NOR AZIZAH ALI FC - Academia.edu (original) (raw)
Papers by NOR AZIZAH ALI FC
2021 4th International Conference of Computer and Informatics Engineering (IC2IE), 2021
One of the topics covered in forensic anthropology is an investigation of skeletal remains where ... more One of the topics covered in forensic anthropology is an investigation of skeletal remains where various properties of the skeleton are to be determined. Typically, the sample found is incomplete, meaning some bone parts are missing or destroyed, and the analysis needs to depend on limited information obtained from what is available. This research focuses on arm, leg, clavicle, and scapula bones, with 8 bone parts in total. Each part is either used independently from the other or considered altogether (aggregate) to test its usability in finding out the owner’s identity when facing such a situation. Bone measurements obtained from the database were used as input data for two different classifiers, namely artificial neural networks and supporting vector machines, with two identification targets, namely sex and race. All of the input data came from publicly available Robert J. Terry Anatomical Skeletal Collection Postcranial Osteo-metric database. Accuracies of 86.67% and 70.78% are obtained for those targets using clavicle and aggregate, respectively, showing that using all information possible from the sample rather than focusing on a single bone part is sometimes useful in improving identification accuracy.
This paper comes up with new approach on predicting of hardness performances for Titanium Alumini... more This paper comes up with new approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, hence by applying the ANN model using back propagation (BP) algorithm have been developed in predicting the hardness of TiA1N coatings. For the process, the selected input parameters are the substrate sputtering power, bias voltage and temperature while the output parameter is the coating hardness. Twenty set of dataset from experimental result (A.S.M Jaya et al., 2011) were used for the training network process to get the ANN ...
2017 6th ICT International Student Project Conference (ICT-ISPC), 2017
This paper describes a simple object detection for a 2D image from a bovine using the simple sour... more This paper describes a simple object detection for a 2D image from a bovine using the simple source code in software Matlab R2015a platform. The focus of this research work is on by basic code in the image processing namely; find objects, labeling objects, and region props of certain sizes. Through this preliminary segmentation indicated that an automatic cropped resultant bone morphology images were of size 1200×900 in dimension and file format of Portable Network Graphics extension has is perfect simple code for the beginner practitioner for find simple object, however, it needs improvement in thresholding to detect all twelve morphology objects.
Image segmentation is the procedure of separating an image into significant areas based on simila... more Image segmentation is the procedure of separating an image into significant areas based on similarity or heterogeneity measures and it is widely used in many fields that involve digital imaging including the medical field. Medical images from Computed Tomography, Magnetic Resonance Imaging and Mammogram require a proper segmentation technique to decompose the images into parts for further analysis. However, a standard methodology for any type of medical image segmentation is yet to be developed. The current image segmentation techniques and its efficiency will be evaluated in order to discover the technique that is most appropriate to be used for medical image segmentation. Researches carried out on image segmentation techniques between the periods of 2000 to 2016 are analysed and examined. This study specifically compares the techniques by analysing the performance of each algorithm on breast cancer modalities.
2017 6th ICT International Student Project Conference (ICT-ISPC), 2017
In the recent past, crime analyses are required to reveal the complexities in the crime dataset. ... more In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical perspectives. Nevertheless, to predict future crime accurately with a better performance, it is a challenging task because of the increasing numbers of crime in present days. Therefore, crime prediction method is important to identify the future crime and reduces the numbers of crime. Currently, some researchers have been conducted a study to predict crime based on particular inputs. The performance of prediction models can be evaluated using a variety of different prediction methods such as support vector machine, multivariate time series and artificial neural network. However, there are still some limitations on their findings to provide an accurate prediction for the location of crimes. A large number of research papers on this topic have already been published previously. Thus, in this paper, we thoroughly review each of them and summarized the outcomes. Our objective is to identify current implementations of crime prediction method and the possibility to enhance it for future needs.
