Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

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Simultaneous spectrophotometric quantitative analysis of velpatasvir and sofosbuvir in recently approved FDA pharmaceutical preparation using artificial neural networks and genetic algorithm artificial neural networks

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2021.119465 ◽

2021 ◽

Vol 251 ◽

pp. 119465

Author(s):

Khalid A.M. Attia ◽

Nasr M. El-Abasawi ◽

Ahmed El-Olemy ◽

Ahmed H. Abdelazim ◽

Abdelrahman I. Goda ◽

...

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Artificial Neural Networks ◽

Quantitative Analysis ◽

Pharmaceutical Preparation ◽

Artificial Neural

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Authentication of Douro DO monovarietal red wines based on anthocyanin profile: Comparison of partial least squares – discriminant analysis, decision trees and artificial neural networks

Food Control ◽

10.1016/j.foodcont.2021.107979 ◽

2021 ◽

Vol 125 ◽

pp. 107979

Author(s):

Fernanda Cosme ◽

Juliana Milheiro ◽

Joana Pires ◽

Fernanda Isabel Guerra-Gomes ◽

Luís Filipe-Ribeiro ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Discriminant Analysis ◽

Least Squares ◽

Decision Trees ◽

Partial Least Squares ◽

Red Wines ◽

Anthocyanin Profile ◽

Artificial Neural

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Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽

10.3390/en14082332 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2332

Author(s):

Cecilia Martinez-Castillo ◽

Gonzalo Astray ◽

Juan Carlos Mejuto

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Random Forests ◽

Prediction Models ◽

Meteorological Station ◽

Ann Model ◽

Global Irradiation ◽

Artificial Neural ◽

Input Variables ◽

Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

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