What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Sung-Kwun Oh focuses on Fuzzy logic, Artificial intelligence, Artificial neural network, Fuzzy set and Cluster analysis.
Sung-Kwun Oh has researched Fuzzy logic in several fields, including Genetic algorithm, Group method of data handling and Control theory, Control theory.
In general Artificial intelligence, his work in Neuro-fuzzy is often linked to Process linking many areas of study.
His Neuro-fuzzy research includes elements of Fuzzy set operations, Fuzzy classification and Adaptive neuro fuzzy inference system.
The study incorporates disciplines such as Computational intelligence, Fuzzy control system, Synthetic data and Fuzzy clustering in addition to Artificial neural network.
His research integrates issues of Particle swarm optimization, Data mining and Nonlinear system in his study of Cluster analysis.
His most cited work include:
- The design of self-organizing polynomial neural networks (154 citations)
- Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems (151 citations)
- Polynomial neural networks architecture: analysis and design (139 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Fuzzy logic, Neuro-fuzzy, Artificial neural network and Pattern recognition.
Sung-Kwun Oh interconnects Group method of data handling and Machine learning in the investigation of issues within Artificial intelligence.
Sung-Kwun Oh has included themes like Genetic algorithm, Algorithm and Cluster analysis in his Fuzzy logic study.
His work investigates the relationship between Cluster analysis and topics such as Data mining that intersect with problems in Fuzzy clustering.
His work deals with themes such as Fuzzy number, Fuzzy set operations, Fuzzy classification and Adaptive neuro fuzzy inference system, which intersect with Neuro-fuzzy.
His Artificial neural network study combines topics in areas such as Computational intelligence and Hybrid system.
He most often published in these fields:
- Artificial intelligence (63.11%)
- Fuzzy logic (59.84%)
- Neuro-fuzzy (28.14%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (63.11%)
- Pattern recognition (27.87%)
- Classifier (17.49%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Classifier, Fuzzy logic and Cluster analysis.
His Artificial intelligence study frequently links to adjacent areas such as Computer vision.
His work on Principal component analysis as part of general Pattern recognition research is frequently linked to Design methods, thereby connecting diverse disciplines of science.
His Classifier research incorporates themes from Partial discharge, Machine learning, Computational intelligence and Softmax function.
A large part of his Fuzzy logic studies is devoted to Fuzzy clustering.
His Cluster analysis research includes themes of Evolutionary algorithm, Neuro-fuzzy, Support vector machine and Radial basis function.
Between 2014 and 2021, his most popular works were:
- Fuzzy Wavelet Polynomial Neural Networks: Analysis and Design (29 citations)
- Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons (24 citations)
- Optimized face recognition algorithm using radial basis function neural networks and its practical applications (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary scientific interests are in Artificial intelligence, Fuzzy logic, Pattern recognition, Cluster analysis and Classifier.
Artificial intelligence and Data mining are frequently intertwined in his study.
His Fuzzy logic study combines topics from a wide range of disciplines, such as Algorithm and Particle swarm optimization.
When carried out as part of a general Pattern recognition research project, his work on Principal component analysis, Linear discriminant analysis and Feature extraction is frequently linked to work in Fourier transform, therefore connecting diverse disciplines of study.
As part of one scientific family, Sung-Kwun Oh deals mainly with the area of Neuro-fuzzy, narrowing it down to issues related to the Adaptive neuro fuzzy inference system, and often Fuzzy rule, Inference and Fuzzy number.
While the research belongs to areas of Fuzzy classification, he spends his time largely on the problem of Fuzzy set operations, intersecting his research to questions surrounding Membership function, Defuzzification and Key.
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