English Vocabulary Learning Aid System Using Digital Twin Wasserstein Generative Adversarial Network Optimized With Jelly Fish Optimization Algorithm

Figures & data

Table 1. Some of the Benchmark Table using literature support.

Performance Analysis	Authors
	Wang, Su, and Yu (2020)	Chotirat, et al., (2021)	Maulud et al. (2021)	Ait-Mlouk and Jiang (2020)	Lauriola, Lavelli, and Aiolli (2022)	Danenas, Skersys, and Butleris (2020)	Dessì et al. (2021)
Accuracy (%)	84.23	82.71	80.01	74.12	84.12	73.29	85.63
Sensitivity (%)	56.57	61.23	81.22	74.29	59.98	69.96	83.56
Specificity (%)	83.27	81.04	78.43	73.21	75.29	57.31	72.34
Precision (%)	74.46	80.03	70.15	85.67	80.23	77.26	80.23
F-measure (%)	78.15	71.22	77.68	72.21	76.28	–	74.63
ROC	0.45	0.56	21.32	0.52	0.67	0.73	–
Error rate (%)	82.45	76.28	85.56	8789	–	–	–
Computation time (%)	13.25	18.74	21.31	18.26	17.33	26.23	–

Figure 1. Block diagram of the proposed EVLS-DtwinWGAN-NLP English learning vocabulary system.

Figure 2. JSOA for optimizing parameters from DtwinWGAN classifier.

Figure 3. Accuracy analysis.

Figure 4. F1 score analysis.

Figure 5. Precision analysis.

Figure 6. Sensitivity analysis.

Figure 7. Specificity analysis.

Figure 8. Computation time analysis.

Figure 9. ROC analysis.

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Data Availability Statement

Data sharing does not apply to this article as no new data has been created or analyzed in this study.