# Automated Design of the Deep Neural Network Pipeline
## 4 Results
### Experiment 3--Automated Design with Transfer Learning
#### 4.3.1 Text Processing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Target** & **Dataset Source** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{6}{*}{Enron} & Enron & 97.45 & 0.86 & 0.87 & 0.87 \\ \cline{2-6}  & SMS Spam & **97.83** & 0.91 & 0.90 & 0.90 \\ \cline{2-6}  & Spam & 96.39 & 0.84 & 0.85 & 0.85 \\ \cline{2-6}  & YouTube & & & & \\ \cline{2-6}  & Spam & 97.50 & 0.88 & 0.88 & 0.88 \\ \hline \multirow{6}{*}{SMS Spam} & Enron & 92.11 & 0.92 & 0.92 & 0.92 \\ \cline{2-6}  & SMS Spam & **93.17** & 0.95 & 0.95 & 0.95 \\ \cline{2-6}  & Spam & 92.11 & 0.95 & 0.95 & 0.95 \\ \cline{2-6}  &  YouTube & & & & \\ \cline{2-6}  & Comments & 91.83 & 0.92 & 0.92 & 0.92 \\ \cline{2-6}  & Spam & 91.3 & 0.83 & 0.82 & 0.82 \\ \cline{2-6}  & SMS Spam & 91.21 & 0.84 & 0.83 & 0.83 \\ \cline{2-6} Spam & Spam & **91.37** & 0.84 & 0.83 & 0.84 \\ \cline{2-6}  & assassin & & & & \\ \cline{2-6}  & YouTube & & & & \\ \cline{2-6}  & Comments & 90.7 & 0.81 & 0.91 & 0.86 \\ \cline{2-6}  & Spam & & & & \\ \hline \multirow{6}{*}{YouTube} & Enron & **98.8** & 0.82 & 0.89 & 0.86 \\ \cline{2-6}  & SMS Spam & 94.17 & 0.80 & 0.94 & 0.86 \\ \cline{2-6}  & Spam & 94.72 & 0.91 & 0.94 & 0.93 \\ \cline{2-6} Spam & YouTube & & & & \\ \cline{2-6}  & Comments & 98.78 & 0.92 & 0.88 & 0.90 \\ \cline{2-6}  & Spam & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of running the SPHH with transfer learning for the spam detection datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Dataset Source for Design**} \\ \cline{2-6}  & **ACL** & **Amazon** & **Cronavirus** & **Sentiment 140** & **Yelp** \\ \cline{2-6}  & **Movie** & **Reviews** & **Reviews** & **Reviews** & **Reviews** \\ \hline \hline \multirow{2}{*}{**ACL**} & \multirow{2}{*}{**IMDB**} & \multirow{2}{*}{-} & \multirow{2}{*}{0.8112} & \multirow{2}{*}{0.00002065} & \multirow{2}{*}{0.9877} & \multirow{2}{*}{1} \\  & & & & & \\ \cline{2-6}  & & & & & \\ \hline Amazon & \multirow{2}{*}{0.9747} & \multirow{2}{*}{-} & \multirow{2}{*}{0.9994} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\  & **reviews** & & & & \\ \hline Coronavirus & 0.9959 & 0.9997 & - & 0.9998 & 1 \\ \hline
**Sentiment 140** & 1 & 0.376 & 0.998 & - & 1 \\ \hline
**Yelp** & 1 & 1 & 0.9747 & 0.7767 & - \\ \hline \multicolumn{6}{c}{**Dataset source for design**} \\ \cline{2-6}  & \multirow{2}{*}{**Enron**} & \multirow{2}{*}{**SMS Spam**} & \multirow{2}{*}{**Syam**} & \multirow{2}{*}{**sassasin**} & **YouTube** \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline
**Enron** & **-** & 0.00016 & 0.9499 & 0.05382 \\ \hline SMS Spam & 1 & - & 1 & 1 \\ \hline
**Spam** & \multirow{2}{*}{0.445} & \multirow{2}{*}{0.9086} & \multirow{2}{*}{-} & \multirow{2}{*}{0.997} \\  & **assasin** & & & & \\ \hline
**YouTube** & \multirow{2}{*}{0.838} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{-} \\  & **spam** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of a Mann–Whitney U test conducted at a statistical significance of 5% when comparing running the SPHH with transfer learning and without transfer learning for the text processing datasets.