## 5 Brief Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **First Author** & **Year** & **Model** & **Application** & **Dataset** & **Accuracy** & **Details** \\ \hline
[84] & [PERSON] & 2018 & DBN model & Monitoring tool wear in a high-speed CNC milling machine & Dataset from thermometer and a accelerometers and an acoustic emission sensor & \(\text{R}^{2}\) value is 98.9\% & DBN model, which is featured a low runtime, a high accuracy and a high high stability & DBN model, which is featured a low runtime, a high accuracy and a high stability & DBN model, which is featured a low runtime, a high accuracy and a high stability & DBN model, which is featured a low runtime, a high accuracy and a high stability \\ \hline
[85] & [PERSON] & 2017 & \begin{tabular}{c} D-DBN-PF \\ model (DBN \\ training method \\ both in pre-training and fine-tuning phases) \\ \end{tabular} & \begin{tabular}{c} Hyperspectral \\ image \\ classification \\ classification \\ \end{tabular} & \
\begin{tabular}{c} Dataset of hyperspectral \\ images from multivariate \\ optical sensors \\ \end{tabular} & Classification accuracy is 93.1\% &
\begin{tabular}{c} Deep features, that model multiple \\ ground-truth classes, are extracted from hyperspectral images \\ \end{tabular} \\ \hline
[87] & Jiao & 2022 & \begin{tabular}{c} GCN-LSTM \\ model \\ \end{tabular} & \begin{tabular}{c} Transformer for a \\ group of PV \\ panels, based on their temporal \\ and spatial \\ information \\ \end{tabular} & \begin{tabular}{c} Solar irradiance dataset, \\ sourced from the U.S.’s \\ National Renewable \\ Energy Laboratory [88] \\ \end{tabular} & \
\begin{tabular}{c} Root mean \\ squared error \\ is 0.0052 \\ \end{tabular} &
\begin{tabular}{c} Model is a group \\ solar irradiance \\ neural network \\ (GSINN), which \\ integrates a GCN \\ model with a \\ LSTM model \\ \end{tabular} \\ \hline
[89] & Shi & 2020 & \begin{tabular}{c} CNN-GNN \\ model \\ \end{tabular} & \begin{tabular}{c} Monitoring of \\ objects from a 3D \\ point cloud \\ \end{tabular} & \begin{tabular}{c} KITTI dataset [100] (Lidar \\ point clouds and the \\ camera images) \\ \end{tabular} & \
\begin{tabular}{c} Monitoring \\ accuracy is \\ between 75\% \\ and 90\% \\ \end{tabular} &
\begin{tabular}{c} Single-stage \\ monitoring method: \\ a Point-GNN extracts \\ features of the point \\ cloud by iteratively \\ updating vertex \\ features on the \\ same graph \\ \end{tabular} \\ \hline
[90] [PERSON] & 2022 & \begin{tabular}{c} Discriminative \\ features from \\  models \\ \end{tabular} & \begin{tabular}{c} Monitoring \\ binding sites with \\ different methods) \\ \end{tabular} & \
\begin{tabular}{c} Monitoring \\ accuracy is 87.9\% \\ (mean value for \\ 24 datasets) \\ \end{tabular} &
\begin{tabular}{c} Two-layer CNN and \\ GCN are used in \\ parallel to extract \\ hidden features \\ \end{tabular} \\ \hline
[91] [PERSON] & 2021 & \begin{tabular}{c} ICDBN-IDM \\ (improved \\ convolutional \\ deep belief \\ network-based \\ intrusion \\ detection model) \\ \end{tabular} & \begin{tabular}{c} Intrusion \\ monitoring via \\ wireless sensor \\ networks \\ \end{tabular} & \begin{tabular}{c} Dataset on environmental \\ information, collected in \\ real-time from sensor \\ node of an WSN \\ \end{tabular} & \
\begin{tabular}{c} Monitoring \\ accuracy is 96.8\% \\ \end{tabular} &
\begin{tabular}{c} Redundancy \\ monitoring \\ algorithm, based on \\ the convolutional \\ deep belief network \\ and a performance \\ evaluation strategy \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)Data Availability Statement: Data sharing is not applicable to this article as no new data were created or analyzed in this study. The authors declare no conflict of interest.