## 4 Experimental Results
As reported in Table 6, we compared our architecture with ones based on deep LSTM, multi-layer perceptron (classical backpropagation MLP with one hidden layer of 200 neurons) and with a classification based on support vector machine (SVM). Our proposed solution outperforms the other machine learning approaches, confirming the effectiveness of the designed pipeline. In order to validate the whole pipeline and the contribution of each block in achieving the target of the GAIA system, we proceeded to execute an ablation session, removing individual subsystems of the GAIA system and validating the system's ability to correctly calculate risk assessment. In reference to the dataset used in this testing, we used a short data history of a sales point of a local holding in the large-scale food distribution sector (see Acknowledgments section). Throughout the history of data, both video clips, and financial flow and orders, we simulated a real scenario by obtaining real risk assessments. In detail, we selected some products from the store and structured the dataset by cataloging the input data (video frames of the area of the store where these products are located, financial flows related to the purchases of these products, dynamics of orders executed). Then, the risk assessments were calculated by evaluating, for each time, if there had been a significant out-of-stock or surplus of stock (high risk) or if such phenomena occurred in a moderate (medium risk) or negligible (low risk) form. To discriminate between the three classes, we used thresholds related to the quantity of residual stock. Having constructed the dataset in this way, we proceeded to validate the GAIA system both in integral form and by removing individual subsystems in order to validate its specific contribution. We collected 2 weeks of historical data with a sampling rate of data of 15 min for each day. The collected data were split into 65% for training and 35% for validation and testing. K-fold (\(k=3\)) cross validation has been applied. The collected performance results (including ablation study) are reported in Table 7. \begin{table}
\begin{tabular}{c c} \hline \hline
**Deep Network** & **Accuracy** \\ \hline Deep LSTM & 91.25\% \\ \hline MLP & 90.96\% \\ \hline SVM & 87.91\% \\ \hline Proposed & **94.32\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Intelligent stock management system (Mask-R-CNN with ResNet-101 Criss-Cross)—NTU-120 dataset (1-clip). As evident from Table 7, the GAIA system without each of the specific subsystems of which the full pipeline is composed of significantly degrades in performance both in reference to the visual part and in reference to the analytical part composed of financial flow and orders (stock management). Therefore, the need to equip the GAIA pipeline with each designed subsystem in order to maximize the performance of the entire solution remains confirmed. All the mentioned experimental sessions, including training, validation and testing of the single deep models as well as the full pipeline, have been executed over a server INTEL MultiCores embedding a GPU RTX 2080 with 24 GB of video memory and in Pytorch/Matlab rev. 2021 frameworks.