APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF POSITIVE DYNAMICS FOR TREATMENT OF PATIENTS WITH TUBERCULOSIS

Mean Square Error ◽

Network Model ◽

Neural Network Model ◽

Test Sample ◽

Mean Square ◽

Tuberculosis Patients ◽

The Mean

Using the RStudio program, a neural network model has been developed that predicts positive dynamics in the treatment of tuberculosis patients in a tuberculosis dispensary hospital. The accuracy of the presented model on the test sample is 99.4%, the mean square error (MSE) is 0.013.

USING ARTIFICIAL NEURAL NETWORKS TO PREDICT POSITIVE DYNAMICS IN THE TREATMENT OF TUBERCULOSIS PATIENT

10.22250/isu.2020.66.139-147 ◽

2020 ◽

pp. 139-147

Author(s):

M.Z. Ermolitskaya ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Mean Square Error ◽

Neural Network Model ◽

Test Sample ◽

Tuberculosis Patient ◽

Mean Square ◽

Tuberculosis Patients ◽

The Mean

R Studio program was used to develop a neural network model that predicts positive dynamics in the treatment of tuberculosis patients in the hospitals. The accuracy of the presented model in the test sample is 99.4%, and the mean square error is 0.013.

Establish an Artificial Neural Networks Model to Make Quantitative Analysis about the Capillary Electrophoresis Spectrum

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.452-453.1116 ◽

2012 ◽

Vol 452-453 ◽

pp. 1116-1120

Author(s):

Hong Ping Li ◽

Hong Li

Keyword(s):

Neural Network ◽

Neural Networks ◽

Capillary Electrophoresis ◽

Quantitative Analysis ◽

Network Model ◽

Neural Network Model ◽

Network Training ◽

Dissociation Degree ◽

Simulating the overlapping capillary electrophoresis spectrogram under the dissimilar conditions by the computer system , Choosing the overlapping capillary electrophoresis spectrogram simulated under the different conditions , processing the data to compose a neural network training regulations, Applying the artificial neural networks method to make a quantitative analysis about the multi-component in the overlapping capillary electrophoresis spectrogram,Using: Radial direction primary function neural network model and multi-layered perceptron neural network model. The findings indicated that, along with the increasing of the capillary electrophoresis spectrogram noise level, the related components’ ability of the two kinds of the overlapping capillary electrophoresis spectrogram by neural network model quantitative analysis drop down. Along with the increasing of the capillary electrophoresis spectrogram’s total dissociation degree, the multi-layered perceptron neural network model to the related components’ ability of the overlapping capillary electrophoresis spectum by quantitative analysis raise up.

High-Resolution PM2.5 Estimation Based on the Distributed Perception Deep Neural Network Model

Sustainability ◽

10.3390/su132413985 ◽

2021 ◽

Vol 13 (24) ◽

pp. 13985

Author(s):

Jiwei Liu ◽

Yong Sun ◽

Qun Li

Keyword(s):

Neural Network ◽

Missing Data ◽

Mean Square Error ◽

Relative Error ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Mean Square ◽

Linear Fitting ◽

Ground Monitoring

The accurate measurement of the PM2.5 individual exposure level is a key issue in the study of health effects. However, the lack of historical data and the minute coverage of ground monitoring points are obstacles to the study of such issues. Based on the aerosol optical depth provided by NASA, combined with ground monitoring data and meteorological data, it is an effective method to estimate the near-ground concentration of PM2.5. With the deepening of related research, the models used have developed from univariate and multivariate linear models to nonlinear models such as support vector machine, random forest model, and deep learning neural network model. Among them, the depth neural network model has better performance. However, in the existing research, the variables used are input into the same neural network together, that is, the complex relationship caused by the lag effect of features and the correlation and partial correlation between features have not been considered. The above neural network framework can not be well applied to the complex situation of atmospheric systems and the estimation accuracy of the model needs to be improved. This is the first problem that we need to be overcome. Secondly, in the missing data value processing, the existing studies mostly use single interpolation methods such as linear fitting and Kriging interpolation. However, because the time and place of data missing are complex and changeable, a single method is difficult to deal with a large area of strip and block missing data. Moreover, the linear fitting method is easy to smooth out the special data in bad weather. This is the second problem that we need to overcome. Therefore, we construct a distributed perception deep neural network model (DP-DNN) and spatiotemporal multiview interpolation module to overcome problems 1 and 2. In empirical research, based on the Beijing–Tianjin–Hebei–Shandong region in 2018, we introduce 50 features such as meteorology, NDVI, spatial-temporal feature to analyze the relationship between AOD and PM2.5, and test the performance of DP-DNN and spatiotemporal multiview interpolation module. The results show that after applying the spatiotemporal multiview interpolation module, the average proportion of missing data decreases from 52.1% to 4.84%, and the relative error of the results is 27.5%. Compared with the single interpolation method, this module has obvious advantages in accuracy and level of completion. The mean absolute error, relative error, mean square error, and root mean square error of DP-DNN in time prediction are 17.7 μg/m3, 46.8%, 766.2 g2/m6, and 26.9 μg/m3, respectively, and in space prediction, they are 16.6 μg/m3, 41.8%, 691.5 μg2/m6, and 26.6 μg/m3. DP-DNN has higher accuracy and generalization ability. At the same time, the estimation method in this paper can estimate the PM2.5 of the selected longitude and latitude, which can effectively solve the problem of insufficient coverage of China’s meteorological environmental quality monitoring stations.

Neural network modeling of the implosion process in the tasks of optimization of oil production process

Nonlinear World ◽

10.18127/j20700970-202101-03 ◽

2021 ◽

Author(s):

A.R. Mukhutdinov ◽

Z.R. Vakhidova ◽

M.G. Efimov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Model ◽

Neural Network Model ◽

Network Modeling ◽

Water Hammer ◽

Design Parameters ◽

Neural Network Modeling ◽

An increase in the productivity of oil wells is possible with the use of a promising technology based on implosion and a device for its implementation. It is known that the effectiveness of the technology depends on the design parameters of the device. Currently, a promising way to study processes is computer modeling based on modern information technologies. Therefore, solving forecasting problems using modern software based on artificial neural networks (ANNs) is an urgent task of scientific and practical interest. In this regard, the aim of the work is to develop a neural network model and its application to identify the features of the influence of the diameter and length of the implosion chamber of the device on the pressure of a water hammer during implosion. In the software environment, the following have been created and tested: a method for developing a neural network model; a method of conducting a computational experiment with it. The possibility of neural network modeling of the implosion process has been studied. The results of predicting the output parameter, in this case the pressure of the water hammer, on a pre-trained network, with a relative error of 3.5%, using the knowledge base are demonstrated. The results of applying the methodology for solving forecasting problems using software based on artificial neural networks are presented. It was found that the diameter and length of the implosion chamber significantly affect the pressure of the water hammer. The practical significance of the work lies in the ability to determine the required values of the diameter and length of the implosion chamber of the device at a given level of water hammer pressure.

The Estimation of the Critical Flashover Voltage of Insulators Using the Computational Intelligence

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666181219093455 ◽

2020 ◽

Vol 13 (4) ◽

pp. 550-556

Author(s):

Louiza Dehyadegari ◽

Somayeh Khajehasani

Keyword(s):

Neural Network ◽

Neural Networks ◽

Fuzzy Logic ◽

Network Model ◽

Neural Network Model ◽

Regression Coefficient ◽

Economic Feasibility ◽

Flashover Voltage ◽

Background: Electric insulation is generally a vital factor in both the technical and economic feasibility of complex power and electronic systems. Several researches focus on the behavior of insulators under polluted conditions. That they are mathematical and physical models of insulators, experiments and simulation programs. Also experiments on critical flashover voltage are timeconsuming and have more limitations such as high cost and need for especial equipment’s. Objective: This paper focused on optimized predicting of critical flashover voltage of Polluted insulators based on artificial intelligence. Methods: Fuzzy logic and artificial neural networks are used in order to have the best estimation of the critical flashover. Results: In this way the correlation index (regression coefficient) improved about 2% toward previous works with same experimental data sets. Additionally, with using the properties of nonlinear artificial neural networks we can have the perfect (R=100%) prediction of the critical flashover voltage on experimental dataset. Conclusion: In this paper two methods for the estimation of critical flashover voltage of polluted insulators using fuzzy logic and neural networks was presented. the regression coefficient R achieved by the optimal parameters is 98.4% while in previous work is 96.7%. In neural network model we have regression coefficient 100% and in previous neural network model it was 99%. our test set is the same as previous works and achieved from experiments. These results show that fuzzy proposed methods are powerful and useful tools lead to a more accurate, generalized and objective estimation of the critical flashover voltage.

NEURAL NETWORK MODEL DEVELOPMENT FOR DISEASE RATE MONITORING AND PREDICTION OF ANTIEPIDEMIC MEASURE EFFECTIVENESS

Bulletin of Bryansk state technical university ◽

10.30987/1999-8775-2020-10-42-50 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 42-50

Author(s):

Nataliya Sukhanova

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Model ◽

Neural Network Model ◽

Model Development ◽

Computing System ◽

Disease Rate ◽

Artificial Neural ◽

Visualization Of Data

There is developed a neural network model for disease rate prediction and assessment of antiepidemic measure effectiveness. As basis of the development there were adopted the existing automated information systems which are used for monitoring and visualization of data on Moscow population disease rate. Under conditions of the emergence and propagation of new dangerous infectious and virus diseases the information processing must be carried out in real time, a prediction for future is required. It is necessary to create, update and adjust rapidly a set of anti-epidemic measures offered. The investigation purpose consists in the prediction of infection spreading and the assessment of anti-epidemic measures based on data on the population disease rate. There is offered a neural network model realized on the basis of the modular computing system and artificial neural networks. A modular computing system includes modules of different types connected between each other with a switch network. In the modular computing system there are included modules of artificial neural networks with the special switch structure. Switchboards allow connecting and disconnecting single modules and elements of neural networks. A neural network model changes dynamically its structure and adapted to a current epidemic situation.

