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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Fuzzy FMEA with a guided rules reduction system for prioritization of failures
Kai Meng Tay;Chee Peng Lim.
International Journal of Quality & Reliability Management (2006)
A hybrid intelligent system for medical data classification
Manjeevan Seera;Chee Peng Lim.
Expert Systems With Applications (2014)
A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition
Kamlesh Mistry;Li Zhang;Siew Chin Neoh;Chee Peng Lim.
IEEE Transactions on Systems, Man, and Cybernetics (2017)
Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model
M. Seera;Chee Peng Lim;D. Ishak;H. Singh.
IEEE Transactions on Neural Networks (2012)
Synchronization of an Inertial Neural Network With Time-Varying Delays and Its Application to Secure Communication
Shanmugam Lakshmanan;Mani Prakash;Chee Peng Lim;Rajan Rakkiyappan.
IEEE Transactions on Neural Networks (2018)
A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification
Anas Quteishat;Chee Peng Lim.
soft computing (2008)
Credit Card Fraud Detection Using AdaBoost and Majority Voting
Kuldeep Randhawa;Chu Kiong Loo;Manjeevan Seera;Chee Peng Lim.
IEEE Access (2018)
An incremental adaptive network for on-line supervised learning and probability estimation
Chee Peng Lim;Robert F. Harrison.
Neural Networks (1997)
Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models
Manjeevan Seera;Chee Peng Lim;Saeid Nahavandi;Chu Kiong Loo.
Expert Systems With Applications (2014)
A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification
Anas Quteishat;Chee Peng Lim;Kay Sin Tan.
systems man and cybernetics (2010)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: