Mohd Saifullah Rusiman

Determinants status of patient after receiving treatment at Intensive Care Unit: A case study in Johor Bahru

ICU in terms of Intensive Care Unit was established in Malaysia since 1968. The number of patients receiving treatment at ICU had been increased day by days until now. Rapid development of medical and surgical subspecialties in the last decade resulted in increasing demands for more ICU beds and provides momentum for its development. This study aims to identify the determinants of patient status (alive or died) after receiving treatment in ICU. Secondary data of 1314 patients who received ICU treatment recorded by nurses and doctors using cluster sampling was used. Binary logistic regression was applied in order to identify the determinants of patient status. Based on the results of the logistic regression, this study discovered that patients age, days of patients in ICU, comorbid diseases and patients discharge score are the factors contributing patients to die after received treatment in ICU.

Algebraic properties of finite switchboard state machine

A finite switchboard state machine is a specialized finite state machine. The algebraic approach of finite switchboard state machine is still lacking in literature. In this paper, we applied the algebraic approach of finite switchboard state machine and study its related properties. Real life examples of the applications are given by discussing the algebraic properties used.

The Impact of Time Management on Students’ Academic Achievement

Time management is very important and it may actually affect individuals overall performance and achievements. Students nowadays always commented that they do not have enough time to complete all the tasks assigned to them. In addition, a university environments flexibility and freedom can derail students who have not mastered time management skills. Therefore, the aim of this study is to determine the relationship between the time management and academic achievement of the students. The factor analysis result showed three main factors associated with time management which can be classified as time planning, time attitudes and time wasting. The result also indicated that gender and races of students show no significant differences in time management behaviours. While year of study and faculty of students reveal the significant differences in the time management behaviours. Meanwhile, all the time management behaviours are significantly positively related to academic achievement of students although the relationship is weak. Time planning is the most significant correlated predictor.

Forecasting Natural Rubber Price In Malaysia Using Arima

This paper contains introduction, materials and methods, results and discussions, conclusions and references. Based on the title mentioned, high volatility of the price of natural rubber nowadays will give the significant risk to the producers, traders, consumers, and others parties involved in the production of natural rubber. To help them in making decisions, forecasting is needed to predict the price of natural rubber. The main objective of the research is to forecast the upcoming price of natural rubber by using the reliable statistical method. The data are gathered from Malaysia Rubber Board which the data are from January 2000 until December 2015. In this research, average monthly price of Standard Malaysia Rubber 20 (SMR20) will be forecast by using Box-Jenkins approach. Time series plot is used to determine the pattern of the data. The data have trend pattern which indicates the data is non-stationary data and the data need to be transformed. By using the Box-Jenkins method, the best fit model for the time series data is ARIMA (1, 1, 0) which this model satisfy all the criteria needed. Hence, ARIMA (1, 1, 0) is the best fitted model and the model will be used to forecast the average monthly price of Standard Malaysia Rubber 20 (SMR20) for twelve months ahead.

Spatio-temporal modelling of dengue fever incidence in Malaysia

Previous studies reported significant relationship between dengue incidence rate (DIR) and both climatic and non-climatic factors. Therefore, this study proposes a generalised additive model (GAM) framework for dengue risk in Malaysia by using both climatic and non-climatic factors. The data used is monthly DIR for 12 states of Malaysia from 2001 to 2009. In this study, we considered an annual trend, seasonal effects, population, population density and lagged DIR, rainfall, temperature, number of rainy days and El Nino-Southern Oscillation (ENSO). The population density is found to be positively related to monthly DIR. There are generally weak relationships between monthly DIR and climate variables. A negative binomial GAM shows that there are statistically significant relationships between DIR with climatic and non-climatic factors. These include mean rainfall and temperature, the number of rainy days, sea surface temperature and the interaction between mean temperature (lag 1 month) and sea surface temperature (lag 6 months). These also apply to DIR (lag 3 months) and population density.

Treatment of Outliers via Interpolation Method with Neural Network Forecast Performances

Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the suitable ways in treating outliers so as to ensure that the quality of the analyzed data is indeed high. As such, this paper discusses an alternative method to treat outliers via linear interpolation method. In fact, assuming outlier as a missing value in the dataset allows the application of the interpolation method to interpolate the outliers thus, enabling the comparison of data series using forecast accuracy before and after outlier treatment. With that, the monthly time series of Malaysian tourist arrivals from January 1998 until December 2015 had been used to interpolate the new series. The results indicated that the linear interpolation method, which was comprised of improved time series data, displayed better results, when compared to the original time series data in forecasting from both Box-Jenkins and neural network approaches.

The analysis of morphometric data on rocky mountain wolves and artic wolves using statistical method

Morphometrics is a quantitative analysis depending on the shape and size of several specimens. Morphometric quantitative analyses are commonly used to analyse fossil record, shape and size of specimens and others. The aim of the study is to find the differences between rocky mountain wolves and arctic wolves based on gender. The sample utilised secondary data which included seven variables as independent variables and two dependent variables. Statistical modelling was used in the analysis such was the analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). The results showed there exist differentiating results between arctic wolves and rocky mountain wolves based on independent factors and gender.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy cmeans cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

MADM For Solving Fourth-Order ODE

In this paper, we present the new reliable modification of Adomian decomposition method for solving fourth order ordinary deferential equations. The modification of Adomian decomposition method is an effective procedure to be applied in the singular or nonsingular problems within given the initial value problem. The results are compared with the existing exact or numerical method. Thus, the Modification of Adomian decomposition method are found to converge very quickly and more accurate compared to Adomian decomposition method. Several examples are given to show the ability and efficiency of the proposed method.

ADM For Solving Linear Second-Order Fredholm Integro-Differential Equations

In this paper, we apply Adomian Decomposition Method (ADM) as numerically analyse linear second-order Fredholm Integro-differential Equations. The approximate solutions of the problems are calculated by Maple package. Some numerical examples have been considered to illustrate the ADM for solving this equation. The results are compared with the existing exact solution. Thus, the Adomian decomposition method can be the best alternative method for solving linear second-order Fredholm Integro-Differential equation. It converges to the exact solution quickly and in the same time reduces computational work for solving the equation. The result obtained by ADM shows the ability and efficiency for solving these equations.