Muhammad Hisyam Lee

Muhammad Hisyam Lee
Universiti Teknologi Malaysia | UTM · Department of Mathematics

PhD

About

168
Publications
50,258
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2,561
Citations
Additional affiliations
January 1991 - May 2015
Universiti Teknologi Malaysia
Position
  • Professor Dr

Publications

Publications (168)
Article
An adaptive cumulative sum (CUSUM) control chart based on the classical exponential weighted moving average (EWMA) statistic and Huber’s function, symbolized as an ACUSUME control chart, is an enhanced form of the classical CUSUM control chart that can identify different sizes of shift. However, the classical EWMA statistic for the ACUSUME control...
Article
Analysis without adequate handling of missing values may lead to inconsistent and biased estimates. Despite multiple imputations becoming a widely used approach in handling missing data, manuscript researchers generally encounter missing data in their respective studies. In high-dimensional data, penalized regression is a popular technique for perf...
Article
Full-text available
In this study, we have proposed skewness correction-based various location control charts for monitoring process characteristics with unknown probability distribution. The location control charts include mean, median, and Hodges–Lehmann. For designing purposes, we have involved advanced skewness correction methods to relax from restricted assumptio...
Article
The effect of parameter estimation emanating from the retrospective stage on the monitoring stage of control charts cannot be overemphasized. These effects are born of but are not limited to the practitioner-to-practitioner variations in the amount and type of samples employed to estimate the process parameters. Another major factor to this effect...
Article
This article proposes efficient computational methods for designing and evaluating phases of Shewhart type control charts under runs rules. The efficient computational methods include exact equations or formulas for computing the probability of single-point and run-length properties of control charts. The run-length properties include average, vari...
Chapter
This research aims to propose hybrid machine learnings for forecasting and monitoring air pollution in Surabaya. In particular, we introduce two hybrid machine learnings, i.e. hybrid Time Series Regression – Feedforward Neural Network (TSR-FFNN) and hybrid Time Series Regression – Long Short-Term Memory (TSR-LSTM). TSR is used to capture linear pat...
Article
Full-text available
Stock market is found in many financial studies. Nonetheless, many of these literatures do not consider on the highly correlated stock market price. In particular, the studies on variable selection, grouping effects and robust dedicated to high dimension stock market price can be considered as scarce. Penalized linear regression using elastic net i...
Article
An adaptive CUSUM (ACUSUM) control chart got special attention against classical CUSUM control chart to detect a shift of different sizes in the process location. Similarly, an ACUSUM based on classical EWMA statistic and score function, denoted as a \({\text{ACUSUM}}_{{\text{E}}}\) control chart, is improved form of classical CUSUM control chart a...
Article
In this work, the combination between the Principal Component Analysis (PCA) and the Hotelling’s T² chart is proposed to solve problems caused by the many highly correlated network traffic features and to reduce the computational time without reducing its accuracy detection. However, a new issue arises due to the difficulty of the network traffic o...
Article
The removal of irrelevant and insignificant genes has always been a major step in microarray data analysis. The application of gene selection methods in biological datasets has greatly increased, supporting expert systems in cancer diagnostic capability with high classification accuracy. Penalized logistic regression (PLR) using the elastic net (EN...
Article
Lately, the multivariate setup of control charts, especially the memory‐less chart has received less attention of researchers as compared to the univariate setup. However, the multivariate setup is of paramount importance in this big‐data era. In this research work, we study the multivariate Shewhart chart for monitoring location parameter by exami...
Article
In this study, we have introduced a generalized Hotelling T ² control chart based on bivariate ranked set techniques with runs rules to identify small and moderate variations in a process mean vector. To achieve this aim, plotting statistic and control limits are formulated in generalized approaches. For evaluation purposes, power and power curves...
Article
High-dimensionality is one of the major problems which affect the quality of the classification and prediction modeling. Support vector regression has been applied in several real problems. However, it is usually needed to tune manually the hyperparameters.In addition, SVR cannot perform feature selection. Nature-inspired algorithms have been used...
Article
Full-text available
Continuous depletion in tin productions has led to a newly emerging industry that is a tin by-product (amang) processing industry to harness mega tons of tin by-products produced in the past. Amang composed of profitable multi-heavy minerals and rare-earth elements. With poorly established safety and health practices in operating plant, amang poses...
Article
In this article we have proposed multivariate cumulative sum control chart based on bivariate ranked set schemes for quick identification of small variation in the process mean vector. Also, we have offered multivariate measure of process capability based on bivariate ranked set schemes for testing the customer requirements. In the construction of...
Poster
Full-text available
1. The industry predominately engendered by extremely high radioactivity problem i.e, for 238 U and 232 Th decay nuclides could span up to 200,000 and 4,000,000 Bq per kg, respectively. 2. Exposure to 400,000 nGy h-1 of γ radiation which roughly 4000 times higher than the background rate in Peninsular Malaysia. 3. Continuous inhalation and inelucta...
Article
Full-text available
