Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

Temporary storage for collection of municipal solid waste is designed with various parameters, such as size of bin, collection frequency, average filling rate, number of bins, volume of material needed for bin production, bin density, and catchment area. The environmental impact of temporary storage is evaluated with emissions generated due to the production of net material used for bin production. An artificial neural network-back propagation method is proposed for modeling the temporary storage. In this proposed artificial neural network-back propagation network, the number of bins and global warming potential (GWP) based on the quantity of generated wastes are predicted. Thus, a set of different structures of artificial neural network-back propagation are investigated and then the best model is developed for temporary storage of municipal solid waste. The artificial neural network-back propagation model is trained using various parameters, such as number of neurons in the hidden layers, training tolerance, momentum, and learning rate. This model is helpful for the decision makers in choosing the environmentally sound options in the design of temporary storage for municipal solid waste management. Artificial neural network-back propagation can be successfully used to predict number of bins, GWP from waste generation rate of the city.

<p>Predicting river water quality in the Special Region of Yogyakarta (DIY) is crucial. In this research, we modeled a river water quality prediction system using the artificial neural network (ANN) backpropagation method. Backpropagation is one of the developments of the multilayer perceptron (MLP) network, which can reduce the level of prediction error by adjusting the weights based on the difference in output and the desired target. Water quality parameters included biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), dissolved oxygen (DO), total phosphate, fecal coliforms, and total coliforms. The research object was the upstream, downstream, and middle parts of the Oya River. The data source was secondary data from the DIY Environment and Forestry Service. Data were in the form of time series data for 2013–2023. Descriptive data results showed that the water quality of the Oya River in 2020–2023 was better than in previous years. However, increasing community and industrial activities can reduce water quality. This was concluded based on the prediction results of the ANN backpropagation method with a hidden layer number of 4. The prediction results for period 3 in 2023 and period 1 in 2024 are that 1) the concentrations of BOD, fecal coli, and total coli will increase and exceed quality standards, 2) COD and TSS concentrations will increase but will still be below quality standards, 3) DO and total phosphate concentrations will remain constant and still on the threshold of quality standards. The possibility of several water quality parameters increasing above the quality standards remains, so the potential for contamination of the Oya River is still high. Therefore, early prevention of river water pollution is necessary.</p>

DSS has low machinability characteristics due to its high strength, machining is complicated, and careful attention is required when selecting machining parameters. The main criteria discussed in this paper concern the turning optimization parameters and machining time reduction of DSS 2205 as the work material. The input parameters are cutting velocity, feeds, cutting depth, and tooltip nose radius of the cutting tool. The design of experiments methodology is employed to design the experiments using Design‐Expert V12 software. The second‐order mathematical model was developed, and analysis of variance was performed to analyze the performance characteristics to recognize the critical variables influencing the output parameter. An artificial neural network (ANN) backpropagation algorithm using MATLAB software was used to develop the mathematical model and optimize the output. The model was developed, and the results were optimized using MATLAB software’s ANN back propagation method to find the best possible solutions. The generated models were significant based on the analysis of variance and the R‐squared value, and these results indicate that the cutting velocity is the most critical factor. For a low machining time, the cutting velocity should be between 100 and 140 m/min, and the tooltip nose radius should be 2.8 mm. The optimal parameter settings are validated by performing a lower is better confirmation test using gray relational analysis (GRA). The GRA exposed the lower machining time at a cutting velocity of 140 m/min, rate of feed of 0.5 mm/rev, cutting depth of 0.5 mm, and tooltip nose radius of 2.4 mm. The predicted values were close to the experimental values, and the result indicates the optimal level of the highest GRA grade of the machining variable.

Preparation and artificial neural networks analysis of ultrafine β-Sialon powders by microwave-assisted carbothermal reduction nitridation of sol–gel derived powder precursors

The clinical application shows that it is possible to differentiate between patients suffering from schizophrenia, depression and normal healthy persons on the basis of EEG rhythms. This paper describes the application of two artificial neural networks (ANN) approaches, BP ANN and self-organizing competitive ANN for the discrimination of three kinds of subjects (including 10 normal control, 10 schizophrenic patients and 10 depressive patients), with EEG rhythms used as feature vectors. In addition, the comparison between two ANNs is illustrated in this paper. The results show that ANN is an effective approach for discrimination of these three kinds of objects and BP ANN has better comprehensive performance than self-organizing competitive ANN technique in this study. Therefore, the ANN technique could be used as a new tool for computer-aided diagnosis for some psychosis.

