scholarly journals Peramalan Penjualan Kendaraan Mobil Segmen B2B dengan Metode Regresi Linear Berganda, Jaringan Saraf Tiruan dan Jaringan Saraf Tiruan – Algoritma Genetika

2020 ◽  
Vol 3 (2) ◽  
pp. 83
Author(s):  
Muhammad Agung Nugraha ◽  
Farizal Farizal ◽  
Djoko Sihono Gabriel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Decision Making Unit ◽  
Car Sales ◽  
Artificial Neural

This study aims to create an effective forecasting model in predicting sales of car products in the B2B segment (Business to Business) to obtain estimates of product sales in the future. This research uses multiple linear regression and artificial neural networks that are optimized by genetic algorithms. Forecasting factors for car sales are generally issued by total national car sales, the Consumer Price Index, the Consumer Confidence Index, the Inflation Rate, Gross Domestic Product (GDP), and Fuel Oil Price. The author has also gotten the factors that play a role in the sale of B2B segment by diverting the survey to 106 DMU (Decision Making Unit) who decide to purchase cars in their company. Then we evaluate the results of the questionnaire in training data and simulations on the Artificial Neural Network. Optimized Artificial Neural Networks with Genetic Algorithms can improve B2B segment car sales' accuracy when comparing error values in the ordinary Artificial Neural Network and Multiple Linear Regression.

scholarly journals Prediction of sweet corn yield depending on cultivation technology parameters by using linear regression and artificial neural network methods

2018 ◽  
Vol 26 (1) ◽  
pp. 11-15 ◽  
Author(s):  
P. V. Lykhovyd
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Data Processing ◽  
Field Experiments ◽  
Sweet Corn ◽  
Corn Yield ◽  
Artificial Neural

Artificial neural networks and linear regression are widely used in particularly all branches of science for modeling and prediction. Linear regression is an old data processing tool, and artificial neural networks are a comparatively new one. The goal of the study was to determine whether artificial neural networks are more accurate than linear regression in sweet corn yield prediction. In the study we used a dataset obtained from field experiments on the technological improvement of sweet corn cultivation. The field experiments were conducted during the period from 2014 to 2016 on dark-chestnut soil under drip irrigated conditions in the Steppe Zone of Ukraine. We studied the impact of the moldboard plowing depths, mineral fertilizer application rates and plant densities on the crop yield. A significant impact of all the studied factors on the sweet corn productivity was proved by using the analysis of variance. The highest yield of sweet corn ears without husks (10.93 t ha–1) was under the moldboard plowing at the depth of 20–22 cm, mineral fertilizers application rate of N120P120, plant density of 65,000 plants ha–1. Data processing by using the linear regression and artificial neural network methods showed that the latter is a great deal better than linear regression in sweet corn yield prediction. Higher accuracy of the artificial neural network prediction was proved by the higher value of the coefficient of determination (R2) – 0.978, in comparison to 0.897 for the linear regression prediction model. We conclude that artificial neural networks are a much better data processing tool, especially, in the life sciences and for prediction of the non-linear natural processes and phenomena. The main disadvantage of the neural network models is their “black box” nature. However, linear regression will not lose its popularity among scientists in the nearest future. Linear regression is a much simpler data analysis tool, it is easier to perform the prediction, but it still provides a sufficiently high level of accuracy.

scholarly journals Qsar Analysis of the Estrogen Activities of Bisphenol a Analogs using Multiple Linear Regression and Artificial Neural Network

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Vu Van Dat ◽  
Le Kim Long ◽  
Doan Van Phuc ◽  
Nguyen Hoang Trang ◽  
Nguyen Van Trang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Bisphenol A ◽  
Multiple Linear Regression ◽  
Bisphenol S ◽  
Qsar Analysis ◽  
Chemical Structures ◽  
Artificial Neural

