Výsledky vyhledávání - "Toomela, Aaro"

Akademický článek

Vygotskian (but only partly Vygotsky’s) understanding of special education = A compreensão Vygotskyana (mas só em parte de Vygotsky) da educação especial = La comprensión Vygotskyana (pero sólo en parte de Vygotsky) de la educación especial

Autor: Toomela, Aaro

Publikováno v: Educação, Vol 41, Iss 2, Pp 347-361 (2018)

Neste artigo, os princípios gerais da educação especial são discutidos a partir da perspectiva Vygotskyana. Em primeiro lugar, sugere-se que, para a compreensão da educação especial em particular e da psique e seu desenvolvimento em geral, a e

Externí odkaz: https://doaj.org/article/72718116aed84454ba0a57860c07e451

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LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis

Autor: Wang, Xuechao, Huang, Junqing, Nomm, Sven, Chatzakou, Marianna, Medijainen, Kadri, Toomela, Aaro, Ruzhansky, Michael

Background and objectives: Dynamic handwriting analysis, due to its non-invasive and readily accessible nature, has recently emerged as a vital adjunctive method for the early diagnosis of Parkinson's disease. In this study, we design a compact and e

Externí odkaz: http://arxiv.org/abs/2311.11756

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Comparison of One- Two- and Three- Dimensional CNN models for Drawing-Test-Based Diagnostics of the Parkinson's Disease

Autor: Wang, Xuechao, Huang, Junqing, Chatzakou, Marianna, Nomm, Sven, Valla, Elli, Medijainen, Kadri, Taba, Pille, Toomela, Aaro, Ruzhansky, Michael

Subject: In this article, convolutional networks of one, two, and three dimensions are compared with respect to their ability to distinguish between the drawing tests produced by Parkinson's disease patients and healthy control subjects. Motivation:

Externí odkaz: http://arxiv.org/abs/2309.14288

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A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics

Autor: Wang, Xuechao, Huang, Junqing, Chatzakou, Marianna, Medijainen, Kadri, Taba, Pille, Toomela, Aaro, Nomm, Sven, Ruzhansky, Michael

In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a se

Externí odkaz: http://arxiv.org/abs/2302.00973

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