# Technology Commercialization Activation Model Using Imagification of Variables
## 4 Experimental Results

Table 7: Result of optimal number of clusters. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Cluster**} & **Top-Frequency Words** & **Elementary Technology** \\ \hline
0 & Signal, Sound, Speech & Auditory intelligence \\
1 & Image, Detect, Motion & Visual intelligence \\
2 & Language, Translate, Generate & Language intelligence \\
3 & Device, Control, Circuit & AI semiconductor \\
4 & Assist, Person, Digit & Intelligent agent \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Elementary technologies. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Model**} & **Parameters** \\ \hline Logistic regression (LR) & L2 Penalty \\ K-Nearest neighbor (KNN) & K = 3, Distance = Minkowski \\ Decision tree (DT) & Criterion = Gini \\ Random forest (RF) & Criterion = Gini, \# of features = sqrt \\ AdaBoost (AB) & Criterion = Gini, \# of features = sqrt \\ Convolutional neural network (CNN) & Layers = 3, activation function = relu/softmax, \\ Optimizer = Adam, Epochs = 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameters for comparison models. The values colored in yellow in Table 10 indicate the best performance for each indicator. In general, the predictive performance of the models trained with image data was better than that of the models trained with non-image data. RF performed best, and its accuracy and precision improved when trained with image data. In addition, all measurement indices for KNN were better when it was trained with image data than when it was trained with non-image data. CNN, which is specialized in image data analysis, had an accuracy of 0.75, the second-highest prediction performance after RF, and its recall and F1-score were the best of all the models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multirow{2}{*}{**Measure**} & \multicolumn{6}{c}{**Model**} \\ \cline{3-8}  & & **LR** & **KNN** & **DT** & **RF** & **AB** & **CNN** \\ \hline \multirow{3}{*}{Non-image data} & Accuracy & 0.73 & 0.66 & 0.66 & 0.76 & 0.74 & \\  & Precision & 0.63 & 0.53 & 0.57 & 0.7 & 0.75 & \\  & Recall & 0.52 & 0.52 & 0.58 & 0.61 & 0.54 & \\  & F1-score & 0.47 & 0.52 & 0.57 & 0.62 & 0.5 & \\ \hline \multirow{3}{*}{Image data} & Accuracy & 0.73 & 0.7 & 0.66 & 0.77 & 0.74 & 0.75 \\  & Precision & 0.63 & 0.61 & 0.58 & 0.73 & 0.75 & 0.68 \\ \cline{1-1}  & Recall & 0.52 & 0.59 & 0.58 & 0.61 & 0.54 & 0.62 \\ \cline{1-1}  & F1-score & 0.47 & 0.59 & 0.58 & 0.62 & 0.5 & 0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Prediction performance by data type. Figure 3: Samples of patent information images: (**a**) transferred patent’s image; (**b**) non-transferred patent’s images. A significant proportion of the previously learned dataset was labeled as transferred technology, at a ratio of 7:3. In general, technology transfer in industry is infrequent. Therefore, it is necessary to prepare for cases where the labels are imbalanced. In this study, the image data were augmented using the oversampling techniques SMOTE and RS, and the training and prediction performance of the models were compared. Table 11 summarizes the data augmented by oversampling. As shown in Table 11, data augmentation adjusted the ratio of the technology transfer label from 7:3 to 5:5. In other words, a total of 12,154 training data elements were augmented to 17,690. Table 12 summarizes the prediction performance of each model based on the data augmentation technique. Table 12 shows that RF outperformed the other models. The accuracy of the models was lower than when trained on the data before data augmentation. However, the F1-scores were higher in most models than when trained with the original training dataset. The CNN prediction performance was evaluated after learning with data augmented by the commonly used Keras ImageDataGenerator [45]. Table 13 summarizes the parameters used in ImageDataGenerator and the prediction performance of CNNs trained on the augmented data.