IOP Conference Series: Materials Science and Engineering, 2020
Feature selection determines the most significant features for a given task while rejecting the n... more Feature selection determines the most significant features for a given task while rejecting the noisy, irrelevant and redundant features of the dataset that might mislead the classifier. Besides, the technique diminishes the dimensionality of the attribute of the dataset, thus reducing computation time and improving prediction performance. This paper aims to perform a feature selection for classification more accurately with an optimal features subset using Multivariate Adaptive Regression Splines (MARS) in Spline Model (SM) classifier. A comparative study of prediction performance was conducted with other classifiers including Decision Tree (DT), Neural Network (NN) and Support Vector Machine (SVM) with similar optimal feature subset produced by MARS. From the results, the MARS technique demonstrated the features reduction up to 87.76% and improved the classification accuracy. Based on the comparative analysis conducted, the Spline classifier shows better performance by achieving t...
IOP Conference Series: Materials Science and Engineering, 2020
Medical image registration is one of the processes involved in medical image analysis. During the... more Medical image registration is one of the processes involved in medical image analysis. During the process, an image will be computed and transform it for mapping the reference image to the target image to analyze the similarity merits as to help in diagnosis the situation in the medical field. However, the accuracy of the image registration is in question, might be improved if we can make use some optimization during the image registration process. In this research, we propose an enhancement of image registration algorithms based on monomodal registration by incorporating Cuckoo Search (CS) method for Lévy flight generation while simultaneously modifying and optimizing it to work on MRI image scanners, specifically to detect brain cancer. The performance of the proposed monomodal registration with CS algorithm was compared with basic traditional monomodal registration. The experimental results were validated by measuring the Normalized Mutual Information (NMI) and CPU run-time for a...
IOP Conference Series: Materials Science and Engineering, 2020
Journal of Computer Science, 2019
Crime forecasting and analysis are very important in predicting future crime patterns and benefic... more Crime forecasting and analysis are very important in predicting future crime patterns and beneficial to the authorities in planning effective crime prevention measures. One of the challenges found in crime analysis is the crime data itself as its form, representation and distribution are varied and unpredictable. To handle such data, most researchers have been focusing on applying various Artificial Intelligence (AI) techniques as an analytical tool. Among them, Gradient Tree Boosting (GTB) is a newly emerged AI technique for forecasting especially in crime analysis. GTB possesses a unique feature among other AI techniques which is its robustness towards any data representation and distribution. Subsequently, this study would like to adopt GTB in modelling crime rates based on 8 defined crime types. Similar to other AI techniques, GTB's overall performance is heavily influenced by its input parameter configuration. To assess such a challenge, this study would like to propose a hybrid DA-GTB crime forecasting model that is equipped with a metaheuristic optimization algorithm called Dragonfly Algorithm (DA) in optimizing GTB's three main parameters namely number of trees, size of individual trees and learning rate. From the experimental result obtained, the application of DA for parameter optimization yielded a positive impact in enhancing GTB forecasting performance as it produced the smallest error compared to nonoptimized GTB. This indicates that the proposed model is able to perform well using time series data with a limited and small sample size.
IOP Conference Series: Materials Science and Engineering, 2019
Nowadays the significant trend of the effort estimation is in demand. It needs more data to be co... more Nowadays the significant trend of the effort estimation is in demand. It needs more data to be collected and the stakeholders require an effective and efficient software for processing, which makes the hardware and software cost development becomes steeply increasing. This scenario is true especially in the area of large industry, as the size of a software project is becoming more complex and bigger, the complexity of estimation is continuously increased. Effort estimation is part of the software engineering economic study on how to manage limited resources in a way a project could meet its target goal in a specified schedule, budget and scope. It is necessary to develop or adopt a useful software development process in executing a software development project by acting as a key constraint to the project. The accuracy of estimation is the main critical evaluation for every study. Recently, the machine learning techniques are becoming widely used in many effort estimation problems bu...
