A Neural Network Approach on Analyzing and Reducing Signalized Intersection Crashes

Operational parameter impact and back propagation artificial neural network modeling for phosphate adsorption onto acid-activated neutralized red mud

Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet delay prediction and interpolation are more difficult than those of the dry component due to the rapid temporal and spatial variation of the water vapor content. Wet delay estimation and interpolation with a sufficient accuracy is an important issue for all parameters obtained by GNSS positioning techniques. In particular, in real-time positioning applications, errors caused by interpolation of the wet troposphere delay are reflected in the height component of about 1 to 2 cm. Furthermore, the amount of water vapor in the troposphere is very important information in weather forecast applications obtained as a function of wet delay. Therefore, real-time monitoring of the troposphere can be achieved with a higher resolution and accuracy by utilizing neural network models for interpolation of the wet tropospheric delay. In addition, in the absence of the GNSS station, wet delays can be interpolated by means of the surrounding stations to the desired location. In this study, a back propagation artificial neural network (BPNN) model based on meteorological parameters obtained from The New Austrian Meteorological Measuring Network (TAWES) was used to interpolate wet troposphere delay. Analysis was carried out at 40 reference stations of the Echtzeit Positionierung Austria (EPOSA) GNSS Network covering the whole of Austria. The interpolation of zenith wet delays based on the artificial neural network was performed by using latitude, longitude, altitude and meteorological parameters (temperature, pressure, weighted mean temperature, and water vapor pressure). These parameters were then subtracted from the artificial neural network model one by one and six different artificial neural networks were designed. In addition, the linear interpolation method (LIN) and inverse distance weighted interpolation method (IDW) were used as conventional interpolation methods. In order to investigate the effect of the network density on interpolation methods, three networks, including 40, 30, and 20 reference stations, were formed and the increased distance effect on interpolation methods was evaluated. In addition, analyses were conducted in winter, spring, and summer to evaluate the seasonal effects on interpolation methods. According to the statistical analysis, the root mean square error (RMSE) values of the IDW, LIN, and BPNN methods were found to be 12.6, 13.4, and 5.9 mm, respectively.

Post-plasma catalytic removal of methanol over Mn–Ce catalysts in an atmospheric dielectric barrier discharge

Near infrared spectroscopy and the back propagation artificial neural network model in conjunction with backward interval partial least squares algorithm were used to estimate the purchasing price of Enshi yulu young tea shoots. The near-infrared spectra regions most relevant to the tea shoots price model (5700.5–5935.8, 7613.6–7848.9, 8091.8–8327.1, 8331–8566.2, 9287.5–9522.5, and 9526.6–9761.9 cm–1) were selected using backward interval partial least squares algorithm. The first five principal components that explained 99.96% of the variability in those selected spectral data were then used to calibrate the back propagation artificial neural tea shoots purchasing price model. The performance of this model (coefficient of determination for prediction 0.9724; root-mean-square error of prediction 4.727) was superior to those of the back propagation artificial neural model (coefficient of determination for prediction 0.8653, root-mean-square error of prediction 5.125) and the backward interval partial least squares model (coefficient of determination for prediction 0.5932, root-mean-square error of prediction 25.125). The acquisition price model with the combined backward interval partial least squares–back propagation artificial neural network algorithms can evaluate the price of Enshi yulu tea shoots accurately, quickly and objectively.

Impact operational parameters such as initial dyes concentration, adsorbent mass, pH and sonication time on the efficiency of ultrasound-assisted adsorption of sunset yellow (SY) and azur (II) (AZ) onto MWCNT and MWCNT-Pd-NPs was studied by central composite design (CCD) and back propagation artificial neural network (ANN) models and resulted for tow adsorbent were compared to each other. Optimize conditions from CCD in presence of MWCNT as adsorbent was found to be 6.01 and 9.98mgL−1 AZ and SY concentration, 0.02 of adsorbent mass, pH 7.0 and 3.8min sonication time, respectively, while in the presence of MWCNT-Pd-NPs optimize value were obtained at 10.0 and 8.0mgL−1 AZ and SY concentration, 0.018 of adsorbent mass and pH 5.0 and 3.5min sonication time, respectively. In these conditions and at desirability value of 0.99, the maximum adsorption efficiency for AZ onto MWCNT and MWCNT-Pd-NPs are 82.97 and 94.95, respectively. Also, the maximum adsorption efficiency for SY onto MWCNT and MWCNT-Pd-NPs are 73.13 and 85.55, respectively. This values show that MWCNT-Pd-NPs in comparison with MWCNT at the lower contact time and adsorbent mass was able to remove greater amounts of under study dyes. Based on the ANN model the absolute average deviations (AADs) of AZ and SY dyes adsorption by MWCNT from experimental data are 0.27% and 0.43%, and the R2 values are 0.911 and 0.954, respectively. Also, the AADs of AZ and SY dyes adsorption by MWCNTs-Pd-NPs are 0.44% and 0.65%, and the R2 values are 0.925% and 0.927%, respectively. This values show that ANN for MWCNT-Pd-NPs in comparison with MWCNT was a more powerful tool for building up to construct an empirical model to predict under study dyes adsorption behavior. In addition, the obtained results have good agreement with experimental data for MWCNT-Pd-NPs in comparison with MWCNT. Finally, the study of equilibrium isotherm and kinetic models of adsorption process are shown that the Langmuir isotherm model and pseudo second order kinetic model were the best models for both adsorbent.

