Binish Fatimah
CMR Institute of Technology
31 Papers
17 Citations
Binish Fatimah is an academic researcher from CMR Institute of Technology. The author has contributed to research in topics: Computer science & Filter bank. The author has an hindex of 4, co-authored 21 publications.
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Papers
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Priya Aggarwal,Narendra Kumar Mishra,Binish Fatimah,Pushpendra Singh,Anubha Gupta,Shiv Dutt Joshi +5 more
TL;DR: In this article , a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images are summarized and reviewed, and an outline of the current state-of-the-art advances and a critical discussion of open challenges are presented.
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Detection of apnea events from ECG segments using Fourier decomposition method
TL;DR: The single-lead ECG signal is divided into 1-min segments, and separated into frequency bands using Fourier decomposition method, which makes it computationally efficient and can be used for real-time sleep apnea detection.
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Efficient detection of myocardial infarction from single lead ECG signal
TL;DR: This work uses single channel electrocardiogram (ECG) signal to develop two automated MI detection algorithms, namely, primary and modified, which perform better than the existing state-of-the-art techniques and have the potential for efficient real-time implementation in MI detection systems.
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Mental Arithmetic Task Classification using Fourier Decomposition Method
Binish Fatimah,Abhishek Javali,Haaris Ansar,B G Harshitha,Hemant Kumar +4 more
- 28 Jul 2020
TL;DR: A mental arithmetic task detection algorithm from a single lead EEG signal used to decompose the signal into M uniform sub-bands and features, like energy, entropy, and variance, are computed from each of these sub-band.
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An automatic siren detection algorithm using Fourier Decomposition Method and MFCC
Binish Fatimah,Preethi A,Hrushikesh,Akhilesh Singh B,Harshanikethan R Kotion +4 more
- 01 Jul 2020
TL;DR: This work has compared the performance of various machine learning algorithms like kNN, SVM and ensemble bagged trees to select the best model to train a machine learning model to identify siren sounds from the background traffic noise.
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