Deep Learning Methods for the Prediction of Information Display Type Using Eye Tracking Sequences-Reference-Cited by-同舟云学术

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Deep Learning Methods for the Prediction of Information Display Type Using Eye Tracking Sequences

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)
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Author:

Yin Yuehan,Alqahtani Yahya,Feng Jinjuan Heidi,Chakraborty Joyram,McGuire Michael P.

Funder

Towson University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9679834/9679948/09680256.pdf?arnumber=9680256

Reference20 articles.

1. Generative Modeling of Synthetic Eye-tracking Data: NLP-based Approach with Recurrent Neural Networks

2. Gesture recognition with a convolutional long short-term memory recurrent neural network;tsironi;ESANN,2016

3. CNN and GRU based Deep Neural Network for Electricity Theft Detection to Secure Smart Grid

4. Toward a deep convolutional lstm for eye gaze spatiotemporal data sequence classification;sodoké;EDM,2020

5. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Real-time Prediction of Students' Math Difficulties using Raw Data from Eye Tracking and Neural Networks;Proceedings of the ACM on Human-Computer Interaction;2024-05-20

2. Detecting Learning Stages within a Sensor-Based Mixed Reality Learning Environment Using Deep Learning;Journal of Computing in Civil Engineering;2023-07

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