What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Programming language
His primary areas of study are Artificial intelligence, Pattern recognition, Parallel computing, CAD and Population.
His research ties Computer vision and Artificial intelligence together.
His work carried out in the field of Pattern recognition brings together such families of science as Image and BitTorrent tracker.
His research integrates issues of Algorithm, Optimization problem, Ant colony optimization algorithms and General-purpose computing on graphics processing units in his study of Parallel computing.
In his study, which falls under the umbrella issue of CAD, User interface and Collaborative software is strongly linked to Human–computer interaction.
His Mixture model research includes themes of Data mining and Feature.
His most cited work include:
- Service-Oriented Feature-Based Data Exchange for Cloud-Based Design and Manufacturing (103 citations)
- Dynamic strategy based parallel ant colony optimization on GPUs for TSPs (86 citations)
- Quantitative optimization of interoperability during feature-based data exchange (73 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, CAD, Computer vision, Consistency and Feature.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
The concepts of his Pattern recognition study are interwoven with issues in Video tracking, BitTorrent tracker and Computer graphics.
His study in CAD is interdisciplinary in nature, drawing from both Software engineering, Computer Aided Design, Cloud computing and Distributed computing.
His Consistency research also works with subjects such as
- Undo which is related to area like State, Database, Solid modeling, Correctness and Collaborative software,
- Structure, which have a strong connection to Tree.
Fazhi He usually deals with Feature and limits it to topics linked to Data mining and Data exchange and Cad system.
He most often published in these fields:
- Artificial intelligence (31.93%)
- CAD (17.65%)
- Computer vision (15.97%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (31.93%)
- Computer vision (15.97%)
- Pattern recognition (11.76%)
In recent papers he was focusing on the following fields of study:
Fazhi He mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Feature.
His research on Artificial intelligence frequently links to adjacent areas such as Machine learning.
His work in Image segmentation and Pixel are all subfields of Computer vision research.
His research investigates the connection with Pattern recognition and areas like Computer graphics which intersect with concerns in Residual and Process.
He combines subjects such as Software, Hardware software, Cluster analysis and Benchmark with his study of Algorithm.
As a member of one scientific family, Fazhi He mostly works in the field of Feature, focusing on Differential evolution and, on occasion, Optimization algorithm, Data mining and Dual.
Between 2018 and 2021, his most popular works were:
- A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation (52 citations)
- A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning (51 citations)
- DRCDN: learning deep residual convolutional dehazing networks (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Programming language
Fazhi He mostly deals with Artificial intelligence, Computer vision, Machine learning, Recommender system and Autoencoder.
Fazhi He has researched Artificial intelligence in several fields, including Residual and Pattern recognition.
He interconnects Process, Subnetwork, Task, Computer graphics and Image in the investigation of issues within Pattern recognition.
His Image segmentation, Segmentation, Pixel and Feature study are his primary interests in Computer vision.
In general Machine learning study, his work on Artificial neural network often relates to the realm of Matrix decomposition, Social influence, Social media and Learning to rank, thereby connecting several areas of interest.
His Recommender system research is multidisciplinary, incorporating perspectives in Structure, Layer, Noise and Set.
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