Transfer of higher education system and switching of education model based on multi-scale feature fusion network
In this paper, we use computer techniques to extract the features of each convolutional layer of CNN, analyze the feature variations of different depth convolutions, and propose a new network framework to improve the head pose estimation task performance by combining the task characteristics of head...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00777 |
| _version_ | 1797340720003022848 |
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| author | Zhang Yuankui Zhang Yuting |
| author_facet | Zhang Yuankui Zhang Yuting |
| author_sort | Zhang Yuankui |
| collection | DOAJ |
| description | In this paper, we use computer techniques to extract the features of each convolutional layer of CNN, analyze the feature variations of different depth convolutions, and propose a new network framework to improve the head pose estimation task performance by combining the task characteristics of head pose estimation. Multi-scale feature information fusion is the basis of the proposed head pose estimation method (IRHP-Net), which consists of a feature extraction module and a multi-scale feature information fusion module. In the smart classroom learning environment, the algorithm is used to identify students’ attention areas and construct the distraction index and threshold parameters to determine the inattentive state and provide relevant teaching measures. Smart classroom teaching resulted in a 39.69% increase in students’ attention, as shown in the results. Students in the traditional teaching mode showed a 16.7% lower level of learning engagement. A 17.24% increase in academic performance was also a result of the increase in attention and learning engagement. |
| first_indexed | 2024-03-08T10:07:18Z |
| format | Article |
| id | doaj.art-fe12382d31004348bcca5427ee13464d |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:07:18Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-fe12382d31004348bcca5427ee13464d2024-01-29T08:52:36ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00777Transfer of higher education system and switching of education model based on multi-scale feature fusion networkZhang Yuankui0Zhang Yuting11Education and Teaching Department, Zhengzhou Preschool Education College, Zhengzhou, Henan, 450099, China.2Intelligent Manufacturing Institute, Hebei Vocational University of Industry and Technology, Shijiazhuang, Hebei, 050000, China.In this paper, we use computer techniques to extract the features of each convolutional layer of CNN, analyze the feature variations of different depth convolutions, and propose a new network framework to improve the head pose estimation task performance by combining the task characteristics of head pose estimation. Multi-scale feature information fusion is the basis of the proposed head pose estimation method (IRHP-Net), which consists of a feature extraction module and a multi-scale feature information fusion module. In the smart classroom learning environment, the algorithm is used to identify students’ attention areas and construct the distraction index and threshold parameters to determine the inattentive state and provide relevant teaching measures. Smart classroom teaching resulted in a 39.69% increase in students’ attention, as shown in the results. Students in the traditional teaching mode showed a 16.7% lower level of learning engagement. A 17.24% increase in academic performance was also a result of the increase in attention and learning engagement.https://doi.org/10.2478/amns.2023.2.00777multi-scale feature fusionhead pose estimationirhp-netcnnsmart classroom97d60 |
| spellingShingle | Zhang Yuankui Zhang Yuting Transfer of higher education system and switching of education model based on multi-scale feature fusion network Applied Mathematics and Nonlinear Sciences multi-scale feature fusion head pose estimation irhp-net cnn smart classroom 97d60 |
| title | Transfer of higher education system and switching of education model based on multi-scale feature fusion network |
| title_full | Transfer of higher education system and switching of education model based on multi-scale feature fusion network |
| title_fullStr | Transfer of higher education system and switching of education model based on multi-scale feature fusion network |
| title_full_unstemmed | Transfer of higher education system and switching of education model based on multi-scale feature fusion network |
| title_short | Transfer of higher education system and switching of education model based on multi-scale feature fusion network |
| title_sort | transfer of higher education system and switching of education model based on multi scale feature fusion network |
| topic | multi-scale feature fusion head pose estimation irhp-net cnn smart classroom 97d60 |
| url | https://doi.org/10.2478/amns.2023.2.00777 |
| work_keys_str_mv | AT zhangyuankui transferofhighereducationsystemandswitchingofeducationmodelbasedonmultiscalefeaturefusionnetwork AT zhangyuting transferofhighereducationsystemandswitchingofeducationmodelbasedonmultiscalefeaturefusionnetwork |