U. Rajendra Acharya
Ngee Ann Polytechnic
730 Papers
1.7K Citations
U. Rajendra Acharya is an academic researcher from Ngee Ann Polytechnic. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 90, co-authored 570 publications. Previous affiliations of U. Rajendra Acharya include Kumamoto University & University of Southern Queensland.
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Papers
Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients.
Roohallah Alizadehsani,Zahra Alizadeh Sani,Mohaddeseh Behjati,Zahra Roshanzamir,Sadiq Hussain,Niloofar Abedini,Fereshteh Hasanzadeh,Abbas Khosravi,Afshin Shoeibi,Afshin Shoeibi,Mohamad Roshanzamir,Pardis Moradnejad,Saeid Nahavandi,Fahime Khozeimeh,Assef Zare,Maryam Panahiazar,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Sheikh Mohammed Shariful Islam +19 more
TL;DR: A number of factors associated with mortality due to CO VID‐19 have been investigated for the first time in this study and might be helpful in early prediction and risk reduction of mortality in patients infected with COVID‐19.
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An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank.
Manish Sharma,Deepanshu Goyal,P.V. Achuth,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: A new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset is developed and yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively.
171
Automatic identification of epileptic and background EEG signals using frequency domain parameters.
TL;DR: This paper used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples, and found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results.
Decision support system for the glaucoma using Gabor transformation
U. Rajendra Acharya,U. Rajendra Acharya,E. Y. K. Ng,Lim Wei Jie Eugene,Kevin Noronha,Lim Choo Min,K. Prabhakar Nayak,Sulatha V. Bhandary +7 more
TL;DR: A novel automated glaucoma diagnosis method using various features extracted from Gabor transform applied on digital fundus images and a GRI developed using principal components to classify the two classes using just one number is proposed.
169
Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring
Paweł Pławiak,Moloud Abdar,U. Rajendra Acharya +2 more
- 01 Nov 2019
TL;DR: The proposed approach is a hybrid model which merges the benefits of evolutionary computation, ensemble learning, and deep learning and can be employed in the banking system to evaluate the bank credits of the applicants and aid the bank managers in making correct decisions.
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