Results of Fitted Neural Network Models on Malaysian Aggregate Dataset

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures.  This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset

Factors contributing towards research productivity in higher education

Research Productivity (RP) is the key element in the establishment of ranking and rating system in the Higher Education (HE) sector. Despite of the many initiatives taken to enliven the research culture among academic staff, there are still constraints and resistance towards conducting research. Therefore, this study attempts to identify the factors affecting RP and develop an appropriate model to determine the RP of an academic staff in Universiti Teknologi MARA (UiTM). In this study, 5 research related indicators were used in the determination of RP. Since the population size of UiTM is large, the primary data was collected by using questionnaire survey and stratified random sampling. The variables that were found to be significant in determining RP of an academic staff were age cohort, highest qualification, cluster and track emphasis. Satisfaction towards annual KPI, UiTM current policy and monthly income were also found to influence the RP of an academic staff. In addition, perceiving the role of principal investigator as a chore and burden and supervising and graduating a PhD student perception as burden and pleasure were also found to be affecting RP. Using these variables, Logistic Regression Model was used to determine the RP of an academic staff in UiTM. In conclusion, personal, environmental and behavioural factors were found to have influence on the RP among academic staff of UiTM. Therefore, generally it is possible to maximize the RP of academic staff by identifying the factors influencing RP followed by strategic management and proper monitoring system

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete

A predictive model for prediction of heart surgery procedure

Coronary heart disease (CHD) is a disease in which plague in the form of waxy substance builds up inside the coronary arteries. Coronary artery bypass grafting (CABG) is used as treatment on CHD patients but the role of CABG has been challenged by percutaneous coronary intervention (PCI) when it was introduced in 1977. Drug eluting stents (DES) was introduced with the development of PCI. The purpose of this study was to find the potential risk factors that associated with the procedures (CABG and DES) and to model procedure (CABG vs DES) on coronary heart disease male patients aged 45 years old and below. The study sample was among male patients aged 45 years old and below who has undergone CABG or DES procedure at either IJN or HUKM from January 2007 until December 2010. Logistic regression was used to model treatment selection on coronary heart disease with 87.3% of the classification rate. Patient who i) smoke, ii) obese, or ii) had dyslipidemia was significantly associated with DES, and the other factors were prone to have CABG as their treatment

An artificial neural network approach on catering premises inspection in Pahang state

Background: The hygiene level of the premise reflect the safety and quality of the food served in the food services kitchen and the poor sanitary condition can contribute to food poisoning outbreaks. Recently, many food poisoning cases reported from food services sector and most of the cases are from institutional food services. These premises sometimes are graded as clean or very clean which can be questioned, mostly at institutions such as schools. Objective; The aim of this research is to identify the level of significance among the contributing factors which influence the caterers’ grading score in Pahang as the biggest state in Malaysian Peninsular using artificial neural network (ANN). Methods: In this research, the premises have been categorised into 3 categories namely Rest and Rescue Area (RnR) premises along the East Coast Highway, event caterers and institutional. A total of 268 premises were involved in this research with 66 (24.63%) RnR, 63 (23.51%) event caterers, and 139 (51.87%) institutional caterers. The instrument used in this research is based on the official risk based premise inspection form currently used by Ministry of Health Malaysia (MOH). The important items in the inspection form are process control, building and facilities, equipment and utensils, cleaning and maintenance, as well as food handler’s requirements. These items consist a total of thirty-one (31) elements with respected weightage score based on risk to food safety. The collected data is analysed using two-layer neural network with tansig-linear configurations, with trainlm activation function. Results: Prior to data normalization, the dataset is partitioned according 70-30-30 sets. In this research, the final model is reliable where the relative error of the training set is 0.076. The five most significant factors influencing the premises grades are critical control points (CCP), transportation condition, risky other related activity, adequate toilets, as well as adequate and safe water supply. Conclusion: As a conclusion, it is expected that the results will assist the related authorities to take appropriate actions prior to the important and compliance information, especially the significant aspects with respect to public health, permit, inspection and other related legal issues. It is suggested that the result can be improved by using other type of training functions such trainscg and trainbfg

Return on investment from educational research grant funding: deliverables and measurement