The need for a control chart that can visualize and recognize the symmetric or asymmetric pattern of the monitoring process with more than one type of quality characteristic is a necessity in the era of Industry 4.0. In the past, the control charts were only developed to monitor one kind of quality characteristic. Several control charts were create...
Article
High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity (property) relationship (QSAR/QSPR) classification methods in chemometrics. Applying variable selection is essential to improve the performance of the classification task. Variable selection is well-known as an NP-hard optimization probl...
Article
In this study, we have conducted comparative analysis between false alarm rate (FAR) and average run length (ARL) based control charts with runs rules. In this regard, we have considered various univariate and multivariate control charts which include mean, standard deviation, variance, Hotelling, and generalized variance. For evaluation purpose, w...
Conference Paper
Full-text available
It is noting that the response variable and the explanatory variables are highly correlated in high dimension data. Hence, the selection of informative variables is important in order to achieve a better model interpretation and concomitantly improve the accuracy of the prediction. In this study, the variable selection in stock market price using s...
Article
Full-text available
This study attempted to combine SSA (Singular Spectrum Analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (Two-Level Seasonal Autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (Two-Level Seasonal Neural...
Preprint
Full-text available
It is noting that the response variable and the explanatory variables are highly correlated in high dimension data. Hence, the selection of informative variables is important in order to achieve a better model interpretation and concomitantly improve the accuracy of the prediction. In this study, the variable selection in stock market price using s...
Article
High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has...
Article
One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized me...
Article
Full-text available
Hybrid methodologies have become popular in many fields of research as they allow researchers to explore various methods, understand their strengths and weaknesses and combine them into new frameworks. Thus, the combination of different methods into a hybrid methodology allows to overcome the shortcomings of each singular method. This paper present...
Article
Full-text available
Shewhart control charts with estimated control limits are widely used in practice. However, the estimated control limits are often affected by phase-I estimation errors. These estimation errors arise due to variation in the practitioner’s choice of sample size as well as the presence of outlying errors in phase-I. The unnecessary variation, due to...
Article
Full-text available
span lang="EN-US">This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing value imputations methods used known...
Article
In this article, we have highlighted limitations of existing structures of X¯ control chart for unknown parameters by considering various circumstances of a process. The circumstances include availability of limited samples for estimating control limits, probability distribution is unknown and collected data are highly skewed. To tackle with the li...
Article
Memory control chart such as multivariate CUSUM (MCUSUM) and multivariate EWMA (MEWMA) control charts are considered superior for the detection of small-to-moderate variation in the process mean vector. In this article, we have proposed two advanced forms of memory multivariate charts to identify the small amount of shifts in the process mean vecto...
Article
Full-text available
Total hip replacement (THR) is the most popular surgery been performed in orthopedic surgery due to the inclination of musculoskeletal disorder and the aging population worldwide. However, the implant's cost-burdened the patient, especially in the ASEAN region. The main objective of this study was to fabricate the low-cost hip implant using direct...
Article
The utilization of conventional multivariate control chart in network intrusion detection will deal with two main problems. First, the high false alarm occurs due to the distribution of network traffic data that is not following the theory. Second, the inability of the control chart to detect outliers caused by the masking effect. To overcome these...
Article
Electricity plays a key role in human life. This study presents several methods to forecast Indonesian electricity load demand and compares the performance of the methods. The Indonesian hourly and half-hourly load series tend to have multiple seasonal patterns. Singular Spectrum Analysis (SSA) is chosen because of its capability in decomposing the...
Chapter
The objective of this research is to propose new hybrid model by combining Time Series Regression (TSR) as statistical method and Feedforward Neural Network (FFNN) or Long Short-Term Memory (LSTM) as machine learning for PM10 prediction at three SUF stations in Surabaya City, Indonesia. TSR as an individual linear model is used to capture trend and...
Article
Full-text available
Microplastics are plastic particles less than 5 mm and have been classified as contaminants of emerging concern. In recent years, the ubiquity of microplastics has caused a serious threat to aquatic animals worldwide. Over the past decade, the ingestion of microplastics has been extensively reported in various marine animals. However, studies on in...
Article
Full-text available
The wind speed forecasting is important to observe the wind behaviour and control the harms caused by extreme speeds. A linear ARIMA model is unable to identify the nonlinear pattern of wind speed data. ARIMA modelling process causes the stochastic uncertainty as a second reason of inaccurate forecasting results. In this study, a review of an ARIMA...
Conference Paper
Full-text available
Error magnitude is a measurement commonly used in forecast evaluation. However, the purpose of forecasting air quality is to maintain the air quality within assigned guidelines. Thus, the index measurement is important to be considered. But, the problem arises when the index is used to gauge the values of different offices and these measurements ar...
Chapter