International Journal of Innovative Computing, 2019
This simulation project aims to solve forensic anthropology issues by using the computational met... more This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affect the identification accuracy. The simulation identification method was studied in order to determine the best model, which reduce the total costs of the post-mortem as an objective. The computational method on simulation run improves the identification accuracy as proven by many studies. Fuzzy K-nearest neighbours classifier (FuzzyNN) is such a computational intelligence method and always shows the best performance in many fields including forensic anthropology. Thus, this intelligent identification method was implemented within the determining for best accuracy. The result of this proposed model was compared with raw data collection and standard collections datasets; Goldman Osteometric...
Journal of Physics: Conference Series, 2017
The presence of the missing value in the data set has always been a major problem for precise pre... more The presence of the missing value in the data set has always been a major problem for precise prediction. The method for imputing missing value needs to minimize the effect of incomplete data sets for the prediction model. Many algorithms have been proposed for countermeasure of missing value problem. In this review, we provide a comprehensive analysis of existing imputation algorithm, focusing on the technique used and the implementation of global or local information of data sets for missing value estimation. In addition validation method for imputation result and way to measure the performance of imputation algorithm also described. The objective of this review is to highlight possible improvement on existing method and it is hoped that this review gives reader better understanding of imputation method trend.
This paper presents a computational approach on predicting of hardness performances for Titanium ... more This paper presents a computational approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Support Vector Machine (SVM) and Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, the SVM and ANN model is used in predicting the hardness of TiA1N coatings. The influential factors of three coating process parameter namely substrate sputtering power, substrate bias voltage and substrate temperature were selected as input while the output parameter is the hardness. The results of proposed SVM and ANN models are compared to the experimental result and the hybrid RSM-Fuzzy model from previous work. The comparisons of SVM and ANN models against hybrid RSM-Fuzzy were based on predictive performances in order to obtain the most accurate model for prediction of hardness in TiA1N coating. In terms of predictive performance evaluation, four performances matrix were applied that are percentage error, mean square error (MSE), coefficient determination (R 2) and model accuracy. The result has proved that the proposed SVM model shows the better result compared to the ANN and hybrid RSM-fuzzy model. The good performances of the results obtained by the SVM method shows that this method can be applied for prediction of hardness performances in TiA1N coating process with better predictive performances compared to ANN and hybrid RSM-Fuzzy.
Abstrak: Kajian ini mengaplikasikan teorem Bayesian dalam meramalkan permintaan bagi sistem kawal... more Abstrak: Kajian ini mengaplikasikan teorem Bayesian dalam meramalkan permintaan bagi sistem kawalan inventori alat ganti mesin di kilang pemprosesan bahan kimia. Polisi pesanan ditentukan menggunakan model Kuantiti Pesanan Ekonomi (KPE). Permintaan item dalam model KPE berdasarkan kepada pesanan atau jumlah pengeluaran item dari stok. Dalam kes ini, permintaan adalah kadar kegagalan. Masalah dan polisi semasa digambarkan dengan jelas. Seterusnya, elemen
Jurnal Teknologi, 2013
Accurate diagnosis of cancer plays an importance role in order to save human life. The results of... more Accurate diagnosis of cancer plays an importance role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. From the literature, it has been found that Artificial Intelligence (AI) machine learning classifiers such as an Artificial Neural Network (ANN) and Support Vector Machine (SVM) can help doctors in diagnosing cancer more precisely. Both of them have been proven to produce good performance of cancer classification accuracy. The aim of this study is to compare the performance of the ANN and SVM classifiers on four different cancer datasets. For breast cancer and liver cancer dataset, the features of the data are based on the condition of the organs which is also called as standard data while for prostate cancer and ovarian cancer; both of these datasets are in the form of gene expression data. The datasets i...
