Search

Unintentional poisoning is on the rise globally and thus a statistical analysis has been conducted. This study focuses on implementing a binary logistic regression analysis to study the factors that lead significantly to unintentional and intentional poisoning in Malaysia. The data consists of information from 592 patients with 7 predictor variables and a binary response variable. Preliminary analysis and chi-square test are conducted before performing the analysis. To obtain a good logistic model, there are three main steps: model building, diagnostic checking and drawing inferences from the parameters. The model provides parameter inferences such as odds ratio and estimation of predicted probabilities. Based on the results, unintentional poisoning is significantly influenced by physical form of poison, type of natural toxin and exposure route. Odds ratio estimation indicates that patients exposed to poisoning through bites or stings are more likely to have unintentional poisoning compared to other exposure routes. Generally, estimation of predicted probability are high for unintentional poisoned patients when they are bitten by animals. [ABSTRACT FROM AUTHOR]

The parametric method is often applied to model income data and to assess income inequality. Parametric estimation of income distribution is convenient because it facilitates subsequent inferences about the measured inequality. In this study, we conduct a comparative assessment of four parametric distribution candidates in terms of model fitting for the income of poor households in Malaysia on the basis of survey data of household incomes for the years of 2007, 2009, 2012 and 2014. The considered models involve the reverse Pareto, shifted reverse exponential, shifted reverse stretched exponential and shifted reverse lognormal. We also derive the explicit expression of Lorenz curve and Gini coefficient for all four considered models. Finally, on the basis of the best parametric model, income inequality analysis of poor households in Malaysia is assessed using Lorenz curve and Gini coefficient. [ABSTRACT FROM AUTHOR]

2 Satisfiability (2SAT) is a language that bridges real life application to neural network optimization. 2SAT is an interesting paradigm because the outcome of this logical rule is always positive. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when logical rule is embedded to HNN. Increasing the storage capacity during learning phase of HNN has been a challenging problem for most neural network researchers. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to decipher several combinatorial optimization problems. In this paper, metaheuristic algorithm ACO will be proposed in learning 2SAT programming. To this end, all the learning model will be tested in a new restricted learning environment. In circumstances of where HNN model was not given unlimited iteration until all 2SAT clauses were fully satisfied, we would like to determine the better performing search method between exhaustive search and ACO. Results acquired from the computer simulation showed that ACO outperformed exhaustive search in restricted learning environment. [ABSTRACT FROM AUTHOR]

This paper discusses a prey-predator model with a Type II functional response and environmental suitability components. The stability of equilibrium points and the emergence of bifurcation are analyzed using techniques from dynamical systems. It is discovered that the transcritical and Hopf bifurcations appear as some ecologically- relevant parameters change. In particular, our two-parameter bifurcation analysis shows how the interaction of transcritical and Hopf bifurcations can lead to different dynamics of species interaction e.g. coexistence of species, species exclusion and oscillatory dynamics. Overall, our findings suggest significant roles of predation and abiotic environments in mediating species biodiversity. [ABSTRACT FROM AUTHOR]

Everybody has at least a smartphone and the technology keep on evolving from time to time, same goes to mobile applications. Mobile applications are currently more towards learning, business and entertainment purposes, but why not we extend the use of the technology to forensic investigation? In this paper, we are highlighting the potential of a mobile application on smartphones to support investigation in crime scenes. As we know, crimes involving firearms has been a threat to humanity since time immemorial. The mass production of handguns and other firearms since the nineteenth century has similarly expanded crime numbers globally. Thus, it is critical to identify the firearm used whenever a firearm crime has been reported, which calls for expertise in forensic ballistics. Firearms identity is a crucial goal of firearm analysis. From previous research, the firing pin impact impression on a cartridge case from a fired bullet is one of the most extensive clues in firearms identification. Since nowadays a lot of crime cases involve guns, a portable device should help the police and the forensic teams for immediate firearm identification, whereby they can pre-analyze the firearm impression on the spot to gain some prior information, which is cost, space and time saving. In this paper, mobile application for firearm identification is proposed, named Firearm Identification Mobile Application (FIMA). This firearm identification application will analyze numerical features extracted from the firing pin impression image as the unique features, and immediately catalogues the features using the backpropagation neural network classification technique. It is found that 'trainlm' performed the best compared to the other training algorithms based on average overall correct classification rates for all features. On the other hand, 'trainscg' performed the best based on centre firing pin impression images. In the near future, a portable firearm analysis device will be developed, namely Portable Firearm Analysis Device (PAFAD) to further assist in forensic investigation activities. [ABSTRACT FROM AUTHOR]

