What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
The scientist’s investigation covers issues in Artificial intelligence, Artificial neural network, Fuzzy logic, Machine learning and Pattern recognition.
The concepts of his Artificial intelligence study are interwoven with issues in Particle swarm optimization and Premature convergence.
In general Artificial neural network study, his work on Time delay neural network, Probabilistic neural network, Hybrid neural network and Supervised learning often relates to the realm of Dissolution testing, thereby connecting several areas of interest.
The concepts of his Fuzzy logic study are interwoven with issues in Decision tree, Data mining, Data classification, Fault detection and isolation and Failure mode and effects analysis.
His work deals with themes such as Keystroke dynamics and Condition monitoring, which intersect with Machine learning.
His Pattern recognition research is multidisciplinary, incorporating elements of Image processing, Fuzzy set, Typing and Medical diagnosis.
His most cited work include:
- Fuzzy FMEA with a guided rules reduction system for prioritization of failures (173 citations)
- A hybrid intelligent system for medical data classification (156 citations)
- A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition (141 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Artificial neural network, Fuzzy logic, Machine learning and Data mining.
His work is dedicated to discovering how Artificial intelligence, Pattern recognition are connected with Particle swarm optimization and other disciplines.
His Artificial neural network study which covers Fault detection and isolation that intersects with Induction motor.
His Fuzzy logic research is multidisciplinary, incorporating perspectives in Genetic algorithm, Mathematical optimization, Failure mode and effects analysis and Monotonic function.
Chee Peng Lim has researched Machine learning in several fields, including Neuro-fuzzy, Decision support system and Data set.
His research integrates issues of Fuzzy set operations and Fuzzy classification in his study of Data mining.
He most often published in these fields:
- Artificial intelligence (54.07%)
- Artificial neural network (40.99%)
- Fuzzy logic (34.81%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial neural network (40.99%)
- Artificial intelligence (54.07%)
- Fuzzy logic (34.81%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial neural network, Artificial intelligence, Fuzzy logic, Applied mathematics and Pattern recognition.
His Artificial neural network research integrates issues from Stability, Control theory and Exponential stability.
He has included themes like Machine learning and Particle swarm optimization in his Artificial intelligence study.
His work on Supervised learning as part of general Machine learning research is frequently linked to Zero shot learning, bridging the gap between disciplines.
The various areas that Chee Peng Lim examines in his Fuzzy logic study include Motion and Interval.
His Pattern recognition research includes elements of Outlier and Cluster analysis.
Between 2018 and 2021, his most popular works were:
- Evolving Ensemble Models for Image Segmentation Using Enhanced Particle Swarm Optimization (35 citations)
- Feature selection based on brain storm optimization for data classification (31 citations)
- An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Artificial intelligence, Artificial neural network, Pattern recognition, Feature selection and Particle swarm optimization are his primary areas of study.
His study in Data classification, Fuzzy logic, Benchmark, Reinforcement learning and Local optimum are all subfields of Artificial intelligence.
His biological study spans a wide range of topics, including Incremental learning, Genetic algorithm, Machine learning and Swarm intelligence.
The study incorporates disciplines such as Stability, Exponential stability and Applied mathematics in addition to Artificial neural network.
His Pattern recognition study combines topics in areas such as Outlier, Cluster analysis and Medical imaging.
Chee Peng Lim combines subjects such as Feature and Curse of dimensionality with his study of Feature selection.
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