Chu Kiong Loo

University of Malaya

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i-10 index

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Co-Authors

Mitja Peruš

University of Ljubljana

Manjeevan Seera

Monash University Malaysia

Naoki Masuyama

Osaka Metropolitan University

Naoyuki Kubota

Tokyo Metropolitan University

Wei Shiung Liew

University of Malaya

Kitsuchart Pasupa

King Mongkut's Institute of Technology Ladkrabang

Wai Kit Wong

Multimedia University

Stefan Wermter

Universität Hamburg

Takenori Obo

Tokyo Metropolitan University

Way Soong Lim

Multimedia University

Cooperative Institutions

University of Malaya

Multimedia University

Tokyo Metropolitan University

UNSW Sydney

Hohai University

Information Technology University

United Arab Emirates University

Universiti Tenaga Nasional

Monash University Malaysia

King Mongkut's Institute of Technology Ladkrabang

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Intelligent control of a robot manipulator using active force control with iterative learning

International Conference on Mechatronics and Machine Vision in Practice (2001)

Lee Chung Kwek Chu Kiong Loo Chee Peng Lim Musa Mailah

Iterative Learning Control

Robot manipulator

Manipulator (device)

Intelligent Control

Source

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Citations (0)

Divisive Hierarchical Clustering Based on Adaptive Resonance Theory

Yuna Yamada Naoki Masuyama Narito Amako Yusuke Nojima Chu Kiong Loo

Divisive hierarchical clustering is a powerful tool for extracting knowledge from data with a pluralistic and appropriate information granularity. Recent developments of hierarchical clustering algorithms apply Growing Neural Gas (GNG) to data divisive mechanisms. However, GNG-based algorithms tend to generate nodes excessively and sensitive to the input order of data points. Furthermore, the plasticity-stability dilemma is another unavoidable problem. In this paper, we propose a divisive hierarchical clustering algorithm based on Adaptive Resonance Theory-based clustering. Simulation experiments show that the proposed algorithm can generate an appropriate tree structure depending on data while improving the performance of hierarchical clustering.

Adaptive resonance theory

Hierarchical clustering

Brown clustering

Granularity

Tree (set theory)

10.1109/ccs49175.2020.9231474

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Citations (11)

Weighted error-output recurrent Xavier echo state network for concept drift handling in water level prediction

Applied Soft Computing (2024)

Zongying Liu Wenru Zhang Mingyang Pan Chu Kiong Loo Kitsuchart Pasupa

Echo state network

Echo (communications protocol)

Concept Drift

10.1016/j.asoc.2024.112055

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Citations (0)

CiRA CORE: A Low Code Platform that Makes AI Work for Industry 4.0

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2024)

Chu Kiong Loo Siridech Boonsang Thanyathep Sasisaowapak Santhad Chuwongin Teerawat Tongloy

Code (set theory)

10.1109/smc54092.2024.10831625

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Citations (0)

Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

IEEE Access (2018)

Habeebah Adamu Kakudi Chu Kiong Loo Foong Ming Moy Naoki Masuyama Kitsuchart Pasupa

Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called "genetically optimized Bayesian adaptive resonance theory mapping" (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS.

Abnormality

10.1109/access.2018.2880224

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Citations (4)

Multi-objective genetic algorithm as channel selection method for P300 and motor imagery data set

Neurocomputing (2015)

Chea-Yau Kee S. G. Ponnambalam Chu Kiong Loo

Motor Imagery

Interface (matter)

Implementation

10.1016/j.neucom.2015.02.057

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Citations (81)

Development of Fast Incremental Slow Feature Analysis (F-IncSFA)

2022 International Joint Conference on Neural Networks (IJCNN) (2012)

Bardia Yousefi Chu Kiong Loo

The proposed Fast Incremental Slow Feature Analysis (F-IncSFA) which is considered as unsupervised learning and it can be used for extracting the features. The featurescan represent the fundamental components of the modifications in different aspect and especially in posing and temporally firms and consistent even in high-dimensional input like signal, video, etc. Here, we addressed a development in SFA algorithm as compare with latest one [17] by combining Candid Covariance-Free Incremental Principle components Analysis (CCIPCA) and Minor Components Analysis (MCA).The proposed F-IncSFA can adapts along with non-stationary environments and unlike the latest SFA, which has two times using CCIPCA, has one time using CCIPCA in its algorithm which makes the method simpler yet efficient. We examine the proposed approach by using some video sequences of humanoid robot and also it is compared with CCIPCA in several experiments and the result indicates that it indeed has superior outcome and impart informative slow features that is representing significant abstract from possessions of non-stationary environment and poses. We successfully apply the F-IncSFA on the high-dimensional video and extract abstract object data. We extend our F-IncSFA to networks in hierarchical model, and apply it for extraction of features in the information obtained from high-dimensional video and the results were promising.

Feature (linguistics)

10.1109/ijcnn.2012.6252847

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Citations (3)

CowXNet: An automated cow estrus detection system

Expert Systems with Applications (2022)

Thanawat Lodkaew Kitsuchart Pasupa Chu Kiong Loo

10.1016/j.eswa.2022.118550

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Citations (36)

Defining A Conceptual Framework for Vibration-Based Damage Detection Platforms Using Blockchain

Zenodo (CERN European Organization for Nuclear Research) (2021)