Andrew H. Sung

Artificial Intelligence · 1998–2022

A researcher in the Science corpus: 8 papers; trusts 27 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #5,604 of 35,345 by trust here, #9,760 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Information Systems — 12 citations into it
• Signal Processing — 8 citations into it; #4,453 of 7,572 by trust here, #4,201 by citations
• Computer Networks and Communications — 7 citations into it; #819 of 9,886 by trust here, #1,475 by citations
• Scaling & Learning Theory — 6 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Andrew H. Sung trusts overall

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

1. Vladimir Naumovich Vapni
2. Mark A. Hall
3. Jessica J. Fridrich
4. T. J. Watson
5. Siwei Lyu
6. Yuhai Wu
7. J. H. Harmsen
8. James Cannady
9. Andrew D. Ker
10. Ismail Avcibas

Who trusts Andrew H. Sung overall

1. G. Zhang
2. Fan-Yin Tzeng
3. Shahaboddin Shamshirband
4. Kasra Mohammadi
5. José Jiménez-Luna
6. Taeshik Shon
7. Animesh Patcha
8. Srinivas Mukkamala
9. Monowar Hussain Bhuyan
10. Srilatha Chebrolu

Papers from Andrew H. Sung's perspective

Ordered by how strongly the researchers Andrew H. Sung trusts endorse each paper. Same corpus, a different order for every researcher.

1. Statistical Learning Theory
2. The perceptron: a probabilistic model for information storage and organization in the brain.
3. Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes (Information Hiding)
4. Neural Networks for Pattern Recognition
5. C4.5: Programs for Machine Learning
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Steganalysis using image quality metrics (IEEE Transactions on Image Processing)
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Why phishing works
10. Pattern Classification

Their papers

• Intrusion detection using neural networks and support vector machines (2002) — 901 citations
• Identifying important features for intrusion detection using support vector machines and neural networks (2003) — 460 citations
• Deepfake Detection: A Systematic Literature Review (2022) — 393 citations
• Learning to Detect Phishing Webpages (2014) — 12 citations
• An improved approach to steganalysis of JPEG images (2010) — 66 citations
• Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images (2008) — 131 citations
• Intrusion detection using an ensemble of intelligent paradigms (2005) — 433 citations
• Ranking importance of input parameters of neural networks (1998)