Serkan Kiranyaz

Computer Vision and Pattern Recognition · 2009–2021

A researcher in the Science corpus: 9 papers; trusts 40 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 46 citations into it
• Control and Systems Engineering — 46 citations into it; #2,887 of 8,257 by trust here, #1,475 by citations
• Artificial Intelligence — 42 citations into it
• Mechanical Engineering — 31 citations into it; #5,046 of 8,300 by trust here, #7,024 by citations
• Civil and Structural Engineering — 30 citations into it; #4,180 of 6,160 by trust here, #2,220 by citations
• Mechanics of Materials — 30 citations into it; #4,770 of 6,291 by trust here, #2,227 by citations
• Cardiology and Cardiovascular Medicine — 18 citations into it; #68 of 906 by trust here, #264 by citations
• Cognitive Neuroscience — 14 citations into it; #5,627 of 10,852 by trust here, #7,860 by citations

Whom Serkan Kiranyaz trusts overall

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

1. Geoffrey E. Hinton
2. Levent Tolga Eren
3. Turker Ince
4. Alex Krizhevsky
5. Philip de Chazal
6. Osama Abdeljaber
7. Yann LeCun
8. Ilya Sutskever
9. Wei Jiang
10. Yoshua Bengio

Who trusts Serkan Kiranyaz overall

1. Onur Avcı
2. Turker Ince
3. Osama Abdeljaber
4. Saeed Khaki
5. Saeed Saadatnejad
6. Moncef Gabbouj
7. Jingshan Huang
8. Orestis Tsinalis
9. R. D. Labati
10. Minghang Zhao

Papers from Serkan Kiranyaz's perspective

Ordered by how strongly the researchers Serkan Kiranyaz 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• 1D Convolutional Neural Networks and Applications: A Survey (2019) — 2,635 citations
• Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images (2021) — 1,001 citations
• A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020) — 1,195 citations
• 1-D Convolutional Neural Networks for Signal Processing Applications (2019) — 297 citations
• A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier (2018) — 640 citations
• Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks (2017) — 1,158 citations
• Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks (2016) — 1,648 citations
• Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks (2016) — 1,154 citations
• A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals (2009) — 485 citations