National University of Malaysia

About: National University of Malaysia is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Heat transfer. The organization has 26593 authors who have published 41270 publications receiving 552683 citations. The organization is also known as: NUM & Universiti Kebangsaan Malaysia.

High glycemic load diet, milk and ice cream consumption are related to acne vulgaris in Malaysian young adults: a case control study

Abstract: Background The role of dietary factors in the pathophysiology of acne vulgaris is highly controversial. Hence, the aim of this study was to determine the association between dietary factors and acne vulgaris among Malaysian young adults.

The Use of Social Networking Sites among Malaysian University Students

Abstract: Social networking sites (SNSs) have increasingly become an important tool for young adults to interact and socialize with their peers. As most of these young adults are also learners, educators have been looking for ways to understand the phenomena in order to harness its potential for use in education. This is especially relevant in Malaysia where SNSs are popular among the youths, yet there is little data available to describe patterns of use for the wider segment of the target population. This study presents the results of a nationwide survey on tertiary level students in Malaysia. The results show that SNSs penetration is not at full 100% as initially assumed. The respondents spend the most time online for social networking and learning. The results also indicate that while the respondents are using SNS for the purpose of informal learning activities, only half (50.3%) use it to get in touch with their lecturers in informal learning contexts. The respondents also reported spending more time on SNS for socializing rather than learning and they do not believe the use of SNS is affecting their academic performance.

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring

Abstract: We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R 2), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm

Abstract: State of charge (SOC) is one of the crucial parameters in a lithium-ion battery. The accurate estimation of SOC guarantees the safe and efficient operation of a specific application. However, SOC estimation with high accuracy is a serious concern to the automobile engineer due to the battery nonlinear characteristics and complex electrochemical reactions. This paper presents an improved nonlinear autoregressive with exogenous input (NARX)-based neural network (NARXNN) algorithm for an accurate and robust SOC estimation of lithium-ion battery which is effective and computationally rich for controlling dynamic system and predicting time series. However, the accuracy of recurrent NARXNN depends on the amount of input order, output order, and hidden layer neurons. The unique contribution of the improved recurrent NARXNN-based SOC estimation is developed using lighting search algorithm (LSA) for finding the best value of input delays, feedback delays, and hidden layer neurons. The contributions are summarized as: 1) the computational capability of NARXNN model which does not require battery model and parameters rather only needs current, voltage, and temperature sensors; 2) the effectiveness of LSA which is verified with particle swarm optimization; 3) the adaptability, efficiency, and robustness of the model which are evaluated using FUDS and US06 drive cycles at varying temperatures conditions; and 4) the performance of the proposed model which is compared with back propagation neural network and radial basis function neural network optimized by LSA using different error statistical terms and computational time. Furthermore, a comparative analysis of SOC estimation in proposed method and existing techniques is presented for validation of NARXNN performance. The results prove that the proposed NARXNN model achieves higher accuracy with less computational time than other existing SOC algorithms under different temperature conditions and electric vehicle drive cycles.