Deep learning algorithms and applications
Deep learning architecture algorithms have been extensively developed and applied to various applications. The techniques have successfully improved the performance of difficult computer tasks such as computer vision, natural language processing, and speech recognition. This project aims to apply on...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2018
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| Online Access: | http://hdl.handle.net/10356/75195 |
| _version_ | 1826129254948012032 |
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| author | Santoso, Yosua Nathanael |
| author2 | Tan Yap Peng |
| author_facet | Tan Yap Peng Santoso, Yosua Nathanael |
| author_sort | Santoso, Yosua Nathanael |
| collection | NTU |
| description | Deep learning architecture algorithms have been extensively developed and applied to various applications. The techniques have successfully improved the performance of difficult computer tasks such as computer vision, natural language processing, and speech recognition. This project aims to apply one of the well-known deep learning algorithms, Convolutional Neural Network to detect student engagement which is believed to be an important factor for learning outcome. The input data which is in the form of frontal videos of students watching online recording were collected and pre-processed before being fed into the seven layers of CNN. The trained model reached a considered decent accuracy result. Some applications utilizing the trained model such as real-time engagement detection and graphical representation of student engagement are also introduced in this project. |
| first_indexed | 2024-10-01T07:37:39Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/75195 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:37:39Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/751952023-07-07T16:06:20Z Deep learning algorithms and applications Santoso, Yosua Nathanael Tan Yap Peng School of Electrical and Electronic Engineering DRNTU::Engineering Deep learning architecture algorithms have been extensively developed and applied to various applications. The techniques have successfully improved the performance of difficult computer tasks such as computer vision, natural language processing, and speech recognition. This project aims to apply one of the well-known deep learning algorithms, Convolutional Neural Network to detect student engagement which is believed to be an important factor for learning outcome. The input data which is in the form of frontal videos of students watching online recording were collected and pre-processed before being fed into the seven layers of CNN. The trained model reached a considered decent accuracy result. Some applications utilizing the trained model such as real-time engagement detection and graphical representation of student engagement are also introduced in this project. Bachelor of Engineering 2018-05-30T02:26:15Z 2018-05-30T02:26:15Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75195 en Nanyang Technological University 61 p. application/pdf |
| spellingShingle | DRNTU::Engineering Santoso, Yosua Nathanael Deep learning algorithms and applications |
| title | Deep learning algorithms and applications |
| title_full | Deep learning algorithms and applications |
| title_fullStr | Deep learning algorithms and applications |
| title_full_unstemmed | Deep learning algorithms and applications |
| title_short | Deep learning algorithms and applications |
| title_sort | deep learning algorithms and applications |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/75195 |
| work_keys_str_mv | AT santosoyosuanathanael deeplearningalgorithmsandapplications |