## 5 Results and Analysis
The advanced performances can be analyzed to a great extent with a classification report (CR) for the best two proposed models (model M2 and model K5), which are presented in Tables 3 and 4, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data-Set**} & \multirow{2}{*}{**Learning Rate**} & \multirow{2}{*}{**Optimizer**} & \multirow{2}{*}{**Model Name**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**TPF**} & \multirow{2}{*}{**TFN**} & \multirow{2}{*}{**TTP**} & \multirow{2}{*}{**TTN**} & \multirow{2}{*}{**Overall Accuracy**} \\ \cline{5-5} \cline{8-10}  & & \multicolumn{1}{c}{} & & & & & & & & **(\%)** \\ \hline \multirow{5}{*}{\
\begin{tabular}{c} Kaggle \\ Alphabet \\ Recognition \\ \end{tabular} } & \multirow{2}{*}{0.001} & ‘ADAM’ & K1 & 99.4 & 99.98 & 99.51 & 360 & 360 & 74,130 & 1,861,890 & 99.516 \\ \cline{3-10}  & & RMS\_prop & K2 & 99.2 & 99.97 & 99.29 & 527 & 527 & 73,963 & 1,861,723 & 99.292 \\ \cline{3-10}  & \multirow{2}{*}{0.0001} & ‘ADAM’ & K3 & 99.5 & 99.98 & 99.51 & 364 & 364 & 74,126 & 1,861,886 & 99.511 \\ \cline{3-10}  & & RMS\_prop & K4 & 99.0 & 99.96 & 99.10 & 664 & 664 & 73,826 & 1,861,586 & 99.108 \\ \cline{3-10}  & & ADAM & K5 & 99.5 & 99.98 & 99.56 & 325 & **74,165** & 1,861,925 & 99.563 \\ \cline{3-10}  & & RMS\_prop & K6 & 99.1 & 99.96 & 99.19 & 602 & 602 & 73,888 & 1,861,648 & 99.191 \\ \hline \multirow{5}{*}{
\begin{tabular}{c} MNIST \\ Regiontion \\ \end{tabular} } & \multirow{2}{*}{0.001} & ‘ADAM’ & M1 & 99.5 & 99.95 & 99.57 & 22 & 22 & 4178 & 37,778 & 99.571 \\ \cline{3-10}  & & RMS\_prop & M2 & 99.6 & 99.96 & **99.64** & 15 & **15** & **4185** & **37,785** & **99.642** \\ \cline{3-10}  & & ‘ADAM’ & M3 & 99.2 & 99.92 & 99.30 & 29 & 29 & 4171 & 37,771 & 99.309 \\ \cline{3-10}  & & RMS\_prop & M4 & 99.4 & 99.93 & 99.45 & 23 & 23 & 4177 & 37,777 & 99.452 \\ \cline{3-10}  & & ‘ADAM’ & M5 & 98.2 & 99.80 & 98.23 & 74 & 74 & 4126 & 37,726 & 98.238 \\ \cline{3-10}  & & RMS\_prop & M6 & 98.1 & 99.79 & 98.14 & 78 & 78 & 4122 & 37,722 & 98.142 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix parameters of all the investigated models. \begin{table}
\begin{tabular}{c c c c|c c} \hline \hline \multirow{2}{*}{**Digit (0–9)**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1 Score**} & \multirow{2}{*}{**Support**} & \multirow{2}{*}{**Support**} \\  & & \multicolumn{1}{c}{**/Class**} & \multicolumn{1}{c}{**/Class**} & \multicolumn{1}{c}{**/Class**} & \multicolumn{1}{c}{**/Class**} & \multicolumn{1}{c}{**Proportion/Class**} \\ \hline class 0 & 1.00 & 1.00 & 1.00 & 411 & 0.098 \\ class 1 & 1.00 & 1.00 & 1.00 & 485 & 0.115 \\ class 2 & 1.00 & 1.00 & 1.00 & 403 & 0.096 \\ class 3 & 1.00 & 1.00 & 1.00 & 418 & 0.1 \\ class 4 & 1.00 & 0.99 & 0.99 & 461 & 0.11 \\ class 5 & 1.00 & 0.99 & 1.00 & 372 &The columns marked in yellow in Tables 3 and 4 indicate the score for each of the 10 classes for digits (0-9) and each of the 26 classes for alphabet, A-Z. In our case, using multiclass classification calculation, we pursued averaging methods for the \(F1\) score, resulting in different average scores, i.e., micro, macro, and weighted averaging. Macro averaging is possibly the most straightforward among the averaging methods. Regardless of their support values, this method treats all classes without differentiation. Support denotes the number of actual occurrences of the class in the dataset. The macro \(F1\) score is totaled by taking the unweighted arithmetic mean of all the per-class \(F1\) scores. We calculated macro \(F1\) scores of 0.998 and 0.992 for model \(M2\) and model K5, respectively. \[\textit{Macro }F1\textit{ score }(\textit{model }M2)=\frac{8*1+2*99}{10}=0.998\]  
\[\textit{Macro }F1\textit{ score }(\textit{model K5})=\frac{8*1+17*99+.97}{26}=0.992\]  
The weighted average \(F1\) score was computed by counting the mean of all per-class \(F1\) scores while considering each classes' support.