Browsing by Author "Lazim Abdullah"

Now showing 1 - 7 of 7
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    A Choquet Integral with DEMATEL-Based Method for Coastal Erosion Decision Problem
    (Indian Journal of Science and Technology, 2016-07) Liana Najib; Ahmad Termimi Ab Ghani; Lazim Abdullah; Mohammad Fadhli Ahmad
    Background/Objectives: This study aims to investigate the cause-effect relationship risk factors associated to coastal erosion decision problem using a decision model based on Choquet integral and Decision-making Trial and Evaluation Laboratory (DEMATEL) method. Methods/Statistical Analysis: In this study, DEMATEL method is used to determine the cause-effect diagram of the risk factor. DEMATEL is known as one of the tools in dealing with digraph visualization of the degree importance for factor influence. Meanwhile, a Choquet integral is used to yield the aggregation among the subjective preference judgement made by the decision makers based on normal capacity. The process of combining a several preference values into a single value is called as an aggregation process. Findings: The Choquet integral DEMATEL-based approach is applied to coastal erosion decision problem to evaluate both interactive and causal relationship of the risk factors. There are two phases involve in this method. The first phase is to illustrate the relationship digraphs of selected risk factors and the implementation of weighted eigenvector matrix comparison in DEMATELbased method. The second phase takes into aggregation of the multi-criteria decision risk factors by fuzzy measure and Choquet integral to obtain the integrated weights value. From the study, it is indicated that shoreline changes has the highest score of Choquet integral by 1.8198 that contribute the most to the coastal erosion followed by relative sea level rise (1.5791), climate change (1.1917) and wave condition (0.9315). Application/Improvements: The feasibility of the developed method can be used to improve the risk factors of coastal erosion management and assessment.
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    Circumcenter of centroid of fuzzy number for identifying risk factors of obesity: a qualitative evaluation
    (Quality and Quantity, 2016) Lazim Abdullah; Fateen Najwa Azman
    Obesity and its associated health problems have been considered as a burden to health care providers where treatment may incur tremendous loss of public fund. Recent studies reveal that authentic factors contributed to obesity are very much inconclusive. Magnitude of risks for each factor remains unknown. This paper aims to propose risk values for the selected risk factors contributed to the development of obesity using an approach of ranking fuzzy number. The method of ranking fuzzy numbers based on circumcenter of centroid is proposed. The proposed model, which takes into account spread, area, and distance of trapezoidal fuzzy numbers was implemented to the case of obesity. Three experts were invited to provide qualitative linguistic evaluation over the importance of risk factors toward development of obesity. The risk values obtained from the proposed method unveiled that the factor of family history is the highest risk factor followed by sedentary life style. The results also indicate that gender is lowest risk factor. An implication for the general public is that the importance of knowing the status of family history and also the awareness in practising healthy life style.
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    Modeling Health Related Quality of Life among Cancer Patients Using an Integrated Inference System and Linear Regression
    (International Journal of Pharma Medicine and Biological Sciences, 2015-01-02) Lazim Abdullah
    Health Related Quality of Life (HRQL) is one of the increasing subjects used for assessing health condition among patients who suffer from specific diseases or ailment. It has been assumed that identification of the variables is able to mirror the one’s overall health conditions. However, devising the extent of contribution of multiple variables towards overall health conditions is not straight forward as the arbitrary nature of HRQL variables.
