## 4 Experimental Results
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},\ x_{t}]+b_{i} \tag{10}\] \[\widetilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},\ x_{t}]+b_{C}\ )\] (11) \[C_{t}=f_{t}\ast C_{t-1}+i_{t}\ast\ \widetilde{C}_{t}\] (12) \[o_{t}=\sigma(W_{o}\cdot[h_{t-1},\ x_{t}]+b_{o}\] (13) \[h_{t}=o_{t}\ast\tan\mathrm{h}(C_{t}) \tag{14}\]  
The diagram of classical vanilla LSTM unit is reported in Figure 9.  
The used sensing camera is the same as described in the previous paragraph. The input frame will be resized through a bicubic algorithm. The dataset is composed of classical gesture recognition NTU-60/NTU-120 datasets [45] extended with locally generated ones in the indoor market stores. The deep network was trained by the SGDM algorithm, while the dataset was split as 70% for training, with the remaining 15% for validation and 15% for testing. A \(k\)-fold (\(k=3\)) cross-validation approach was used. The following tables reported the benchmarks comparison with other classical deep networks. As reported in Tables 4 and 5, our proposed solution outperformed the others for the enhanced NTU-60 dataset but underperformed in the enhanced NTU-120 dataset. This drop in performance with respect to the 3D architecture is probably due to the fact that for a large dataset, a temporal correlation in the input data could improve the \begin{table}
\begin{tabular}{c c} \hline \hline
**Deep Network** & **Accuracy** \\ \hline Graph-Convolutional Network & 91.31\% \\
3D-CNN & 92.07\% \\ Faster-R-CNN & 89.65\% \\ Mask-R-CNN & 90.83\% \\ Proposed & **92.91\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Customer sentiment analysis system (Mask-R-CNN with ResNet-101 Criss-Cross) benchmark comparison—enhanced NTU-60 dataset (1-clip). \begin{table}
\begin{tabular}{c c} \hline \hline
**Deep Network** & **Accuracy** \\ \hline Graph-Convolutional Network & 83.80\% \\
3D-CNN & **84.88\%** \\ Faster-R-CNN & 77.87\% \\ Mask-R-CNN & 79.02\% \\ Proposed & 82.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Customer sentiment analysis system (Mask-R-CNN with ResNet-101 Criss-Cross) benchmark comparison—enhanced NTU-120 dataset (1-clip). Figure 9: Convolutional LSTM: LSTM basic unit. discriminative features. Finally, we tested the deep architecture embedded in the intelligent stock management system. Basically, we designed the 1D-TCNN as follows: input layer of 1 \(\times\) 300 dimension; 12 main convolutional blocks; L2-regularization factor: 0.0005; penalty threshold: 0.25; dropout enabled; batch size: 10. Each of the convolutional blocks is composed of a 3 \(\times\) 3 dilated convolutional layer followed by normalization, spatial dropout, 5 \(\times\) 5 dilated causal convolutional layer, normalization, ReLU activation and spatial dropout. Each block shows residual gradient propagation with a 1 \(\times\) 1 convolutional layer for adapting the dimension of the features. The dilation factor starts from 2 and moves to 16 with increments of 2 for each convolutional block. The dataset is composed of a vector of elements composed of the fields included in Tables 1 and 2 (the vector length was 8), for each stock's category. We collected 5 years of historical data as per Tables 1 and 2 from a local market store backlog for different categories for a total amount of input instances. We divided this dataset as follows: 65% as a training set, with the remaining 30% for validation and testing. K-fold (\(k=3\)) cross-validation was used. We labeled the data, assigning the \"high-risk out-of-stock\" as scenario in which the related stock was sold out despite requests; \"medium-risk out-of-stock\" was the scenario in which the related stock covered 60% of the real requests; \"low-risk out-of-stock\" was the scenario in which the related stock covered all the requests made by the customer. The following table reported the benchmark comparison with other classical deep networks.