Deep Learning-Guided Simulated Annealing for Designing Vocational High Educational System

Area of Science:

• Computer Science
• Educational Technology
• Artificial Intelligence

Background:

• Traditional classroom instruction struggles to meet modern demands due to web technology advancements.
• Online learning platforms face challenges in effective instruction and student-teacher communication.
• Existing emotional literature models exhibit limitations in classifying high-dimensional data.

Purpose of the Study:

• To design and implement a deep learning-based educational control system supporting a Browser/Server (B/S) structure.
• To enhance the prediction accuracy for high-dimensional data classification using a modified brain emotion model.
• To improve the data fitting capacity and prediction ability of algorithms for educational applications.

Main Methods:

• Developed a deep learning-based educational system using Java and SQL Server, adhering to software engineering principles.
• Proposed a modified brain emotion model incorporating simulated annealing algorithm rules.
• Optimized network structure and training processes for improved scientific standard conception and data fitting.

Main Results:

• The implemented system supports online teaching, data management, and assessment functionalities.
• The modified brain emotion model demonstrated refined data fitting capacity and prediction ability.
• Significant improvement in prediction accuracy for high-dimensional data classification problems was achieved.

Conclusions:

• The deep learning-based educational system effectively addresses limitations in traditional instruction and online learning.
• The modified brain emotion model with simulated annealing offers a superior approach for high-dimensional data classification.
• The proposed methods enhance the performance and accuracy of educational technology applications.