Application of statistical and neural network model for oil palm yield study

This thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. This study began by exploring the commonly used statistical models for plant growth such as nonlinear growth model, multiple linear regression models and robust M regression model. Data used were OP yield growth data, foliar composition data and fertiliser treatments data, collected from seven stations in the inland and coastal areas provided by Malaysian Palm Oil Board (MPOB). Twelve nonlinear growth models were used. Initial study shows that logistic growth model gave the best fit for modelling OP yield. This study then explores the causality relationship between OP yield and foliar composition and the effect of nutrient balance ratio to OP yield. In improving the model, this study explores the use of neural network. The architecture of the neural network such as the combination activation functions, the learning rate, the number of hidden nodes, the momentum terms, the number of runs and outliers data on the neural network’s performance were also studied. Comparative studies between various models were carried out. The response surface analysis was used to determine the optimum combination of fertiliser in order to maximise OP yield. Saddle points occurred in the analysis and ridge analysis technique was used to overcome the saddle point problem with several alternative combinations fertiliser levels considered. Finally, profit analysis was performed to select and identify the fertiliser combination that may generate maximum yiel

On Robust Environmental Quality Indices

This paper discusses a formulation of new environmental quality indices, which can be used for monitoring environmental as well as meteorological parameters. The formulation of the indices is based on conventional and robust principal component analysis, which gives the linear combination of environmental parameters. Comparisons are made between the conventional principal component analysis (PCA) indices and robust principal component analysis (RPCA) indices. The results show that the RPCA gave a better alternative linear combination. A numerical example on air quality was used to illustrate the application of the robust environmental indices

An investigation on benefits and future expectation of Industrialised Building System (IBS) implementation in construction practices

Industrialised Building System (IBS) is well known in many developing countries due to the benefits that can be derived from its applications in construction projects. However, the low percentage of IBS usage may be due to lack of awareness and knowledge about IBS among many professionals. There may be factors that contribute to a lack of interest from the client towards IBS. The aim of this study is to improve the application of IBS particularly in private construction projects in Malaysia by determining the benefits and expectation on application of IBS in private construction projects. This study adopts a quantitative method using questionnaires that were sent to 35 construction firms as a sampling frame. Finally, the finding of this study hopefully could assist professional parties in construction industry in providing a better ground knowledge for improving decisions making to achieve the success of IBS construction projects implementation and also this study will achieved the project objectives in terms of predetermined objectives that are mostly within the time, specified budget and standard qualit

ARIMA and VAR Modeling to Forecast Malaysian Economic Growth

This study presents a comparative study on univariate time series via Autoregressive Integrated Moving Average (ARIMA) model and multivariate time series via Vector Autoregressive (VAR) model in forecasting economic growth in Malaysia. This study used monthly economic indicators price from January 1998 to January 2016 and the economic indicators used to measure the economic growth are Currency in Circulation, Exchange Rate, External Reserve and Reserve Money. The aim is to evaluate a VAR and ARIMA model to forecast economic growth and to suggest the best time series model from existing model for forecasting economic growth in Malaysia. The forecast performances of these models were evaluated based on out-of-sample forecast procedure using an error measurement, Mean Absolute Percentage Error (MAPE). Results revealed that VAR model outperform ARIMA model in predicting the economic growth in term of lowest forecasting accuracy measurement

A robust vector autoregressive model for forecasting economic growth in Malaysia

Economic indicator measures how solid or strong an economy of a country is. Basically, economic growth can be measured by using the economic indicators as they give an account of the quality or shortcoming of an economy. Vector Auto-regressive (VAR) method is commonly useful in forecasting the economic growth involving a bounteous of economic indicators. However, problems arise when its parameters are estimated using least square method which is very sensitive to the outliers existence. Thus, the aim of this study is to propose the best method in dealing with the outliers data so that the forecasting result is not biased. Data used in this study are the economic indicators monthly basis starting from January 1998 to January 2016. Two methods are considered, which are filtering technique via least median square (LMS), least trimmed square (LTS), least quartile difference (LQD) and imputation technique via mean and median. Using the mean absolute percentage error (MAPE) as the forecasting performance measure, this study concludes that Robust VAR with LQD filtering is a more appropriate model compare to others model

Fuzzy Entire Sequence Spaces

We first investigate the notion of fuzzy entire sequence space with a suitable example. Also we deal with the properties of the space of fuzzy entire sequences. The concepts of subset and superset of the fuzzy entire sequence spaces are introduced and their properties are discussed

Experimental validation of a theoretical model for flexural modulus of elasticity of thin cement composite

Experimental and analytical investigations for the modulus of elasticity of thin cement composite composed of mesh and mortar are demonstrated. Based on the analyses and experimental data, new equations for the modulus of elasticity of thin cement composite are proposed. It is observed that the flexural modulus of elasticity of thin cement composite depends on the elastic modulus of mortar and some factor of the difference of elastic modulus of mesh and mortar. Results obtained by using the proposed equations are compared to those of the available equations. It has been found that the newly developed equations give relatively conservative results as compared to the typically used ones. A comparison between the analytical and experimental findings further indicates that there is a good agreement between the analytical and experimental result

An Analysis of the Prevalence of Pneumonia for Children under 12 Year Old in Tawau General Hospital, Malaysia

Pneumonia is one of the serious illnesses, which involves lung infection specifically alveoli. Nearly 40,000 to 70,000 people die each year in United State because of pneumonia. Therefore, it is not a surprise that pneumonia is one of the most critical illnesses for children under 12 years old in many parts of the world, including Malaysia and particularly in Tawau, Sabah, Malaysia. The objectives of this study are: to develop a summary on the prevalence of pneumonia in Tawau General Hospital, to analyze the best practice to prevent this illness and lastly to determine an overview of which area that is widely affected by pneumonia. The results can assist doctors and the government to take major precautions and preventive measures efficiently to the full extent. This paper presents a descriptive analysis of the data, which are retrieved from the medical reports at the Tawau General Hospital. Through the findings, pneumonia is widely spread among young children under 12 years old. There are more than one major factor that leads to this critical illness, such as family background, genetic and environment. Therefore, the government, doctors and parents should take major steps to prevent children suffering from pneumonia.Comment: Presented at the International Seminar on the Application of Science & Mathematics 201

Fuzzification of quantitative data to predict tumour size of colorectal cancer

Regression analysis has become more popular among researchers as a standard tool in analyzing data. This paper used fuzzy linear regression model (FLRM) to predict tumour size of colorectal cancer (CRC) data in Malaysia. 180 patients with colorectal cancer received treatment in hospital were recorded by nurses and doctors. Based on the patient records, a triangular fuzzy data will be built toward the size of the tumour. Mean square error (MSE) and root mean square error (RMSE) will be measured as a part of the process for predicting the size of the tumour. The degree of fitting adjusted is set between 0 and 1 in order to find the least error. It was found that the combination of FLRM model with fuzzy data provided a better prediction compared to the FLRM model alone. Hence, this study concluded that the tumour size is directly proportional to several factors such as gender, ethnic, icd 10, TNM staging, diabetes mellitus, Crohn’s disease

Forecasting currency in circulation in Malaysia using arch and garch models

The monthly economic time series commonly contains the volatility periods and it is suitable to apply the Heteroscedastic model to it where the conditional variance is not constant throughout the time trend. The aim of this study is to model and forecast the currency in circulation (CIC) in Malaysia over the time period, from January 1998 to January 2016. Two methods are considered, which are Autoregressive Conditional Heteroscedastic (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Using the Root Mean Square Error (RMSE) as the forecasting performance measure, this study concludes that GARCH is a more appropriate model compared to ARCH