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Volume 20, Issue 97 (3-2021)                   علوم روانشناختی 2021, 20(97): 39-46 | Back to browse issues page

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Esmaeeli Abdar M, Taheri H R, sohrabi M, Ghanaei A. The effects of different levels of difficulty on learning the task of throwing in elderly: emphasis on machine learning methods. علوم روانشناختی. 2021; 20 (97) :39-46
URL: http://psychologicalscience.ir/article-1-796-en.html
Professor. Department of Motor Behavior, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract:   (402 Views)
Background: Numerous studies have examined the difficulty levels of homework and homework learning. But research that uses machine learning approaches, and in particular neural-fuzzy inference systems, to model the difficulty of throwing a task with different difficulty conditions among the elderly has been overlooked. Aims: The aim of the present study was to investigate the effects of different levels of difficulty on learning the task of throwing in the elderly: Emphasis on machine learning methods. Method: The research was quasi-experimental with pre-test and post-test design with 4 control groups. The statistical population of the study included all right-handed elderly men in 1397 with an age range of 70-60 years in Mashhad. 120 elderly people were selected by available sampling method and randomly divided into 8 groups of 15 people (4 experimental groups and 4 control groups). The instrument of the present study was the task of discus throwing participants derived from the research of Sanli and Lee (Sanli and Lee, 2015). Data analysis was performed using encryption using an sophisticated system. Results: Individuals in the experimental group were better off than the control group in terms of learning conditions. Also, the best scenario of the adaptive fuzzy-neural inference system in the learning phase was selected in the experimental group No. 1 with the lowest error RMSE= 0/73 for the survey data. Conclusions: The use of fuzzy neural inference system and the creation of neural-fuzzy networks has been successful and significantly reduced the prediction error, which has a significant feature of rapid convergence and high accuracy
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Type of Study: Research | Subject: Special
Received: 2020/05/26 | Accepted: 2021/03/30 | Published: 2021/02/20

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