## 3 Proposed Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Dropout = 0.01**} & \multicolumn{6}{c}{**Dropout = 0.02**} & \multicolumn{6}{c}{**Dropout = 0.03**} \\ \hline
**Models** & **Dataset 1** & **Dataset 2** & **Dataset 3** & **Models** & **Dataset 1** & **Dataset 2** & **Dataset 3** & **Models** & **Dataset 1** & **Dataset 2** & **Dataset 3** \\ \hline M01 & 64.32 & 66.33 & 65.24 & M01 & 66.55 & 67.36 & 68.22 & M01 & 62.35 & 66.35 & 64.21 \\ \hline M02 & 66.55 & 64.32 & 62.33 & M02 & 68.36 & 67.21 & 69.36 & M02 & 66.32 & 67.24 & 65.32 \\ \hline M03 & 68.36 & 68.32 & 70.56 & M03 & 70.22 & 71.56 & 72.32 & M03 & 70.25 & 69.68 & 71.56 \\ \hline M04 & 70.25 & 71.52 & 72.22 & M04 & 72.36 & 73.32 & 71.35 & M04 & 74.65 & 73.22 & 74.01 \\ \hline M05 & 74.36 & 76.32 & 72.52 & M05 & 75.62 & 78.32 & 74.22 & M05 & 76.32 & 77.25 & 74.35 \\ \hline M06 & 76.32 & 78.25 & 77.85 & M06 & 77.55 & 75.22 & 76.32 & M06 & 81.65 & 82.54 & 80.26 \\ \hline M07 & 84.66 & 85.65 & 83.26 & M07 & 79.65 & 80.25 & 91.56 & M07 & 84.68 & 85.10 & 86.32 \\ \hline M08 & 89.56 & 88.32 & 90.23 & M08 & 84.32 & 82.35 & 83.77 & M08 & 89.62 & 90.21 & 91.25 \\ \hline M09 & 94.35 & 94.56 & 93.26 & M09 & 90.21 & 91.36 & 91.55 & M09 & 94.56 & 95.21 & 94.96 \\ \hline \multicolumn{6}{c}{(a)} & \multicolumn{6}{c}{(b)} & \multicolumn{6}{c}{(c)} \\ \hline  & \multicolumn{6}{c}{Dropout = 0.04} & \multicolumn{6}{c}{Dropout = 0.05} & \multicolumn{6}{c}{Dropout = 0.06} \\ \hline Models & Dataset 1 & Dataset 2 & Dataset 3 & Models & Dataset 1 & Dataset 2 & Dataset 3 & Models & Dataset 1 & Dataset 2 & Dataset 3 \\ \hline M01 & 67.87 & 68.68 & 69.54 & M01 & 69.25 & 66.32 & 68.32 & M01 & 66.32 & 68.21 & 65.22 \\ \hline M02 & 70.76 & 68.53 & 66.54 & M02 & 72.65 & 73.26 & 71.25 & M02 & 68.21 & 69.32 & 70.21 \\ \hline M03 & 73.54 & 74.88 & 75.64 & M03 & 74.32 & 74.21 & 74.88 & M03 & 73.32 & 70.54 & 72.25 \\ \hline M04 & 78.53 & 79.65 & 77.52 & M04 & 77.36 & 78.65 & 79.32 & M04 & 76.95 & 75.32 & 74.55 \\ \hline M05 & 81.32 & 82.52 & 93.56 & M05 & 81.54 & 80.32 & 81.01 & M05 & 81.65 & 80.32 & 84.32 \\ \hline M06 & 86.32 & 86.21 & 86.55 & M06 & 84.56 & 85.65 & 86.32 & M06 & 86.32 & 87.21 & 85.32 \\ \hline M07 & 88.25 & 87.36 & 89.32 & M07 & 88.32 & 87.36 & 90.32 & M07 & 88.51 & 89.32 & 88.81 \\ \hline M08 & 91.52 & 91.98 & 92.65 & M08 & 92.65 & 93.25 & 91.35 & M08 & 91.56 & 93.35 & 92.80 \\ \hline M09 & 94.23 & 95.88 & 96.21 & M09 & 92.54 & 94.36 & 95.21 & M09 & 96.21 & 95.18 & 94.21 \\ \hline \multicolumn{6}{c}{(d)} & \multicolumn{6}{c}{(e)} & \multicolumn{6}{c}{(f)} \\ \hline  & \multicolumn{6}{c}{Dropout = 0.07} & \multicolumn{6}{c}{Dropout = 0.08} & \multicolumn{6}{c}{Dropout = 0.09} \\ \hline Models & Dataset 1 & Dataset 2 & Dataset 3 & Models & Dataset 1 & Dataset 2 & Dataset 3 & Models & Dataset 1 & Dataset 2 & Dataset 3 \\ \hline M01 & 69.21 & 70.25 & 68.11 & M01 & 65.32 & 67.21 & 66.25 & M01 & 70.50 & 71.61 & 72.50 \\ \hline M02 & 70.22 & 71.56 & 72.54 & M02 & 68.22 & 69.01 & 70.15 & M02 & 73.27 & 72.36 & 75.60 \\ \hline M03 & 71.32 & 70.41 & 73.65 & M03 & 70.21 & 71.15 & 69.32 & M03 & 75.79 & 74.50 & 76.90 \\ \hline M04 & 76.32 & 79.25 & 78.22 & M04 & 72.54 & 71.25 & 73.65 & M04 & 80.00 & 80.70 & 78.90 \\ \hline M05 & 78.00 & 79.15 & 77.25 & M05 & 78.65 & 79.35 & 77.55 & M05 & 83.19 & 81.97 & 82.66 \\ \hline M06 & 80.21 & 81.56 & 83.32 & M06 & 81.36 & 83.35 & 82.65 & M06 & 87.64 & 87.53 & 87.87 \\ \hline M07 & 83.55 & 84.32 & 85.11 & M07 & 85.65 & 84.32 & 86.35 & M07 & 90.23 & 89.34 & 91.30 \\ \hline M08 & 88.55 & 89.56 & 88.36 & M08 & 89.32 & 90.35 & 90.99 & M08 & 94.88 & 96.67 & 96.12 \\ \hline M09 & 92.25 & 93.36 & 93.35 & M09 & 94.21 & 92.25 & 91.36 & M09 & 94.75 & 96.57 & 97.42 \\ \hline \multicolumn{6}{c}{(g)} & \multicolumn{6}{c}{(h)} & \multicolumn{6}{c}{(i)} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance accuracy (%) for an embedding size of 400. Table 8 above shows the average performance accuracy of each model for the three datasets.