Abdulhamit Subasi

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 8 papers; trusts 55 researchers and is trusted by 64 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #3,032 of 35,345 by trust here, #10,430 by citations
• Signal Processing — 20 citations into it; #370 of 7,572 by trust here, #1,219 by citations
• Cognitive Neuroscience — 18 citations into it; #303 of 10,852 by trust here, #2,220 by citations
• Statistical Learning Theory — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Control and Systems Engineering — 8 citations into it
• Statistics and Probability — 8 citations into it
• Model Architectures — 7 citations into it

Whom Abdulhamit Subasi trusts overall

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

1. Vladimir Naumovich Vapni
2. A. Kandaswamy
3. M. Stone
4. Hsuan-Tien Lin
5. Farid Melgani
6. Yakoub Bazi
7. Nancy A. Obuchowski
8. Michael I. Jordan
9. Hojjat Adeli
10. Cheng-Lung Huang

Who trusts Abdulhamit Subasi overall

1. Umut Orhan
2. Amjed Subhi Al-Fahoum
3. Alexandros T. Tzallas
4. Usha R. Acharya
5. Mahmut Hekim
6. Zerina Mašetić
7. Ling Guo
8. Essam H. Houssein
9. Ausilah Alfraihat
10. Yudong Zhang

Papers from Abdulhamit Subasi's perspective

Ordered by how strongly the researchers Abdulhamit Subasi 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. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013) — 488 citations
• Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients (2005) — 313 citations
• Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction (2018) — 367 citations
• Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system (2017) — 394 citations
• Congestive heart failure detection using random forest classifier (2016) — 303 citations
• EEG signal classification using PCA, ICA, LDA and support vector machines (2010) — 1,193 citations
• EEG signal classification using wavelet feature extraction and a mixture of expert model (2007) — 1,251 citations
• Classification of EEG signals using neural network and logistic regression (2005) — 588 citations