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20192021
most citedAn EMD-based Method for the Detection of Power Transformer Faults with a Hierarchical Ensemble Classifier

10 citations · 15 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG20212 cited

Power Transformer Fault Diagnosis with Intrinsic Time-scale Decomposition and XGBoost Classifier

Shoaib Meraj Sami, Mohammed Imamul Hassan Bhuiyan

An intrinsic time-scale decomposition (ITD) based method for power transformer fault diagnosis is proposed. Dissolved gas analysis (DGA) parameters are ranked according to their sk…

cs.LG202110 cited

An EMD-based Method for the Detection of Power Transformer Faults with a Hierarchical Ensemble Classifier

Shoaib Meraj Sami, Mohammed Imamul Hassan Bhuiyan

In this paper, an Empirical Mode Decomposition-based method is proposed for the detection of transformer faults from Dissolve gas analysis (DGA) data. Ratio-based DGA parameters ar…

eess.IV20213 cited

Weighted Contourlet Parametric (WCP) Feature Based Breast Tumor Classification from B-Mode Ultrasound Image

Shahriar Mahmud Kabir, Md. Sayed Tanveer, ASM Shihavuddin +1

Automated detection of breast tumor in early stages using B-Mode Ultrasound image is crucial for preventing widespread breast cancer specially among women. This paper is primarily…

eess.SP2020

A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram

Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil +2

Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to th…

cs.CV2019

X-Ray Image Compression Using Convolutional Recurrent Neural Networks

Asif Shahriyar Sushmit, Shakib Uz Zaman, Ahmed Imtiaz Humayun +2

In the advent of a digital health revolution, vast amounts of clinical data are being generated, stored and processed on a daily basis. This has made the storage and retrieval of l…

cs.LG2019

End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

Ahmed Imtiaz Humayun, Asif Shahriyar Sushmit, Taufiq Hasan +1

Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture…