Md. Omaer Faruq Goni
Rajshahi University of Engineering & Technology
6 Papers
Md. Omaer Faruq Goni is an academic researcher from Rajshahi University of Engineering & Technology. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 1, co-authored 5 publications.
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Papers
Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm
Md. Nahiduzzaman,Md. Robiul Islam,S. M. Riazul Islam,Md. Omaer Faruq Goni,Md. Shamim Anower,Kyung Sup Kwak +5 more
TL;DR: Zhang et al. as mentioned in this paper exploited the extreme learning machine (ELM) approach to address diabetic retinopathy (DR), a medical condition in which impairment occurs to the retina caused by diabetes.
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A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images
Md. Nahiduzzaman,Md. Omaer Faruq Goni,Md. Shamim Anower,Md. Robiul Islam,Mominul Ahsan,Julfikar Haider,Saravanakumar Gurusamy,Rakibul Hassan,Md. Rakibul Islam +8 more
TL;DR: In this article, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia).
Graduate Admission Chance Prediction Using Deep Neural Network
Md. Omaer Faruq Goni,Abdul Matin,Tonmoy Hasan,Md. Abu Ismail Siddique,Oishi Jyoti,Fahim Md. Sifnatul Hasnain +5 more
- 26 Dec 2020
TL;DR: In this article, a deep neural network (DNN) was proposed to predict the chance of getting admitted to a university according to the students portfolio, which has shown the most promising results that includes R-squared score of 0.8538 and MSE of0.0031.
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Breast Cancer Detection using Deep Neural Network
Md. Omaer Faruq Goni,Fahim Md. Sifnatul Hasnain,Md. Abu Ismail Siddique,Oishi Jyoti,Md. Habibur Rahaman +4 more
- 19 Dec 2020
TL;DR: In this article, a deep neural network with feature selection techniques was used to predict breast cancer and achieved an accuracy of 99.42% on different evaluation benchmarks like train accuracy, test accuracy, precision, recall, specificity, sensitivity, f measure and MCC.
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A Comparative Analysis of Feature Extraction Methods for Human Opinion Grouping Using Several Machine Learning Techniques
Tonmoy Hasan,Abdul Matin,M. Kamruzzaman,Sanzida Islam,Md. Omaer Faruq Goni +4 more
- 26 Dec 2020
TL;DR: In this paper, the authors compared the performance of four well-known feature extraction techniques (i.e. BOW, TF-IDF, Skip-Gram, and CBOW) for binary opinion classification.
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