M
Mominul Ahsan
Researcher at Manchester Metropolitan University
Publications - 46
Citations - 417
Mominul Ahsan is an academic researcher from Manchester Metropolitan University. The author has contributed to research in topics: Computer science & Prognostics. The author has an hindex of 6, co-authored 27 publications receiving 120 citations. Previous affiliations of Mominul Ahsan include Dublin City University & University of Greenwich.
Papers
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COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.
TL;DR: In this paper, a feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%.
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Smart Monitoring and Controlling of Appliances Using LoRa Based IoT System
TL;DR: The proposed smart system with modular design proved to be highly effective in controlling and monitoring home appliances from a longer distance with relatively lower power consumption.
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Reliability Assessment of IGBT Through Modelling and Experimental Testing
TL;DR: This paper presents two case studies to demonstrate the reliability assessment of IGBT and a new driving strategy for operating IGBT based power inverter module is proposed to mitigate wire-bond thermal stresses.
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Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images
Md. Robiul Islam,Lway Faisal Abdulrazak,Md. Nahiduzzaman,Md. Omaer Faruq Goni,Md. Shamim Anower,Mominul Ahsan,Julfikar Haider,Marcin Kowalski +7 more
TL;DR: In this paper , a two-stage training method with supervised contrastive loss function was proposed for the first time to the best of authors' knowledge to identify the diabetic retinopathy and its severity stages from fundus images (FIs) using "APTOS 2019 Blindness Detection" dataset.
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Frequency Adaptive Parameter Estimation of Unbalanced and Distorted Power Grid
TL;DR: A state observer-based approach for the parameter estimation of unbalanced three-phase grid voltage signal and the chosen frequency adaptation law ensures the global asymptotic convergence of the estimated parameters in the fundamental frequency case.