Soluzioni di Machine Learning per il Patrimonio Culturale. Il progetto ArtI4EO. by Nais Solutions

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"Il Service Learning per l’innovazione scolastica. Le proposte del Movimento delle Avanguardie educative", a cura di Lorenza Orlandini, Stefania Chipa e Chiara Giunti, Carocci Editore, 2020 by Irene Culcasi, Claudia Russo

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Mirror Visual Feedback Training Improves Intermanual Transfer in a Sport-Specific Task: A Comparison between Different Skill Levels by Fabian Steinberg, Nils Henrik Pixa, Michael Doppelmayr

“…However, this effect cannot be generalized to motor learning per se since it is modulated by individuals’ skill level, a factor that might be considered in mirror therapy research.…”
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تأثیر استخدام التعلم الاتقاني في تعلم مھارتي الطبطبة والتصویب بكرة الید by حاتم شوكت ابراهيم, رافد حبيب قدوري, عبدالكريم ابراهيم جاسم

“…The research sample consisted of students Division (f) havebeen selected from among the people of school year second's (6) people, and at a rate of (25-35) called for each division, and this amounted to the sample (30), student, and the sample exploratory experiments being researchers working on totaling 10 students were from the Division (d) after the researchers that ruled out a number of students from the research sample, has homogenization process between the two groups of the research sample to adjust the following chronological age, height and weight variables, was parity with between the two sets of research in learning my skills clapotement and correction hand reel, and the art teacher to apply the curriculum of the College has been organizing exercises and repeat them on the use of style Alleghany learning the experimental group the control group used the technique, and the curriculum took (12) educational unit and by (2) unit learning per week for each group, and that the educational unit time (90 minutes), was the use of the user's program in the areas of statistics and system (spss) to extract the data for research. …”
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Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning by Leandro Parente, Lindsey Sloat, Vinicius Mesquita, Davide Consoli, Radost Stanimirova, Tomislav Hengl, Carmelo Bonannella, Nathália Teles, Ichsani Wheeler, Maria Hunter, Steffen Ehrmann, Laerte Ferreira, Ana Paula Mattos, Bernard Oliveira, Carsten Meyer, Murat Şahin, Martijn Witjes, Steffen Fritz, Ziga Malek, Fred Stolle

“…The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3 M reference samples (visually interpreted in Very High Resolution imagery). …”
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Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus by Cui Wang, Xinping Xu, Shuai Luo, Man Luo, Sha Li, Jianhong Si

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