An Intelligent Approach Using Machine Learning Techniques to Predict Flow in People

被引：0

作者：

Pegalajar, M. C. ^{[1
]}

Ruiz, L. G. B. ^{[2
]}

Perez-Moreiras, E. ^{[3
]}

Boada-Grau, J. ^{[4
]}

Serrano-Fernandez, M. J. ^{[4
]}

机构：

[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada 18014, Spain

[2] Univ Granada, Dept Software Engn, Granada 18014, Spain

[3] Inst Desarrollo Talento, RH Asesores Improving SL, Madrid 28046, Spain

[4] Rovira & Virgili Univ, Dept Psychol, Tarragona 43002, Spain

来源：

BIG DATA AND COGNITIVE COMPUTING | 2023年 / 7卷 / 02期

关键词：

machine learning; artificial neural networks; flow; psychology; data mining; NEURAL-NETWORKS; SATISFACTION; PSYCHOLOGY; WORK; SCALE; MODEL;

D O I：

10.3390/bdcc7020067

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The goal of this study is to estimate the state of consciousness known as Flow, which is associated with an optimal experience and can indicate a person's efficiency in both personal and professional settings. To predict Flow, we employ artificial intelligence techniques using a set of variables not directly connected with its construct. We analyse a significant amount of data from psychological tests that measure various personality traits. Data mining techniques support conclusions drawn from the psychological study. We apply linear regression, regression tree, random forest, support vector machine, and artificial neural networks. The results show that the multi-layer perceptron network is the best estimator, with an MSE of 0.007122 and an accuracy of 88.58%. Our approach offers a novel perspective on the relationship between personality and the state of consciousness known as Flow.

引用

页数：15

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