Table 2 Comparison of relevant old models with proposed architecture.

From: Deep learning optimization of teaching schedules in sports dance education

Ref

Sched. Eff.

Comp. Perf.

Conf. Res.

Scalab.

Dyn. Adapt.

Sports Ed.

AI Opt.

19

\(\checkmark\)

\(\checkmark\)

X

X

X

\(\checkmark\)

X

20

X

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

X

X

\(\checkmark\)

21

\(\checkmark\)

X

X

\(\checkmark\)

\(\checkmark\)

X

X

22

X

\(\checkmark\)

\(\checkmark\)

X

X

X

\(\checkmark\)

23

X

X

\(\checkmark\)

\(\checkmark\)

X

\(\checkmark\)

X

24

X

\(\checkmark\)

X

\(\checkmark\)

\(\checkmark\)

X

\(\checkmark\)

25

\(\checkmark\)

\(\checkmark\)

X

X

\(\checkmark\)

\(\checkmark\)

X

26

X

X

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

X

\(\checkmark\)

Proposed

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

  1. Notes: This table compares previous scheduling approaches with the proposed deep learning-powered scheduling model. The proposed model improves adaptability, scalability, and AI-driven optimization for sports dance education. Abbreviations: Sched. Eff. (Scheduling Efficiency), Comp. Perf. (Computational Performance), Conf. Res. (Conflict Resolution), Scalab. (Scalability), Dyn. Adapt. (Dynamic Adaptability), Sports Ed. (Sports Dance Education), AI Opt. (AI-based Optimization).
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