scholarly journals Comparison of Artificial Neural Networks and Logistic Regression Analysis in PISA Science Literacy Success Prediction

2020 ◽  
Author(s):  
Ali BOZAK ◽  
Eren Can AYBEK
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Logistic Regression Analysis ◽  
Science Literacy ◽  
Success Prediction ◽  
Literacy Success ◽  
Artificial Neural

scholarly journals ESTIMATION OF THE SECTORS OF THE INVESTMENTS MADE ON VENTURE CAPITAL COMPANIES WITH ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LOGISTIC REGRESSION ANALYSIS

2020 ◽  
Vol 7 (4) ◽  
pp. 297-308
Author(s):  
Kubilay ERİSLİK ◽  
Özlem DENİZ BAŞAR
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Venture Capital ◽  
Logistic Regression Analysis ◽  
Multiple Logistic Regression Analysis ◽  
Multiple Logistic Regression ◽  
Core Phase ◽  
Artificial Neural

Venture capital companies undergo three different phases as core, growth and maturity phases as of their establishment. There are different stages in these phases in terms of providing the finance. The stage of providing finance for the first introduction of the product to the market in the core phase is called Serial A, the stage of providing the increasing finance need during the continuation of the growth is called Serial B and the stage of providing the finance needed in the growth and maturity phases is called Serial C and it continues as Serial D. In this study, it has been aimed to estimate the sectors of the venture capital companies by benefiting from the phases and amounts of the investments made by the investors to the venture capital companies. In the study, 5 sectors with the highest investment from investors have been selected and the investment data of 709 venture capital companies taking place in this sector have been benefited. Artificial Neural Networks and Multiple Logistic Regression Analysis have been used in the estimation of the sectors covering the companies with the data attained from the investment series. When the attained results have been examined, it has been determined that the results attained with Artificial Neural Networks are more successful than the results attained with Multiple Logistic Regression analysis.

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.

Comparison of Artificial Neural Networks with Logistic Regression for Detection of Obesity

2011 ◽  
Vol 36 (4) ◽  
pp. 2449-2454 ◽  
Author(s):  
Seyed Taghi Heydari ◽  
Seyed Mohammad Taghi Ayatollahi ◽  
Najaf Zare
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Artificial Neural

Methods of Evaluation for Region’s Landslide Susceptibility. Short Overview

2017 ◽  
Vol 6 (3) ◽  
pp. 57-60
Author(s):  
Денис Кривогуз ◽  
Denis Krivoguz
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Linear Discriminant ◽  
Landslide Susceptibility Assessment ◽  
Artificial Neural

Modern approaches to the region’s landslide susceptibility assessment are considered in this paper. Have been presented descriptions of the most used techniques for landslide susceptibility assessment: logistic regression, indicator validity, linear discriminant analysis and application of artificial neural networks. These techniques’ advantages and disadvantages are discussed in the paper. The most suitable techniques for various conditions of analysis have been marked. It has been concluded that the most acceptable techniques of analysis for a large number of input data related to the studied region are the method of logistic regression and indicator validity method. With these methods the most accurate results are achieved. When there is a lack of information, it is more expedient to use linear discriminant analysis and artificial neural networks that will minimize potential analysis inaccuracies.

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