Abstract. This study gives a quantitative structure-activity relationship (QSAR) analysis of the estrogen activities of bisphenol A and analogs. The chemical structures of 23 Bisphenol A analogs have been characterized by quantum-electronic descriptors. The present study was performed using multiple regression analysis (MLR) and artificial neural network (ANN). The quantitative model was accordingly proposed and the toxicity of the compounds was interpreted based on the multivariate statistical analysis. This study shows that the results obtained by MLR were suitable and have served to predict estrogen activities, but compared to the results of the ANN model, we conclude that the prediction achieved by the latter is more effective and better than MLR model. Following to the obtained results, our proposed model may be useful to predict of Estrogen activities and risk assessment of chemicals. Keywords QSAR, Bisphenol A, multiple linear regression, artificial neural network References [1] Rezg R, El-Fazaa S, Gharbi N, Mornagui B (March 2014). "Bisphenol A and human chronic diseases: Current evidences, possible mechanisms, and future perspectives".Environment International 2014, 64, 83–90. [2] Melzer D, Rice NE, Lewis C, Henley WE, Galloway TS (2010). Zhang, Baohong, ed."Association of Urinary Bisphenol a Concentration with Heart Disease: Evidence from NHANES 2003/06". PLoS ONE 5 (1). [3] Manikkam, M.; Tracey, R.; Guerrero-Bosagna, C.; Skinner, M. K. (January 24, 2013). "Plastics derived endocrine disruptors (BPA, DEHP and DBP) induce epigenetic transgenerational inheritance of obesity, reproductive disease and sperm epimutations".PLoS ONE 8 (1). 1–16. [4] D.R. Doerge, N.C. Twaddle, M. Vanlandingham, R.P. Brown, J.W. Fisher, Toxicol. Appl. Pharmacol. 2011, 255, 261.[5] Ho SM, Tang WY, Belmonte de Frausto J, Prins GS (2006). "Developmental exposure to estradiol and bisphenol A increases susceptibility to prostate carcinogenesis and epigenetically regulates phosphodiesterase type 4 variant 4". Cancer Res. 66 (11): 5624–32. [6] Johanna R. Rochester and Ashley L. Bolden (2015 Jul) “Bisphenol S and F: A Systematic Review and Comparison of the Hormonal Activity of Bisphenol A Substitutes”. Environ Health Perspect123(7):643-50.[7] Kelly, P. C., William, A. T., Thomas, E. W., QSAR models of thein vitro estrogen activity of bisphenol A analogs, QSAR Comb.Sci., 2003, 22: 78―88.[8]. Frisch, M. J. T., G. W. et al , Gaussian 09, Revision D.01. Gaussian, Inc., Wallingford CT, 2009.[9]. Zhao, Y.; Truhlar, D., The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor Chem[10] J. Devillers (1996), Strengths and Weaknesses of the Backpropagation Neural Network in QSAR and QSPR Studies, in: J. Devillers (Ed.) Neural Networks in QSAR and Drug Design, Academic Press, London, pp.1-46.[11] K. Hornik (1991), AppSroximation capabilities of multilayer feedforward networks, Neural Networks, 4 251-257.[12] A. Adad, M. Larif, R. Hmamouchi, M. Bouachrine, T. Lakhlifi, J. Comp. Meth. Mol. Des. 4(3) (2014) 72-83.[13] A. Golbraikh, A. Tropsha, Beware of q2. J. Mol. Graphics Model. 20 (2002) 269–276.  

scholarly journals USE OF ARTIFICIAL NEURAL NETWORKS TO PREDICT TERRITORIAL ECONOMIC INDICATORS

2019 ◽  
Vol 4 (8) ◽  
pp. 143-146
Author(s):  
Gocha Ugulava
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Economic Indicators ◽  
Regional Economic ◽  
The Neural Network ◽  
Artificial Neural ◽  
Predicted Values

