Mohamed S. Kamel

Artificial Intelligence · 2004–2011

A researcher in the Science corpus: 10 papers; trusts 108 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #3,909 of 35,345 by trust here, #4,772 by citations
• Computer Vision and Pattern Recognition — 49 citations into it; #8,440 of 21,575 by trust here, #16,787 by citations
• Signal Processing — 40 citations into it; #2,722 of 7,572 by trust here, #2,001 by citations
• Information Systems — 31 citations into it; #1,762 of 8,771 by trust here, #5,942 by citations
• Statistical Learning Theory — 19 citations into it
• Experimental and Cognitive Psychology — 18 citations into it; #4,723 of 9,731 by trust here, #3,388 by citations
• Optimization & Training — 16 citations into it
• Computational Theory and Mathematics — 13 citations into it

Whom Mohamed S. Kamel trusts overall

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

1. Robert E. Schapire
2. Leo Breiman
3. Yoav Freund
4. Jerome H. Friedman
5. Anil K. Jain
6. David Dapeng Zhang
7. Kai Ming Ting
8. J. Ross Quinlan
9. Nitesh V. Chawla
10. David G. Lowe

Who trusts Mohamed S. Kamel overall

1. Victoria López
2. Alberto Fernández
3. Shuo Wang
4. Bartosz Krawczyk
5. Seonguk Park
6. Paula Branco
7. Jingrui He
8. Loet Leydesdorff
9. Hung Chim
10. Mikel Galar

Papers from Mohamed S. Kamel's perspective

Ordered by how strongly the researchers Mohamed S. Kamel trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Shape retrieval using triangle-area representation and dynamic space warping (2007) — 300 citations
• Survey on speech emotion recognition: Features, classification schemes, and databases (2011) — 2,137 citations
• Cost-sensitive boosting for classification of imbalanced data (2007) — 1,536 citations
• Classification of Imbalanced Data: a Review (2009) — 1,588 citations
• Boosting for Learning Multiple Classes with Imbalanced Class Distribution (2006) — 322 citations
• An Efficient Greedy Method for Unsupervised Feature Selection (2011) — 91 citations
• A survey of palmprint recognition (2009) — 612 citations
• Fuzzy preferences in conflicts (2008) — 50 citations
• Palmprint identification using feature-level fusion (2006) — 385 citations
• Efficient phrase-based document indexing for Web document clustering (2004) — 375 citations