## 5 Discussion and Conclusions  
In this paper, the contribution developed entirely by the authors and called GAIA is disclosed. It is an innovative system based on artificial intelligence for the management of a commercial store. Specifically, GAIA manages the market store in various areas, from the dynamic reconstruction of customer sentiment, to the anomalies in the management of warehouse stock and to the reconstruction of the correlation of cashflow with respect to the real interest of customers in the store. Using sophisticated deep learning techniques that include attention mechanisms and multi-modal analysis of input data, GAIA allows for an efficient monitoring of the market store combined with a robust management of the classic issue of out-of-stock that usually affects large-scale commercial distribution. By means of an intelligent control panel and with real-time updates, the GAIA system is able to monitor the various sectors of the market store both through computer vision systems that characterize the sentiment and interest of customers and through convolutional temporal deep networks for the predictive and efficient estimation of warehouse orders. From a benchmark comparison with similar deep learning models, GAIA's AI backbone shows greater performance compared to the state-of-the-art, both in relation to the computer vision part and in relation to the predictive and understanding part of customer sentiment. Through the designed self-attention mechanisms embedded in the deep architectures implemented in the GAIA framework, the discriminating capacity of the features map has been significantly increased, thus allowing for high performance indicators in terms of accuracy and average precision. By means of the virtual sectorization supported by the GAIA system, it is possible to define the level of granularity of the product sector to be monitored. Finally, for the application of GAIA, no special hardware equipment is required at the commercial point, but the normally supplied security video cameras with the financial and warehouse flow management systems can be used. The GAIA system is currently being integrated into some market stores. Specifically, the integration is quite simple and does not require high costs. As regards the visual part (therefore the computer vision and sentiment reconstruction systems based on the analysis of the customer-skeletons), the framework of security cameras that the market stores are already equipped with was used. Using the calibration system, we adopted the frames according to the characteristics specified in this paper. As for the part of the analysis of the financial flows of purchases and orders, we interfaced through a simple software wrapper with the management system that the market stores are usually equipped with. As for the computational part, we used a server as described in the previous section containing the experimental results. Therefore,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GAIA System Structure** & **High Risk** & **Medium Risk** & **Low Risk** \\ \hline full pipeline & **91.42\%** & **93.54\%** & **90.00\%** \\ \hline without computer vision block & 85.71\% & 87.09\% & 86.66\% \\ \hline without customer sentiment block & 82.85\% & 90.32\% & 83.33\% \\ \hline without cashflow analyzer block & 80.09\% & 80.64\% & 83.33\% \\ \hline without intelligent stock management & 85.71\% & 87.09\% & 83.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation analysis of the GAIA system.  
the integration of the GAIA system in any market store is quite simple and does not require particular costs, as it integrates perfectly into the hardware and software frameworks that market stores are usually equipped with.  
Future works intend to further expand GAIA with additional control modules of the commercial point for optimal waste management, optimal customer profiling, as well as for the characterization of promotions tailored to customers.