Flavonoids, the most diverse class of plant secondary metabolites, exhibit high affinity toward the purified cytosolic NBD2(C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationships (QSARs) models were developed using back-propagation artificial neural networks (BPANN) and multiple linear regression (MLR). Molecular descriptors were calculated using PaDELDescriptor, and the number of descriptors was then reduced using a genetic algorithm (GA) and stepwise regression. The MLR (R(2)=0.855, q(2)=0.8138, Rext(2)=0.6916), 14-3-1 BPANN (R(2)=0.8514, q(2)=0.7695, Rext (2)=0.8142), 14-4-1 BPANN(R(2)=0.9199, q(2)=0.7733, Rext(2) =0.8731), and 14-5-1 BPANN (R(2)=0.8660, q(2)=0.7432, Rext(2)=0.8292) models all showed good robustness. While BPANN models exceeded significantly MLR in predictable performance for their flexible characters, could be used to predict the affinity of flavonoids for P-gp and applied in further drug screening.

BackgroundThe transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors.Methodology/Principal FindingsWe aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected.Conclusion/SignificanceBoth BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control.

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.

Artificial neural network modeling of photocatalytic removal of a disperse dye using synthesized of ZnO nanoparticles on montmorillonite

In this study, a feed-forward backpropagation (FFBP) artificial neural network (ANN) model based on multilayer perceptron (MLP) for computing the pull-in voltage value of fixed–fixed micro-actuators is presented. The proposed ANN model is trained using the numerous pull-in voltage value results of clamped–clamped actuators, which have various physical properties, simulated by a software that employs the finite element method (FEM). As the presented ANN model has been trained for the simulation data, it implicitly takes into account the fringing field, mid-plane stretching and size effects. The accuracy and robustness of the results obtained by the proposed ANN model are verified by comparing with those of the theoretical ones through the simulation and experimental studies previously presented in the literature. The average percentage errors for training and testing data are found to be 0.30 and 0.40 %, respectively, while the maximum percentage error for literature data is calculated as 1.96 %. These results show that the main advantage of the presented ANN model gives satisfying pull-in voltage results over solution space of the simulations with effortless way.

Predicting seismic interaction effect between soil and structure group using convolutional neural network

The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids. Researchers rely on experimental investigations to explore nanofluid properties, as it is a necessary step before their practical application. As these investigations are time and resource-consuming undertakings, an effective prediction model can significantly improve the efficiency of research operations. In this work, an Artificial Neural Network (ANN) model is developed to predict the thermal conductivity of metal oxide water-based nanofluid. For this, a comprehensive set of 691 data points was collected from the literature. This dataset is split into training (70%), validation (15%), and testing (15%) and used to train the ANN model. The developed model is a backpropagation artificial neural network with a 4–12–1 architecture. The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence. It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.

In this research work, we developed a mathematical model of a digital currency market, involving daily closing price as a function of time. We proposed the Artificial Neural Network (ANN) model. We observed that our ANN model was able to predict the daily closing price of Bitcoin and also make six weeks forecast to a reasonable degree of accuracy. We equally observe that the time dependent ANN model can actually give digital currency traders and investors a clue on when to trade off their digital assets with minimum risk. We therefore, recommend that ANN model should be incorporated into digital currency trading platforms as a signal tool to enable digital currency traders take more informed and less risky trading decisions. From our findings, we would advise traders who wish to employ ANN model to consider a smaller time frame say a few weeks’ time interval for their predictions. We observed also that ANN models have limitations when it comes to manual computation or implementation in Microsoft Excel, especially when dealing with very large input values. This is because of the saturation characteristic of our ANN inner layer activation function (viz; tanh function) which can lead to identical output values for different input values, making it difficult to replicate the ANN model's behavior. Furthermore, ANN models often involve complex interactions between multiple neurons, layers, and activation functions, which can be challenging to replicate manually.