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

A population is a group of individuals who occupy or live in a place or area that interacts with one another. Because the population has a very important role in an area, it is important to make predictions to find out how much the level of increase or descent of the population in an area, especially in Pematangsiantar. Therefore this research was conducted. This study uses population data in 8 Sub-Districts in Pematangsiantar. Data was taken from the Central Statistics Agency (BPS) of Pematangsiantar city in 2011-2017. The method used is the Artificial Neural Network (ANN) Backpropagation. These data will be processed into 2 parts namely training data and Testing data. This research will use 5 architectural models namely, 3-25-1, 3-30-1, 3-45-1, 3-54-1 and 3-68-1. From these 5 architectural models, after analysis, models 3-45-1 were chosen as the best models with epoch 553 values, MSE training 0,0001108768, MSE testing 0.0012355953 and an accuracy rate of 88%. The results of this paper are expected to be widely useful, especially for academics as further research material, especially those related to population in Pematangsiantar, because this research is still limited to discussing the level of accuracy, not prediction results.

Traditional methods of multivariate statistical analyses, used to recognize anomalous hydrocarbon signatures in petroleum exploration, have at least four shortcomings: (1) it is difficult to isolate anomalies where the data are not normally distributed; (2) it is difficult to separate distinct anomaly populations corresponding to distinct formation mechanisms while separating anomalies from background; (3) it is not fitting to present illustrations of multivariate anomalies on contour maps; and (4) it is not suitable for preparation for multivariate pattern recognition. These present serious obstacles to the application of exploration geochemistry in hydrocarbon exploration. This study demonstrates that the Back Propagation Artificial Neural Network (BP-ANN) with logic multiplication cluster analysis (a new cluster analysis proposed in this paper) overcomes the difficulties exhibited by traditional multivariate methods. The logic multiplication cluster analysis was designed to produce a training set for the BP-ANN. This approach was established on the basis of geochemical characteristics and origin of the various populations in geochemical data, such as background, micro-seepage anomalies and seepage anomalies. The topology of the BP-ANN was optimized using the outputs of the BP-ANN and the correct rate. In order to illustrate the multivariate anomalies recognized using BP-ANN on contour maps, we designed the expression functions for BP-ANN application in this field. With traditional methods of anomaly recognition, acid-extractable hydrocarbons in soils have not proven to be efficient indicators for hydrocarbon potential in East Anan Sag, Inner Mongolia. However, the BP-ANN has indicated that these indicators are efficient and that areas of East Anan Sag have potential reserves of petroleum.

There has been a need for geodetic network densification since the early days of traditional surveying. In order to densify geodetic networks in a way that will produce the most effective reference frame improvements, the crustal velocity field must be modelled. Artificial Neural Networks (ANNs) are widely used as function approximators in diverse fields of geoinformatics including velocity field determination. Deciding the number of hidden neurons required for the implementation of an arbitrary function is one of the major problems of ANN that still deserves further exploration. Generally, the number of hidden neurons is decided on the basis of experience. This paper attempts to quantify the significance of pruning away hidden neurons in ANN architecture for velocity field determination. An initial back propagation artificial neural network (BPANN) with 30 hidden neurons is educated by training data and resultant BPANN is applied on test and validation data. The number of hidden neurons is subsequently decreased, in pairs from 30 to 2, to achieve the best predicting model. These pruned BPANNs are retrained and applied on the test and validation data. Some existing methods for selecting the number of hidden neurons are also used. The results are evaluated in terms of the root mean square error (RMSE) over a study area for optimizing the number of hidden neurons in estimating densification point velocity by BPANN.

Unemployment is a serious social and economic problem faced by the Pematangsiantar City government, high unemployment is also caused by the low education and skills of the workforce. To be able to reduce the number of unemployed, especially in the city of Pematangsiantar, it is necessary to predict the unemployment rate based on urban villages in the three sub-districts of the city of Pematangsiantar, so that the government has a policy so that it can tackle the number of unemployed. The data used in this study are unemployment data based on 19 urban areas from 2013-2017 in 3 districts in Pematangsiantar City. Data sources were obtained from the Pematangsiantar 03 / SS Koramil Office. The research method used is Backpropagation Artificial Neural Network. Data analysis was performed with backpropagation algorithm using Matlab. There are 5 network architecture used, namely 2-35-1, 2-38-1, 2-41-1, 2-43-1, 2-46-1 with the best model is 2-38-1 which produces accuracy by 79%. Thus this model is good enough to be used to predict the unemployment rate based on wards in 3 sub-districts in the city of Pematangsiantar.

