## 4 Results and Analysis
### Experimental Results  
The experiments were carried out on the total dataset comprising 1200 articles written by 50 authors using three different features: 12 statistical-based features, 7 internal features, \begin{table}
\begin{tabular}{c c} \hline \hline
**Layers** & **Parameters** \\ \hline Convolution1 & 2432 \\ Convolution2 & 4624 \\ Dense1 & 3,936,512 \\ Dense2 & 25,700 \\ Dense3 & 5050 \\ Total & 3,974,318 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The number of parameters for different layers of the network. Figure 9: The architecture of the proposed network. and a combination of both features (\(12+7\)). These features were visualized by representing them in a 2D plane, using, as already mentioned, three types of charts (line, imagesc, and pie). Those image-based features were then provided to the CNN network. The results obtained for three charts using three feature sets based on a 80%-20% train-test split are illustrated in Table 5. The experiment was carried out by considering a batch size, image size and epochs of 100 and dropouts of 0.5. The variation in accuracy for a combination of features using a line chart is portrayed through the heat map presented in Figure 10. The x-axis represents the features and the y-axis represents the number of authors. Each cell of a heat map represents a grouping of data. It can be observed that the identification of authors is quite encouraging, as there was either only a slight variation, or the accuracy was maintained for individual authors. In real-world scenarios, the dataset keeps on changing both in terms of volume and variety. To test the performance of the proposed system for such scenarios, the dataset was gradually increased regarding the number of authors from five with a step of five. It was noted that the system maintained its overall accuracy with an increase in the volume of data. Since the maximum accuracy of 93.58% was obtained for the line chart using the combination of both feature sets, further experiments were done by tuning the parameters. Figure 10: The heat map obtained for the proposed approach. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Types of Charts**} & \multicolumn{3}{c}{**Accuracy (in \%)**} \\ \cline{2-4}  & **Statistical Features** & **Internal Features** & **Combination of Both** \\ \hline line & 91.92 & 86.67 & 93.58 \\ imagesc & 86.50 & 81.67 & 89.92 \\ pie & 91.75 & 88.42 & 92.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained for three charts using three feature sets. Figure 11: The inter- and intraclass similarity analysis. In this figure, four instances of four different authors are shown ((**a**)–(**d**) from [PERSON]; (**e**)–(**h**) from [PERSON]; (**i**)–(**l**) from [PERSON]; and (**m**)–(**p**) from [PERSON]). It can be observed that the interclass similarity is quite high among the authors. There is also a high similarity between different authors such as (**d**) and (**h**) marked in orange boxes, (**j**) and (**n**) marked in red boxes, and (**l**) and (**p**) marked in green boxes, which leads to confusion. Furthermore, an intraclass difference is observed, adding more challenge to our task: the difference can be observed for the pairs (**c**)–(**d**), (**e**)–(**f**), (**i**)–(**j**), and (**n**)–(**o**), all pairs from the same author, which further adds to the chances of misclassification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Authors** & **No. of Articles** & **Accuracy (in \%)** & **Deviation from Past Accuracy** \\ \hline
5 & 55 & 100.00 & — \\
10 & 160 & 100.00 & 0.00 \\
15 & 273 & 100.00 & 0.00 \\
20 & 368 & 94.84 & \(-\)5.16 \\
25 & 468 & 94.44 & \(-\)0.40 \\
30 & 572 & 91.44 & \(-\)3.00 \\
35 & 753 & 94.29 & 2.85 \\
40 & 905 & 90.50 & \(-\)3.79 \\
45 & 1055 & 93.46 & 2.96 \\
50 & 1200 & 93.58 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The results obtained for the partitioned datasets.