Kajian Terhadap Perbandingan Prestasi Teknik Pensaizan Lot di dalam Persekitaran Rolling Horizon.... more Kajian Terhadap Perbandingan Prestasi Teknik Pensaizan Lot di dalam Persekitaran Rolling Horizon. Ali, Nor Azizah (2006) Kajian Terhadap Perbandingan Prestasi Teknik Pensaizan Lot di dalam Persekitaran Rolling Horizon. ...
Enterprise resource planning: trend and challenge in teaching and project supervision. Haron, Hab... more Enterprise resource planning: trend and challenge in teaching and project supervision. Haron, Habibollah and Ali, Nor Azizah (2006) Enterprise resource planning: trend and challenge in teaching and project supervision. In: Seminar Nasional Manufaktur II , 2006, n/a. ...
Mohd. Zain, Azlan and Haron, Habibollah and Ali, Nor Azizah and Mohamed Radzi, Nor Haizan (2006) ... more Mohd. Zain, Azlan and Haron, Habibollah and Ali, Nor Azizah and Mohamed Radzi, Nor Haizan (2006) Computerized quality control system using control charts in statistical proses control. In: The International Conference on Computer and Communication Engineering 2006, ...
... Hasan, Haswadi and Ahmad, Ab. Rahman and Ali, Nor Azizah (2006) Pembangunan sistem drp-MRP un... more ... Hasan, Haswadi and Ahmad, Ab. Rahman and Ali, Nor Azizah (2006) Pembangunan sistem drp-MRP untuk industri kecil dan sederhana (IKS). Project Report. ... Tujuan utama sistem ini adalah untuk menggantikan sistem semasa yang dilakukan secara manual. ...
Discrete-Event Simulation (DES) has been used extensively by companies for modelling and analysis... more Discrete-Event Simulation (DES) has been used extensively by companies for modelling and analysis of manufacturing systems, especially in evaluating the performance of a proposed system and choosing an appropriate design before actually implementing the ...
2021 4th International Conference of Computer and Informatics Engineering (IC2IE), 2021
One of the topics covered in forensic anthropology is an investigation of skeletal remains where ... more One of the topics covered in forensic anthropology is an investigation of skeletal remains where various properties of the skeleton are to be determined. Typically, the sample found is incomplete, meaning some bone parts are missing or destroyed, and the analysis needs to depend on limited information obtained from what is available. This research focuses on arm, leg, clavicle, and scapula bones, with 8 bone parts in total. Each part is either used independently from the other or considered altogether (aggregate) to test its usability in finding out the owner’s identity when facing such a situation. Bone measurements obtained from the database were used as input data for two different classifiers, namely artificial neural networks and supporting vector machines, with two identification targets, namely sex and race. All of the input data came from publicly available Robert J. Terry Anatomical Skeletal Collection Postcranial Osteo-metric database. Accuracies of 86.67% and 70.78% are obtained for those targets using clavicle and aggregate, respectively, showing that using all information possible from the sample rather than focusing on a single bone part is sometimes useful in improving identification accuracy.
This paper comes up with new approach on predicting of hardness performances for Titanium Alumini... more This paper comes up with new approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, hence by applying the ANN model using back propagation (BP) algorithm have been developed in predicting the hardness of TiA1N coatings. For the process, the selected input parameters are the substrate sputtering power, bias voltage and temperature while the output parameter is the coating hardness. Twenty set of dataset from experimental result (A.S.M Jaya et al., 2011) were used for the training network process to get the ANN ...
2017 6th ICT International Student Project Conference (ICT-ISPC), 2017
This paper describes a simple object detection for a 2D image from a bovine using the simple sour... more This paper describes a simple object detection for a 2D image from a bovine using the simple source code in software Matlab R2015a platform. The focus of this research work is on by basic code in the image processing namely; find objects, labeling objects, and region props of certain sizes. Through this preliminary segmentation indicated that an automatic cropped resultant bone morphology images were of size 1200×900 in dimension and file format of Portable Network Graphics extension has is perfect simple code for the beginner practitioner for find simple object, however, it needs improvement in thresholding to detect all twelve morphology objects.