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

Computational Intelligence and Neuroscience ◽

10.1155/2016/3868519 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 20

Author(s):

Petr Maca ◽

Pavel Pech

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Model ◽

Neural Network Model ◽

Multilayer Perceptron ◽

Standardized Precipitation Index ◽

Model Performance ◽

Drought Indices ◽

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Theoretical and Applied Mathematics in International Business - Advances in Business Strategy and Competitive Advantage ◽

Designing a Neural Network Model for Time Series Forecasting

10.4018/978-1-5225-8458-2.ch012 ◽

2020 ◽

pp. 259-284 ◽

Cited By ~ 2

Author(s):

Paola Andrea Sanchéz Sanchéz ◽

José Rafael García González ◽

Carlos Hernán Fajardo-Toro ◽

Paloma María Teresa Martínez Sánchez

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

Time Series Forecasting ◽

Extant Literature ◽

Statistical Procedures ◽

Linear Behavior

Artificial neural networks are highly flexible and efficient tools in the approximation of time series patterns. In recent years, more than 5,000 studies oriented to the use of neural networks in time series forecasting have been evidenced in the extant literature. However, the methodology used for its specification and construction still involves a lot of trial and error or is inherited from econometric and statistical procedures that do not fit perfectly to the characteristics of the time series. This is especially true when they present non-linear behavior; moreover, it is not designed for working with neural networks. The objective of this chapter is to present a five-step guide for the specification, design, and validation of a neural network model for forecasting time series.

Assessment of dissolved oxygen in Shatt Al-Arab River by other quality parameters of water using Artificial Neural Networks

Muthanna Journal of Engineering and Technology ◽

10.52113/3/mjet/2020-8-2/08-16 ◽

2020 ◽

Vol 8 (2) ◽

pp. 8-16

Author(s):

Zaynab A. Khudhur ◽

Saad A. Arab ◽

Ammar S. Dawood

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Dissolved Oxygen ◽

Mean Square Error ◽

Network Model ◽

Neural Network Model ◽

Back Propagation ◽

Quality Parameters ◽

Mean Square ◽

The Major sources of water are surface and subsurface. Surface water includes Rivers, Reservoirs, Creek, Streams, etc. This paper deals with using a neural network model to recognize dissolved oxygen in Shatt Al-Arab. Within the present study, Shatt Al-Arab River (Basrah-Iraq) is considered as the study area with monthly observed data from 2009-2014. Artificial Neural Network (ANN) has been applied to pattern the relations among eight (8) water quality parameters which are devoted for predicting one parameter (1) so that to decrease the load of long experimental procedure. Physical and chemical parameters that are inserted in the model are: pH, total dissolved solids, electrical conductivity, sulphate, phosphate, calcium, magnesium and nitrate. Dissolved oxygen (DO) is included in the output models. The three layered feed-forward model with back-propagation multi-layer perception (MLP) models architecture of 8-8-1 for DO. The artificial neural network has got training successfully and has been tested with 70{1524fc3db9b9185e4da51c194ca3b05c06ae483421403c447a0666442f370a52} and 30{1524fc3db9b9185e4da51c194ca3b05c06ae483421403c447a0666442f370a52} of the data groups. Statistical criteria of correlation coefficient (R2) and mean square error (MSE) are used to evaluate performance of the models. The correlation coefficients of the artificial neural network model for predicting DO have been 0.99354 and 0.98237, and mean square error for the model are 0.007698 and 0.00122 respectively. It can be concluding that these techniques provide similar accuracy in estimating DO concentration and predicting the dissolved oxygen (DO) in Shatt Al-Arab

Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-019-00802-6 ◽

2019 ◽

Vol 10 (3) ◽

pp. 1081-1095 ◽

Cited By ~ 2

Author(s):

Okorie E. Agwu ◽

Julius U. Akpabio ◽

Adewale Dosunmu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

High Pressure ◽

High Temperature ◽

Mean Square Error ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Mean Square ◽

High Temperature High Pressure

AbstractIn this paper, an artificial neural network model was developed to predict the downhole density of oil-based muds under high-temperature, high-pressure conditions. Six performance metrics, namely goodness of fit (R2), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), sum of squares error (SSE) and root mean square error (RMSE), were used to assess the performance of the developed model. From the results, the model had an overall MSE of 0.000477 with an MAE of 0.017 and an R2 of 0.9999, MAPE of 0.127, RMSE of 0.022 and SSE of 0.056. All the model predictions were in excellent agreement with the measured results. Consequently, in assessing the generalization capability of the developed model for the oil-based mud, a new set of data that was not part of the training process of the model comprising 34 data points was used. In this regard, the model was able to predict 99% of the unfamiliar data with an MSE of 0.0159, MAE of 0.101, RMSE of 0.126, SSE of 0.54 and a MAPE of 0.7. In comparison with existing models, the ANN model developed in this study performed better. The sensitivity analysis performed shows that the initial mud density has the greatest impact on the final mud density downhole. This unique modelling technique and the model it evolved represents a huge step in the trajectory of achieving full automation of downhole mud density estimation. Furthermore, this method eliminates the need for surface measurement equipment, while at the same time, representing more accurately the downhole mud density at any given pressure and temperature.