M
Muhammad Hisyam Lee
Researcher at Universiti Teknologi Malaysia
Publications - 152
Citations - 2820
Muhammad Hisyam Lee is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Control chart & Time series. The author has an hindex of 25, co-authored 147 publications receiving 2132 citations. Previous affiliations of Muhammad Hisyam Lee include Mathematical Sciences Research Institute.
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Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA
TL;DR: In this article, an artificial neural network (ANN) and Kalman filter (KF) were used to handle nonlinearity and uncertainty problems in wind speed forecasting in order to improve the accuracy of wind power generation.
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Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
Hossein Javedani Sadaei,Petrônio Cândido de Lima e Silva,Frederico Gadelha Guimarães,Muhammad Hisyam Lee +3 more
TL;DR: In the proposed method, multivariate time series data which include hourly load data, hourly temperature time series and fuzzified version of load time series, was converted into multi-channel images to be fed to a proposed deep learning CNN model with proper architecture.
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Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification
TL;DR: The proposed CBPLR has significant impact in penalized logistic regression by selecting fewer genes with high area under the curve and low misclassification rate, which could conceivably be used in other research that implements gene selection in the field of high dimensional cancer classification.
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Mixed CUSUM-EWMA chart for monitoring process dispersion
TL;DR: A new control chart named as mixed CUSUM-EWMA (called MCE) control chart is proposed for the efficient monitoring of process dispersion and is compared with other existing control charts and some of their modifications.
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Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
TL;DR: In this article, adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously, the real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods.