Usha R. Acharya

Cognitive Neuroscience · 2005–2022

A researcher in the Science corpus: 22 papers; trusts 232 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 164 citations into it; #1,736 of 10,852 by trust here, #1,280 by citations
• Artificial Intelligence — 161 citations into it; #19,550 of 35,345 by trust here, #10,667 by citations
• Computer Vision and Pattern Recognition — 107 citations into it
• Signal Processing — 89 citations into it; #3,471 of 7,572 by trust here, #1,299 by citations
• Cardiology and Cardiovascular Medicine — 78 citations into it; #181 of 906 by trust here, #189 by citations
• Statistical and Nonlinear Physics — 24 citations into it
• Biomedical Engineering — 23 citations into it; #6,229 of 10,734 by trust here, #6,028 by citations
• Molecular Biology — 22 citations into it; #9,033 of 17,773 by trust here, #10,047 by citations

Whom Usha R. Acharya trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Samanwoy Ghosh-Dastidar
2. Hojjat Adeli
3. Hasan Ocak
4. Ross B. Girshick
5. Abdulhamit Subasi
6. Alex Krizhevsky
7. Kaiming He
8. Ling Guo
9. Geoffrey E. Hinton
10. Hirotugu Akaike

Who trusts Usha R. Acharya overall

1. Poomipat Boonyakitanont
2. Shu Lih Oh
3. Dr. Prabira Kumar Sethy
4. Sebastiano Massaro
5. Yuki Hagiwara
6. Sajad Mousavi
7. Fatin A. Elhaj
8. Florian Beissner
9. Apiwat Lek-uthai
10. Shivarudhrappa Raghu

Papers from Usha R. Acharya's perspective

Ordered by how strongly the researchers Usha R. Acharya trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Going deeper with convolutions
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Heart rate variability: a review (2006) — 2,892 citations
• Entropies for detection of epilepsy in EEG (2005) — 800 citations
• SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach (2019) — 477 citations
• A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension (2017) — 355 citations
• Application of entropies for automated diagnosis of epilepsy using EEG signals: A review (2015) — 460 citations
• Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022) (2022) — 613 citations
• The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020) — 344 citations
• Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks (2020) — 734 citations
• Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020) — 2,261 citations
• A new approach for arrhythmia classification using deep coded features and LSTM networks (2019) — 345 citations
• Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals (2019) — 356 citations
• A deep learning approach for Parkinson’s disease diagnosis from EEG signals (2018) — 571 citations
• Automated EEG-based screening of depression using deep convolutional neural network (2018) — 526 citations
• Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017) — 1,449 citations
• Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015) — 299 citations
• Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015) — 517 citations
• Automated EEG analysis of epilepsy: A review (2013) — 816 citations
• ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform (2013) — 709 citations
• Application of principal component analysis to ECG signals for automated diagnosis of cardiac health (2012) — 300 citations
• Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine (2012) — 296 citations