In this research will show a method for sound-recognition with artificial neural network back propagation concept. The artificial neural network use sigmoid activation function to all layer. Steps to the extraction, first, divide into ten component, second do the Fast Fourier Transform, third continue with Power Spectral Density, fourth count the average. The end result show that the pattern will recognize by the Artificial Neural Network. Extraction performed, as well as Fourier transform, and also calculate the power density, and then process them with artificial neural networks, allows the system to do the identification of voice data Gamelan Bonang using In this research will show a method for sound-recognition with artificial neural network back propagation concept.

Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations.

In this study, a back propagation artificial neural network model utilizing characteristics of urban signalized intersections for occurrence prediction of intersection - related traffic crashes, along with its application for crash reduction, are proposed. The 1,593 traffic crashes reported at 62 signalized intersections in Taichung city, Taiwan during year 2000 - 2001 were analyzed. These intersections were decomposed into 636 approaching direction combinations (ADCs). Characteristics, as well as the number of crashes, corresponding to each ADC were investigated. A back propagation artificial neural network (ANN) model was then generated. It was found that the ANN model exhibited great prediction ability with 0.992 correlation coefficient, and 3.38E- 06 mean square error. With the ANN model, a proposed decision-making scheme for intersection rehabilitation was suggested. Case study indicated that the suggested scheme could serve well as a referable tool.

Variants of feedforward backpropagation (BP) artificial neural network (ANNs) such as pseudo Mac Laurin power series order 1 (McL1BP), order 2 (McL2BP), order 3 (McL3BP) and order 4 (McL4BP) models are used to fill in mean annual streamflows. The baseline of modeling process is the standard feedforward backpropagation ANN (StandBP). The sigmoid function is used as activation function. Performance comparisons of data infilling models (ANNs) are conducted using the Root Mean Square Error of Predictions (RMSEp) and graphical plots. To test the performance of the data infilling techniques, selected streamflow gauges (i.e. the Diepkloof (control) gauge on the Wonderboomspruit River and the Molteno (target) gauge on Stormbergspruit River) of the Orange drainage river systems of South Africa have been used. The results demonstrated that relatively higher order ANN models; i.e. McL3BP, McL4BP can still outperform the rest of techniques for 7 % (except StandBP), 20 % and 30 % missing data proportions at Molteno gauge. At the same time they have been outperformed by the rest of ANN models at 13 %. Generally higher and low order Pseudo MacLaurin ANNs are acceptable to fill in missing mean annual flows at Molteno gauge. The accuracy of estimated mean annual values at Molteno is substantially negatively affected beyond 20 % gap size for all ANNs. Hence gap size beyond 20 % yields to relatively higher value of RMSEp for both high and low MacLaurin feedforward ANN techniques. A linear relationship could describe accuracy of estimated values and gap size at Molteno. Further work should include other data regimes as well as other South African catchment areas. Other activation functions should also be tested.

To date the use of artificial neural networks (ANNs) in quantitative structure-activity relationship (QSAR) studies has been primarily concerned in comparing the predictive accuracy of the technique using known data sets where the data set parameters had been preselected and optimized for use with other statistical methods. Little effort has been directed at optimizing the input parameters for use with ANNs or exploring other potential strengths of ANNs. In this study, back-propagation ANNs and multilinear regression (MLR) were used to examine the QSAR between substituent constants and random noise at six positions on 57 1,4-benzodiazepin-2-ones (1,4-BZs) and their binding affinities (log IC50) for benzodiazepine GABAA receptor preparations. By using selective pruning and cross-validation techniques, it was found possible to use ANNs to indicate an optimum set of 10 input parameters from a choice of 48 which were then used to train back-propagation ANNs that best predicted the receptor binding affinity with a high correlation between known and predicted data sets. Using the optimum set of input parameters, three-layer ANNs performed no better than the two-layer ANNs which gave marginally better results than MLR. Using the trained ANNs to examine the individual parameters showed that increases in the lipophilicity and F polar value at position 7, F polar value at position 2', and dipole at position 1 on the molecule all enhanced receptor binding affinity of 1,4-BZ ligands. Increases in molar refractivity and resonance parameters at position 1, molar refractivity at positions 6' and 2', Hammet meta constant at position 3', and Hammet para constant at position 8 on the molecule all caused decreases in receptor binding affinity. By considering the optimal ANNs as pharmacophore models representing the internal physicochemical structure of the receptor site, it was found that they could be used to critically examine the properties of the receptor site.