Image segmentation is the procedure of separating an image into significant areas based on simila... more Image segmentation is the procedure of separating an image into significant areas based on similarity or heterogeneity measures and it is widely used in many fields that involve digital imaging including the medical field. Medical images from Computed Tomography, Magnetic Resonance Imaging and Mammogram require a proper segmentation technique to decompose the images into parts for further analysis. However, a standard methodology for any type of medical image segmentation is yet to be developed. The current image segmentation techniques and its efficiency will be evaluated in order to discover the technique that is most appropriate to be used for medical image segmentation. Researches carried out on image segmentation techniques between the periods of 2000 to 2016 are analysed and examined. This study specifically compares the techniques by analysing the performance of each algorithm on breast cancer modalities.
2017 6th ICT International Student Project Conference (ICT-ISPC), 2017
In the recent past, crime analyses are required to reveal the complexities in the crime dataset. ... more In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical perspectives. Nevertheless, to predict future crime accurately with a better performance, it is a challenging task because of the increasing numbers of crime in present days. Therefore, crime prediction method is important to identify the future crime and reduces the numbers of crime. Currently, some researchers have been conducted a study to predict crime based on particular inputs. The performance of prediction models can be evaluated using a variety of different prediction methods such as support vector machine, multivariate time series and artificial neural network. However, there are still some limitations on their findings to provide an accurate prediction for the location of crimes. A large number of research papers on this topic have already been published previously. Thus, in this paper, we thoroughly review each of them and summarized the outcomes. Our objective is to identify current implementations of crime prediction method and the possibility to enhance it for future needs.
IOP Conference Series: Materials Science and Engineering, 2020
Feature selection determines the most significant features for a given task while rejecting the n... more Feature selection determines the most significant features for a given task while rejecting the noisy, irrelevant and redundant features of the dataset that might mislead the classifier. Besides, the technique diminishes the dimensionality of the attribute of the dataset, thus reducing computation time and improving prediction performance. This paper aims to perform a feature selection for classification more accurately with an optimal features subset using Multivariate Adaptive Regression Splines (MARS) in Spline Model (SM) classifier. A comparative study of prediction performance was conducted with other classifiers including Decision Tree (DT), Neural Network (NN) and Support Vector Machine (SVM) with similar optimal feature subset produced by MARS. From the results, the MARS technique demonstrated the features reduction up to 87.76% and improved the classification accuracy. Based on the comparative analysis conducted, the Spline classifier shows better performance by achieving t...
IOP Conference Series: Materials Science and Engineering, 2020
Medical image registration is one of the processes involved in medical image analysis. During the... more Medical image registration is one of the processes involved in medical image analysis. During the process, an image will be computed and transform it for mapping the reference image to the target image to analyze the similarity merits as to help in diagnosis the situation in the medical field. However, the accuracy of the image registration is in question, might be improved if we can make use some optimization during the image registration process. In this research, we propose an enhancement of image registration algorithms based on monomodal registration by incorporating Cuckoo Search (CS) method for Lévy flight generation while simultaneously modifying and optimizing it to work on MRI image scanners, specifically to detect brain cancer. The performance of the proposed monomodal registration with CS algorithm was compared with basic traditional monomodal registration. The experimental results were validated by measuring the Normalized Mutual Information (NMI) and CPU run-time for a...
