New electrotopological descriptor for prediction of boiling points of alkanes and aliphatic alcohols through artificial neural network and multiple linear regression analysis

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

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Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

SN Applied Sciences ◽

10.1007/s42452-020-03974-7 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Charles Gbenga Williams ◽

Oluwapelumi O. Ojuri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hydraulic Conductivity ◽

Linear Regression ◽

Multiple Linear Regression ◽

Coarse Grained ◽

Soil Types ◽

Wide Range ◽

Artificial Neural ◽

Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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

Metals ◽

10.3390/met11010018 ◽

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.

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