared publications - Martino Vaglio
AMPS-LLC, New York, NY, USA
1 shared publication - Md Aktaruzzaman
Department of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh
1 shared publication - Mohammad Arifuzzaman
Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi Arabia
1 shared publication
Latest Publications
- Open Access
- 13 November 2025
- Published by Elsevier in Biomedical Signal Processing and Control
- Vol. 113
Uncertainty estimation of deep learning models for atrial fibrillation detection from Holter recordings: A benchmark study
- 13 November 2025
- Published by Elsevier in Biomedical Signal Processing and Control
- Vol. 113
Open AccessUncertainty estimation of deep learning models for atrial fibrillation detection from Holter recordings: A benchmark study
- 10 January 2025
- Published by Elsevier in Journal of Electrocardiology
- Vol. 89, 153876
Residual-attention deep learning model for atrial fibrillation detection from Holter recordings
- 10 January 2025
- Published by Elsevier in Journal of Electrocardiology
- Vol. 89, 153876
Residual-attention deep learning model for atrial fibrillation detection from Holter recordings
- Top Cited PapersOpen Access
A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
Top Cited PapersOpen AccessA Systematic Survey of Data Augmentation of ECG Signals for AI Applications
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
- 8 October 2021
- Published by Taylor & Francis in Particulate Science and Technology
- Vol. 40 (5) , 609-619
A decision support system for predicting settling velocity of spherical and non-spherical particles in Newtonian fluids
An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML.
- 8 October 2021
- Published by Taylor & Francis in Particulate Science and Technology
- Vol. 40 (5) , 609-619
A decision support system for predicting settling velocity of spherical and non-spherical particles in Newtonian fluids
An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML.