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

2012 ◽  
Vol 452-453 ◽  
pp. 1116-1120
Author(s):  
Hong Ping Li ◽  
Hong Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Capillary Electrophoresis ◽  
Artificial Neural Networks ◽  
Quantitative Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Network Training ◽  
Dissociation Degree ◽  
Artificial Neural

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.

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

2021 ◽  
Author(s):  
A.R. Mukhutdinov ◽  
Z.R. Vakhidova ◽  
M.G. Efimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
Water Hammer ◽  
Design Parameters ◽  
Neural Network Modeling ◽  
Artificial Neural

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

2020 ◽  
Vol 13 (4) ◽  
pp. 550-556
Author(s):  
Louiza Dehyadegari ◽  
Somayeh Khajehasani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Regression Coefficient ◽  
Economic Feasibility ◽  
Flashover Voltage ◽  
Artificial Neural

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

2020 ◽  
Vol 2020 (10) ◽  
pp. 42-50
Author(s):  
Nataliya Sukhanova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial 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.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Petr Maca ◽  
Pavel Pech
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Standardized Precipitation Index ◽  
Model Performance ◽  
Drought Indices ◽  
Artificial Neural

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.

scholarly journals Estimation of burned areas in forest fires using artificial neural networks

2020 ◽  
Vol 16 (3) ◽  
pp. 1-22
Author(s):  
M. Hanefi Calp ◽  
Utku Kose
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Forest Fires ◽  
Traditional Methods ◽  
Burned Areas ◽  
Artificial Neural

Introduction: This article is the product of the research “Developing an Artificial Neural Network Based Model for Estimating Burned Areas in Forest Fires”, developed at Karadeniz Technical University in the year 2020. Problem: Forest Fires are an issue that greatly affect human life and the ecological order, leaving long-term issues. It should be estimated because it is not known when, where and how much the fire will be in the area. Objective: The objective of the research is to use artificial neural networks to estimate the burned areas in forest fires. Methodology: A feed-forward backpropagation neural network model was used for estimating the burned areas. Results: We performed a performance evaluation over the proposed model by considering Regression values, Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE). The results show that the model is efficient in terms of its estimation of burnt areas. Conclusions: The proposed artificial neural network model has low error rate and high estimation accuracy. It is more effective than traditional methods for estimating burned areas in forests. Originality: To the best of our knowledge, this is the first time that this real, unique data has been used for building and testing the model’s estimations and the improvements that have been made in producing results faster and more accurately than with traditional methods. Limitations: Since there are regional differences over different forest areas, effective criteria need to be analysed regarding the target regions.  

scholarly journals Do Artificial Neural Networks Always Provide High Prediction Performance? An Experimental Study on the Insufficiency of Artificial Neural Networks in Capacitance Prediction of the 6H-SiC/MEH-PPV/Al diode

2021 ◽  
Author(s):  
Andaç Batur Çolak ◽  
Tamer Güzel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Organic Polymer ◽  
High Prediction ◽  
Artificial Neural

Abstract Recently, studies on artificial neural network model, which is one of the most effective artificial intelligence tools applied in many fields, reported that artificial neural networks are tools that offer very high prediction performance compared to traditional models. In this study, an artificial neural network model has been developed to predict the capacitance voltage outputs of the 6H-SiC/MEH-PPV/Al diode with organic polymer interface, depending on the frequency. In the multi-layer network model developed with a total of 186 experimental data, 70% of the data used for training, 15% for validation and 15% for testing. The prediction performances of three different artificial neural networks developed with 5, 10 and 15 neurons in their hidden layers have been analyzed. The results obtained, for the first time in the literature, show that the artificial neural network model cannot predict the capacitance voltage outputs of the organic polymer interface 6H-SiC/MEH-PPV/Al diode depending on the frequency.

Health Insurance Claim Prediction Using Artificial Neural Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 40-57
Author(s):  
Sam Goundar ◽  
Suneet Prakash ◽  
Pranil Sadal ◽  
Akashdeep Bhardwaj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Insurance Company ◽  
Feed Forward ◽  
Medical Claims ◽  
Artificial Neural

A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This study presents the development of artificial neural network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models was finished, the focus was to decrease the mean absolute percentage error by adjusting the parameters, such as epoch, learning rate, and neurons in different layers. Both feed forward and recurrent neural networks were implemented to forecast the yearly claims amount. In conclusion, the artificial neural network model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims for BSP Life. Recurrent neural network outperformed the feed forward neural network in terms of accuracy and computation power required to carry out the forecasting.

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

2020 ◽  
Author(s):  
Marina Ermolickaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
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.

Prediction of Dynamic Impedances Functions Using an Artificial Neural Network (ANN)

2012 ◽  
Vol 170-173 ◽  
pp. 3588-3593
Author(s):  
Sbartai Badreddine ◽  
Kamel Goudjil
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dynamic Properties ◽  
Surface Foundation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Impedance Functions

Artificial Neural Networks (ANN) has seen an explosion of interest over the last few years. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. Hence, the main objective of this paper is to develop a model to predict the response of the soil-structure interaction system without using the calculate code based on sophisticate numerical methods by the employment of a statistical approach based on an Artificial Neural Network model (ANN). In this study, a data base which relates the impedance functions to the geometrics characteristic of the foundation and the dynamic properties of the soil is implemented. This leads to develop a neural network model to predict impedances functions (all modes) of a rectangular surface foundation. Then the results are compared with unused data to check the ANN model’s validity.

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