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    Prediction of Carbon Dioxide Emissions Using Two Linear Regression-based Models
    (Journal of Applied Engineering, 2016-04) Chee San Choi; Lazim Abdullah
    Carbon dioxide (CO2) emissions have been continuously escalating in recent years. The escalating trend is consistent with the current economic activities and other uncertain variables such as demand and supply in businesses and energy needs. Linear model is one of the most commonly used methods to explain the relationship between CO2 emissions and the related economic variables. The conventional linear regression model has a disadvantage in describing the relationships due to the variables’ uncertainty and vague information. To address this problem, the fuzzy linear regression model has been proposed for explaining the relationships. However, the performance of the two linear models for predicting CO2 emissions is not immediately known. This paper presents a comparative study of conventional linear regression model and linear regression with fuzzy numbers model for predicting CO2 emissions in Malaysia. Twenty five years data from 1981 to 2005 of CO2 emissions, fuel mix, transportation, gross domestic product, and population have been used to develop the model of possibilistic fuzzy linear regression (PFLR) and multiple linear regression (MLR). The criteria of performance evaluation are calculated for estimating and comparing the performances of PFLR and MLR models. The performance comparison of PFLR and MLR models due to mean absolute percentage errors, root mean squared error criteria; indicate that MLR performed better on CO2 emissions prediction. A considerable further work needs to be done to determine the flexibility of fuzzy numbers in enhancing the performance of PFLR against the MLR.
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    Ranking Causes of Road Accident Occurrence Using Extended Interval Type-2 Fuzzy TOPSIS
    (Malaysian Journal of Applied Sciences, 2016) Nurnadiah Zamri; Syibrah Naim; Lazim Abdullah
    Over the past century there has been a dramatic increase in the number of road accidents in Malaysia. Hence, it is necessary to create a decision making method which can consider various preferences and criteria in order to identify the main causes of the accidents. This paper proposes an Interval Type-2 Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IT2FTOPSIS) method which provides a comprehensive valuation from experts. This method is developed based on the aggregation of experts’ opinions on preferred causes of road accidents. The extended IT2FTOPSIS employs a linguistic scales of positive and negative Interval Type-2 Trapezoidal Fuzzy Number (IT2TrFN) and hybrid averaging approach (from an ambiguity and type-reduction methods) to formulate a collective decision environment. Three authorised personnel from three Malaysian Government agencies were interviewed where they were asked to rank the causes. The analysis shows that the linguistic scales of positive and negative Interval Type-2 Trapezoidal Fuzzy Number (IT2TrFN) and hybrid averaging approach are effective in measuring the uncertainties in the interviewees’ responses. Thus this paper concludes that the extended IT2FTOPSIS is more aligned with the users’ decisions compared to the earlier IT2FTOPSIS.
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    Ranking of the Factors Associated with Road Accidents using Correlation Analysis and Fuzzy TOPSIS
    (Terengganu Universiti Malaysia Terengganu, 2010) Lazim Abdullah
    Road accident is one of the major causes of death and injuries in Malaysia. The increase of road accidents is said to be associated with the factors of rapid growth in population, economic development, and motorization. However no specific literature was found to specify weights and subsequently rank the factors. This paper proposes a ranking of three selected factors associated with road accidents using the correlation analysis and Multi Criteria Decision Making, fuzzy TOPSIS. Statistical accident data issued by Royal Malaysian Police and linguistic judgement data collected from three authorised personnel of three Malaysian Government agencies were considered in analysis. The ranks ane be drawn using the strength of correlation coefficients and the magnitude of closeness coefficients in fuzzy TOPSIS. The results from two analyses indicate that registered vehicles yielded the highest ranking followed by population and road length. This ranking gives rise to concerns about the relevance of the factors in reducing accidents rate.
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    A step-wise multiple linear regression analysis for identifying predictors of employees’ intention to undertake further study
    (Journal of Current Research in Science, 2016) Lazim Abdullah; Ashraf Fahmi Rahim
    The aim of the current study is to identify predictors of intention to undertake further study among employees. Data regarding employees’ intention and related variables were collected from five private companies and eight government agencies at a municipality council in Malaysia. In this study, hundred and twenty employees were fully completed the questionnaire. Ten predictors and one dependent variable (Intention) were defined. A series of six-step analyses of step-wise multiple linear regression was performed. The findings show the linearity assumption about the association between predictors and dependent variable (Intention) is fulfilled. A significant portion of the variability in dependent variable (Intention) is explained by multiple regressions on the predictors. A positive 0.817 regression coefficient suggests that motivation level is the most influential predictor in predicting employees’ intention to undertake further study.