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.

scholarly journals Artificial Neural Network Model for the Prediction of Groundwater Quality

2018 ◽  
Vol 4 (12) ◽  
pp. 2959 ◽  
Author(s):  
Basim Hussein Khudair ◽  
Mustafa Malik Jasim ◽  
Awatif Soaded Alsaqqar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Groundwater Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Coefficient Of Determination ◽  
Groundwater Samples ◽  
High Prediction ◽  
Artificial Neural

The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be 14.9 %, 39.5 %, 22.8 %, 6.1 %, and 16.7 %, respectively. Additionally, to anticipate changes in groundwater WQI, IBM® SPSS® Statistics 19 software (SPSS) was used to develop an artificial neural network model (ANNM). With the application of this ANNM model, the results obtained illustrated high prediction efficiency, as the sum of squares error functions (for training and testing samples) and coefficient of determination (R2), were found to be (0.038 and 0.005) and 0.973, respectively. However, the parameters pH and Cl influenced model prediction significantly, thereby becoming crucial factors in the anticipation carried out by using ANNM model.

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 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.  

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