What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Data mining, Pattern recognition, Rough set and Feature selection.
His Artificial intelligence study incorporates themes from Algorithm and Machine learning.
His study in Data mining is interdisciplinary in nature, drawing from both Fuzzy set, Preprocessor, Mutual information, Reduction and Pattern recognition.
Augmented Lagrangian method and Data point is closely connected to Cluster analysis in his research, which is encompassed under the umbrella topic of Pattern recognition.
His Rough set research incorporates themes from Fuzzy number, Fuzzy set operations, Fuzzy classification and Fuzzy logic.
The study incorporates disciplines such as Feature, Feature, k-nearest neighbors algorithm, Categorical variable and Robustness in addition to Feature selection.
His most cited work include:
- Neighborhood rough set based heterogeneous feature subset selection (558 citations)
- Information-preserving hybrid data reduction based on fuzzy-rough techniques (340 citations)
- Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (333 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Data mining, Rough set and Machine learning.
As part of the same scientific family, Qinghua Hu usually focuses on Artificial intelligence, concentrating on Computer vision and intersecting with Drone.
His research in Pattern recognition intersects with topics in Regularization and Cluster analysis.
His Data mining research includes elements of Defuzzification, Mutual information, Reduction and Pattern recognition.
His work focuses on many connections between Rough set and other disciplines, such as Fuzzy classification, that overlap with his field of interest in Membership function.
His work on Feature vector, Ensemble learning and Margin as part of general Machine learning research is frequently linked to Metric, bridging the gap between disciplines.
He most often published in these fields:
- Artificial intelligence (54.03%)
- Pattern recognition (32.52%)
- Data mining (20.54%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (54.03%)
- Pattern recognition (32.52%)
- Computer vision (6.36%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Drone and Convolutional neural network.
His work in Artificial intelligence addresses subjects such as Machine learning, which are connected to disciplines such as Pattern recognition.
His Pattern recognition study combines topics in areas such as Regularization, Pascal and Feature.
In his study, which falls under the umbrella issue of Regularization, Feature selection is strongly linked to Tree structure.
He interconnects Video tracking, Object and Crowd counting in the investigation of issues within Drone.
His Convolutional neural network research is multidisciplinary, incorporating elements of Kernel, Covariance, Pooling and Face.
Between 2018 and 2021, his most popular works were:
- Generalized Latent Multi-View Subspace Clustering (137 citations)
- Progressive Image Deraining Networks: A Better and Simpler Baseline (130 citations)
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (123 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Pattern recognition, Object detection, Convolutional neural network and Theoretical computer science.
His Artificial intelligence study frequently links to other fields, such as Computer vision.
The various areas that he examines in his Pattern recognition study include Artificial neural network, Matrix, Robustness and Regularization.
His work deals with themes such as MNIST database, Data mining and Deep neural networks, which intersect with Robustness.
Qinghua Hu has researched Theoretical computer science in several fields, including Representation and Graph.
Qinghua Hu works mostly in the field of Graph, limiting it down to topics relating to Graph and, in certain cases, Feature selection, as a part of the same area of interest.
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