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Can I read Fusion Artificial Intelligence Technology in Music Education Teaching on EtoBox?
Fusion Artificial Intelligence Technology in Music Education Teaching by Liang Zhang, Liang Zhang (author) is a Computer Science article available to read on EtoBox.
What is Fusion Artificial Intelligence Technology in Music Education Teaching about?
This paper proposed innovative EFDfO (Entropy Features Data Fusion Optimized) framework, a data-driven approach aimed at revolutionizing music education teaching. EFDfO combines data fusion, feature extraction, and optimization techniques to customize teaching strategies to individual students' unique learning profiles. The data related to students are collected with different sources and data were fused. The fused data are optimized with the Whale optimization technique to estimate the performance of the students. Simualtion analysis of the EFDfO demonstrates its potential to enhance student performance, with an average improvement of approximately 18% to 20% observed in pre-test and post-test scores. Moreover, the classification results indicate that, in most cases, EFDfO accurately categorizes students based on their performance and learning characteristics, although further refinement is needed to reduce misclassifications. Additionally, with the proposed EFDfO model the performance of the students are improved. EFDfO offers a promising avenue for personalized music education, ultimately enhancing students' learning experiences and outcomes.
Who reads Fusion Artificial Intelligence Technology in Music Education Teaching?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Liang Zhang, Liang Zhang (author)
- Publisher
- Science Research Society
- Published
- 2024
- Language
- EN
- Field
- Computer Science (Physical Sciences)