This paper investigates the return on investment (ROI) paradigm from fundamental educational research grant funding perspectives. The researchers conducted a wide-ranging literature search regarding the educational research grant funding ROI from public policy and economics viewpoints. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) were utilized to identify and analyze research articles and original reports related to ROI from educational research grant funding. The research data were screened through a literature review based on the inclusion criteria, namely study focusing on return on investment of educational research funding and study published between 2001 and 2021 (as of December 2022). This study found evidence that ROI calculation from fundamental educational research grants is not straightforward and varies. Besides, most available research focuses on research impact rather than economic and intrinsic research value. Based on the compiled literature on research-related performance attributes, this study identified three distinct deliverables of educational research funding: tangible output, intangible output, and research outcome. The present research proposes more robust and reliable methods for measuring the ROI of fundamental educational research impact, potentially generating a much-inform decision-making and resource allocation in educational research grant funding

Firing Pin Impression Segmentation using Canny Edge Detection Operator and Hough Transform

Firearms identification based on the forensic ballistics specimen is crucial in solving criminal case in a short time. Currently, the firearms examiners perform authentication by visual observation. Due to observation of large evidence database, the experts normally take a long time to identify the firearms. As a result, computerized firearms identification should be implemented in order to perform the identification faster. The computerized identification involves image preprocessing, segmentation, feature extraction and classification. Therefore, in order to reduce computational time, the segmentation has to be performed automatically. The main objective of this study is to perform the segmentation of firing pin impression by using Canny edge detection operator improvised with Hough transform. The performance of segmentation in detecting the central image of firing pin impression has achieved 93% segmentation accurac

Multiclass Prediction Model for Student Grade Prediction Using Machine Learning

This work was supported in part by the Ministry of Higher Education through the Fundamental Research Scheme under Grant FRGS/1/2018/ICT04/UTM/01/1, in part by the Speci~c Research Project (SPEV) at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic, under Grant 2102-2021, in part by the Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, and in part by the Malaysia Research University Network (MRUN) under Grant Vot 4L876.Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive analytics used advanced analytics that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. Mostly know that student grade is one of the key performance indicators that can help educators monitor their academic performance. During the past decade, researchers have proposed many variants of machine learning techniques in education domains. However, there are severe challenges in handling imbalanced datasets for enhancing the performance of predicting student grades. Therefore, this paper presents a comprehensive analysis of machine learning techniques to predict the nal student grades in the rst semester courses by improving the performance of predictive accuracy. Two modules will be highlighted in this paper. First, we compare the accuracy performance of six well-known machine learning techniques namely Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN), Logistic Regression (LR) and Random Forest (RF) using 1282 real student's course grade dataset. Second, we proposed a multiclass prediction model to reduce the over tting and misclassi cation results caused by imbalanced multi-classi cation based on oversampling Synthetic Minority Oversampling Technique (SMOTE) with two features selection methods. The obtained results showthat the proposed model integrates with RF give signi cant improvement with the highest f-measure of 99.5%. This proposed model indicates the comparable and promising results that can enhance the prediction performance model for imbalanced multi-classi cation for student grade prediction.Science and Technology Development Fund (STDF)Ministry of Higher Education & Scientific Research (MHESR) FRGS/1/2018/ICT04/UTM/01/1Specific Research Project (SPEV) at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic 2102-2021Universiti Teknologi Malaysia (UTM) Vot-20H04Malaysia Research University Network (MRUN) 4L87

The quadriceps muscle of knee joint modelling using neural network approach: Part 2

— Artificial neural network has been implemented in many filed, and one of the most famous estimators. Neural network has long been known for its ability to handle a complex nonlinear system without a mathematical model and has the ability to learn sophisticated nonlinear relationships provides. Theoretically, the most common algorithm to train the network is the backpropagation (BP) algorithm which is based on the minimization of the mean square error (MSE). Subsequently, this paper displays the change of quadriceps muscle model by using fake savvy strategy named backpropagation neural system nonlinear autoregressive (BPNN-NAR) model in perspective of utilitarian electrical affectation (FES). A movement of tests using FES was driven. The data that is gotten is used to develop the quadriceps muscle model. 934 planning data, 200 testing and 200 endorsement data set are used as a part of the change of muscle model. It was found that BPNNNARMA is suitable and efficient to model this type of data. A neural network model is the best approach for modelling nonlinear models such as active properties of the quadriceps muscle with one input, namely output namely muscle force