H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 69 Citations 14,640 287 World Ranking 910 National Ranking 52

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Cross-validation, Support vector machine and Principal component analysis. The study incorporates disciplines such as Machine learning, Particle swarm optimization, Magnetic resonance imaging and Computer vision in addition to Artificial intelligence. His Particle swarm optimization research is multidisciplinary, relying on both Genetic algorithm, Ant colony optimization algorithms and Metaheuristic.

The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network, Feedforward neural network, Entropy and Biogeography-based optimization. His studies in Cross-validation integrate themes in fields like Discrete wavelet transform and Chaotic. Shuihua Wang combines subjects such as Decision tree, Feature extraction, Eigenvalues and eigenvectors and Radial basis function with his study of Support vector machine.

His most cited work include:

  • A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (467 citations)
  • A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (467 citations)
  • Binary PSO with mutation operator for feature selection using decision tree applied to spam detection (272 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Artificial intelligence, Pattern recognition, Artificial neural network, Convolutional neural network and Support vector machine. His Artificial intelligence research includes elements of Machine learning and Computer vision. His study in the field of Wavelet transform, Classifier, Feature extraction and Principal component analysis is also linked to topics like Sensitivity.

His Artificial neural network research focuses on subjects like Particle swarm optimization, which are linked to Feedforward neural network. His studies deal with areas such as Identification, Normalization, Pooling and Test set as well as Convolutional neural network. His study looks at the relationship between Support vector machine and topics such as Computer-aided diagnosis, which overlap with Hearing loss.

He most often published in these fields:

  • Artificial intelligence (99.01%)
  • Pattern recognition (78.95%)
  • Artificial neural network (25.99%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (99.01%)
  • Pattern recognition (78.95%)
  • Convolutional neural network (26.32%)

In recent papers he was focusing on the following fields of study:

Shuihua Wang mostly deals with Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Pooling. Shuihua Wang has researched Artificial intelligence in several fields, including Machine learning and Identification. His work on Normalization as part of his general Pattern recognition study is frequently connected to Sensitivity, thereby bridging the divide between different branches of science.

Test set and Hyperparameter optimization is closely connected to Normalization in his research, which is encompassed under the umbrella topic of Convolutional neural network. His Deep learning research is multidisciplinary, incorporating elements of Classifier, Image, Magnetic resonance imaging, Transfer of learning and Euclidean distance. As part of his studies on Artificial neural network, Shuihua Wang frequently links adjacent subjects like Particle swarm optimization.

Between 2019 and 2021, his most popular works were:

  • Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization (65 citations)
  • Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling (43 citations)
  • Covid-19 Classification by FGCNet with Deep Feature Fusion from Graph Convolutional Network and Convolutional Neural Network. (34 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of investigation include Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Pooling. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Machine learning. His research in Pattern recognition intersects with topics in Extreme learning machine, Feedforward neural network, Test set and Hyperparameter.

His Convolutional neural network study incorporates themes from Normalization, Speech recognition and Overfitting. His Deep learning research is multidisciplinary, incorporating perspectives in Artificial neural network and Feature extraction. His Support vector machine study integrates concerns from other disciplines, such as Discriminative model and Outlier.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Yudong Zhang;Shuihua Wang;Shuihua Wang;Genlin Ji.
Mathematical Problems in Engineering (2015)

722 Citations

Binary PSO with mutation operator for feature selection using decision tree applied to spam detection

Yudong Zhang;Shuihua Wang;Preetha Phillips;Genlin Ji.
Knowledge Based Systems (2014)

366 Citations

A hybrid method for MRI brain image classification

Yudong Zhang;Zhengchao Dong;Lenan Wu;Shuihua Wang.
Expert Systems With Applications (2011)

364 Citations

Fruit classification using computer vision and feedforward neural network

Yudong Zhang;Shuihua Wang;Genlin Ji;Preetha Phillips.
Journal of Food Engineering (2014)

235 Citations

Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)

Yudong Zhang;Zhengchao Dong;Shuihua Wang;Genlin Ji.
Entropy (2015)

230 Citations

MAGNETIC RESONANCE BRAIN IMAGE CLASSIFICATION BY AN IMPROVED ARTIFICIAL BEE COLONY ALGORITHM

Yudong Zhang;Lenan Wu;Shuihua Wang.
Progress in Electromagnetics Research-pier (2011)

218 Citations

Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning.

Yudong Zhang;Zhengchao Dong;Preetha Phillips;Shuihua Wang;Shuihua Wang.
Frontiers in Computational Neuroscience (2015)

201 Citations

Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation

Yu-Dong Zhang;Zhang-Jing Yang;Hui-Min Lu;Xing-Xing Zhou.
IEEE Access (2016)

199 Citations

Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation

Yu-Dong Zhang;Zhengchao Dong;Xianqing Chen;Wenjuan Jia.
Multimedia Tools and Applications (2019)

183 Citations

Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree

Yudong Zhang;Shuihua Wang;Zhengchao Dong.
Progress in Electromagnetics Research-pier (2014)

180 Citations

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