The cumulative sum (CUSUM) control charts are widely used for the monitoring of normal processes for changes in the location and dispersion parameters. This study presents several CUSUM charts designed structures based on the ranked set sampling (RSS) data for overall efficient detection of changes in the process mean and variance. The run-length p...
Article
Full-text available
The fast-growing urbanization has contributed to the construction sector becoming one of the major sectors traded in the world stock market. In general, non-stationarity is highly related to most of the stock market price pattern. Even though stationarity transformation is a common approach, yet this may prompt to originality loss of the data. Henc...
Article
Full-text available
Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, μ, is added in the origi...
Article
We propose a combined method that is based on the fuzzy time series (FTS) and convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, 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 mult...
Article
Full-text available
The study of SSA-based forecasting model is always interesting due to its capability in modeling trend and multiple seasonal time series. The aim of this study is to propose an iterative ordinary least square (OLS) for estimating the oscillatory with time-varying amplitude model that usually found in SSA decomposition. We compare the results with t...
Article
Full-text available
An improved binary differential search (improved BDS) algorithm is proposed for QSAR classification of diverse series of antimicrobial compounds against Candida albicans inhibitors. The transfer functions is the most important component of the BDS algorithm, and converts continuous values of the donor into discrete values. In this paper, the eight...
Chapter
This study proposes hybrid methods by combining Singular Spectrum Analysis and Neural Network (SSA-NN) to forecast the currency circulation in the community, i.e. inflow and outflow. The SSA technique is applied to decompose and reconstruct the time series factors which including trend, cyclic, and seasonal into several additive components, i.e. tr...
Chapter
Multivariate time series modeling is quite challenging particularly in term of diagnostic checking for assumptions required by the underlying model. For that reason, nonparametric approach is rapidly developed to overcome that problem. But, feature selection to choose relevant input becomes new issue in nonparametric approach. Moreover, if the mult...
Article
Full-text available
Maximum multivariate cumulative sum (Max-MCUSUM) is one of the single control charts proposed for joint monitoring the mean and variability of independent observation. Since many applications yield time series data, it is important to develop Max-MCUSUM control chart for monitoring multivariate autocorrelated processes. In this paper, we propose a...
Article
Full-text available
SSA (Singular Spectrum Analysis) starts to become a popular method in decomposing time series into some separable and interpretable series. This study provides an error evaluation in the SSA-based model for trend and multiple seasonal time series forecasting. This error evaluation is obtained by means of a numerical study on the mean square error o...
Article
Full-text available
Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SS...
Article
In this study, we have considered two design structures of control chart by covering the situations of known and unknown parameters, variety of probability distributions, and runs rules. The design structures are dependent on constants which generally considered hard to compute analytically. For construction of constants and also for evaluating per...
Article
Full-text available
The majority of stock market price is nonstationary, while only few have stationary pattern. It is noted that past researches usually transformed the stock market price into stationary prior to analysis which may lead to the loss of data originality. Thus, a direct application of the nonstationary stock market price is of main interest in this stud...
Article
Full-text available
Some problems arise in time series analysis are nonlinearity and heteroscedasticity. Methods that can be used to analyze such problems are neural network and quantile regression. There are a lot of studies and developments on both methods, but the study that focuses on the performances of combination of these two methods applied in real case are st...
Article
The common issues of high-dimensional gene expression data are that many of the genes may not be relevant, and there exists a high correlation among genes. Gene selection has been proven to be an effective way to improve the results of many classification methods. Sparse logistic regression using least absolute shrinkage and selection operator (las...
Article
Full-text available
Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spa...
Article
Full-text available
A penalized quantitative structure–property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ( ) as an initi...
Article
Full-text available
Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling...
Article
Plant extracts as corrosion inhibitors have been extensively investigated and are found as an alternative to synthetic organic compounds. The corrosion inhibition of mild steel in 1 M HCl by 15 compounds comprising of five phenylpropanoids from Alpinia galanga and other related compounds was explored experimentally using potentiodynamic polarisatio...
Article
Full-text available
The examination of product characteristics using a statistical tool is an important step in a manufacturing environment to ensure product quality. Several methods are employed for maintaining product quality assurance. Quality control charts, which utilize statistical methods, are normally used to detect special causes. Shewhart control charts are...
Article
Full-text available
A robust screening approach and a sparse quantitative structure–retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-S...
Article
Molecular descriptor selection is a pivotal tool for quantitative structure–activity relationship modeling. This paper proposes a novel molecular descriptor selection method on the basis of taking into account the information of the group type that the descriptor belongs to. This descriptor selection method is on the basis of combining penalized lo...