Pre-dispersive near-infrared light sensing in non-destructively classifying the brix of intact pineapples

Exported fresh intact pineapples must fulfill the minimum internal quality requirement of 12 degree brix. Even though near-infrared (NIR) spectroscopic approaches are promising to non-destructively and rapidly assess the internal quality of intact pineapples, these approaches involve expensive and complex NIR spectroscopic instrumentation. Thus, this research evaluates the performance of a proposed pre-dispersive NIR light sensing approach in non-destructively classifying the Brix of pineapples using K-fold cross-validation, holdout validation, and sensitive analysis. First, the proposed pre-dispersive NIR sensing device that consisted of a light sensing element and five NIR light emitting diodes with peak wavelengths of 780, 850, 870, 910, and 940 nm, respectively, was developed. After that, the diffuse reflectance NIR light of intact pineapples was non-destructively acquired using the developed NIR sensing device before their Brix values were conventionally measured using a digital refractometer. Next, an artificial neural network (ANN) was trained and optimized to classify the Brix values of pineapples using the acquired NIR light. The results of the sensitivity analysis showed that either one wavelength that was near to the water absorbance or chlorophyll band was redundant in the classification. The performance of the trained ANN was tested using new pineapples with the optimal classification accuracy of 80.56%. This indicates that the proposed predispersive NIR light sensing approach coupled with the ANN is promising to be an alternative to non-destructively classifying the internal quality of fruits

Causative factors of construction and demolition waste generation in Iraq Construction Industry

The construction industry has hurt the environment from the waste generated during construction activities. Thus, it calls for serious measures to determine the causative factors of construction waste generated. There are limited studies on factors causing construction, and demolition (C&D) waste generation, and these limited studies only focused on the quantification of construction waste. This study took the opportunity to identify the causative factors for the C&D waste generation and also to determine the risk level of each causal factor, and the most important minimization methods to avoiding generating waste. This study was carried out based on the quantitative approach. A total of 39 factors that causes construction waste generation that has been identified from the literature review were considered which were then clustered into 4 groups. Improved questionnaire surveys by 38 construction experts (consultants, contractors and clients) during the pilot study. The actual survey was conducted with a total of 380 questionnaires, received with a response rate of 83.3%. Data analysis was performed using SPSS software. Ranking analysis using the mean score approach found the five most significant causative factors which are poor site management, poor planning, lack of experience, rework and poor controlling. The result also indicated that the majority of the identified factors having a high-risk level, in addition, the better minimization method is environmental awareness. A structural model was developed based on the 4 groups of causative factors using the Partial Least Squared-Structural Equation Modelling (PLS-SEM) technique. It was found that the model fits due to the goodness of fit (GOF ≥ 0.36= 0.658, substantial). Based on the outcome of this study, 39 factors were relevant to the generation of construction and demolition waste in Iraq. These groups of factors should be avoided during construction works to reduce the waste generated. The findings of this study are helpful to authorities and stakeholders in formulating laws and regulations. Furthermore, it provides opportunities for future researchers to conduct additional research’s on the factors that contribute to construction waste generation

Multivariate time series analysis for short-term forecasting of ground level ozone (O3) in Malaysia

The declining of air quality mostly affects the elderly, children, people with asthma, as well as a restriction on outdoor activities. Therefore, there is an importance to provide a statistical modelling to forecast the future values of surface layer ozone (O3) concentration. The objectives of this study are to obtain the best multivariate time series (MTS) model and develop an online air quality forecasting system for O3 concentration in Malaysia. The implementations of MTS model improve the recent statistical model on air quality for short-term prediction. Ten air quality monitoring stations situated at four (4) different types of location were selected in this study. The first type is industrial represent by Pasir Gudang, Perai, and Nilai, second type is urban represent by Kuala Terengganu, Kota Bharu, and Alor Setar. The third is suburban located in Banting, Kangar, and Tanjung Malim, also the only background station at Jerantut. The hourly record data from 2010 to 2017 were used to assess the characteristics and behaviour of O3 concentration. Meanwhile, the monthly record data of O3, particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), temperature (T), wind speed (WS), and relative humidity (RH) were used to examine the best MTS models. Three methods of MTS namely vector autoregressive (VAR), vector moving average (VMA), and vector autoregressive moving average (VARMA), has been applied in this study. Based on the performance error, the most appropriate MTS model located in Pasir Gudang, Kota Bharu and Kangar is VAR(1), Kuala Terengganu and Alor Setar for VAR(2), Perai and Nilai for VAR(3), Tanjung Malim for VAR(4) and Banting for VAR(5). Only Jerantut obtained the VMA(2) as the best model. The lowest root mean square error (RMSE) and normalized absolute error is 0.0053 and <0.0001 which is for MTS model in Perai and Kuala Terengganu, respectively. Meanwhile, for mean absolute error (MAE), the lowest is in Banting and Jerantut at 0.0013. The online air quality forecasting system for O3 was successfully developed based on the best MTS models to represent each monitoring station

Best Architecture Recommendations of ANN Backpropagation Based on Combination of Learning Rate, Momentum, and Number of Hidden Layers

