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.

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.

Predicting Behavior of Plasma Nitrided Ni+Al Composite Coating Using an Artificial Neural Network Model

2010 ◽  
Vol 297-301 ◽  
pp. 1127-1132 ◽  
Author(s):  
H. Bolvardi ◽  
Ali Shokuhfar ◽  
N. Daemi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Plasma Nitriding ◽  
Numerical Results ◽  
Nickel Aluminum ◽  
Aluminum Composite ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Plasma nitriding is a powerful process for surface modification of different materials. In this study, plasma nitriding is applied on a Nickel-Aluminum composite, coated on ST37 steel. Ni+Al composites were fabricated by electrodeposition process in watts bath containing Al particles. For prediction of electrodeposited Al% during the electroplating and microhardness of coatings after plasma nitriding process artificial neural network (ANN) was used. The numerical results obtained via a neural network model were compared with the experimental results. Agreement between the experimental and numerical results was reasonably good.

Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model

RSC Advances ◽  
2015 ◽  
Vol 5 (101) ◽  
pp. 82654-82665 ◽  
Author(s):  
Ghaidaa S. Daood ◽  
Hamidon Basri ◽  
Johnson Stanslas ◽  
Hamid Reza Fard Masoumi ◽  
Mahiran Basri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Statistical Technique ◽  
Brain Delivery ◽  
Multivariate Statistical ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

For the purpose of brain delivery via intravenous administration, the formulation of an azithromycin-loaded nanoemulsion system was optimized utilizing the artificial neural network (ANN) as a multivariate statistical technique.

Health Insurance Claim Prediction Using Artificial Neural Networks

2022 ◽  
pp. 1174-1193
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 ◽  
Feed Forward Neural Network ◽  
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.

scholarly journals Real-Time Quality Control and Infilling of Precipitation Data Using Neural Networks

10.29007/t5k7 ◽  
2018 ◽  
Author(s):  
Mohammadreza Moslemi ◽  
Darko Joksimovic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Superior Performance ◽  
Precipitation Data ◽  
Artificial Neural

Due to advancements in instrumentation and communication technologies, monitoring of water infrastructure is experiencing a significant growth worldwide and water managers are increasingly deploying monitoring equipment for decision-making purposes. Hydrological events and relevant datasets including rainfall data are of a complex nature and are potentially susceptible to errors from various sources. Hence, it is essential to develop efficient methods for the quality control of the acquired data. The present work introduces an artificial neural network-based approach for real-time quality control and infilling of rain gauge data. Available rainfall measurements from neighboring rain gauges are employed to train and develop the neural network model. Trained artificial neural network model was able to validate up to about 97% of the data using 95% confidence intervals. This finding suggests that artificial neural networks can be successfully implemented for erroneous data identification/correction and reconstruction of missing data points. Given its short processing time and reportedly superior performance to traditional quality control strategies, neural network methodology can be deployed as an efficient tool for the processing and control of large sets of timeseries with complex natures including precipitation data.

scholarly journals Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1906 ◽  
Author(s):  
Christian Giovanelli ◽  
Seppo Sierla ◽  
Ryutaro Ichise ◽  
Valeriy Vyatkin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Energy Market ◽  
Market Prices ◽  
Artificial Neural

The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively.

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.

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