scholarly journals Forecasting BIST 100 Index With Artificial Neural Networks and Regression Analysis

2022 ◽  
Author(s):  
Yüksel Akay ÜNVAN ◽  
Cansu ERGENÇ
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Artificial Neural

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Artificial neural networks and regression analysis for volume estimation in native species

2021 ◽  
Vol 25 (10) ◽  
pp. 664-669
Author(s):  
Lucas M. Amorim ◽  
Elton da S. Leite ◽  
Deoclides R. de Souza ◽  
Liniker F. da Silva ◽  
Carlos R. de Mello ◽  
...  
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Native Species ◽  
Middle Layer ◽  
Volume Measurement ◽  
Volume Estimation ◽  
Individual Volume ◽  
Artificial Neural ◽  
The Individual

ABSTRACT Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.

scholarly journals About combining determinated and stochastic approaches for prediction of the heating balance of the building for water sports

2020 ◽  
Vol 22 (1) ◽  
pp. 103-112
Author(s):  
S. V. Guzhov
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Initial Data ◽  
Thermal Energy ◽  
Heat Balance ◽  
Stochastic Method ◽  
Stochastic Methods ◽  
Artificial Neural ◽  
Water Sports

Forecasting the demand for thermal energy by energy complexes of buildings and structures is an urgent task. To achieve the necessary accuracy of the calculation, it is customary to use various deterministic methods based on the available changing and slightly changing data about the object of study. At the same time, statistical data can also be used in analysis by stochastic methods. The purpose of this article is to analyze the question of the admissibility of combining deterministic and stochastic approaches in order to increase the accuracy of the calculation. Formulas for calculating the components of the expenditure part of the heat balance are shown on the example of a building for water sports. Based on the above formulas, a calculation with a monthly discretization in the period from January 2009 is carried out. until January 2019. An example is given of calculating the accuracy of the forecast of demand for thermal energy through multivariate regression analysis and the use of artificial neural networks. Based on the same data, an artificial neural network was trained on seven different factors: six independent and seventh — the idealized value of the building’s heat loss through the building envelope. An example of the analysis of a building for practicing water sports shows the inadmissibility of the described approach if the same initial data are used in the deterministic and stochastic method. Results: the accuracy of the forecast made using regression analysis increases with an increase in the number of factors. However, the use of an additional group of factors in the stochastic method, for example, which are numerically processed climate data that are already used as initial data, will lead to an unreasonable overestimation of the significance of the twice used factor. The presence in the predictive models using artificial neural networks of collinearity and multicollinearity of variables does not negatively affect the forecast. Conclusion: the combination of the deterministic and stochastic approaches in preparing the predicted heat balance by using only the same input data that is used in the stochastic approach in the deterministic approach is unacceptable.

scholarly journals Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249206
Author(s):  
Linus Aronsson ◽  
Roland Andersson ◽  
Daniel Ansari
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Variable Selection ◽  
Lasso Regression ◽  
Term Survival ◽  
Long Term Survival ◽  
Artificial Neural ◽  
Invasive Ipmn

Prediction of long-term survival in patients with invasive intraductal papillary mucinous neoplasm (IPMN) of the pancreas may aid in patient assessment, risk stratification and personalization of treatment. This study aimed to investigate the predictive ability of artificial neural networks (ANN) and LASSO regression in terms of 5-year disease-specific survival. ANN work in a non-linear fashion, having a potential advantage in analysis of variables with complex correlations compared to regression models. LASSO is a type of regression analysis facilitating variable selection and regularization. A total of 440 patients undergoing surgical treatment for invasive IPMN of the pancreas registered in the Surveillance, Epidemiology and End Results (SEER) database between 2004 and 2016 were analyzed. The dataset was prior to analysis randomly split into a modelling and test set (7:3). The accuracy, precision and F1 score for predicting mortality were 0.82, 0.83 and 0.89, respectively for ANN with variable selection compared to 0.79, 0.85 and 0.87, respectively for the LASSO-model. ANN using all variables showed similar accuracy, precision and F1 score of 0.81, 0.85 and 0.88, respectively compared to a logistic regression analysis. McNemar´s test showed no statistical difference between the models. The models showed high and similar performance with regard to accuracy and precision for predicting 5-year survival status.

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