## 4 Methods and Experiment
### Keyframe Extraction
Finally, the BiLSTM-AT network is used to classify human action recognition. The dataset is split into three groups consisting of \"Training\", \"Validation\", and \"Testing\", which have percentage data values of 70:15:15, respectively. The number of video clips is shown in Table 5.

Our proposed KMFF-BiLSTM-AT model obtained high accuracy compared with the state-of-the-art models. The model achieved the highest accuracy of 97.54% with the UCF101 dataset and 72.43% with the HMDB51 dataset. Table 6 summarizes the comparative results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **UCF101 (\%)** & **HMDB51 (\%)** \\ \hline iDT+HSV [51] & 87.90 & 61.10 \\ C3D [52] & 85.20 & - \\ Deeper temporal net [53] & 84.90 & - \\ Two-stream [54] & 88.00 & 54.90 \\ FstCN [55] & 88.10 & 59.10 \\ TDD+FV [56] & 90.30 & 63.20 \\ scLSTM [57] & 84.00 & 55.10 \\ VideoLSTM [58] & 89.20 & 56.40 \\ L2 STM [59] & 93.60 & 66.20 \\ STS [60] & 90.10 & 62.40 \\ STS-ALSTM [60] & 92.70 & 64.40 \\ S3D+RANs [61] & 93.30 & 71.20 \\ TSN [62] & 93.90 & 69.90 \\ TSN+AS [62] & 94.50 & 71.60 \\
3 DCNN+Two-level+AT [63] & 95.68 & **72.60** \\ KMFF-BiLSTM-AT (10 Keyframes) (Ours) & **97.54** & 72.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of our methods with the state-of-the-art models with the UCF101 and HMDB51 public datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Classes** & **Training (70\%)** & \begin{tabular}{c} **Validation** \\ **(15\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Testing (15\%)** \\ \end{tabular} &
\begin{tabular}{c} **Total Clips** \\ **(100\%)** \\ \end{tabular} \\ \hline UCF101 & 101 & 9664 & 1705 & 1951 & 13,320 \\ HMDB51 & 51 & 4907 & 865 & 994 & 6766 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dataset settings for training, validation, and testing. ### Discussion

When comparing the results of the student's behavior dataset with only a single pretrained model, we found that VGG-16 with the BiLSTM-AT model had a higher accuracy than the others. However, even though the VGG-16 pretrained model is excellent for extracting features, it is still insufficient for identifying and classifying students' behaviors in the classroom using the BiLSTM-AT model. When we apply the multiple pretraining models based on multi-feature fusion and keyframe selection methods to address this limitation, the MFF-BiLSTM-AT and KMFF-BiLSTM-AT models achieve a higher accuracy than the S-BiLSTM-AT model. Nevertheless, of the multiple pretrained models, the KMFF-BiLSTM-AT obtained an accuracy higher than the MFF-BiLSTM-AT. The model prediction results for KMFF-BiLSTM-AT are higher than all of our proposed models, indicating that this is an excellent, accurate, and robust model for classifying the complexity of our student behavior datasets. Moreover, our proposed framework adopted keyframe extraction and selection methods using the SADLK algorithm with multiple feature fusion and applied the data augmentation technique combination with optical flow motion identification on BiLSTM-AT networks--the overall accuracy evaluation with the two largest datasets, HMDB51 and UCF101, of our proposed framework is excellent compared with state-of-the-art models. ## 6 Conclusions and Future Work

There have been several attempts to analyze students' behavior in a classroom. However, it has been difficult to find an effective method for this analysis due to the limited classroom environment and the interactions between many students and a teacher. We propose a novel MultiFusedNet network model that can optimally perform individual behavior analysis in situations where lighting changes are diverse and groups of students are intermingled. To select keyframes that have representativeness from a video input of arbitrary length, the SADLK algorithm was designed as an efficient tool to select a specified number of keyframes by identifying the spatial and color differences between frames and applying the k-mean method.