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IEEE/ICACT20250017 Slide.18        [Big Slide]       [YouTube]	Oral Presentation
	Our analysis revealed several key insights: GPT-2 Model: Achieved highest performance across tasks with 100% accuracy in both gap filling and full sequence generation Transformer Model: Demonstrated balanced performance with strong generalization capability (96.42% gap filling, 96.72% full sequence accuracy) CDA Model: Proved effective for gap filling with competitive accuracy (100% gap filling, 95.33% full sequence accuracy) Based on these findings, we recommend: For high-accuracy gap filling: GPT-2 or CDA models For complete sequence generation: GPT-2 or Transformer models For resource-constrained scenarios: Random Forest or CNN-LSTM approaches

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