| Autor: |
Jin Li, Jingxin Wang, Longjiang Guo, Meirui Ren, Fei Hao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
ICT Express, Vol 10, Iss 4, Pp 909-915 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2405-9595 |
| DOI: |
10.1016/j.icte.2024.04.006 |
| Popis: |
Mobile learning allows for an interactive way of learning through devices like smartphones. However, current methods usually rely on pre-set situations and struggle to recognize new contexts when they come up during testing. To solve this, we suggest the Open-set Learning Context Recognition Model (OLCRM). This model uses data extracted from smartphone sensors to identify whether a learning context is known or unknown. It also uses a Dual Discriminator Generative Adversarial Network (DDGAN) to create high-quality fake examples, which helps improve the accuracy of recognizing contexts. Experimental results demonstrate the effectiveness of OLCRM in open-set learning context recognition problems. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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