munira razali - Academia.edu (original) (raw)
Papers by munira razali
Perubahan sistem pendidikan dan latihan di institusi latihan teknik dan vokasional menyebabkan pe... more Perubahan sistem pendidikan dan latihan di institusi latihan teknik dan vokasional menyebabkan perubahan kaedah pengajaran dan pembelajaran di bilik kuliah. Perubahan sistem pendidikan ini juga turut mempengaruhi tahap kemahiran, pengetahuan dan kompetensi tenaga pengajar di institusi tersebut. Institusi latihan kemahiran seperti Kolej Komuniti perlu sentiasa peka dan inovatif terhadap pendidikan dan latihan tenaga pengajar untuk memastikan kemahiran dan pengetahuan tenaga pengajar sentiasa relevan dengan sistem pendidikan. Dalam Rancangan Malaysia Kesepuluh, transformasi telah dilakukan terhadap Kolej Komuniti di mana perubahan sistem pendidikan latihan dan kemahiran sijil sepenuh masa kepada Sijil Modular Kebangsaan. Kertas kerja ini membincangkan cadangan kajian yang akan dijalankan bagi program Sijil Modular Kebangsaan khusus bagi kemahiran sedia ada tenaga pengajar dan keperluan latihan bagi memenuhi piawaian program. Tujuan utama kajian ini perlu dijalankan adalah untuk mengka...
IOP Conference Series: Earth and Environmental Science
Bulletin of Electrical Engineering and Informatics
The widespread use of computer experiments for design optimization has made the issue of reducing... more The widespread use of computer experiments for design optimization has made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling, can approximate the actual simulation model allowing for much faster execution time thus becoming a useful method to mitigate these problems. There are two (2) well-known metamodeling techniques which is kriging and radial basis function (RBF) discussed in this paper based on widely used algorithm tool from previous work in modern engineering design of optimization. An integral part of metamodeling is in the method to sample new data from the actual simulation model. Sampling new data for metamodeling requires finding the location (or value) of one or more new data such that the accuracy of the metamodel can be increased as much as possible after the sampling process. This paper discussed...
MATEMATIKA
Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a pre... more Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of ...
Recently, a computer experiment is ubiquitous in modeling and engineering design. Estimation ofen... more Recently, a computer experiment is ubiquitous in modeling and engineering design. Estimation ofenergy building efficiency using computer experiment is widely used to improve performance andenergy consumption in the residential building. This paper proposed Radial Basis Function NeuralNetwork (RBFNN) for energy building consumption dataset and make comparative studies betweenthe Random Forest algorithm (RF) in previous work. This study using the experimental dataset in theliterature that consists of 768 experimental data with eight input variables and two outputparameters of estimation. The inputs variables are relative compactness, surface area, wall area, roofarea, overall height, orientation, glazing area, and glazing area distribution of a building, whileoutput variables include heating and cooling loads of the building. The analytical result of energybuilding performance shows RBFNN is better than RF algorithm in estimation based on errorvalidation calculation using Mean Square ...
International Journal of Engineering & Technology
Since a pass few decades up to recent, building energy efficiency performance is the top priority... more Since a pass few decades up to recent, building energy efficiency performance is the top priority due to the sustainability of energy and quality of life. According to recent study related to computer experiment, there are various types of the model has been proposed by the researcher to improve the performance of building energy efficiency. However, there is no empirical evidence to prove the best method in prediction and estimation of energy efficiency that ensure adequate energy to meet todays and future needs. The objective of this paper is to propose Radial Basis Function Neural Network (RBFNN) for estimating the heating load and cooling load of a residential building. This study set out to evaluate different estimation methods of residential building energy efficiency using RBFNN. The data of residential building are obtained from UCI Machine Learning Repository. The dataset of simulation using Ecotect consists of 768 samples with 8 input features and 2 output variables were u...
International Journal of Advances in Intelligent Informatics
Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast ... more Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series of differences between observed and forecast values, are very interesting and challenging task. This paper presents an improved lead time flood forecasting using Non-linear Auto Regressive Exogenous Neural Network (NARXNN), which shows better performance in term of forecast precision and produces minimum error compared to neural network method using Radial Basis Function (RBF) in examined 12-hour ahead of time. First, RBF forecasting model was employed to predict the flood water level of Kelantan River at Kuala Krai, Kelantan, Malaysia. The model is tested for 1-hour and 7-hour ahead of time water level at flood location. The same analysis has also been taken by NARXNN method. Then, a non-linear neural network model with exogenous in...
Bulletin of Electrical Engineering and Informatics
The widespread use of computer experiments for design optimization has made the issue of reducing... more The widespread use of computer experiments for design optimization has made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling, can approximate the actual simulation model allowing for much faster execution time thus becoming a useful method to mitigate these problems. There are two (2) well-known metamodeling techniques which is kriging and radial basis function (RBF) discussed in this paper based on widely used algorithm tool from previous work in modern engineering design of optimization. An integral part of metamodeling is in the method to sample new data from the actual simulation model. Sampling new data for metamodeling requires finding the location (or value) of one or more new data such that the accuracy of the metamodel can be increased as much as possible after the sampling process. This paper discussed...
