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Chee Peng Lim
Researcher at Deakin University
Publications - 439
Citations - 10238
Chee Peng Lim is an academic researcher from Deakin University. The author has contributed to research in topics: Artificial neural network & Fuzzy logic. The author has an hindex of 45, co-authored 409 publications receiving 7432 citations. Previous affiliations of Chee Peng Lim include Universiti Sains Malaysia & University of Sheffield.
Papers
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A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition
TL;DR: The empirical results indicate that the proposed mGA-embedded PSO variant outperforms other state-of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.
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A hybrid intelligent system for medical data classification
Manjeevan Seera,Chee Peng Lim +1 more
TL;DR: A hybrid intelligent system that consists of the Fuzzy Min-Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined.
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Credit Card Fraud Detection Using AdaBoost and Majority Voting
TL;DR: Machine learning algorithms are used to detect credit card fraud and results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.
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Synchronization of an Inertial Neural Network With Time-Varying Delays and Its Application to Secure Communication
Shanmugam Lakshmanan,M. Prakash,Chee Peng Lim,Rajan Rakkiyappan,Pagavathigounder Balasubramaniam,Saeid Nahavandi +5 more
TL;DR: The results ascertain that the proposed encryption algorithm based on the piecewise linear chaotic map and the chaotic inertial neural network is efficient and reliable for secure communication applications.
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Fuzzy FMEA with a guided rules reduction system for prioritization of failures
Kai Meng Tay,Chee Peng Lim +1 more
TL;DR: A guided rules reduction system (GRRS) is proposed to regulate the number of rules required during the fuzzy RPN modeling process and the effectiveness of the proposed GRRS is investigated using three real‐world case studies in a semiconductor manufacturing process.