IOP Conference Series: Materials Science and Engineering, 2020
Journal of Computer Science, 2019
Crime forecasting and analysis are very important in predicting future crime patterns and benefic... more Crime forecasting and analysis are very important in predicting future crime patterns and beneficial to the authorities in planning effective crime prevention measures. One of the challenges found in crime analysis is the crime data itself as its form, representation and distribution are varied and unpredictable. To handle such data, most researchers have been focusing on applying various Artificial Intelligence (AI) techniques as an analytical tool. Among them, Gradient Tree Boosting (GTB) is a newly emerged AI technique for forecasting especially in crime analysis. GTB possesses a unique feature among other AI techniques which is its robustness towards any data representation and distribution. Subsequently, this study would like to adopt GTB in modelling crime rates based on 8 defined crime types. Similar to other AI techniques, GTB's overall performance is heavily influenced by its input parameter configuration. To assess such a challenge, this study would like to propose a hybrid DA-GTB crime forecasting model that is equipped with a metaheuristic optimization algorithm called Dragonfly Algorithm (DA) in optimizing GTB's three main parameters namely number of trees, size of individual trees and learning rate. From the experimental result obtained, the application of DA for parameter optimization yielded a positive impact in enhancing GTB forecasting performance as it produced the smallest error compared to nonoptimized GTB. This indicates that the proposed model is able to perform well using time series data with a limited and small sample size.
IOP Conference Series: Materials Science and Engineering, 2019
Nowadays the significant trend of the effort estimation is in demand. It needs more data to be co... more Nowadays the significant trend of the effort estimation is in demand. It needs more data to be collected and the stakeholders require an effective and efficient software for processing, which makes the hardware and software cost development becomes steeply increasing. This scenario is true especially in the area of large industry, as the size of a software project is becoming more complex and bigger, the complexity of estimation is continuously increased. Effort estimation is part of the software engineering economic study on how to manage limited resources in a way a project could meet its target goal in a specified schedule, budget and scope. It is necessary to develop or adopt a useful software development process in executing a software development project by acting as a key constraint to the project. The accuracy of estimation is the main critical evaluation for every study. Recently, the machine learning techniques are becoming widely used in many effort estimation problems bu...
International Journal of Innovative Computing, 2019
This simulation project aims to solve forensic anthropology issues by using the computational met... more This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affect the identification accuracy. The simulation identification method was studied in order to determine the best model, which reduce the total costs of the post-mortem as an objective. The computational method on simulation run improves the identification accuracy as proven by many studies. Fuzzy K-nearest neighbours classifier (FuzzyNN) is such a computational intelligence method and always shows the best performance in many fields including forensic anthropology. Thus, this intelligent identification method was implemented within the determining for best accuracy. The result of this proposed model was compared with raw data collection and standard collections datasets; Goldman Osteometric...
Journal of Physics: Conference Series, 2017
The presence of the missing value in the data set has always been a major problem for precise pre... more The presence of the missing value in the data set has always been a major problem for precise prediction. The method for imputing missing value needs to minimize the effect of incomplete data sets for the prediction model. Many algorithms have been proposed for countermeasure of missing value problem. In this review, we provide a comprehensive analysis of existing imputation algorithm, focusing on the technique used and the implementation of global or local information of data sets for missing value estimation. In addition validation method for imputation result and way to measure the performance of imputation algorithm also described. The objective of this review is to highlight possible improvement on existing method and it is hoped that this review gives reader better understanding of imputation method trend.
This paper presents a computational approach on predicting of hardness performances for Titanium ... more This paper presents a computational approach on predicting of hardness performances for Titanium Aluminium Nitride (TiA1N) coating process. A new application in predicting the hardness performances of TiA1N coatings using a method called Support Vector Machine (SVM) and Artificial Neural Network (ANN) is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent properties in surface hardness and wear resistance. Physical Vapor Deposition (PVD) magnetron sputtering process has been used to produce the TiA1N coatings. Based on the experimental dataset of previous work, the SVM and ANN model is used in predicting the hardness of TiA1N coatings. The influential factors of three coating process parameter namely substrate sputtering power, substrate bias voltage and substrate temperature were selected as input while the output parameter is the hardness. The results of proposed SVM and ANN models are compared to the experimental result and the hybrid RSM-Fuzzy model from previous work. The comparisons of SVM and ANN models against hybrid RSM-Fuzzy were based on predictive performances in order to obtain the most accurate model for prediction of hardness in TiA1N coating. In terms of predictive performance evaluation, four performances matrix were applied that are percentage error, mean square error (MSE), coefficient determination (R 2) and model accuracy. The result has proved that the proposed SVM model shows the better result compared to the ANN and hybrid RSM-fuzzy model. The good performances of the results obtained by the SVM method shows that this method can be applied for prediction of hardness performances in TiA1N coating process with better predictive performances compared to ANN and hybrid RSM-Fuzzy.
