Dr. Khaw Khai Wah

Profile-Details

Position: Senior Lecturer

Academic Qualifications:

Ph.D., Universiti Sains Malaysia
BMgt. (Hons), Universiti Tun Hussein Onn Malaysia

Contacts:
Room: Cabin A-18
Phone: +604-6535941
Fax: +604-6577448
Email: khaiwah@usm.my

Biodata:

Dr. Khaw Khai Wah is a Senior Lecturer in the School of Management, Universiti Sains Malaysia. He holds a Ph.D. in statistical quality control from Universiti Sains Malaysia. He is a coordinator of the Business Analytics Program in the School of Management, USM. His areas of research are in advanced analytics and statistical quality/process control. He has featured in prominent international publications. His efforts and excellence have been acknowledged and awarded at several dignified platforms. He is actively involved in conducting training in statistics and visualization. Prior to his academic career, he worked in a renowned U.S. multinational company as a Data Analytics Team Leader.

Research

Research Area: 

Statistical Quality/Process Control, Advanced Analytics (Machine Learning and Deep Learning), Operations Research and Management

Research Project:

  1. A proposed variable parameters multivariate coefficient of variation chart integrating advanced data visualization platform for sustainable smart manufacturing, Sarawak Media Authority (SMA) 2018-2021

  2. Monitoring the coefficient of variation in short run processes using a variable parameter control chart, Short Terms Grant (STG), USM – 2019-2021

  3. A new hybrid model for monitoring the multivariate coefficient of variation in healthcare surveillance, Fundamental and Research Grant Scheme (FRGS) – 2019-2021

  4. New robust adaptive model for the coefficient of variation in infinite and finite horizon processes, Fundamental and Research Grant Scheme (FRGS) – 2019-2021

  5. Side-sensitive synthetic charts to monitor the coefficient of variation for univariate and multivariate processes, Sunway University Internal Grant – 2020

  6. A new adaptive kernel-distance-based control chart by machine learning techniques to support industry 4.0 sustainable smart manufacturing, Research University Grant (RUI) – 2020-2022

Publication


Selected Publications in Journals:

Chew, X. Y., Khaw, K. W., Lee, M. H. (2020). The efficiency of run rules schemes for the multivariate coefficient of variation in short runs process. Communication in Statistics – Simulation and Computation. [Q4 ISI-indexed] (In Press)

Chew, Y. Y., Khoo, M. B. C., Khaw, K. W., Yeong, W. C. (2020). Economic and economic statistical designs of variable sample size and sampling interval coefficient of variation chart. Communications in Statistics – Theory and Methods. [Q4 ISI-indexed] (In Press).

Yeong, W. C., Lim, S. L., Khoo, M. B. C., Khaw, K. W., Ng, P. S. (2020). An optimal design of the synthetic coefficient of variation chart based on the median run length. International Journal of Reliability, Quality and Safety Engineering. [Scopus-indexed] (In Press)

Chew, X.Y., Khaw, K.W., Yeong, W.C. (2020). The efficiency of run rules schemes for the multivariate coefficient of variation: a Markov chain approach. Journal of Applied Statistics, 47(3), 460-480. [Q3 ISI-indexed]

Chew, X. Y., Khaw, K. W. (2020). One-sided downward variable sample size and sampling interval control chart for monitoring the multivariate coefficient of variation. Journal of Mathematical and Fundamental Sciences, 52(1), 112-130. [Scopus-indexed]

Tan, R. Z., Chew, X. Y., Khaw, K. W. (2020). Quantized deep residual convolutional neural network for image-based dietary assessment. IEEE Access, 8, 111875-111888. [Q1 ISI-indexed]

Khaw, K.W., Chew, X.Y., Yeong, W.C., Lim, S.L. (2019). Optimal design of the synthetic control chart for monitoring the multivariate coefficient of variation. Chemometrics and Intelligent Laboratory Systems, 186, pp.33-40. [Q1 ISI-indexed]

Chew, X.Y., Khoo, M.B.C., Khaw, K.W., Yeong, W.C., Chong Z.L. (2019). A proposed variable parameter control chart for monitoring the multivariate coefficient of variation. Quality and Reliability Engineering International, 35(7), pp. 2442-2461. [Q3 ISI-indexed]

Lim, S.L., Yeong, W.C., Khoo, M.B.C., Chong, Z.L., Khaw, K.W. (2019). An alternative design for the variable sample size coefficient of variation chart based on the median run length and expected median run length. International Journal of Industrial Engineering, 26(2), pp. 199-220. [Q3-ISI-indexed]

Khaw, K.W., Chew, X.Y., Teh, S.Y., Yeong, W.C. (2019). Optimal variable sample size and sampling interval control chart for the process mean based on expected time to signal. International Journal of Machine Learning and Computing, 9(6), 880-885. [Scopus-indexed]

Khaw, K. W., Chew X. Y. (2019). A re-evaluation of the run rules control charts for monitoring the coefficient of variation. Statistics, Optimization & Information Computing, 7(4), 716-730. [Scopus-indexed]

Khaw, K. W., Khoo, M. B. C., Castagliola, P., Rahim, M. A. (2018). New adaptive control charts for monitoring the multivariate coefficient of variation. Computers & Industrial Engineering, 126, 595-610. [Q1 ISI-indexed]

Khaw, K. W., Khoo, M. B. C., Yeong, W. C., Wu, Z. (2017). Monitoring the coefficient of variation using variable sample size and sampling interval control chart. Communication in Statistics – Simulation and Computation, 46(7), 5772-5794. [Q4 ISI-indexed]

 

Supervision

Supervision:

Others


Others: