What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Sam Kwong mostly deals with Artificial intelligence, Algorithm, Mathematical optimization, Benchmark and Machine learning. His Artificial intelligence research includes themes of Speech recognition, Computer vision and Pattern recognition. His studies in Algorithm integrate themes in fields like Encoder and Integer dct.
He works mostly in the field of Mathematical optimization, limiting it down to concerns involving Convergence and, occasionally, Optimization problem and Particle swarm optimization. The Machine learning study combines topics in areas such as Classifier and Fuzzy set. The concepts of his Multi-objective optimization study are interwoven with issues in Evolutionary computation and Selection.
His most cited work include:
- Genetic algorithms and their applications (915 citations)
- Gbest-guided artificial bee colony algorithm for numerical function optimization (855 citations)
- Genetic algorithms: concepts and applications [in engineering design] (725 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Algorithm, Pattern recognition, Computer vision and Mathematical optimization. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Speech recognition. In Algorithm, Sam Kwong works on issues like Coding, which are connected to Video quality.
As a member of one scientific family, Sam Kwong mostly works in the field of Pattern recognition, focusing on Cluster analysis and, on occasion, Data mining. Mathematical optimization and Benchmark are frequently intertwined in his study. His research integrates issues of Convergence and Selection in his study of Multi-objective optimization.
He most often published in these fields:
- Artificial intelligence (45.08%)
- Algorithm (28.62%)
- Pattern recognition (19.50%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (45.08%)
- Pattern recognition (19.50%)
- Algorithm (28.62%)
In recent papers he was focusing on the following fields of study:
Sam Kwong spends much of his time researching Artificial intelligence, Pattern recognition, Algorithm, Computer vision and Image. The Classifier research Sam Kwong does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Domain, therefore creating a link between diverse domains of science. His Algorithm study combines topics in areas such as Point cloud, Random access, Distortion, Upsampling and Coding.
His Coding research is multidisciplinary, relying on both Encoder, Rate–distortion optimization and Data compression. His study in the fields of Light field and Stereoscopy under the domain of Computer vision overlaps with other disciplines such as Underwater. His research in Benchmark intersects with topics in Convergence and Optimization problem, Particle swarm optimization, Mathematical optimization.
Between 2018 and 2021, his most popular works were:
- An Underwater Image Enhancement Benchmark Dataset and Beyond (103 citations)
- Coevolutionary Particle Swarm Optimization With Bottleneck Objective Learning Strategy for Many-Objective Optimization (52 citations)
- Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement (50 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Algorithm, Benchmark, Pattern recognition and Mathematical optimization are his primary areas of study. He has researched Artificial intelligence in several fields, including Field and Computer vision. His study on Computational complexity theory is often connected to Differentiable function as part of broader study in Algorithm.
His Benchmark study incorporates themes from Evolutionary computation, Evolutionary algorithm and Differential evolution. His Pattern recognition study combines topics from a wide range of disciplines, such as Precision and recall, Hash function, Hash table and Image retrieval. His Multi-objective optimization, Particle swarm optimization and Memetic algorithm study in the realm of Mathematical optimization connects with subjects such as Domain and Test suite.
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