This article discusses the results of research on the combination of learning rate values, momentum, and the number of neurons in the hidden layer of the ANN Backpropagation (ANN-BP) architecture using meta-analysis. This study aims to find out the most recommended values at each learning rate and momentum interval, namely [0.1], as well as the number of neurons in the hidden layer used during the data training process. We conducted a meta-analysis of the use of learning rate, momentum, and number of neurons in the hidden layer of ANN-BP. The eligibility data criteria of 63 data include a learning rate of 44 complete data, the momentum of 30 complete data, and the number of neurons in the hidden layer of 45 complete data. The results of the data analysis showed that the learning rate value was recommended at intervals of 0.1-0.2 with a RE model value of 0.938 (very high), the momentum at intervals of 0.7-0.9 with RE model values of 0.925 (very high), and the number of neurons in the input layer that was smaller than the number of neurons in the hidden layer with a RE model value of 0.932 (very high). This recommendation is obtained from the results of data analysis using JASP by looking at the effect size of the accuracy level of research sample data

A Hybridized Forecasting Model for Metal Commodity Prices: An Empirical Model Evaluation

Appropriate decision on perfect commodity prediction under market’s constant fluctuations intensifies the need for efficient methods. The main objective of this study is to apply different optimization algorithms such as conventional particle swarm optimization (PSO), bat algorithm (BAT) and ant colony optimization (ACO) algorithms on back propagation neural network (BPNN) to enhance the accuracy of prediction and minimize the error. In this paper, a model has been proposed for volatility forecasting using PSO algorithm to train the BPNN and predict the commodities’ closing price. The proposed PSO-BPNN model is considered as best forecasting model compared to BPNN, BAT-BPNN and ACO-BPNN. The experiment has been carried out upon five publicly available metal datasets (gold, silver, lead, aluminium, and copper) to forecast the price return volatility of those five metals challenging the effectiveness. Here, three technical indicators and four filters, such as; moving average convergence/divergence (MACD), williams %R (W%), bollinger (B), least mean squares (LMS), finite impulse response (FIR), Kalmanand recursive least square (RLS) have been applied for providing an additional degree of freedom to train and test the classifiers. From the experimental result analysis it has been found that the proposed PSO-BPNN produces promising output while comparing with BPNN, BAT-BPNN and ACO-BPNN

A Hybridized Forecasting Model for Metal Commodity Prices: An Empirical Model Evaluation

945-950Appropriate decision on perfect commodity prediction under market&rsquo;s constant fluctuations intensifies the need for efficient methods. The main objective of this study is to apply different optimization algorithms such as conventional particle swarm optimization (PSO), bat algorithm (BAT) and ant colony optimization (ACO) algorithms on back propagation neural network (BPNN) to enhance the accuracy of prediction and minimize the error. In this paper, a model has been proposed for volatility forecasting using PSO algorithm to train the BPNN and predict the commodities&rsquo; closing price. The proposed PSO-BPNN model is considered as best forecasting model compared to BPNN, BAT-BPNN and ACO-BPNN. The experiment has been carried out upon five publicly available metal datasets (gold, silver, lead, aluminium, and copper) to forecast the price return volatility of those five metals challenging the effectiveness. Here, three technical indicators and four filters, such as; moving average convergence/divergence (MACD), williams %R (W%), bollinger (B), least mean squares (LMS), finite impulse response (FIR), Kalmanand recursive least square (RLS) have been applied for providing an additional degree of freedom to train and test the classifiers. From the experimental result analysis it has been found that the proposed PSO-BPNN produces promising output while comparing with BPNN, BAT-BPNN and ACO-BPNN

Cause and Effect Prediction in Manufacturing Process Using an Improved Neural Networks

The limitations of the existing Knowledge Hyper-surface method in learning cause and effect relationships in the manufacturing process is explored. A new approach to enhance the performance of the current Knowledge Hyper-surface method has been proposed by constructing midpoints between each primary weight along each dimension by using a quadratic Lagrange interpolation polynomial. The new secondary-weight values, generated due to the addition of midpoints, were also represented as a linear combination of the corresponding primary/axial weight values. An improved neural networks in learning from examples have also been proposed where both of the proposed algorithms able to constrain the shape of the surface in two-dimensional and multi-dimensional cases and produced more realistic and acceptable results as compared to the previous version. The ability of the proposed approach to models the exponential increase/decrease in the belief values by using high-ordered polynomials without introducing ‘over-fitting’ effects was investigated. The performance of the proposed method in modelling the exponential increase/decrease in belief values was carried out on real cases taken from real casting data. The computed graphical results of the proposed methods were compared with the current Knowledge Hyper-surface and neural-network methods. As a result, the proposed methods correctly predict the sensitivity of process-parameter variations with the occurrence of a defect and very important area of research in a robust design methodology.

An Optimized Back Propagation Learning Algorithm with Adaptive Learning Rate

Back Propagation (BP) is commonly used algorithm that optimize the performance of network for training multilayer feed-forward artificial neural networks. However, BP is inherently slow in learning and it sometimes gets trapped at local minima. These problems occur mailnly due to a constant and non-optimum learning rate (a fixed step size) in which the fixed value of learning rate is set to an initial starting value before training patterns for an input layer and an output layer. This fixed learning rate often leads the BP network towrds failure during steepest descent. Therefore to overcome the limitations of BP, this paper introduces an improvement to back propagation gradient descent with adapative learning rate (BPGD-AL) by changing the values of learning rate locally during the learning process. The simulation results on selected benchmark datasets show that the adaptive learning rate significantly improves the learning efficiency of the Back Propagation Algorith