## 3 Results and Discussion  
In this section, experimental results are presented along with the explanation in detail. Table 1 shows the overall accuracy of each classifier from the features of FC1 and FC2 layers. Deep burn features from FC1 and FC2 layers for classification using the DT algorithm are presented in the Table 1. Both FC layers had 4096 feature vectors, but FC1 had 102,764,544 trainable parameters while FC2 had 16,781,312 trainable parameters. The DT classifier performed marginally better using deep FC2 features than FC1 features. Similarly, SVM and KNN achieved higher classification accuracy using FC2 features. It is also worth noting that the training of the classification algorithms had no random effect; hence, a random seed was used to ensure that the result was reproducible. By doing so, each algorithm was trained using the same splits and in exactly the same way. This implies that execution started at the same place every time for each classifier, which simply means that the experiment was fully deterministic.  
The result in Table 1 shows that DT performed slightly better with 4096 features of the FC2 layer, achieving 88.93% accuracy, than when using the same number of features from the FC1 layer (88.86%). SVM achieved 96.19% accuracy with FC2 features compared to 95.83% with FC1 features. Similarly, an accuracy of 87.38% was achieved using 4096 FC2 features by KNN compared to 82.44% using 4096 FC1 features. Two classification algorithms (NB and LR), in contrast, achieved higher accuracy using FC1 features than FC2 features. The NB classifier recorded an accuracy of 92.32% using FC1 features compared to 91.85% with FC2 features, whereas LR achieved an accuracy of 96.07% using FC1 features, which was marginally better than 96.01% with FC2 features.  
Moreover, it is also obvious from Figure 6 that the best classification outputs came from the SVM classifier, followed by LR. The results suggest that, for FC2 features, SVM was most appropriate and effective, but LR was more suitable for use with FC1 features. This vitally shows that a good result can only be determined through trial and error rather than using theoretical and mathematic proofs.  
Furthermore, training time varied even though the same number of features was used from each FC layer, but Table 2 shows that all classifiers that returned the most impressive classification output using 4096 FC2 features took less time than when using FC1. DT was trained in 75.78 s with FC2 features compared to 104.75 s with FC1 features, SVM completed the training in 321.11 s with FC2  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **DT** & **SVM** & **NB** & **LR** & **KNN** \\ \hline FC1 & 88.86\% & 95.83\% & 92.32\% & 96.07\% & 82.44\% \\ FC2 & 88.93\% & 96.19\% & 91.85\% & 96.01\% & 87.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy. DT, decision tree; SVM, support vector machine; NB, naive Bayes; LR, logistic regression; KNN, \(k\)-nearest neighbour; FC, fully connected.  
Figure 6: Comparison of transferable features and classifier performance.  
compared to 323.99 s with FC1 features, and KNN completed the training in 108.92 s with FC2 features compared to 117.09 s with FC1 features. Similarly, those classifiers that performed better with FC1 features also indicated more efficiency in terms of training time. NB completed the training in 2.91 s with FC1 features compared to 3.06 s with FC2 features, and LR completed the training in 26.10 s with FC1 features compared to 26.47 s with FC2 features.  
Figure 7 presents a graphical visualisation of the training time complexity with NB as the most efficient classifier, taking a very short time to complete the training.