Adolescents Psychological Well-Being Estimation Based on a Data Mining Algorithm; Computer Science and Information Technologies (CSIT); Vol. 1
| Parent link: | Computer Science and Information Technologies (CSIT): proceedings of the XIIIth International Scientific and Technical Conference CSIT 2018, 11-14 September 2018, Lviv, Ukraine Vol. 1.— 2018.— [P. 475-478] |
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| Ente Autore: | |
| Altri autori: | , , , |
| Riassunto: | Title screen Control of the risks for reducing mental health and psychological well-being of young people allows making timely managerial decisions aimed at reducing social tensions and increasing the safety of communities. Effective implementation of projects at the national and regional level is possible if there is relevant and dynamically updated information on the state of mental health of young people. The authors develop a special questionnaire for gathering initial data on psychological wellbeing of adolescents. However, for final conclusion about wellbeing, a qualified psychologist is needed who is not always available for organizations (especially for rural schools). In this regard, the use of methods of machine learning and data mining to create software that automatically assesses well-being according to results of respondents' responses is relevant. Within this study, the group method of data handling (GMDH) is used. The algorithm of twice-multilayered modified polynomial neural network with active neurons is applied to construct classifiers for 4 classes of well-being of schoolchildren. The data contain responses of about 200 adolescents aged 12-17 years from 11 rural schools. The results of this study demonstrate the percentage of correct classification for the two extreme classes of well-being (“well-being”, “not well-being”) not worse than 90% for an independent control sample of data. |
| Lingua: | inglese |
| Pubblicazione: |
2018
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| Soggetti: | |
| Accesso online: | https://doi.org/10.1109/STC-CSIT.2018.8526628 |
| Natura: | Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=379604 |
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| 200 | 1 | |a Adolescents Psychological Well-Being Estimation Based on a Data Mining Algorithm |f S. V. Tyulyupo, A. A. Andrakhanov, B. A. Dashieva, A. V. Tyryshkin | |
| 203 | |a Текст |c электронный | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 4 tit.] | ||
| 330 | |a Control of the risks for reducing mental health and psychological well-being of young people allows making timely managerial decisions aimed at reducing social tensions and increasing the safety of communities. Effective implementation of projects at the national and regional level is possible if there is relevant and dynamically updated information on the state of mental health of young people. The authors develop a special questionnaire for gathering initial data on psychological wellbeing of adolescents. However, for final conclusion about wellbeing, a qualified psychologist is needed who is not always available for organizations (especially for rural schools). In this regard, the use of methods of machine learning and data mining to create software that automatically assesses well-being according to results of respondents' responses is relevant. Within this study, the group method of data handling (GMDH) is used. The algorithm of twice-multilayered modified polynomial neural network with active neurons is applied to construct classifiers for 4 classes of well-being of schoolchildren. The data contain responses of about 200 adolescents aged 12-17 years from 11 rural schools. The results of this study demonstrate the percentage of correct classification for the two extreme classes of well-being (“well-being”, “not well-being”) not worse than 90% for an independent control sample of data. | ||
| 461 | |t Computer Science and Information Technologies (CSIT) |o proceedings of the XIIIth International Scientific and Technical Conference CSIT 2018, 11-14 September 2018, Lviv, Ukraine | ||
| 463 | |t Vol. 1 |v [P. 475-478] |d 2018 | ||
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a psychological well-being | |
| 610 | 1 | |a risk factors | |
| 610 | 1 | |a questionnaire | |
| 610 | 1 | |a machine learning | |
| 610 | 1 | |a data mining | |
| 610 | 1 | |a GMDH | |
| 610 | 1 | |a neural network | |
| 610 | 1 | |a active neuron | |
| 610 | 1 | |a психологическое благополучие | |
| 610 | 1 | |a факторы риска | |
| 610 | 1 | |a анкеты | |
| 610 | 1 | |a машинное обучение | |
| 610 | 1 | |a сбор данных | |
| 610 | 1 | |a нейронные сети | |
| 610 | 1 | |a активные нейроны | |
| 701 | 1 | |a Tyulyupo |b S. V. | |
| 701 | 1 | |a Andrakhanov |b A. A. |c Specialist in the field of electrical engineering |c Assistant of the Department of Tomsk Polytechnic University |f 1982- |g Anatoliy Aleksandrovich |3 (RuTPU)RU\TPU\pers\38561 |9 20819 | |
| 701 | 1 | |a Dashieva |b B. A. | |
| 701 | 1 | |a Tyryshkin |b A. V. |c Specialist in the field of electrical engineering |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1954- |g Aleksandr Vasilievich |3 (RuTPU)RU\TPU\pers\38559 |9 20817 | |
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