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Application of deep learning and cloud data platform in college teaching quality evaluation
- Source :
- Journal of Intelligent & Fuzzy Systems. 39:5547-5558
- Publication Year :
- 2020
- Publisher :
- IOS Press, 2020.
-
Abstract
- In this paper, the author introduces the theory of fuzzy mathematics into the evaluation of higher education. By determining the set of evaluation factors and comments, the author constructs the relevant mathematical model and processes the data, thus turning the evaluation problem into the multiplication problem of the fuzzy matrix. Deep learning is a very active branch of machine learning research in recent years. By increasing the depth and breadth of the model, i.e. increasing the number of operations from the input end to the output end and the number of channels of the model, the scale of parameters of the model is increased, so that the model has the ability to express complex functions. It is appropriate to use deep learning in teaching quality evaluation. The simulation results show that the deep learning model is very effective in dealing with data diversity and extracting complex implicit rules. It can effectively model experts’ professional knowledge and experience. Deep neural network has powerful expressive ability, and can effectively extract the deep-seated laws affecting the teaching quality. It can be used as an assistant technology for the evaluation of teaching quality in Colleges.
- Subjects :
- Statistics and Probability
Multimedia
Computer science
business.industry
020209 energy
media_common.quotation_subject
Deep learning
05 social sciences
General Engineering
02 engineering and technology
computer.software_genre
Cloud data
Artificial Intelligence
0502 economics and business
0202 electrical engineering, electronic engineering, information engineering
Quality (business)
Artificial intelligence
business
computer
050203 business & management
media_common
Subjects
Details
- ISSN :
- 18758967 and 10641246
- Volume :
- 39
- Database :
- OpenAIRE
- Journal :
- Journal of Intelligent & Fuzzy Systems
- Accession number :
- edsair.doi...........1c64793fb5de80bce33898393f6e70cd