Huiling Chen
Artificial Intelligence · 2011–2022
A researcher in the Science corpus: 17 papers; trusts 74 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 336 citations into it; #18,915 of 35,345 by trust here, #4,722 by citations
- Computational Theory and Mathematics — 222 citations into it
- Computer Vision and Pattern Recognition — 55 citations into it; #18,396 of 21,575 by trust here, #9,435 by citations
- Industrial and Manufacturing Engineering — 26 citations into it
- Biomedical Engineering — 23 citations into it
- Civil and Structural Engineering — 21 citations into it
- Molecular Biology — 21 citations into it
- Control and Systems Engineering — 16 citations into it
Whom Huiling Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Seyedali Mirjalili
- Seyed Mohammad Mirjalili
- Xin-She Yang
- Chih-Jen Lin
- Vladimir Naumovich Vapni
- Carlos Artemio Coello Coello
- Andrew Lewis
- Carlos Andrés Peña-Reyes
- Kemal Polat
- Murat Karabatak
Who trusts Huiling Chen overall
- H. Hannah Inbarani
- Ali Asghar Heidari
- Amin Ul Haq
- Iman Ahmadianfar
- Yanan Zhang
- Zhou Wei
- Mingjing Wang
- Ahmad Taher Azar
- Tansel Dökeroğlu
Papers from Huiling Chen's perspective
Ordered by how strongly the researchers Huiling Chen trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- The Nature of Statistical Learning Theory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Genetic Algorithms in Search Optimization and Machine Learning
- A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms (Swarm and Evolutionary Computation)
- Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Support-Vector Networks
Their papers
- Slime mould algorithm: A new method for stochastic optimization (2020) — 2,690 citations
- Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies (2020) — 300 citations
- Enhanced Moth-flame optimizer with mutation strategy for global optimization (2019) — 393 citations
- Boosted binary Harris hawks optimizer and feature selection (2020) — 297 citations
- A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis (2011) — 375 citations
- INFO: An efficient optimization algorithm based on weighted mean of vectors (2022) — 723 citations
- Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules (2021) — 56 citations
- Parameters extraction of three diode photovoltaic models using boosted LSHADE algorithm and Newton Raphson method (2021) — 85 citations
- Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts (2021) — 920 citations
- A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique (2021) — 326 citations
- RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method (2021) — 988 citations
- Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis (2020) — 425 citations
- Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy (2020) — 300 citations
- Harris hawks optimization: Algorithm and applications (2019) — 5,546 citations
- Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients (2019) — 302 citations
- Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses (2017) — 433 citations
- Evolving support vector machines using fruit fly optimization for medical data classification (2016) — 511 citations