Zobrazeno 1 - 10
of 1 486
pro vyhledávání: '"Mutual Learning"'
Autor:
Casper G. Schoemaker, Inge Schalkers, Brigit A. de Jong, Wilma Wissink, Sacha le Loux, Ronald A. M. Buijsen, Klaas Russcher, Frank A. M. van der Steeg, Janine Blom, Eva Vroonland
Publikováno v:
Research Involvement and Engagement, Vol 10, Iss 1, Pp 1-13 (2024)
Abstract Background Patient involvement is most common during the first phases of a research project. Despite good intentions, maintaining meaningful collaborations throughout the research process doesn't seem easy. Several training programmes for re
Externí odkaz:
https://doaj.org/article/ee6e3845f807453e807ac51baf83caf1
Autor:
Faranak Shapouran, Roya Ranjbar Mohammadi Bonab, Mohammad Hossein Yousefi, Hassan Asadollahfam
Publikováno v:
Journal of Modern Research in English Language Studies, Vol 11, Iss 3, Pp 101-124 (2024)
Teachers' professional learning plays an important role in their attempt to change their teaching practices; however, few studies have been conducted on how teachers prepare themselves for the vast initiatives in ameliorating students' outcomes. This
Externí odkaz:
https://doaj.org/article/752f8290dd494cc2bb7315d0098361df
Publikováno v:
Revista Tempo do Mundo, Iss 34, Pp 119-147 (2024)
The Global Alliance against hunger and poverty launched by Brazil’s Group of Twenty (G20) Presidency builds on a long history of G20 efforts to address global food and nutritional insecurity. However, unlike previous initiatives, which focused prim
Externí odkaz:
https://doaj.org/article/14d22824da144ed5adbcb51084b8ea22
Autor:
Ananya Ghosh, Parthiban Krishnamoorthy
Publikováno v:
IEEE Access, Vol 12, Pp 135040-135062 (2024)
Federated learning presents a potent avenue for addressing challenges in waste classification, where diverse datasets are distributed across sources. This paper introduces the Federated Average Knowledge Distilled Mutual Conditional Learning (FedADC)
Externí odkaz:
https://doaj.org/article/69cd5693c9854d97b3f2b57400377a9c
Publikováno v:
IEEE Access, Vol 12, Pp 120788-120803 (2024)
Object detection is a crucial task in mobile augmented reality (MAR), where achieving both speed and accuracy with limited computational resources is essential. However, applying object detection models to new domains or reducing the model size tends
Externí odkaz:
https://doaj.org/article/c4dffbb3da754851a9aad8ff181724d6
Publikováno v:
IET Image Processing, Vol 18, Iss 1, Pp 34-46 (2024)
Abstract Most existing domain adaption methods solve the problem of data distribution similarity in single‐source to single‐target domain or multi‐source to single‐target domain adaption, but the more realistic multi‐source to multi‐targe
Externí odkaz:
https://doaj.org/article/6fc6108b11d342fa90f04a114ca436ca
Publikováno v:
Remote Sensing, Vol 16, Iss 21, p 4029 (2024)
Hyperspectral image (HSI) and light detection and ranging (LiDAR) data have gained significant attention due to their excellent complementarity, which can be combined to improve the accuracy of land cover classification. However, there are still many
Externí odkaz:
https://doaj.org/article/e7113e2210bd43a3bf60addd1d65e807
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Effectively monitoring pest-infested areas by computer vision is essential in precision agriculture in order to minimize yield losses and create early scientific preventative solutions. However, the scale variation, complex background, and dense dist
Externí odkaz:
https://doaj.org/article/d81c0deb97fb4039b78c6a1a321cbbcb
Autor:
Shinsuke Kyoi, Koichiro Mori
Publikováno v:
World Development Sustainability, Vol 4, Iss , Pp 100118- (2024)
Pro-environmental behavior does not diffuse sufficiently in society. Is there a way to enhance the degree of people's pro-environmental behavior? This study aims to develop a dynamic model of mutual learning in social networks to simulate the diffusi
Externí odkaz:
https://doaj.org/article/1bdd3567afd941bf87aebc0fd9d31e40