# Automated Design of the Deep Neural Network Pipeline
## 5 Discussion
### Experiment 1--Basic Automated Design
#### 5.1.2 Image Processing  
From Table 8, we can see that the SPHH exceeds the performance achieved by previous work for all datasets listed. As for the text processing, comparisons were made on the basis of \(p\)-values from a left one-tailed Mann-Whitney U test at a statistical significance of 0.05 (5%). As for the text processing domain above, when considering the difference between the automated design time and manual design time, we can see that the SPHH delivers results in a far shorter timeframe. The results again confirm that automation of the deep neural network pipeline for image processing is possible, and effective at that.  
When considering the difference between the application time for the pipeline designs produced by the SPHH and the manually derived design in the literature, we can see that in the instances where application times are available, the SPHH designs pipelines have application times that are either equal to or lower than the manually derived pipelines from the literature.  
Table 18 breaks down the best-performing pipeline designs produced by the SPHH for a more holistic view of the experimentation. The most popular techniques per stage are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Best Performing Design**} \\ \cline{2-4}
**Dataset** & **Preprocessing Stage** & **Feature Engineering Stage** & **Classification Stage** \\ \hline ACL & Stemming, convert text & & \\ IMDB & to lowercase, remove stop words, & & \\ movie & remove punctuation, remove & & \\ reviews & URI-like objects, lemmatization & & \\ \hline Amazon & Remove URI like objects, & & \\ product & lemmatization, stemming, & & \\ reviews & remove shops, & & \\ \hline Coronavirus & Stemming, remove stop words, & & \\ remove URL-like objects, & & & \\ spelling correction, remove hashtags & & & \\ hashtags & & & \\ \hline Sentiment 140 & Stemming, lemmatization, & & \\ text to lowercase & & & \\ \hline \multirow{4}{*}{Yelp} & Spelling correction, remove & & \\ punctuation, convert text to & & & \\ lowercase, remove stop words, & & & \\ remove hashtags, stemming, & & & \\ \hline \hline \end{tabular}
\end{table}
Table 16: Best-performing designs produced by the SPHH for the sentiment analysis datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Best Performing Design**} \\ \cline{2-4}
**Dataset** & **Preprocessing Stage** & **Feature Engineering Stage** & **Classification Stage** \\ \hline \multirow{4}{*}{Enron} & Stemming, convert text & & \\ to lowercase, remove stop words, & & & \\ remove punctuation, remove & & & \\ URI-like objects, & & & \\ lemmatization & & & \\ \hline \multirow{4}{*}{SMS Spam} & Remove URI like objects, & & \\ lemmatization, stemming, & & & \\ remove stop words, remove & & & \\ hashtags & & & \\ \hline \multirow{4}{*}{Spam assassin} & Stemming, remove stop & & \\ words, remove URI like objects, spelling correction, & & \\ remove hashtags & & & \\ remove hashtags & & & \\ \hline YouTube & & & \\ comments & Stemming, lemmatization, & & \\ text to lowercase & & & \\ \hline \hline \end{tabular}
\end{table}
Table 17: Best-performing designs produced by the SPHH for the spam detection datasets.