Modern economic science is unthinkable without predicting and planning the prospects for economic life development. There are many different mathematical and statistical tools in the arsenal of scientists as well as practitioners and economists today in purpose of forecasting. To date, one of the most prominent effective tools for data analytics is artificial neural networks. Artificial Neural Network - is a mathematical mod- el created in the likeness of a human neural network, and its software and hardware implementation. We carried out modeling and forecasting of regional economic indicators using the artificial neural network of the three-layer perceptron architecture. The network architecture and neuron settings were automatically formatted through the programming language R and its package - Neuralnet. During the forecasting phase, the data vectors were presented as data frame in five input parameters (DFI, FAI, EMP, BT, CPI), according to the neural network forecast of the regional gross domestic product (RGDP_NN) was calculated. All data are from the Imereti region and are taken from official GeoStat sources. Forecasting was done at the same time scale (2006-2017) to enable us to compare the predicted values with the actual ones to verify the level of fore- cast accuracy. We also tested the results of the neural network in another way - compared to the predicted values using multiple linear regression on the same data. The accuracy of the predicted values calculated by the neural network was quite high, which was not declining but slightly ahead of the accuracy coefficients of the predicted values obtained through linear regression. Also, the predictive values calculated by the neural network with high adequacy and accuracy were compared with actual, existing ones. Presented material shows that the use of artificial neural networks for the prediction of territorial economic indicators is reasonable and justified. Their role in analyzing and predicting indicators that are characterized by nonstationarity, dynamism, lack of a definite trend, periodicity, nonlinear structure is especially increased. It is therefore advisable to apply this method in regional economic studies, in predicting territorial development plans, strategies, targets and indicators.

Prediction of the Consistency of Concrete by Means of the Use of Artificial Neural Networks

2011 ◽  
pp. 1484-1493
Author(s):  
Bele´n Gonzalez ◽  
Ma Isabel Martinez ◽  
Diego Carro
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Fresh Concrete ◽  
Rheological Behaviour ◽  
Simple Approach ◽  
Artificial Neural

This chapter displays an example of application of the ANN in civil engineering. Concretely, it is applied to the prediction of the consistency of the fresh concrete through the results that slump test provides, a simple approach to the rheological behaviour of the mixtures. From the previously done tests, an artificial neural network trained by means of genetic algorithms adjusts to the situation, and has the variable value of the cone as an output, and as an input, diverse variables related to the composition of each type of concrete. The final discussion is based on the quality of the results and its possible application.

scholarly journals Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

2019 ◽  
Vol 54 ◽  
Author(s):  
Carlos Alberto Araújo Júnior ◽  
Pábulo Diogo de Souza ◽  
Adriana Leandra de Assis ◽  
Christian Dias Cabacinha ◽  
Helio Garcia Leite ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Quantile Regression ◽  
Site Index ◽  
Permanent Plots ◽  
Artificial Neural ◽  
The Stability

Abstract: The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also better than the one obtained by the other methods, regardless of the presence or the absence of outliers in the database. This shows that the artificial neural network is a solid modelling technique in the presence of outliers. When the cause of the presence of outliers in the database is not known, they can be kept in it if techniques as artificial neural networks or quantile regression are used.

Prediction of the Consistency of Concrete by Means of the Use of Artificial Neural Networks

2006 ◽  
pp. 188-201
Author(s):  
Belén Gonzalez ◽  
M. Isabel Martinez ◽  
Diego Carro
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Fresh Concrete ◽  
Rheological Behaviour ◽  
Simple Approach ◽  
Artificial Neural

This chapter displays an example of application of the ANN in civil engineering. Concretely, it is applied to the prediction of the consistency of the fresh concrete through the results that slump test provides, a simple approach to the rheological behaviour of the mixtures. From the previously done tests, an artificial neural network trained by means of genetic algorithms adjusts to the situation, and has the variable value of the cone as an output, and as an input, diverse variables related to the composition of each type of concrete. The final discussion is based on the quality of the results and its possible application.

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

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.

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 Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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