What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Gene
Artificial intelligence, Machine learning, Statistical physics, Data mining and Average path length are his primary areas of study.
The study incorporates disciplines such as Sequence and Pattern recognition in addition to Artificial intelligence.
His Machine learning research includes themes of Classifier, Subcellular localization and Computational biology.
His Statistical physics research is multidisciplinary, relying on both Statistics, Random walk, Scale-free network and Random graph.
The Data mining study combines topics in areas such as Amino acid, Vertex, Theoretical computer science and Cluster analysis.
He interconnects Clustering coefficient, Degree and Complex network in the investigation of issues within Average path length.
His most cited work include:
- Focusing Attention: Towards Accurate Text Recognition in Natural Images (172 citations)
- Cassava Genome From a Wild Ancestor to Cultivated Varieties (152 citations)
- AON: Towards Arbitrarily-Oriented Text Recognition (141 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Data mining, Machine learning, Statistical physics and Degree distribution.
His Artificial intelligence study incorporates themes from Sequence and Pattern recognition.
His Data mining study combines topics in areas such as Peer-to-peer, Set, Information retrieval and Cluster analysis.
His research investigates the connection between Statistical physics and topics such as Random walk that intersect with problems in First-hitting-time model.
His Degree distribution research includes elements of Discrete mathematics, Average path length, Fractal, Clustering coefficient and Scale-free network.
His Average path length study combines topics from a wide range of disciplines, such as Degree and Complex network.
He most often published in these fields:
- Artificial intelligence (19.86%)
- Data mining (18.69%)
- Machine learning (9.35%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (19.86%)
- Data mining (18.69%)
- Machine learning (9.35%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Data mining, Machine learning, Pattern recognition and Computational biology.
As a part of the same scientific family, Shuigeng Zhou mostly works in the field of Artificial intelligence, focusing on Sequence and, on occasion, Orientation.
The study incorporates disciplines such as Set, Theoretical computer science, Pruning and Protein–protein interaction in addition to Data mining.
His work carried out in the field of Machine learning brings together such families of science as Novelty detection, Semantic consistency and Embedding.
His Omics data study in the realm of Computational biology interacts with subjects such as Cellular heterogeneity.
His Feature research is multidisciplinary, incorporating perspectives in Scalability, Representation, Index and Exploratory search, Information retrieval.
Between 2016 and 2021, his most popular works were:
- Focusing Attention: Towards Accurate Text Recognition in Natural Images (172 citations)
- AON: Towards Arbitrarily-Oriented Text Recognition (141 citations)
- Edit Probability for Scene Text Recognition (97 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Gene
Shuigeng Zhou focuses on Artificial intelligence, Computational biology, Machine learning, Data mining and Set.
His biological study spans a wide range of topics, including Sequence and Pattern recognition.
His Computational biology research incorporates themes from Enhancer, Targeted proteomics, Function and Value.
His research in Machine learning intersects with topics in Enhanced Data Rates for GSM Evolution and Constraint.
His Data mining study deals with Protein–protein interaction intersecting with Biological system, Protein protein interaction network and Degree.
His work deals with themes such as Discrete mathematics, Kernel and Statistics, Regression, Curse of dimensionality, which intersect with Set.
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