Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin

Artificial neural network (ANN) is one of the most prominent universal approximators, and has been implemented tremendously in forecasting arena. The aforementioned neural network forecasting models are feedforward (nonlinear autoregressive) and recurrent (nonlinear autoregressive moving average). Theoretically, the most common algorithm to train the network is the backpropagation (BP) algorithm which is based on the minimization of the ordinary least squares (LS) estimator in terms of mean squared error (MSE). However, this algorithm is not totally robust in the presence of outliers that usually exist in the routine time series data, and this may cause false prediction of future values. Therefore, the main objective of this research is to modify the backpropagation algorithm of nonlinear autoregressive (NAR) and autoregressive moving average (NARMA) models using Tukey-bisquare estimator and a proposed hybrid firefly algorithm on the least median of squares (FFA-LMedS), in order to manage outlying data efficiently, hence produce more accurate forecasted values. The proposed neural network models are named as modified NAR and NARMA models, which able to handle various degrees of outliers problem in time series data. The performance of the fitted neural network models are examined on both real and simulated datasets. The error measures to assess the performance are Root Mean Square Errors (RMSE), Mean Square Prediction Error (MSPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Geometric Root Mean Square Error (GRMSE)

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

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

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

A Preliminary Study on Understanding the Consumptions of Therapeutic Essential Oils During Covid-19 Pandemic Among Adults Using ANN

The COVID-19 pandemic has emphasized the significance of utilizing essential oils (EO) as one of the holistic ways of supporting and enhancing health. As a consequence of growing knowledge of connected health concerns, people all over the world are looking for natural ways to avoid different ailments. It has been proven that excellent health and psychological awareness increase the human body's immune response, therefore boosting disease resistance. Essential oils are derived in a number of ways from valued plants containing active chemicals with medicinal qualities. In Malaysia, many have used EO in their daily lives. This paper identifies the hierarchy of importance among factors which contribute towards the usage frequency of essential oils in Malaysia using an artificial neural network. Two-layer neural network (NN) models have been applied, which are multilayer perceptron (MLP) and radial basis function (RBF). Based on the analysis done, RBF-NN performed the best with SSE=4.436 and RE=0.548. It can be concluded that, based on sensitivity analysis, the top five factors toward usage frequency are consumption, age, external use, clinic visit, and occasion, with normalized importance of 100%, 90.8%, 89.3%, 68.2%, and 42.2% respectively

The quadriceps muscle of knee joint modelling using hybrid particle swarm optimization-neural network (PSO-NN)

Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force

Artificial neural network and its Islamic relevance

The complex workings and wonders of the human brain have become mysterious since time immemorial. Human brain is a thinking organ that learns and grows by interacting with the world through perception and action; and it is able to continually adapt and rewire itself. Only recently scientists have been able to learn how the neural network of the brain forms. Artificial neural network (ANN) is a mathematical model or computational model that inspired by the structure and functional aspects of biological neural networks. As a final verdict, the complex structure of ANN which resembles the ordinary human brain mechanism may help for better understanding of the creative Power and lead to the enhancement of our faith in God as the Mighty Creator. The objective of this paper is to relate ANN with the Islamic worldview and its relevance for learning and education. The method used was analytical outlook to comprehend ANN in an Islamic approach

Consolidated backpropagation neural network for Malaysian construction costs indices data with outliers problem

Neurocomputing has been adjusted effectively in time series forecasting activities, yet the vicinity of exceptions that frequently happens in time arrangement information might contaminate the system preparing information. This is because of its capacity to naturally realise any example without earlier suspicions and loss of sweeping statement. In principle, the most widely recognised calculation for preparing the system is the backpropagation (BP) calculation, which inclines toward minimisation of standard slightest squares (OLS) estimator, particularly the mean squared mistake (MSE). Regardless, this calculation is not by any stretch of the imagination strong when the exceptions are available, and it might prompt bogus expectation of future qualities. In this paper, we exhibit another calculation which controls the firefly algorithm of least median squares (FFA-LMedS) estimator for neural system nonlinear autoregressive moving average (ANN-NARMA) model enhancement to provide betterment for the peripheral issue in time arrangement information. Moreover, execution of the solidified model in correlation with another hearty ANN-NARMA models, utilising M-estimators, Iterative LMedS and Particle Swarm Optimisation on LMedS (PSO-LMedS) with root mean squared blunder (RMSE) qualities, is highlighted in this paper. In the interim, the actual monthly information of Malaysian Aggregate, Sand and Roof Materials value was taken from January 1980 to December 2012 (base year 1980=100) with various levels of anomaly issues. It was found that the robustified ANN-NARMA model utilising FFA-LMedS delivered the best results, with the RMSE values having almost no mistakes at all in all the preparation, testing and acceptance sets for every single distinctive variable. Findings of the studies are hoped to assist the regarded powers including the PFI development tasks to overcome cost overwhelms