Perubahan sistem pendidikan dan latihan di institusi latihan teknik dan vokasional menyebabkan pe... more Perubahan sistem pendidikan dan latihan di institusi latihan teknik dan vokasional menyebabkan perubahan kaedah pengajaran dan pembelajaran di bilik kuliah. Perubahan sistem pendidikan ini juga turut mempengaruhi tahap kemahiran, pengetahuan dan kompetensi tenaga pengajar di institusi tersebut. Institusi latihan kemahiran seperti Kolej Komuniti perlu sentiasa peka dan inovatif terhadap pendidikan dan latihan tenaga pengajar untuk memastikan kemahiran dan pengetahuan tenaga pengajar sentiasa relevan dengan sistem pendidikan. Dalam Rancangan Malaysia Kesepuluh, transformasi telah dilakukan terhadap Kolej Komuniti di mana perubahan sistem pendidikan latihan dan kemahiran sijil sepenuh masa kepada Sijil Modular Kebangsaan. Kertas kerja ini membincangkan cadangan kajian yang akan dijalankan bagi program Sijil Modular Kebangsaan khusus bagi kemahiran sedia ada tenaga pengajar dan keperluan latihan bagi memenuhi piawaian program. Tujuan utama kajian ini perlu dijalankan adalah untuk mengka...
IOP Conference Series: Earth and Environmental Science
Bulletin of Electrical Engineering and Informatics
The widespread use of computer experiments for design optimization has made the issue of reducing... more The widespread use of computer experiments for design optimization has made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling, can approximate the actual simulation model allowing for much faster execution time thus becoming a useful method to mitigate these problems. There are two (2) well-known metamodeling techniques which is kriging and radial basis function (RBF) discussed in this paper based on widely used algorithm tool from previous work in modern engineering design of optimization. An integral part of metamodeling is in the method to sample new data from the actual simulation model. Sampling new data for metamodeling requires finding the location (or value) of one or more new data such that the accuracy of the metamodel can be increased as much as possible after the sampling process. This paper discussed...
MATEMATIKA
Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a pre... more Prediction analysis has drawn significant interest in numerous field. Taguchi’s T-Method is a prediction tool that developed practically but not limited to small sample analysis. It was developed explicitly for multidimensional system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as help in eliminating variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multidimensionality restraint the optimization accuracy. Binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process to better prediction accuracy. The results show that if the historical data consist of samples with higher correlation of ...
Recently, a computer experiment is ubiquitous in modeling and engineering design. Estimation ofen... more Recently, a computer experiment is ubiquitous in modeling and engineering design. Estimation ofenergy building efficiency using computer experiment is widely used to improve performance andenergy consumption in the residential building. This paper proposed Radial Basis Function NeuralNetwork (RBFNN) for energy building consumption dataset and make comparative studies betweenthe Random Forest algorithm (RF) in previous work. This study using the experimental dataset in theliterature that consists of 768 experimental data with eight input variables and two outputparameters of estimation. The inputs variables are relative compactness, surface area, wall area, roofarea, overall height, orientation, glazing area, and glazing area distribution of a building, whileoutput variables include heating and cooling loads of the building. The analytical result of energybuilding performance shows RBFNN is better than RF algorithm in estimation based on errorvalidation calculation using Mean Square ...
International Journal of Engineering & Technology
Since a pass few decades up to recent, building energy efficiency performance is the top priority... more Since a pass few decades up to recent, building energy efficiency performance is the top priority due to the sustainability of energy and quality of life. According to recent study related to computer experiment, there are various types of the model has been proposed by the researcher to improve the performance of building energy efficiency. However, there is no empirical evidence to prove the best method in prediction and estimation of energy efficiency that ensure adequate energy to meet todays and future needs. The objective of this paper is to propose Radial Basis Function Neural Network (RBFNN) for estimating the heating load and cooling load of a residential building. This study set out to evaluate different estimation methods of residential building energy efficiency using RBFNN. The data of residential building are obtained from UCI Machine Learning Repository. The dataset of simulation using Ecotect consists of 768 samples with 8 input features and 2 output variables were u...
International Journal of Advances in Intelligent Informatics
Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast ... more Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series of differences between observed and forecast values, are very interesting and challenging task. This paper presents an improved lead time flood forecasting using Non-linear Auto Regressive Exogenous Neural Network (NARXNN), which shows better performance in term of forecast precision and produces minimum error compared to neural network method using Radial Basis Function (RBF) in examined 12-hour ahead of time. First, RBF forecasting model was employed to predict the flood water level of Kelantan River at Kuala Krai, Kelantan, Malaysia. The model is tested for 1-hour and 7-hour ahead of time water level at flood location. The same analysis has also been taken by NARXNN method. Then, a non-linear neural network model with exogenous in...
Bulletin of Electrical Engineering and Informatics
The widespread use of computer experiments for design optimization has made the issue of reducing... more The widespread use of computer experiments for design optimization has made the issue of reducing computational cost, improving accuracy, removing the “curse of dimensionality” and avoiding expensive function approximation becoming even more important. Metamodeling also known as surrogate modeling, can approximate the actual simulation model allowing for much faster execution time thus becoming a useful method to mitigate these problems. There are two (2) well-known metamodeling techniques which is kriging and radial basis function (RBF) discussed in this paper based on widely used algorithm tool from previous work in modern engineering design of optimization. An integral part of metamodeling is in the method to sample new data from the actual simulation model. Sampling new data for metamodeling requires finding the location (or value) of one or more new data such that the accuracy of the metamodel can be increased as much as possible after the sampling process. This paper discussed...