Machine and Deep Learning Techniques to Classify Arousal Judgments in Dynamic Virtual Experience of Architecture-Reference-Cited by-同舟云学术

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Machine and Deep Learning Techniques to Classify Arousal Judgments in Dynamic Virtual Experience of Architecture

Published:2023-12-15 Issue: Volume:193 Page:2087-2094
ISSN:
Container-title:2023 International Conference on Machine Learning and Applications (ICMLA)
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Author:

Pecori Riccardo¹,Presti Paolo²,Avanzini Pietro³,Aversano Lerina⁴,Caruana Fausto³,Cimitile Marta⁵,Montano Debora⁶,Ruzzon Davide⁷,Bernardi Mario Luca⁴,Vecchiato Giovanni³

Affiliation:

1. SMARTEST Research Centre eCampus University Novedrate (CO),Italy

2. University of Parma,Dept. of Medicine and Surgery,Parma,Italy

3. Institute of Neuroscience National Research Council of Italy,Parma,Italy

4. University of Sannio,Dept. of Engineering,Benevento,Italy

5. UniTelma Sapienza University,Dept. of Law and Digital Society,Rome,Italy

6. Faculty of Economics Universitas Mercatorum,Rome,Italy

7. Iuav University of Venice,Venice,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10459339/10459727/10459962.pdf?arnumber=10459962

Reference40 articles.

1. Miguel Angel Campano, Russell Leslie, and Leora Radetsky;Acosta;Daylighting design for healthy environments: Analysis of educational spaces for optimal circadian stimulus. Solar Energy,2019

2. Land cover classification and monitoring through multisensor image and data combination

3. Convolutional Neural Network-Based Methods for Eye Gaze Estimation: A Survey

4. Attention mediates the effect of emotional arousal on learning outcomes in multimedia learning: an eye-tracking study

5. Neural Networks for Emotion Recognition Based on Eye Tracking Data

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