Artificial neural network modeling for prediction and optimization of biochar yield and properties

Neural network model for atmospheric residue desulfurization conversion prediction using real plant data

A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

Stock market analysis and prediction are crucial for understanding business ownership and financial performance, this study proposes an optimised CNN-stacked LSTM neural network model for accurate stock market trend prediction. The initial challenge lies in designing a customised CNN-stacked LSTM model for stock data prediction due to the abundance of non-optimised algorithms. To address this, we conducted training and testing using diverse datasets, including NYSE, NASDAQ, and NIFTY-50, observing variations in model accuracy based on the dataset. Remarkably, our model demonstrated exceptional performance with the NIFTY-50 dataset, accurately predicting up to 99% of stocks even during the testing phase. Throughout training and validation, we measured mean squared error (MSE) values ranging from 0.001 to 0.05 and 0.002 to 0.1, depending on the dataset. Our proposed CNN-stacked LSTM model presents a promising solution for accurate prediction of stock market trends, addressing the limitations of previous methods.

In infectious disease outbreak modeling, there remains a gap in addressing spatiotemporal challenges present in established models. This study addresses this gap by evaluating four established hybrid neural network models for predicting influenza outbreaks. These models were analyzed by employing time series data from eight different countries to challenge the models with imposed spatial difficulties, in a month-on-month structure. The models' predictions were compared using MAPE, and RMSE, as well as graphical representations generated by employed models. The SARIMA-LSTM model excelled in achieving the lowest average RMSE score of 66.93 as well as reporting the lowest RMSE score for three out of eight countries studied. In this case also, GA-ConvLSTM-CNN model comes in second place with an average RMSE score of 68.46. Considering these results and the ability to follow the seasonal trends of the actual values, this study suggests the SARIMA-LSTM model to be more robust to spatiotemporal challenges compared with the other models under examination. This study•Evaluated established methods with unique imposed difficulty.•Addressed spatiotemporal characteristics of the data.•Proposed the SARIMA-LSTM model based on evaluation metrics.

Water consumption of plants is a key parameter for formulating irrigation system, and the precise prediction play a important role in improving the use efficiency of limited water resources. In this experiment, by using the method of artificial neural network and MATLAB DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.×euramericana cv."74/76", and through the experiments it has been examined that two neural network system models can be applied in forecasting water consumption of seedlings, and the average relative error of Back Propagation (BP) neural network prediction model was 0.07, the General Regression Neural Network (GRNN) prediction model was 0.05, moreover, the latter had good stability, while that of the former was poor. Therefore, we propose that GRNN model can be used in prediction of seedling water consumption. Furthermore, the maximum relative error of GRNN predication model was 0.106, the minimum relative error was 0.015. The GRNN model is superior to the BP neural network model that the former performs a higher forecasting accuracy with relatively shorter time consumption and faster speed in training.

Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.)

Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch-based foods

Service quality is one of the main issues that today's world. Firms operating in the transportation sector are also trying to improve the quality of the service they provide to their passengers. It is crucial to determine the passengers' service quality perceptions and priorities to evaluate and improve the service in this context. In this study, Kocaeli's tram service users' service quality perceptions have been evaluated by applying a survey consisting of 20 questions and user satisfaction levels from different service dimensions. Later, an artificial neural network model was developed using the users' demographic data and their responses to the survey questions to mimic their service quality satisfaction. The artificial neural network model developed has been examined to understand the importance that tram users give to service quality. Using the developed the “change of score” method, how the changes to be made in the tram system will affect the quality of service and how the opinions of different user groups will be affected can be examined in detail. The artificial neural network model's prediction capability was compared with the multiple linear regression model and found superior. According to the developed Change of Score Method, the most frequent user attaches the highest importance to the service dimensions of the convenience to pay for the tram, getting his/her destination on time, and reducing environmental pollution.

Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28 th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS), and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c) is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

A neural network model is developed for prediction of solid oxide fuel cell performance. The back propagation algorithm is used for the cell voltage and power prediction. As the model is developed, the neural network model's prediction is presented and compared with the physical non-linear model results. Hence, the neural network structure based on the Levenberg Marquardt back propagation algorithm has been concluded to be more appropriate for modeling the dependencies of the non-linearity on the performance of solid oxide fuel cell.

The aim of the study was to examine the possibilities of the development of a prognostic instrument for the air quality management in big cities. The models ANNs were tested (MLP and RBF type). The study was focused on the development of the neural network models for prediction of the classes of the air quality state in relation to mean daily PM10 concentration. The air quality class was predicted for the next day in relation to mean daily concentrations. The tests were carried out in the city of Lodz in central Poland. The results of the modelling were very satisfactory. In the optimally constructed models, false prognosis were only 1.9% in testing series and 1.4% in training series. A low level of error prediction confirmed the fact that the neural network models are an effective instrument of the air quality management in big cities.

This study relates circuit board design features to assembly yields. Data used were collected over a period of one year from two circuit board assembly shops at AT&T. Design parameters that may affect the assembly yield were identified using knowledge of the assembly process. These parameters were then quantified for a set of board designs and related to the actual assembly yield by the statistical regression models and the artificial neural network (ANN) models. These models are able to predict the assembly yield with a root mean square (RMS) error of less than 5%. They can be used to predict the assembly yield for new board designs on the same line. Alternatively, they can be used to compare the performance of different lines by comparing the expected yield for a given design with the actual yield. >

Stone Mastic Asphalt (SMA), firstly introduced in the 1960s, is a durable and rut-resistant asphalt mixture that uses stone-on-stone contact to improve strength, and a rich mortar binder to provide durability. Marshall parameters, such as Marshall Stability (MS) and Marshall Flow (MF) are critical mechanical properties of SMA, representing the performance of asphalt concrete. The two Marshall parameters are widely used for the evaluation of resistance to displacement, distortion, rutting, and shearing stresses of SMA. As the pavement is frequently subjected to traffic loads, it is highly required to find out an optimum manner to determine these Marshall parameters. However, such a procedure is complicated, costly, and time-consuming. The primary aim of the present work is to develop an alternative numerical tool using artificial neural network (ANN) to predict the MS and MF of SMA mixtures. The results show that the ANN algorithm is an excellent predictor based on the excellent values of statistical criteria such as root mean square error, and the Pearson correlation coefficient. This study's results pave the way towards selecting a suitable machine learning approach to accurately determine the Marshall parameters of SMA mixtures.KeywordsStone Mastic Asphalt (SMA)Marshall parametersArtificial neural network (ANN)

Application of Neural Network Models in Modelling Economic Time Series with Non-constant Volatility

Land salinization is one of the most important factors in reducing the soil quality of agricultural land. Accordingly, these regions affected agricultural production and ecological development. Therefore, it is important to assess soil salinity driving factors. However, it is difficult to characterize soil salinity using single-factor and linear models. This research was carried out for soil digital mapping using remote sensing to classify saline lands and their spatial distribution in SharifAbad plain. The methodology is based on the differentiation of saline soils by combining the Landsat 8 data, fieldwork, and neural network model for prediction of soil salinity. The Latin hypercube method is based on the stratified sampling method. Based on this technique, 63 samples were selected from 0 to 30 cm of the soil surface. In the ANN model, considered 30% of the soil EC as the validation set and the rest (70%) for the testing set. To model soil salinity, auxiliary variables such as Landsat 8 satellite image of 2016 including 2–5 main bands and band 7, topographic auxiliary data includes DEM, TWI, TCI, and spectral parameters were extracted. The result revealed that the GFF algorithm which, according to R2 and MSE statistics, the best way to prepare a soil salinity map in Sharif Abad plain. The ANN model in most cases satisfies the EC amount less than the real value. We found that the average values of SI5, TCI, and TWI values in the non-saline, the class were lower than the saline class, and the average values of DEM and NDVI indices in the non-saline class were higher than the saline class and showed a statistically significant difference.