Abstrak: Kajian ini mengaplikasikan teorem Bayesian dalam meramalkan permintaan bagi sistem kawal... more Abstrak: Kajian ini mengaplikasikan teorem Bayesian dalam meramalkan permintaan bagi sistem kawalan inventori alat ganti mesin di kilang pemprosesan bahan kimia. Polisi pesanan ditentukan menggunakan model Kuantiti Pesanan Ekonomi (KPE). Permintaan item dalam model KPE berdasarkan kepada pesanan atau jumlah pengeluaran item dari stok. Dalam kes ini, permintaan adalah kadar kegagalan. Masalah dan polisi semasa digambarkan dengan jelas. Seterusnya, elemen
Jurnal Teknologi, 2013
Accurate diagnosis of cancer plays an importance role in order to save human life. The results of... more Accurate diagnosis of cancer plays an importance role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. From the literature, it has been found that Artificial Intelligence (AI) machine learning classifiers such as an Artificial Neural Network (ANN) and Support Vector Machine (SVM) can help doctors in diagnosing cancer more precisely. Both of them have been proven to produce good performance of cancer classification accuracy. The aim of this study is to compare the performance of the ANN and SVM classifiers on four different cancer datasets. For breast cancer and liver cancer dataset, the features of the data are based on the condition of the organs which is also called as standard data while for prostate cancer and ovarian cancer; both of these datasets are in the form of gene expression data. The datasets i...
Kajian Terhadap Perbandingan Prestasi Teknik Pensaizan Lot di dalam Persekitaran Rolling Horizon.... more Kajian Terhadap Perbandingan Prestasi Teknik Pensaizan Lot di dalam Persekitaran Rolling Horizon. Ali, Nor Azizah (2006) Kajian Terhadap Perbandingan Prestasi Teknik Pensaizan Lot di dalam Persekitaran Rolling Horizon. ...
Enterprise resource planning: trend and challenge in teaching and project supervision. Haron, Hab... more Enterprise resource planning: trend and challenge in teaching and project supervision. Haron, Habibollah and Ali, Nor Azizah (2006) Enterprise resource planning: trend and challenge in teaching and project supervision. In: Seminar Nasional Manufaktur II , 2006, n/a. ...
Mohd. Zain, Azlan and Haron, Habibollah and Ali, Nor Azizah and Mohamed Radzi, Nor Haizan (2006) ... more Mohd. Zain, Azlan and Haron, Habibollah and Ali, Nor Azizah and Mohamed Radzi, Nor Haizan (2006) Computerized quality control system using control charts in statistical proses control. In: The International Conference on Computer and Communication Engineering 2006, ...
... Hasan, Haswadi and Ahmad, Ab. Rahman and Ali, Nor Azizah (2006) Pembangunan sistem drp-MRP un... more ... Hasan, Haswadi and Ahmad, Ab. Rahman and Ali, Nor Azizah (2006) Pembangunan sistem drp-MRP untuk industri kecil dan sederhana (IKS). Project Report. ... Tujuan utama sistem ini adalah untuk menggantikan sistem semasa yang dilakukan secara manual. ...
Discrete-Event Simulation (DES) has been used extensively by companies for modelling and analysis... more Discrete-Event Simulation (DES) has been used extensively by companies for modelling and analysis of manufacturing systems, especially in evaluating the performance of a proposed system and choosing an appropriate design before actually implementing the ...