## 4 Deep Learning
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & \
\begin{tabular}{c} **First** \\ **Author** \\ \end{tabular} & **Year** & **Model** &
\begin{tabular}{c} **Application** \\ **Field** \\ \end{tabular} & **Dataset** & **Accuracy** & **Details** \\ \hline
[48] [PERSON] & 2020 & \begin{tabular}{c} Hierarchical \\ clustering \\ sensors \\ \end{tabular} & \begin{tabular}{c} IoT \\ applications with \\ data from \\ sensors \\ \end{tabular} & \begin{tabular}{c} Dataset, related to \\ vehicular traffic \\ count, and \\ environmental \\ pollutant CO level \\ \end{tabular} & \
\begin{tabular}{c} Accuracy is more \\ than 90\% \\ \end{tabular} &
\begin{tabular}{c} Combination of a \\ hierarchical \\ clustering \\ and an LSTM \\ neural network \\ \end{tabular} \\ \hline
[49] & [PERSON] & 2020 & \begin{tabular}{c} Hierarchical \\ clustering \\ \end{tabular} & \begin{tabular}{c} M13 \\ bacteriophage- \\ based \\ colorimetric \\ sensors \\ \end{tabular} & \begin{tabular}{c} Dataset from sensing \\ experiments at four \\ different \\ temperatures \\ \end{tabular} & \
\begin{tabular}{c} Effective \\ classification \\ \end{tabular} &
\begin{tabular}{c} Hierarchical \\ clustering is used to \\ classify types of \\ medical chemicals \\ \end{tabular} \\ \hline
[51] & [PERSON] & 2021 & \begin{tabular}{c} Distance-and \\ grey-constrained \\ k-means \\ clustering \\ \end{tabular} & \begin{tabular}{c} Lifetime of \\ WUSNs \\ \end{tabular} & \
\begin{tabular}{c} AUV-based dataset \\ from underwater \\ clusters \\ \end{tabular} &
\begin{tabular}{c} Essentially \\ outperformed \\ k-means clustering \\ k-means is used for \\ cluster head selection \\ \end{tabular} \\ \hline
[52] & [PERSON] & 2020 & \begin{tabular}{c} Sub-trajectory \\ clustering \\ \end{tabular} & \begin{tabular}{c} Ship-handling \\ behavior \\ monitoring \\ \end{tabular} & \begin{tabular}{c} Trajectory dataset \\ from multiple \\ sensors \\ \end{tabular} & \
\begin{tabular}{c} Effective \\ monitoring \\ \end{tabular} &
\begin{tabular}{c} Sub-handling \\ behavior monitoring \\ with multi-step \\ sub-trajectory \\ clustering analysis \\ \end{tabular} \\ \hline
[53] & [PERSON] & 2020 & \begin{tabular}{c} Spectral \\ clustering \\ \end{tabular} & \begin{tabular}{c} Hyperspectral \\ image \\ classification and \\ monitoring \\ \end{tabular} & \begin{tabular}{c} Indian Pines and \\ Salinas datasets \\ \end{tabular} & \
\begin{tabular}{c} Effective \\ classification and \\ monitoring \\ \end{tabular} &
\begin{tabular}{c} Unsupervised band \\ selection technique \\ with a hierarchical \\ clustering \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the advances in unsupervised machine learning for sensing and condition monitoring. or object, or pedestrian detection, human activity identification, fault diagnosis, drug discovery, genomics, multi-task and transfer learning, domain adaptation, etc. Input data quality is fundamental for a good functioning of deep learning. Consequently, the tools that enable data acquisition also play a key role. Depending on the field of application and the purpose, the nature of the data and of the sensors/acquisition devices is different. The use of data from sensors/sources of different nature is increasingly frequent and therefore data fusion, which merges complementary information, as spatial-temporal-spectral resolution data, often occurs. DL models extract abstract features from multiple input streams and can establish robust relationships between dissimilar input signals, not influenced by sensor type and spatial scale. Moreover, DL is robust even in cases of missing or corrupted sensor data. Starting from the classical NNs, such as the back-propagation feedforward NN (BPNN) or the radial basis type known as a generalized regression neural network (GRNN), different DL architectures have been developed to address different kinds of problems.