The association between dietary habits and cardiovascular diseases (CVDs) has been established. However, the dietary habits vary among countries, therefore, there is scarcity in the literature for a standardized instrument to assess the dietary habits. This paper is aimed to evaluate the psychometric properties of modified dietary habits instrument, originally based on a food frequency questionnaire (FFQ). In this case-control study, it was used to find the most prevalent dietary habits in CVDs patients and their corresponding controls. The modified instrument consisted of 13 items representing thirteen food groups identified from CVDs related studies and FFQ. A sample of 180 (90 cases and 90 controls) was selected from the largest cardiac hospital of Pakistan through systematic sampling. Data was collected through modified instrument using direct investigation method. Twelve food groups were validated which further constituted two main factors labelled; healthy and unhealthy dietary habits. Exploratory factor analysis explained 70% and 68% variation through these two main identified dietary habits in cases and control, respectively. Internal consistency reliability for healthy and unhealthy dietary habits in cases were 0.905 and 0.879 and almost similar coefficient of reliability was recorded in the control group. Further, the mean score of healthy dietary habits was significantly less than unhealthy dietary habits (p<0.001) in the cases and vice versa in controls. This study concludes that the modified instrument is valid and reliable and should be used to assess the dietary habits in CVDs related studies. Further, mean score of unhealthy dietary habits can help in disease prediction models and early diagnosis of cardiac problems. [ABSTRACT FROM AUTHOR]

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 statistical approach was carried out. It is pertinent since most of the previous study only concerns on the financial interests of the stock market. Therefore, this study considers the homogeneity structure in the highly correlated data on yearly stock market price by applying ordered homogenous pursuit lasso (OHPL) method. The performance results of OHPL were compared with lasso and elastic net. As a result, OHPL a had higher number of selected variables and a better prediction power than of lasso and elastic net. In conclusion, OHPL shows its capability to enhance variable selection while increasing the prediction power of the selected variables than its counterpart. [ABSTRACT FROM AUTHOR]

The field of survival analysis has made a tremendous progress during the latter half of the twentieth century. Weibull distribution and parametric models (proportional hazards models) have played important roles in the analysis of survival data. In this paper, a two-parameter modified Weibull distribution, three-parameter modified Weibull distri- butions and a four-parameter modified Weibull distribution are demonstrated that can be adopted in the proportional hazards models. Smoothed hazard estimate is used to indicate that Weibull and modified Weibull models are preferred to apply in the coronary heart disease (CHD) data. Weibull and modified Weibull proportional hazards models are fitted to the CHD data. It is found that the two-parameter modified Weibull proportional hazards model has a better fit to the CHD data than Weibull and other modified Weibull proportional hazards models. [ABSTRACT FROM AUTHOR]

There were many cases reported for ovarian cysts and it was difficult to recognize the size of cyst whether it is a large or a small cyst. Thus, this research was conducted to predict the size of benign cysts according to the patient's symptoms and its risk factors, to analyze the relationship between the size of benign cyst and the type of surgical procedures that the patient needs to undergo and to obtain which variable gives most important effect to the model. Retrospective observation study has been applied in this research since the data was collected from the monthly census file for year 2016 in Ward of Obstetrics and Gynecology from Hospital A, Johor Bahru. From the 89 patients of benign ovarian cysts, only 26 of the patient have small cysts and the other 63 patients have large benign cysts. From analysis, it found that the significant symptoms towards the predicting of Size of Benign Cyst were Abdominal Pain and Intermenstrual Bleeding. There were no significant for the risk factors. There was a significant relationship between the Size of Benign Cysts and the Type of Surgical Procedure. From the association, it can be concluded, if the patient had a large size of cysts, they will need to undergo laparotomy surgery. By using log-linear analysis, it is found that the most important effect in model was Type of Operation. [ABSTRACT FROM AUTHOR]

Portfolio investment is one of the important topics in financial management. Its aim of having the wealth in a period of time with maximum return and minimum risk has been well-defined in the literature. Specifically, the portfolio selection is arisen to be an interesting investment issue. Since the risk is defined as the probability of losses relative to the expected return in the portfolio investment, the existing risk would influence the rate of return and affect the investment decision. In this paper, the portfolio selection problem is discussed. The aim is to provide an effective quantitative study in managing the portfolio. In the beginning, the rates of return are calculated and it is followed by computing the expected rates of return and the related covariance matrix. Accordingly, the mean-variance optimization model, which is the quadratic programming model, is formulated and solved by the quadratic programming technique. Here, the objective function to be minimized represents the risk of the portfolio selection, while the weights of the portfolio selection, which are defined as the decision variables, are normalized with the total amount of investment, and the expected return is assigned to a certain targeted return. For illustration, a set of historical data on the stock prices in the Financial Times Stock Exchange (FTSE) Bursa Malaysia Kuala Lumpur Composite Index (KLCI) is considered. Through simulation and optimization of the mean-variance portfolio model, the result shows the relationship between risk and return with the various targeted returns. Moreover, by applying the Sharpe ratio, the optimal weight of the portfolio selection, which is the optimal portfolio decision, is properly suggested. In conclusion, the simulation optimization model for the portfolio selection problem is highly recommended. [ABSTRACT FROM AUTHOR]

Income inequality is a matter of concern for any society and an interesting discussion topic, particularly for developing countries, since high-income inequality could be detrimental to economic growth. In Malaysia, upper-income earners are classified as the T20 group. This study analyzes the demographic profile of members of the T20 group living in urban areas in Malaysia. The upper-income distribution is described using the Pareto model, and a robust and efficient estimator called the probability integral transform statistic estimator is utilized to estimate the shape parameter of this model. Thereafter, a Gini index and Lorenz curve are derived to analyze the severity of income inequality among T20 group members. [ABSTRACT FROM AUTHOR]